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Sample records for volume cbv-weighted fmri

  1. Enhancing the Detection of BOLD Signal in fMRI by Reducing the Partial Volume Effect

    Directory of Open Access Journals (Sweden)

    Yiping P. Du

    2014-01-01

    Full Text Available Purpose. To investigate the advantages of reducing the partial volume effect (PVE to enhance the detection of the BOLD signal in fMRI. Methods. A linear phase term was added in k-space to obtain half-voxel shifting of 64 × 64 T2*-weighted echo-planar images. Three sets of image data shifted in the x, y, and diagonal direction, respectively, are combined with the original 64 × 64 data to form the 128 × 128 voxel-shifted interpolated data. Results. A simulation of a synthetic fMRI dataset shows that the voxel-shifted interpolation (VSI can increase the t-score up to 50% in single-voxel activations. An fMRI study (n=7 demonstrates that 20.4% of the interpolated voxels have higher t-scores than their nearest neighboring voxels in the original maps. The average increase of the t-score in these interpolated voxels is 13.3%. Conclusion. VSI yields increased sensitivity in detecting voxel-size BOLD activations, improved spatial accuracy of activated regions, and improved detection of the peak BOLD signal of an activated region. VSI can potentially be used as an alternative to the high-resolution fMRI studies in which reduction in SNR and increase in imaging time become prohibitive.

  2. Motor fMRI and cortical grey matter volume in adults born very preterm

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    E.J. Lawrence

    2014-10-01

    Full Text Available The primary aim of this study was to investigate the functional neuroanatomy of motor planning, initiation and execution in a cohort of young adults (mean age 20 years who were born very preterm (VPT; <33 weeks of gestation, as these individuals are at increased risk of experiencing neuromotor difficulties compared to controls. A cued motor task was presented to 20 right-handed VPT individuals and 20 controls within a functional magnetic resonance imaging (fMRI paradigm. Whole-brain grey matter volume was also quantified and associations with functional data were examined. Despite comparable task performance, fMRI results showed that the VPT group displayed greater brain activation compared to controls in a region comprising the right cerebellum and the lingual, parahippocampal and middle temporal gyri. The VPT group also displayed decreased grey matter volume in the right superior frontal/premotor cortex and left middle temporal gyri. Grey matter volume in the premotor and middle temporal clusters was significantly negatively correlated with BOLD activation in the cerebellum. Overall, these data suggest that preterm birth is associated with functional neuronal differences that persist into adulthood, which are likely to reflect neural reorganisation following early brain injury.

  3. Mapping the MRI voxel volume in which thermal noise matches physiological noise--implications for fMRI.

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    Bodurka, J; Ye, F; Petridou, N; Murphy, K; Bandettini, P A

    2007-01-15

    This work addresses the choice of the imaging voxel volume in blood oxygen level dependent (BOLD) functional magnetic resonance imaging (fMRI). Noise of physiological origin that is present in the voxel time course is a prohibitive factor in the detection of small activation-induced BOLD signal changes. If the physiological noise contribution dominates over the temporal fluctuation contribution in the imaging voxel, further increases in the voxel signal-to-noise ratio (SNR) will have diminished corresponding increases in temporal signal-to-noise (TSNR), resulting in reduced corresponding increases in the ability to detect activation induced signal changes. On the other hand, if the thermal and system noise dominate (suggesting a relatively low SNR) further decreases in SNR can prohibit detection of activation-induced signal changes. Here we have proposed and called the "suggested" voxel volume for fMRI the volume where thermal plus system-related and physiological noise variances are equal. Based on this condition we have created maps of fMRI suggested voxel volume from our experimental data at 3T, since this value will spatially vary depending on the contribution of physiologic noise in each voxel. Based on our fast EPI segmentation technique we have found that for gray matter (GM), white matter (WM), and cerebral spinal fluid (CSF) brain compartments the mean suggested cubical voxel volume is: (1.8 mm)3, (2.1 mm)3 and (1.4 mm)3, respectively. Serendipitously, (1.8 mm)3 cubical voxel volume for GM approximately matches the cortical thickness, thus optimizing BOLD contrast by minimizing partial volume averaging. The introduced suggested fMRI voxel volume can be a useful parameter for choice of imaging volume for functional studies.

  4. Comparison of volume-selective z-shim and conventional EPI in fMRI studies using face stimuli

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    Fukunaga, Hu Cheng Srikanth Padmala Rena

    2013-01-01

    Single-shot gradient recalled echo planar imaging (EPI) is the primary tool for functional magnetic resonance imaging (fMRI). The image often suffers from signal drop near the air-tissue interface, such as the amygdala and regions of the orbitofrontal lobe. An effective way to correct for this type of artifact is by applying multi-shot EPI using different z-shimming. Unfortunately, the scanning efficiency is significantly lowered. More recently, a new technique called volume-selective z-shim was proposed to implement z-shim compensation to only specific slices with large susceptibility effects. The high imaging efficiency of volume selective z-shim makes it possible to substitute conventional EPI for whole brain studies. In this study two fMRI experiments were conducted to compare volume- selective z-shim and conventional EPI while subjects performed tasks on face stimuli. The comparison was focused on three brain regions: amygdala, hippocampus, and fusiform gyrus. Our results indicate that despite fewer volu...

  5. Real-time cardiac synchronization with fixed volume frame rate for reducing physiological instabilities in 3D FMRI.

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    Tijssen, Rob H N; Okell, Thomas W; Miller, Karla L

    2011-08-15

    Although 2D echo-planar imaging (EPI) remains the dominant method for functional MRI (FMRI), 3D readouts are receiving more interest as these sequences have favorable signal-to-noise ratio (SNR) and enable imaging at a high isotropic resolution. Spoiled gradient-echo (SPGR) and balanced steady-state free-precession (bSSFP) are rapid sequences that are typically acquired with highly segmented 3D readouts, and thus less sensitive to image distortion and signal dropout. They therefore provide a powerful alternative for FMRI in areas with strong susceptibility offsets, such as deep gray matter structures and the brainstem. Unfortunately, the multi-shot nature of the readout makes these sequences highly sensitive to physiological fluctuations, and large signal instabilities are observed in the inferior regions of the brain. In this work a characterization of the source of these instabilities is given and a new method is presented to reduce the instabilities observed in 3D SPGR and bSSFP. Rapidly acquired single-slice data, which critically sampled the respiratory and cardiac waveforms, showed that cardiac pulsation is the dominant source of the instabilities. Simulations further showed that synchronizing the readout to the cardiac cycle minimizes the instabilities considerably. A real-time synchronization method was therefore developed, which utilizes parallel-imaging techniques to allow cardiac synchronization without alteration of the volume acquisition rate. The implemented method significantly improves the temporal stability in areas that are affected by cardiac-related signal fluctuations. In bSSFP data the tSNR in the brainstem increased by 45%, at the cost of a small reduction in tSNR in the cortical areas. In SPGR the temporal stability is improved by approximately 20% in the subcortical structures and as well as cortical gray matter when synchronization was performed.

  6. Quantification of fMRI BOLD signal and volume applied to the somatosensory cortex

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    Luedemann, L.; Wust, P. [Universitaetsklinikum Charite, CVK, Berlin (Germany). Klinik fuer Radiologie, Nuklearmedizin und Strahlenheilkunde; Foerschler, A.; Zimmer, C. [Universitaetsklinikum Leipzig (Germany). Abt. fuer Neuroradiologie

    2007-07-01

    Functional magnetic resonance imaging based on blood-oxygenation-level-dependent (BOLD) signal variations is clinically used to investigate the impact of neurological disorders on brain function. Such disorders effect not only the localization but also the amplitude and extent of the BOLD signal. Statistical methods are useful to localize the BOLD signal but fail to quantify functional activity because they rely on arbitrary thresholds. This article presents a method that uses a priori defined VOI (volume of interest) and independently quantifies the mean BOLD signal and extent of the activated volume. The technique is based on the separation of the VOI signal difference distribution into a noise and an activation contribution. The technique does not require any threshold and is nearly independent of the preselected VOI size. The technique was verified in a test group of 17 subjects performing bilateral finger tapping. The results were compared with those of conventional analysis based on statistical tools. A standard imaging technique using FID-EPI (free induction decay echo-planar imaging, TR = 4000 ms, TE = 66 ms, 60 images activation, 60 images rest) was employed. The activated volume, V, and signal difference, {delta}S, of the motor cortex were determined with an accuracy of {sigma}(V)=17.1% and {sigma}({delta}S)=3.6%, respectively. The activated volume of the left hemispheric motor area was significantly greater (P=0.025) then in the right hemispheric, V{sub L} = 7.35 {+-} 2.29 cm{sup 3} versus V{sub L} = 6.39 {+-} 2.34 cm{sup 3}. The result is consistent with the findings obtained by other techniques. On the other hand, the statistical methods did not yield any significant difference in activation between both hemispheres. The VOI-based method presented here is an additional tool to study the extent and amplitude of the BOLD signal. (orig.)

  7. Negative cerebral blood volume fMRI response coupled with Ca²⁺-dependent brain activity in a dopaminergic road map of nociception.

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    Hsu, Yi-Hua; Chang, Chen; Chen, Chiao-Chi V

    2014-04-15

    Decreased cerebral blood volume/flow (CBV/CBF) contributes to negative blood-oxygen-level-dependent (BOLD) functional MRI (fMRI) signals. But it is still strongly debated whether these negative BOLD or CBV/CBF signals are indicative of decreased or increased neuronal activity. The fidelity of Ca(2+) signals in reflecting neuronal excitation is well documented. However, the roles of Ca(2+) signals and Ca(2+)-dependent activity in negative fMRI signals have never been explored; an understanding of this is essential to unraveling the underlying mechanisms and correctly interpreting the hemodynamic response of interest. The present study utilized a nociception-induced negative CBV fMRI response as a model. Ca(2+) signals were investigated in vivo using Mn(2+)-enhanced MRI (MEMRI), and the downstream Ca(2+)-dependent signaling was investigated using phosphorylated cAMP response-element-binding (pCREB) immunohistology. The results showed that nociceptive stimulation led to (1) striatal CBV decreases, (2) Ca(2+) increases via the nigrostriatal pathway, and (3) substantial expression of pCREB in substantia nigra dopaminergic neurons and striatal neurons. Interestingly, the striatal negative fMRI response was abolished by blocking substantia nigra activity but was not affected by blocking the striatal activity. This suggests the importance of input activity other than output in triggering the negative CBV signals. These findings indicate that the striatal negative CBV fMRI signals are associated with Ca(2+) increases and Ca(2+)-dependent signaling along the nigrostriatal pathway. The obtained data reveal a new brain road map in response to nociceptive stimulation of hemodynamic changes in association with Ca(2+) signals within the dopaminergic system.

  8. fMRI Neuroinformatics

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    Nielsen, Finn Årup; Christensen, Mark Schram; Madsen, Kristoffer M.

    2006-01-01

    Functional magnetic resonance imaging (fMRI) generates vast amounts of data. The handling, processing, and analysis of fMRI data would be inconceivable without computer-based methods. fMRI neuroinformatics is concerned with research, development, and operation of these methods. Reconstruction...

  9. fMRI. Basics and clinical applications

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    Ulmer, Stephan; Jansen, Olav (eds.) [University Hospital of Schleswig-Holstein, Kiel (Germany). Inst. of Neuroradiology, Neurocenter

    2010-07-01

    Functional MRI (fMRI) and the basic method of BOLD imaging were introduced in 1993 by Seiji Ogawa. From very basic experiments, fMRI has evolved into a clinical application for daily routine brain imaging. There have been various improvements in both the imaging technique as such as well as in the statistical analysis. In this volume, experts in the field share their knowledge and point out possible technical barriers and problems explaining how to solve them. Starting from the very basics on the origin of the BOLD signal, the book covers technical issues, anatomical landmarks, presurgical applications, and special issues in various clinical fields. Other modalities for brain mapping such as PET, TMS, and MEG are also compared with fMRI. This book is intended to give a state-of-the-art overview and to serve as a reference and guide for clinical applications of fMRI. (orig.)

  10. Optimized design and analysis of sparse-sampling FMRI experiments.

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    Perrachione, Tyler K; Ghosh, Satrajit S

    2013-01-01

    Sparse-sampling is an important methodological advance in functional magnetic resonance imaging (fMRI), in which silent delays are introduced between MR volume acquisitions, allowing for the presentation of auditory stimuli without contamination by acoustic scanner noise and for overt vocal responses without motion-induced artifacts in the functional time series. As such, the sparse-sampling technique has become a mainstay of principled fMRI research into the cognitive and systems neuroscience of speech, language, hearing, and music. Despite being in use for over a decade, there has been little systematic investigation of the acquisition parameters, experimental design considerations, and statistical analysis approaches that bear on the results and interpretation of sparse-sampling fMRI experiments. In this report, we examined how design and analysis choices related to the duration of repetition time (TR) delay (an acquisition parameter), stimulation rate (an experimental design parameter), and model basis function (an analysis parameter) act independently and interactively to affect the neural activation profiles observed in fMRI. First, we conducted a series of computational simulations to explore the parameter space of sparse design and analysis with respect to these variables; second, we validated the results of these simulations in a series of sparse-sampling fMRI experiments. Overall, these experiments suggest the employment of three methodological approaches that can, in many situations, substantially improve the detection of neurophysiological response in sparse fMRI: (1) Sparse analyses should utilize a physiologically informed model that incorporates hemodynamic response convolution to reduce model error. (2) The design of sparse fMRI experiments should maintain a high rate of stimulus presentation to maximize effect size. (3) TR delays of short to intermediate length can be used between acquisitions of sparse-sampled functional image volumes to increase

  11. fMRI adaptation revisited.

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    Larsson, Jonas; Solomon, Samuel G; Kohn, Adam

    2016-07-01

    Adaptation has been widely used in functional magnetic imaging (fMRI) studies to infer neuronal response properties in human cortex. fMRI adaptation has been criticized because of the complex relationship between fMRI adaptation effects and the multiple neuronal effects that could underlie them. Many of the longstanding concerns about fMRI adaptation have received empirical support from neurophysiological studies over the last decade. We review these studies here, and also consider neuroimaging studies that have investigated how fMRI adaptation effects are influenced by high-level perceptual processes. The results of these studies further emphasize the need to interpret fMRI adaptation results with caution, but they also provide helpful guidance for more accurate interpretation and better experimental design. In addition, we argue that rather than being used as a proxy for measurements of neuronal stimulus selectivity, fMRI adaptation may be most useful for studying population-level adaptation effects across cortical processing hierarchies.

  12. Physiologically informed dynamic causal modeling of fMRI data.

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    Havlicek, Martin; Roebroeck, Alard; Friston, Karl; Gardumi, Anna; Ivanov, Dimo; Uludag, Kamil

    2015-11-15

    The functional MRI (fMRI) signal is an indirect measure of neuronal activity. In order to deconvolve the neuronal activity from the experimental fMRI data, biophysical generative models have been proposed describing the link between neuronal activity and the cerebral blood flow (the neurovascular coupling), and further the hemodynamic response and the BOLD signal equation. These generative models have been employed both for single brain area deconvolution and to infer effective connectivity in networks of multiple brain areas. In the current paper, we introduce a new fMRI model inspired by experimental observations about the physiological underpinnings of the BOLD signal and compare it with the generative models currently used in dynamic causal modeling (DCM), a widely used framework to study effective connectivity in the brain. We consider three fundamental aspects of such generative models for fMRI: (i) an adaptive two-state neuronal model that accounts for a wide repertoire of neuronal responses during and after stimulation; (ii) feedforward neurovascular coupling that links neuronal activity to blood flow; and (iii) a balloon model that can account for vascular uncoupling between the blood flow and the blood volume. Finally, we adjust the parameterization of the BOLD signal equation for different magnetic field strengths. This paper focuses on the form, motivation and phenomenology of DCMs for fMRI and the characteristics of the various models are demonstrated using simulations. These simulations emphasize a more accurate modeling of the transient BOLD responses - such as adaptive decreases to sustained inputs during stimulation and the post-stimulus undershoot. In addition, we demonstrate using experimental data that it is necessary to take into account both neuronal and vascular transients to accurately model the signal dynamics of fMRI data. By refining the models of the transient responses, we provide a more informed perspective on the underlying neuronal

  13. fMRI at High Spatial Resolution: Implications for BOLD-Models.

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    Goense, Jozien; Bohraus, Yvette; Logothetis, Nikos K

    2016-01-01

    As high-resolution functional magnetic resonance imaging (fMRI) and fMRI of cortical layers become more widely used, the question how well high-resolution fMRI signals reflect the underlying neural processing, and how to interpret laminar fMRI data becomes more and more relevant. High-resolution fMRI has shown laminar differences in cerebral blood flow (CBF), volume (CBV), and neurovascular coupling. Features and processes that were previously lumped into a single voxel become spatially distinct at high resolution. These features can be vascular compartments such as veins, arteries, and capillaries, or cortical layers and columns, which can have differences in metabolism. Mesoscopic models of the blood oxygenation level dependent (BOLD) response therefore need to be expanded, for instance, to incorporate laminar differences in the coupling between neural activity, metabolism and the hemodynamic response. Here we discuss biological and methodological factors that affect the modeling and interpretation of high-resolution fMRI data. We also illustrate with examples from neuropharmacology and the negative BOLD response how combining BOLD with CBF- and CBV-based fMRI methods can provide additional information about neurovascular coupling, and can aid modeling and interpretation of high-resolution fMRI.

  14. Optimized design and analysis of sparse-sampling fMRI experiments

    Directory of Open Access Journals (Sweden)

    Tyler K Perrachione

    2013-04-01

    Full Text Available Sparse-sampling is an important methodological advance in functional magnetic resonance imaging (fMRI, in which silent delays are introduced between MR volume acquisitions, allowing for the presentation of auditory stimuli without contamination by acoustic scanner noise and for overt vocal responses without motion-induced artifacts in the functional timeseries. As such, the sparse-sampling technique has become a mainstay of principled fMRI research into the cognitive and systems neuroscience of speech, language, hearing, and music. Despite being in use for over a decade, there has been little systematic investigation of the acquisition parameters, experimental design considerations, and statistical analysis approaches that bear on the results and interpretation of sparse-sampling fMRI experiments. In this report, we examined how design and analysis choices related to the duration of repetition time (TR delay (an acquisition parameter, stimulation rate (an experimental design parameter and model basis function (an analysis parameter act independently and interactively to affect the neural activation profiles observed in fMRI. First, we conducted a series of computational simulations to explore the parameter space of sparse design and analysis with respect to these variables; second, we validated the results of these simulations in a series of sparse-sampling fMRI experiments. Overall, these experiments suggest three methodological approaches that can, in many situations, substantially improve the detection of neurophysiological response in sparse fMRI: (1 Sparse analyses should utilize a physiologically-informed model that incorporates hemodynamic response convolution to reduce model error. (2 The design of sparse fMRI experiments should maintain a high rate of stimulus presentation to maximize effect size. (3 TR delays of short to intermediate length can be used between acquisitions of sparse-sampled functional image volumes to improve

  15. Embedded sparse representation of fMRI data via group-wise dictionary optimization

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    Zhu, Dajiang; Lin, Binbin; Faskowitz, Joshua; Ye, Jieping; Thompson, Paul M.

    2016-03-01

    Sparse learning enables dimension reduction and efficient modeling of high dimensional signals and images, but it may need to be tailored to best suit specific applications and datasets. Here we used sparse learning to efficiently represent functional magnetic resonance imaging (fMRI) data from the human brain. We propose a novel embedded sparse representation (ESR), to identify the most consistent dictionary atoms across different brain datasets via an iterative group-wise dictionary optimization procedure. In this framework, we introduced additional criteria to make the learned dictionary atoms more consistent across different subjects. We successfully identified four common dictionary atoms that follow the external task stimuli with very high accuracy. After projecting the corresponding coefficient vectors back into the 3-D brain volume space, the spatial patterns are also consistent with traditional fMRI analysis results. Our framework reveals common features of brain activation in a population, as a new, efficient fMRI analysis method.

  16. Enhanced Thalamic Functional Connectivity with No fMRI Responses to Affected Forelimb Stimulation in Stroke-Recovered Rats

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    Shim, Woo H.; Suh, Ji-Yeon; Kim, Jeong K.; Jeong, Jaeseung; Kim, Young R.

    2017-01-01

    Neurological recovery after stroke has been extensively investigated to provide better understanding of neurobiological mechanism, therapy, and patient management. Recent advances in neuroimaging techniques, particularly functional MRI (fMRI), have widely contributed to unravel the relationship between the altered neural function and stroke-affected brain areas. As results of previous investigations, the plastic reorganization and/or gradual restoration of the hemodynamic fMRI responses to neural stimuli have been suggested as relevant mechanisms underlying the stroke recovery process. However, divergent study results and modality-dependent outcomes have clouded the proper interpretation of variable fMRI signals. Here, we performed both evoked and resting state fMRI (rs-fMRI) to clarify the link between the fMRI phenotypes and post-stroke functional recovery. The experiments were designed to examine the altered neural activity within the contra-lesional hemisphere and other undamaged brain regions using rat models with large unilateral stroke, which despite the severe injury, exhibited nearly full recovery at ∼6 months after stroke. Surprisingly, both blood oxygenation level-dependent and blood volume-weighted (CBVw) fMRI activities elicited by electrical stimulation of the stroke-affected forelimb were completely absent, failing to reveal the neural origin of the behavioral recovery. In contrast, the functional connectivity maps showed highly robust rs-fMRI activity concentrated in the contra-lesional ventromedial nucleus of thalamus (VM). The negative finding in the stimuli-induced fMRI study using the popular rat middle cerebral artery model denotes weak association between the fMRI hemodynamic responses and neurological improvement. The results strongly caution the indiscreet interpretation of stroke-affected fMRI signals and demonstrate rs-fMRI as a complementary tool for efficiently characterizing stroke recovery. PMID:28119575

  17. On clustering fMRI time series

    DEFF Research Database (Denmark)

    Goutte, C; Toft, P; Rostrup, E

    1999-01-01

    Analysis of fMRI time series is often performed by extracting one or more parameters for the individual voxels. Methods based, e.g., on various statistical tests are then used to yield parameters corresponding to probability of activation or activation strength. However, these methods do not indi......Analysis of fMRI time series is often performed by extracting one or more parameters for the individual voxels. Methods based, e.g., on various statistical tests are then used to yield parameters corresponding to probability of activation or activation strength. However, these methods do...... between the activation stimulus and the fMRI signal. We present two different clustering algorithms and use them to identify regions of similar activations in an fMRI experiment involving a visual stimulus....

  18. Functional correlates of cognitive dysfunction in multiple sclerosis: A multicenter fMRI Study.

    Science.gov (United States)

    Rocca, Maria A; Valsasina, Paola; Hulst, Hanneke E; Abdel-Aziz, Khaled; Enzinger, Christian; Gallo, Antonio; Pareto, Debora; Riccitelli, Gianna; Muhlert, Nils; Ciccarelli, Olga; Barkhof, Frederik; Fazekas, Franz; Tedeschi, Gioacchino; Arévalo, Maria J; Filippi, Massimo

    2014-12-01

    In this multicenter study, we applied functional magnetic resonance imaging (fMRI) to define the functional correlates of cognitive dysfunction in patients with multiple sclerosis (MS). fMRI scans during the performance of the N-back task were acquired from 42 right-handed relapsing remitting (RR) MS patients and 52 sex-matched right-handed healthy controls, studied at six European sites using 3.0 Tesla scanners. Patients with at least two abnormal (<2 standard deviations from the normative values) neuropsychological tests at a standardized evaluation were considered cognitively impaired (CI). FMRI data were analyzed using the SPM8 software, modeling regions showing load-dependent activations/deactivations with increasing task difficulty. Twenty (47%) MS patients were CI. During the N-back load condition, compared to controls and CI patients, cognitively preserved (CP) patients had increased recruitment of the right dorsolateral prefrontal cortex. As a function of increasing task difficulty, CI MS patients had reduced activations of several areas located in the fronto-parieto-temporal lobes as well as reduced deactivations of regions which are part of the default mode network compared to the other two groups. Significant correlations were found between abnormal fMRI patterns of activations and deactivations and behavioral measures, cognitive performance, and brain T2 and T1 lesion volumes. This multicenter study supports the theory that a preserved fMRI activity of the frontal lobe is associated with a better cognitive profile in MS patients. It also indicates the feasibility of fMRI to monitor disease evolution and treatment effects in future studies.

  19. Monkey cortex through fMRI glasses.

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    Vanduffel, Wim; Zhu, Qi; Orban, Guy A

    2014-08-06

    In 1998 several groups reported the feasibility of fMRI experiments in monkeys, with the goal to bridge the gap between invasive nonhuman primate studies and human functional imaging. These studies yielded critical insights in the neuronal underpinnings of the BOLD signal. Furthermore, the technology has been successful in guiding electrophysiological recordings and identifying focal perturbation targets. Finally, invaluable information was obtained concerning human brain evolution. We here provide a comprehensive overview of awake monkey fMRI studies mainly confined to the visual system. We review the latest insights about the topographic organization of monkey visual cortex and discuss the spatial relationships between retinotopy and category- and feature-selective clusters. We briefly discuss the functional layout of parietal and frontal cortex and continue with a summary of some fascinating functional and effective connectivity studies. Finally, we review recent comparative fMRI experiments and speculate about the future of nonhuman primate imaging.

  20. Resting state FMRI research in child psychiatric disorders

    NARCIS (Netherlands)

    Oldehinkel, M.; Francx, W.; Beckmann, C.F.; Buitelaar, J.K.; Mennes, M.

    2013-01-01

    Concurring with the shift from linking functions to specific brain areas towards studying network integration, resting state FMRI (R-FMRI) has become an important tool for delineating the functional network architecture of the brain. Fueled by straightforward data collection, R-FMRI analysis methods

  1. Resting state FMRI research in child psychiatric disorders

    NARCIS (Netherlands)

    Oldehinkel, Marianne; Francx, Winke; Beckmann, Christian; Buitelaar, Jan K.; Mennes, Maarten

    2013-01-01

    Concurring with the shift from linking functions to specific brain areas towards studying network integration, resting state FMRI (R-FMRI) has become an important tool for delineating the functional network architecture of the brain. Fueled by straightforward data collection, R-FMRI analysis methods

  2. On clustering fMRI time series

    DEFF Research Database (Denmark)

    Goutte, Cyril; Toft, Peter Aundal; Rostrup, E.

    1999-01-01

    Analysis of fMRI time series is often performed by extracting one or more parameters for the individual voxels. Methods based, e.g., on various statistical tests are then used to yield parameters corresponding to probability of activation or activation strength. However, these methods do...

  3. High spatial resolution increases the specificity of block-design BOLD fMRI studies of overt vowel production.

    Science.gov (United States)

    Soltysik, David A; Hyde, James S

    2008-06-01

    Functional MRI (fMRI) studies of tasks involving orofacial motion, such as speech, are prone to problems related to motion-induced magnetic field variations. Orofacial motion perturbs the static magnetic field, leading to signal changes that correlate with the task and corrupt activation maps with false positives or signal loss. These motion-induced signal changes represent a contraindication for the implementation of fMRI to study the neurophysiology of orofacial motion. An fMRI experiment of a structured, non-semantic vowel production task was performed using four different voxel volumes and three different slice orientations in an attempt to find a set of acquisition parameters leading to activation maps with maximum specificity. Results indicate that the use of small voxel volumes (2 x 2 x 3 mm(3)) yielded a significantly higher percentage of true positive activation compared to the use of larger voxel volumes. Slice orientation did not have as great an impact as spatial resolution, although coronal slices appeared superior at high spatial resolutions. Furthermore, it was found that combining the strategy of high spatial resolution with an optimum task duration and post-processing methods for separating true and false positives greatly improved the specificity of single-subject, block-design fMRI studies of structured, overt vowel production.

  4. Pharmacological fMRI; a clinical exploration

    OpenAIRE

    Goekoop, R.

    2006-01-01

    Dit proefschrift beschrijft de resultaten van een verkennend onderzoek naar een nieuwe techniek die gebruikt kan worden om de effecten van geneesmiddelen op hersenaktiviteit af te beelden: pharmacologische functionele magnetic resonance imaging (farmacologische fMRI of phMRI). Met behulp van deze techniek werden de effecten onderzocht van drie verschillende medicijnen (de bètablokker propranolol, de selectieve oestrogeen-receptor modulator (SERM) raloxifene en de cholinesteraseremmer galantam...

  5. Behavior, neuropsychology and fMRI.

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    Bennett, Maxwell R; Hatton, Sean; Hermens, Daniel F; Lagopoulos, Jim

    Cognitive neuroscientists in the late 20th century began the task of identifying the part(s) of the brain concerned with normal behavior as manifest in the psychological capacities as affective powers, reasoning, behaving purposively and the pursuit of goals, following introduction of the 'functional magnetic resonance imaging' (fMRI) method for identifying brain activity. For this research program to be successful two questions require satisfactory answers. First, as the fMRI method can currently only be used on stationary subjects, to what extent can neuropsychological tests applicable to such stationary subjects be correlated with normal behavior. Second, to what extent can correlations between the various neuropsychological tests on the one hand, and sites of brain activity determined with fMRI on the other, be regarded as established. The extent to which these questions have yet received satisfactory answers is reviewed, and suggestions made both for improving correlations of neuropsychological tests with behavior as well as with the results of fMRI-based observations. Copyright © 2016. Published by Elsevier Ltd.

  6. Quantitative fMRI and oxidative neuroenergetics.

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    Hyder, Fahmeed; Rothman, Douglas L

    2012-08-15

    The discovery of functional magnetic resonance imaging (fMRI) has greatly impacted neuroscience. The blood oxygenation level-dependent (BOLD) signal, using deoxyhemoglobin as an endogenous paramagnetic contrast agent, exposes regions of interest in task-based and resting-state paradigms. However the BOLD contrast is at best a partial measure of neuronal activity, because the functional maps obtained by differencing or correlations ignore the total neuronal activity in the baseline state. Here we describe how studies of brain energy metabolism at Yale, especially with (13)C magnetic resonance spectroscopy and related techniques, contributed to development of quantitative functional brain imaging with fMRI by providing a reliable measurement of baseline energy. This narrative takes us on a journey, from molecules to mind, with illuminating insights about neuronal-glial activities in relation to energy demand of synaptic activity. These results, along with key contributions from laboratories worldwide, comprise the energetic basis for quantitative interpretation of fMRI data. Copyright © 2012 Elsevier Inc. All rights reserved.

  7. A new method for FMRI activation detection

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    Wei, Jianing; Talavage, Thomas M.; Pollak, Ilya

    2009-02-01

    The objective of fMRI data analysis is to detect the region of the brain that gets activated in response to a specific stimulus presented to the subject. We develop a new algorithm for activation detection in event-related fMRI data. We utilize a forward model for fMRI data acquisition which explicitly incorporates physiological noise, scanner noise and the spatial blurring introduced by the scanner. After slice-by-slice image restoration procedure that independently restores each data slice corresponding to each time index, we estimate the parameters of the hemodynamic response function (HRF) model for each pixel of the restored data. In order to enforce spatial regularity in our estimates, we model the prior distribution of the HRF parameters as a generalized Gaussian Markov random field (GGMRF) model. We develop an algorithm to compute the maximum a posteriori (MAP) estimates of the parameters. We then threshold the amplitude parameters to obtain the final activation map. We illustrate our algorithm by comparing it with the widely used general linear model (GLM) method. In synthetic data experiments, under the same probability of false alarm, the probability of correct detection for our method is up to 15% higher than GLM. In real data experiments, through anatomical analysis and benchmark testing using block paradigm results, we demonstrate that our algorithm produces fewer false alarms than GLM.

  8. Functional imaging of olfaction by CBV fMRI in monkeys: insight into the role of olfactory bulb in habituation.

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    Zhao, Fuqiang; Holahan, Marie A; Houghton, Andrea K; Hargreaves, Richard; Evelhoch, Jeffrey L; Winkelmann, Christopher T; Williams, Donald S

    2015-02-01

    Cerebral blood volume (CBV) fMRI with superparamagnetic iron oxide nanoparticles (USPIO) as contrast agent was used to investigate the odorant-induced olfaction in anesthetized rhesus monkeys. fMRI data were acquired in 24 axial slices covering the entire brain, with isoamyl-acetate as the odor stimulant. For each experiment, multiple fMRI measurements were made during a 1- or 2-h period, with each measurement consisting of a baseline period, a stimulation period, and a recovery period. Three different stimulation paradigms with a stimulation period of 1 min, 2 min, or 8 min, respectively, were used to study the olfactory responses in the olfactory bulb (OB). Odorant-induced CBV increases were observed in the OB of each individual monkey. The spatial and temporal activation patterns were reproducible within and between animals. The sensitivity of CBV fMRI in OB was comparable with the sensitivities reported in previous animal fMRI studies. The CBV responses during the 1-min, 2-min, or 8-min odor stimulation period were relatively stable, and did not show attenuation. The amplitudes of CBV response to the repeated stimuli during the 1- or 2-h period were also stable. The stable CBV response in the OB to both continuous and repeated odor stimuli suggests that the OB may not play a major role in olfactory habituation. The technical approach described in this report can enable more extensive fMRI studies of olfactory processing in OB of both humans and non-human primates.

  9. Regional homogeneity on resting state fMRI in patients with tinnitus

    Institute of Scientific and Technical Information of China (English)

    Haidi Yang; Yiqing Zheng; Yongkang Ou; Xiayin Huang

    2014-01-01

    Objective:To study central functional network connections and their alterations in tinnitus patients using fMRI. Methods: Regional homogeneity (ReHo) values on fMRI were obtained from 18 tinnitus patients and 20 age and gender-matched control subjects. ReHo values were compared between tinnitus patients and control subjects to evaluate functional network connection differences. Results:Tinnitus patients showed increased ReHo values in gyrus frontalis inferior and decreased ReHo values in the anterior lobe of cerebellum in comparison with the controls. Analysis of functional network connection from the gyrus frontalis interior shows stronger connections to the middle brain (FWE, P<0.001) and right ventral striatum (FEW, P<0.05, small volume correction). Conclusions: The fMRI results indicate that both auditory and non-auditory centers play important roles in tinnitus. Functional connections among the auditory cortex, thalamus, medial temporal gyrus, parahippocampal gyrus and insula may be an underlying cause for the development of tinnitus.

  10. Optimizing the imaging of the monkey auditory cortex: sparse vs. continuous fMRI.

    Science.gov (United States)

    Petkov, Christopher I; Kayser, Christoph; Augath, Mark; Logothetis, Nikos K

    2009-10-01

    The noninvasive imaging of the monkey auditory system with functional magnetic resonance imaging (fMRI) can bridge the gap between electrophysiological studies in monkeys and imaging studies in humans. Some of the recent imaging of monkey auditory cortical and subcortical structures relies on a technique of "sparse imaging," which was developed in human studies to sidestep the negative influence of scanner noise by adding periods of silence in between volume acquisition. Among the various aspects that have gone into the ongoing optimization of fMRI of the monkey auditory cortex, replacing the more common continuous-imaging paradigm with sparse imaging seemed to us to make the most obvious difference in the amount of activity that we could reliably obtain from awake or anesthetized animals. Here, we directly compare the sparse- and continuous-imaging paradigms in anesthetized animals. We document a strikingly greater auditory response with sparse imaging, both quantitatively and qualitatively, which includes a more expansive and robust tonotopic organization. There were instances where continuous imaging could better reveal organizational properties that sparse imaging missed, such as aspects of the hierarchical organization of auditory cortex. We consider the choice of imaging paradigm as a key component in optimizing the fMRI of the monkey auditory cortex.

  11. Silent speechreading in the absence of scanner noise: an event-related fMRI study.

    Science.gov (United States)

    MacSweeney, M; Amaro, E; Calvert, G A; Campbell, R; David, A S; McGuire, P; Williams, S C; Woll, B; Brammer, M J

    2000-06-05

    In a previous study we used functional magnetic resonance imaging (fMRI) to demonstrate activation in auditory cortex during silent speechreading. Since image acquisition during fMRI generates acoustic noise, this pattern of activation could have reflected an interaction between background scanner noise and the visual lip-read stimuli. In this study we employed an event-related fMRI design which allowed us to measure activation during speechreading in the absence of acoustic scanner noise. In the experimental condition, hearing subjects were required to speechread random numbers from a silent speaker. In the control condition subjects watched a static image of the same speaker with mouth closed and were required to subvocally count an intermittent visual cue. A single volume of images was collected to coincide with the estimated peak of the blood oxygen level dependent (BOLD) response to these stimuli across multiple baseline and experimental trials. Silent speechreading led to greater activation in lateral temporal cortex relative to the control condition. This indicates that activation of auditory areas during silent speechreading is not a function of acoustic scanner noise and confirms that silent speechreading engages similar regions of auditory cortex as listening to speech.

  12. Lying about Facial Recognition: An fMRI Study

    Science.gov (United States)

    Bhatt, S.; Mbwana, J.; Adeyemo, A.; Sawyer, A.; Hailu, A.; VanMeter, J.

    2009-01-01

    Novel deception detection techniques have been in creation for centuries. Functional magnetic resonance imaging (fMRI) is a neuroscience technology that non-invasively measures brain activity associated with behavior and cognition. A number of investigators have explored the utilization and efficiency of fMRI in deception detection. In this study,…

  13. Lying about Facial Recognition: An fMRI Study

    Science.gov (United States)

    Bhatt, S.; Mbwana, J.; Adeyemo, A.; Sawyer, A.; Hailu, A.; VanMeter, J.

    2009-01-01

    Novel deception detection techniques have been in creation for centuries. Functional magnetic resonance imaging (fMRI) is a neuroscience technology that non-invasively measures brain activity associated with behavior and cognition. A number of investigators have explored the utilization and efficiency of fMRI in deception detection. In this study,…

  14. Unified ICA-SPM analysis of fMRI experiments

    DEFF Research Database (Denmark)

    Bjerre, Troels; Henriksen, Jonas; Nielsen, Carsten Haagen

    2009-01-01

    We present a toolbox for exploratory analysis of functional magnetic resonance imaging (fMRI) data using independent component analysis (ICA) within the widely used SPM analysis pipeline. The toolbox enables dimensional reduction using principal component analysis, ICA using several different ICA...... effects in fMRI data from a visual experiment....

  15. Flexible multivariate hemodynamics fMRI data analyses and simulations with PyHRF

    Directory of Open Access Journals (Sweden)

    Thomas eVincent

    2014-04-01

    Full Text Available As part of fMRI data analysis, the pyhrf package provides a set of tools for addressing the two main issues involved in intra-subject fMRI data analysis: (i the localization of cerebral regions that elicit evoked activity and (ii the estimation of activation dynamics also known as Hemodynamic Response Function (HRF recovery. To tackle these two problems, pyhrf implements the Joint Detection-Estimation framework~(JDE which recovers parcel-level HRFs and embeds an adaptive spatio-temporal regularization scheme of activation maps. With respect to the sole detection issue~(i, the classical voxelwise GLM procedure is also available through nipy, whereas Finite Impulse Response~(FIR and temporally regularized FIR models are concerned with HRF estimation~(ii and are specifically implemented in pyhrf. Several parcellation tools are also integrated such as spatial and functional clustering. Parcellations may be used for spatial averaging prior to FIR/RFIR analysis or to specify the spatial support of the HRF estimates in the JDE approach. These analysis procedures can be applied either to volumic data sets or to data projected onto the cortical surface. For validation purpose, this package is shipped with artificial and real fMRI data sets, which are used in this paper to compare the outcome of the different available approaches. The artificial fMRI data generator is also described to illustrate how to simulate different activation configurations, HRF shapes or nuisance components. To cope with the high computational needs for inference, pyhrf handles distributing computing by exploiting cluster units as well as multi-core machines. Finally, a dedicated viewer is presented, which handles $n$-dimensional images and provides suitable features to explore whole brain hemodynamics~(time series, maps, ROI mask overlay.

  16. Hypercapnic normalization of BOLD fMRI: comparison across field strengths and pulse sequences

    DEFF Research Database (Denmark)

    Cohen, Eric R.; Rostrup, Egill; Sidaros, Karam;

    2004-01-01

    The blood oxygenation level-dependent (BOLD) functional magnetic resonance imaging (fMRI) signal response to neural stimulation is influenced by many factors that are unrelated to the stimulus. These factors are physiological, such as the resting venous cerebral blood volume (CBV(v)) and vessel...... for global stimulation, subjects breathed a 5% CO(2) gas mixture. Under all conditions, voxels containing primarily large veins and those containing primarily active tissue (i.e., capillaries and small veins) showed distinguishable behavior after hypercapnic normalization. This allowed functional activity...

  17. Three-dimensional brain mapping using fMRI

    Energy Technology Data Exchange (ETDEWEB)

    Fukunaga, Masaki; Tanaka, Chuzo; Umeda, Masahiro; Ebisu, Toshihiko; Aoki, Ichio [Meiji Univ. of Oriental Medicine, Hiyoshi, Kyoto (Japan); Higuchi, Toshihiro; Naruse, Shoji

    1997-10-01

    Functional mapping of the activated brain, the location and extent of the activated area were determined, during motor tasks and sensory stimulation using fMRI superimposed on 3D anatomical MRI. Twelve volunteers were studied. The fMR images were acquired using a 2D gradient echo echo planar imaging sequence. The 3D anatomical MR images of the whole brain were acquired using a conventional 3D gradient echo sequence. Motor tasks were sequential opposition of fingers, clenching a hand and elbow flexion. Somatosensory stimulation were administered by scrubbing the palm and sole with a washing sponge. Visual stimulation consisted of full visual field stimulation. Data were analyzed by the cross-correlation method. Transversal fMR images and anatomical images were reconstructed using both volume-, surface-rendering methods, and reconstructed for coronal and sagittal sections. Activated areas were expressed using the three primary colors. Motor tasks activated the contralateral primary motor area (M1), the primary somatosensory area (S1) and the supplementary motor area (SMA). Somatosensory tasks activated the contralateral S1, M1 and secondary sensory area (S2). Activated areas during full visual field stimulation was observed in the bilateral occipital lobe, including both the primary cortex. Three-dimensional brain mapping allowed visualization of the anatomical location and extent of the activated brain during both motor task and sensory stimulation. Using this method we could obtain a functional map similar to the Penfield`s schema. (author)

  18. Mechanistic Mathematical Modeling Tests Hypotheses of the Neurovascular Coupling in fMRI.

    Directory of Open Access Journals (Sweden)

    Karin Lundengård

    2016-06-01

    Full Text Available Functional magnetic resonance imaging (fMRI measures brain activity by detecting the blood-oxygen-level dependent (BOLD response to neural activity. The BOLD response depends on the neurovascular coupling, which connects cerebral blood flow, cerebral blood volume, and deoxyhemoglobin level to neuronal activity. The exact mechanisms behind this neurovascular coupling are not yet fully investigated. There are at least three different ways in which these mechanisms are being discussed. Firstly, mathematical models involving the so-called Balloon model describes the relation between oxygen metabolism, cerebral blood volume, and cerebral blood flow. However, the Balloon model does not describe cellular and biochemical mechanisms. Secondly, the metabolic feedback hypothesis, which is based on experimental findings on metabolism associated with brain activation, and thirdly, the neurotransmitter feed-forward hypothesis which describes intracellular pathways leading to vasoactive substance release. Both the metabolic feedback and the neurotransmitter feed-forward hypotheses have been extensively studied, but only experimentally. These two hypotheses have never been implemented as mathematical models. Here we investigate these two hypotheses by mechanistic mathematical modeling using a systems biology approach; these methods have been used in biological research for many years but never been applied to the BOLD response in fMRI. In the current work, model structures describing the metabolic feedback and the neurotransmitter feed-forward hypotheses were applied to measured BOLD responses in the visual cortex of 12 healthy volunteers. Evaluating each hypothesis separately shows that neither hypothesis alone can describe the data in a biologically plausible way. However, by adding metabolism to the neurotransmitter feed-forward model structure, we obtained a new model structure which is able to fit the estimation data and successfully predict new

  19. fMRI repetition suppression: neuronal adaptation or stimulus expectation?

    Science.gov (United States)

    Larsson, Jonas; Smith, Andrew T

    2012-03-01

    Measurements of repetition suppression with functional magnetic resonance imaging (fMRI adaptation) have been used widely to probe neuronal population response properties in human cerebral cortex. fMRI adaptation techniques assume that fMRI repetition suppression reflects neuronal adaptation, an assumption that has been challenged on the basis of evidence that repetition-related response changes may reflect unrelated factors, such as attention and stimulus expectation. Specifically, Summerfield et al. (Summerfield C, Trittschuh EH, Monti JM, Mesulam MM, Egner T. 2008. Neural repetition suppression reflects fulfilled perceptual expectations. Nat Neurosci. 11:1004-1006) reported that the relative frequency of stimulus repetitions and non-repetitions influenced the magnitude of repetition suppression in the fusiform face area, suggesting that stimulus expectation accounted for most of the effect of repetition. We confirm that stimulus expectation can significantly influence fMRI repetition suppression throughout visual cortex and show that it occurs with long as well as short adaptation durations. However, the effect was attention dependent: When attention was diverted away from the stimuli, the effects of stimulus expectation completely disappeared. Nonetheless, robust and significant repetition suppression was still evident. These results suggest that fMRI repetition suppression reflects a combination of neuronal adaptation and attention-dependent expectation effects that can be experimentally dissociated. This implies that with an appropriate experimental design, fMRI adaptation can provide valid measures of neuronal adaptation and hence response specificity.

  20. Sparse representation of group-wise FMRI signals.

    Science.gov (United States)

    Lv, Jinglei; Li, Xiang; Zhu, Dajiang; Jiang, Xi; Zhang, Xin; Hu, Xintao; Zhang, Tuo; Guo, Lei; Liu, Tianming

    2013-01-01

    The human brain function involves complex processes with population codes of neuronal activities. Neuroscience research has demonstrated that when representing neuronal activities, sparsity is an important characterizing property. Inspired by this finding, significant amount of efforts from the scientific communities have been recently devoted to sparse representations of signals and patterns, and promising achievements have been made. However, sparse representation of fMRI signals, particularly at the population level of a group of different brains, has been rarely explored yet. In this paper, we present a novel group-wise sparse representation of task-based fMRI signals from multiple subjects via dictionary learning methods. Specifically, we extract and pool task-based fMRI signals for a set of cortical landmarks, each of which possesses intrinsic anatomical correspondence, from a group of subjects. Then an effective online dictionary learning algorithm is employed to learn an over-complete dictionary from the pooled population of fMRI signals based on optimally determined dictionary size. Our experiments have identified meaningful Atoms of Interests (AOI) in the learned dictionary, which correspond to consistent and meaningful functional responses of the brain to external stimulus. Our work demonstrated that sparse representation of group-wise fMRI signals is naturally suitable and effective in recovering population codes of neuronal signals conveyed in fMRI data.

  1. Optimizing stimulation and analysis protocols for neonatal fMRI.

    Science.gov (United States)

    Cusack, Rhodri; Wild, Conor; Linke, Annika C; Arichi, Tomoki; Lee, David S C; Han, Victor K

    2015-01-01

    The development of brain function in young infants is poorly understood. The core challenge is that infants have a limited behavioral repertoire through which brain function can be expressed. Neuroimaging with fMRI has great potential as a way of characterizing typical development, and detecting abnormal development early. But, a number of methodological challenges must first be tackled to improve the robustness and sensitivity of neonatal fMRI. A critical one of these, addressed here, is that the hemodynamic response function (HRF) in pre-term and term neonates differs from that in adults, which has a number of implications for fMRI. We created a realistic model of noise in fMRI data, using resting-state fMRI data from infants and adults, and then conducted simulations to assess the effect of HRF of the power of different stimulation protocols and analysis assumptions (HRF modeling). We found that neonatal fMRI is most powerful if block-durations are kept at the lower range of those typically used in adults (full on/off cycle duration 25-30s). Furthermore, we show that it is important to use the age-appropriate HRF during analysis, as mismatches can lead to reduced power or even inverted signal. Where the appropriate HRF is not known (for example due to potential developmental delay), a flexible basis set performs well, and allows accurate post-hoc estimation of the HRF.

  2. Classification of mouth movements using 7 T fMRI

    Science.gov (United States)

    Bleichner, M. G.; Jansma, J. M.; Salari, E.; Freudenburg, Z. V.; Raemaekers, M.; Ramsey, N. F.

    2015-12-01

    Objective. A brain-computer interface (BCI) is an interface that uses signals from the brain to control a computer. BCIs will likely become important tools for severely paralyzed patients to restore interaction with the environment. The sensorimotor cortex is a promising target brain region for a BCI due to the detailed topography and minimal functional interference with other important brain processes. Previous studies have shown that attempted movements in paralyzed people generate neural activity that strongly resembles actual movements. Hence decodability for BCI applications can be studied in able-bodied volunteers with actual movements. Approach. In this study we tested whether mouth movements provide adequate signals in the sensorimotor cortex for a BCI. The study was executed using fMRI at 7 T to ensure relevance for BCI with cortical electrodes, as 7 T measurements have been shown to correlate well with electrocortical measurements. Twelve healthy volunteers executed four mouth movements (lip protrusion, tongue movement, teeth clenching, and the production of a larynx activating sound) while in the scanner. Subjects performed a training and a test run. Single trials were classified based on the Pearson correlation values between the activation patterns per trial type in the training run and single trials in the test run in a ‘winner-takes-all’ design. Main results. Single trial mouth movements could be classified with 90% accuracy. The classification was based on an area with a volume of about 0.5 cc, located on the sensorimotor cortex. If voxels were limited to the surface, which is accessible for electrode grids, classification accuracy was still very high (82%). Voxels located on the precentral cortex performed better (87%) than the postcentral cortex (72%). Significance. The high reliability of decoding mouth movements suggests that attempted mouth movements are a promising candidate for BCI in paralyzed people.

  3. How restful is it with all that noise? Comparison of Interleaved silent steady state (ISSS) and conventional imaging in resting-state fMRI.

    Science.gov (United States)

    Andoh, J; Ferreira, M; Leppert, I R; Matsushita, R; Pike, B; Zatorre, R J

    2017-02-15

    Resting-state fMRI studies have become very important in cognitive neuroscience because they are able to identify BOLD fluctuations in brain circuits involved in motor, cognitive, or perceptual processes without the use of an explicit task. Such approaches have been fruitful when applied to various disordered populations, or to children or the elderly. However, insufficient attention has been paid to the consequences of the loud acoustic scanner noise associated with conventional fMRI acquisition, which could be an important confounding factor affecting auditory and/or cognitive networks in resting-state fMRI. Several approaches have been developed to mitigate the effects of acoustic noise on fMRI signals, including sparse sampling protocols and interleaved silent steady state (ISSS) acquisition methods, the latter being used only for task-based fMRI. Here, we developed an ISSS protocol for resting-state fMRI (rs-ISSS) consisting of rapid acquisition of a set of echo planar imaging volumes following each silent period, during which the steady state longitudinal magnetization was maintained with a train of relatively silent slice-selective excitation pulses. We evaluated the test-retest reliability of intensity and spatial extent of connectivity networks of fMRI BOLD signal across three different days for rs-ISSS and compared it with a standard resting-state fMRI (rs-STD). We also compared the strength and distribution of connectivity networks between rs-ISSS and rs-STD. We found that both rs-ISSS and rs-STD showed high reproducibility of fMRI signal across days. In addition, rs-ISSS showed a more robust pattern of functional connectivity within the somatosensory and motor networks, as well as an auditory network compared with rs-STD. An increased connectivity between the default mode network and the language network and with the anterior cingulate cortex (ACC) network was also found for rs-ISSS compared with rs-STD. Finally, region of interest analysis showed

  4. Semi-blind independent component analysis of fMRI based on real-time fMRI system.

    Science.gov (United States)

    Ma, Xinyue; Zhang, Hang; Zhao, Xiaojie; Yao, Li; Long, Zhiying

    2013-05-01

    Real-time functional magnetic resonance imaging (fMRI) is a type of neurofeedback tool that enables researchers to train individuals to actively gain control over their brain activation. Independent component analysis (ICA) based on data-driven model is seldom used in real-time fMRI studies due to large time cost, though it has been very popular to offline analysis of fMRI data. The feasibility of performing real-time ICA (rtICA) processing has been demonstrated by previous study. However, rtICA was only applied to analyze single-slice data rather than full-brain data. In order to improve the performance of rtICA, we proposed semi-blind real-time ICA (sb-rtICA) for our real-time fMRI system by adding regularization of certain estimated time courses using the experiment paradigm information to rtICA. Both simulated and real-time fMRI experiment were conducted to compare the two approaches. Results from simulated and real full-brain fMRI data demonstrate that sb-rtICA outperforms rtICA in robustness, computational time and spatial detection power. Moreover, in contrast to rtICA, the first component estimated by sb-rtICA tends to be the target component in more sliding windows.

  5. Multi-echo fMRI: A review of applications in fMRI denoising and analysis of BOLD signals.

    Science.gov (United States)

    Kundu, Prantik; Voon, Valerie; Balchandani, Priti; Lombardo, Michael V; Poser, Benedikt A; Bandettini, Peter A

    2017-07-01

    In recent years the field of fMRI research has enjoyed expanded technical abilities related to resolution, as well as use across many fields of brain research. At the same time, the field has also dealt with uncertainty related to many known and unknown effects of artifact in fMRI data. In this review we discuss an emerging fMRI technology, called multi-echo (ME)-fMRI, which focuses on improving the fidelity and interpretability of fMRI. Where the essential problem of standard single-echo fMRI is the indeterminacy of sources of signals, whether BOLD or artifact, this is not the case for ME-fMRI. By acquiring multiple echo images per slice, the ME approach allows T2* decay to be modeled at every voxel at every time point. Since BOLD signals arise by changes in T2* over time, an fMRI experiment sampling the T2* signal decay can be analyzed to distinguish BOLD from artifact signal constituents. While the ME approach has a long history of use in theoretical and validation studies, modern MRI systems enable whole-brain multi-echo fMRI at high resolution. This review covers recent multi-echo fMRI acquisition methods, and the analysis steps for this data to make fMRI at once more principled, straightforward, and powerful. After a brief overview of history and theory, T2* modeling and applications will be discussed. These applications include T2* mapping and combining echoes from ME data to increase BOLD contrast and mitigate dropout artifacts. Next, the modeling of fMRI signal changes to detect signal origins in BOLD-related T2* versus artifact-related S0 changes will be reviewed. A focus is on the use of ME-fMRI data to extract and classify components from spatial ICA, called multi-echo ICA (ME-ICA). After describing how ME-fMRI and ME-ICA lead to a general model for analysis of fMRI signals, applications in animal and human imaging will be discussed. Applications include removing motion artifacts in resting state data at subject and group level. New imaging methods such

  6. Give me a sign: decoding complex coordinated hand movements using high-field fMRI.

    Science.gov (United States)

    Bleichner, Martin G; Jansma, Johan M; Sellmeijer, Jim; Raemaekers, Mathijs; Ramsey, Nicolas F

    2014-03-01

    Decoding movements from the human cortex has been a topic of great interest for controlling an artificial limb in non-human primates and severely paralyzed people. Here we investigate feasibility of decoding gestures from the sensorimotor cortex in humans, using 7 T fMRI. Twelve healthy volunteers performed four hand gestures from the American Sign Language Alphabet. These gestures were performed in a rapid event related design used to establish the classifier and a slow event-related design, used to test the classifier. Single trial patterns were classified using a pattern-correlation classifier. The four hand gestures could be classified with an average accuracy of 63 % (range 35–95 %), which was significantly above chance (25 %). The hand region was, as expected, the most active region, and the optimal volume for classification was on average about 200 voxels, although this varied considerably across individuals. Importantly, classification accuracy correlated significantly with consistency of gesture execution. The results of our study demonstrate that decoding gestures from the hand region of the sensorimotor cortex using 7 T fMRI can reach very high accuracy, provided that gestures are executed in a consistent manner. Our results further indicate that the neuronal representation of hand gestures is robust and highly reproducible. Given that the most active foci were located in the hand region, and that 7 T fMRI has been shown to agree with electrocorticography, our results suggest that this confined region could serve to decode sign language gestures for intracranial brain–computer interfacing using surface grids.

  7. fMRI investigation of the effect of local and systemic lidocaine on noxious electrical stimulation-induced activation in spinal cord.

    Science.gov (United States)

    Zhao, Fuqiang; Williams, Mangay; Welsh, Denise C; Meng, Xiangjun; Ritter, Amy; Abbadie, Catherine; Cook, Jacquelynn J; Reicin, Alise S; Hargreaves, Richard; Williams, Donald S

    2009-09-01

    Spinal cord fMRI offers an excellent opportunity to quantify nociception using neuronal activation induced by painful stimuli. Measurement of the magnitude of stimulation-induced activation, and its suppression with analgesics can provide objective measures of pain and efficacy of analgesics. This study investigates the feasibility of using spinal cord fMRI in anesthetized rats as a pain assay to test the analgesic effect of locally and systemically administered lidocaine. Blood volume (BV)-weighted fMRI signal acquired after intravenous injection of ultrasmall superparamagnetic iron oxide (USPIO) particles was used as an indirect readout of the neuronal activity. Transcutaneous noxious electrical stimulation was used as the pain model. BV-weighted fMRI signal could be robustly quantified on a run-by-run basis, opening the possibility of measuring pharmacodynamics (PD) of the analgesics with a temporal resolution of approximately 2 min. Local administration of lidocaine was shown to ablate all stimulation-induced fMRI signals by the total blockage of peripheral nerve transmission, while the analgesic effect of systemically administered lidocaine was robustly detected after intravenous infusion of approximately 3mg/kg, which is similar to clinical dosage for human. This study establishes spinal cord fMRI as a viable assay for analgesics. With respect to the mode of action of lidocaine, this study suggests that systemic lidocaine, which is clinically used for the treatment of neuropathic pain, and believed to only block the peripheral nerve transmission of abnormal neural activity (ectopic discharge) originating from the damaged peripheral nerves, also blocks the peripheral nerve transmission of normal neural activity induced by transcutaneous noxious electrical stimulation.

  8. Visual stimulation, {sup 1}H MR spectroscopy and fMRI of the human visual pathways

    Energy Technology Data Exchange (ETDEWEB)

    Boucard, Christine C.; Cornelissen, Frans W. [University of Groningen, Laboratory for Experimental Ophthalmology, Postbus 30001, Groningen (Netherlands); University of Groningen, BCN Neuro-imaging Center, Postbus 196, Groningen (Netherlands); Mostert, Jop P.; Keyser, Jacques De [University Hospital Groningen, Department of Neurology, Groningen (Netherlands); Oudkerk, Matthijs; Sijens, Paul E. [University Hospital Groningen, Department of Radiology, Groningen (Netherlands)

    2005-01-01

    The purpose was to assess changes in lactate content and other brain metabolites under visual stimulation in optical chiasm, optic radiations and occipital cortex using multiple voxel MR spectroscopy (MRS). {sup 1}H chemical shift imaging (CSI) examinations of transverse planes centered to include the above structures were performed in four subjects at an echo time of 135 ms. Functional MRI (fMRI) was used to confirm the presence of activity in the visual cortex during the visual stimulation. Spectral maps of optical chiasm were of poor quality due to field disturbances caused by nearby large blood vessels and/or eye movements. The optic radiations and the occipital lobe did not show any significant MR spectral change upon visual stimulation, i.e., the peak areas of inositol, choline, creatine, glutamate and N-acetylaspartate were not affected. Reproducible lactate signals were not observed. fMRI confirmed the presence of strong activations in stimulated visual cortex. Prolonged visual stimulation did not cause significant changes in MR spectra. Any signal observed near the 1.33 ppm resonance frequency of the lactate methyl-group was artifactual, originating from lipid signals from outside the volume of interest (VOI). Previous claims about changes in lactate levels in the visual cortex upon visual stimulation may have been based on such erroneous observations. (orig.)

  9. fMRI. Basics and clinical applications. 2. ed.

    Energy Technology Data Exchange (ETDEWEB)

    Ulmer, Stephan [Medizinisch Radiologisces Institut (MRI), Zuerich (Switzerland); Universitaetsklinikum Schleswig-Holstein, Kiel (Germany). Inst. fuer Neuroradiologie; Jansen, Olav (eds.) [Universitaetsklinikum Schleswig-Holstein, Kiel (Germany). Inst. fuer Neuroradiologie

    2013-11-01

    State of the art overview of fMRI. Covers technical issues, methods of statistical analysis, and the full range of clinical applications. Revised and expanded edition including discussion of novel aspects of analysis and further important applications. Includes comparisons with other brain mapping techniques and discussion of potential combined uses. Since functional MRI (fMRI) and the basic method of BOLD imaging were introduced in 1993 by Seiji Ogawa, fMRI has evolved into an invaluable clinical tool for routine brain imaging, and there have been substantial improvements in both the imaging technique itself and the associated statistical analysis. This book provides a state of the art overview of fMRI and its use in clinical practice. Experts in the field share their knowledge and explain how to overcome diverse potential technical barriers and problems. Starting from the very basics on the origin of the BOLD signal, the book covers technical issues, anatomical landmarks, the full range of clinical applications, methods of statistical analysis, and special issues in various clinical fields. Comparisons are made with other brain mapping techniques, such as DTI, PET, TMS, EEG, and MEG, and their combined use with fMRI is also discussed. Since the first edition, original chapters have been updated and new chapters added, covering both novel aspects of analysis and further important clinical applications.

  10. Functional and structural abnormalities associated with empathy in patients with schizophrenia: An fMRI and VBM study.

    Science.gov (United States)

    Singh, Sadhana; Modi, Shilpi; Goyal, Satnam; Kaur, Prabhjot; Singh, Namita; Bhatia, Triptish; Deshpande, Smita N; Khushu, Subash

    2015-06-01

    Empathy deficit is a core feature of schizophrenia which may lead to social dysfunction. The present study was carried out to investigate functional and structural abnormalities associated with empathy in patients with schizophrenia using functional magnetic resonance imaging (fMRI) and voxel-based morphometry (VBM). A sample of 14 schizophrenia patients and 14 healthy control subjects matched for age, sex and education were examined with structural highresolution T1-weighted MRI; fMRI images were obtained during empathy task in the same session. The analysis was carried out using SPM8 software. On behavioural assessment, schizophrenic patients (83.00+-29.04) showed less scores for sadness compared to healthy controls (128.70+-22.26) (p less than 0.001). fMRI results also showed reduced clusters of activation in the bilateral fusiform gyrus, left lingual gyrus, left middle and inferior occipital gyrus in schizophrenic subjects as compared to controls during empathy task. In the same brain areas, VBM results also showed reduced grey and white matter volumes. The present study provides an evidence for an association between structural alterations and disturbed functional brain activation during empathy task in persons affected with schizophrenia. These findings suggest a biological basis for social cognition deficits in schizophrenics.

  11. Functional and structural abnormalities associated with empathy in patients with schizophrenia: An fMRI and VBM study

    Indian Academy of Sciences (India)

    Sadhana Singh; Shilpi Modi; Satnam Goyal; Prabhjot Kaur; Namita Singh; Triptish Bhatia; Smita N Deshpande; Subash Khushu

    2015-06-01

    Empathy deficit is a core feature of schizophrenia which may lead to social dysfunction. The present study was carried out to investigate functional and structural abnormalities associated with empathy in patients with schizophrenia using functional magnetic resonance imaging (fMRI) and voxel-based morphometry (VBM). A sample of 14 schizophrenia patients and 14 healthy control subjects matched for age, sex and education were examined with structural high-resolution T1-weighted MRI; fMRI images were obtained during empathy task in the same session. The analysis was carried out using SPM8 software. On behavioural assessment, schizophrenic patients (83.00±29.04) showed less scores for sadness compared to healthy controls (128.70±22.26) ( < 0.001). fMRI results also showed reduced clusters of activation in the bilateral fusiform gyrus, left lingual gyrus, left middle and inferior occipital gyrus in schizophrenic subjects as compared to controls during empathy task. In the same brain areas, VBM results also showed reduced grey and white matter volumes. The present study provides an evidence for an association between structural alterations and disturbed functional brain activation during empathy task in persons affected with schizophrenia. These findings suggest a biological basis for social cognition deficits in schizophrenics.

  12. Re-examine tumor-induced alterations in hemodynamic responses of BOLD fMRI. Implications in presurgical brain mapping

    Energy Technology Data Exchange (ETDEWEB)

    Wang, Liya [Dept. of Radiology and Imaging Sciences, Emory Univ., School of Medicine, Atlanta (United States); Dept. of Radiology, Baoan Hospital, Shenzhen (China); Ali, Shazia; Fa, Tianning; Mao, Hui [Dept. of Radiology and Imaging Sciences, Emory Univ., School of Medicine, Atlanta (United States)], e-mail: hmao@emory.edu; Dandan, Chen [Dept. of Physics, Emory Univ., Atlanta, (United States); School of Radiation Medicine and Protection, Soochow Univ., Suzhou (China); Olson, Jeffrey [Dept. of Neurosurgery, Emory Univ., School of Medicine, Atlanta (United States)

    2012-09-15

    Background: Blood oxygenation level dependent (BOLD) fMRI is used for presurgical functional mapping of brain tumor patients. Abnormal tumor blood supply may affect hemodynamic responses and BOLD fMRI signals. Purpose: To perform a multivariate and quantitative investigation of the effect of brain tumors on the hemodynamic responses and its impact on BOLD MRI signal time course, data analysis in order to better understand tumor-induced alterations in hemodynamic responses, and accurately mapping cortical regions in brain tumor patients. Material and Methods: BOLD fMRI data from 42 glioma patients who underwent presurgical mapping of the primary motor cortex (PMC) with a block designed finger tapping paradigm were analyzed, retrospectively. Cases were divided into high grade (n = 24) and low grade (n = 18) groups based on pathology. The tumor volume and distance to the activated PMCs were measured. BOLD signal time courses from selected regions of interest (ROIs) in the PMCs of tumor affected and contralateral unaffected hemispheres were obtained from each patient. Tumor-induced changes of BOLD signal intensity and time to peak (TTP) of BOLD signal time courses were analyzed statistically. Results: The BOLD signal intensity and TTP in the tumor-affected PMCs are altered when compared to that of the unaffected hemisphere. The average BOLD signal level is statistically significant lower in the affected PMCs. The average TTP in the affected PMCs is shorter in the high grade group, but longer in the low grade tumor group compared to the contralateral unaffected hemisphere. Degrees of alterations in BOLD signal time courses are related to both the distance to activated foci and tumor volume with the stronger effect in tumor distance to activated PMC. Conclusion: Alterations in BOLD signal time courses are strongly related to the tumor grade, the tumor volume, and the distance to the activated foci. Such alterations may impair accurate mapping of tumor-affected functional

  13. Archetypal Analysis for Modeling Multisubject fMRI Data

    DEFF Research Database (Denmark)

    Hinrich, Jesper Løve; Bardenfleth, Sophia Elizabeth; Røge, Rasmus

    2016-01-01

    Functional magnetic resonance imaging (fMRI) is widely used to measure brain function during various cognitive states. However, it remains a challenge to obtain low-rank models of functional networks in fMRI that have interpretable latent features and generalize across groups of subjects, due...... are assumed to be generated by a set of 'prototype' time series. Archetypal analysis (AA) provides a promising alternative, combining the advantages of component-model flexibility with highly interpretable latent 'archetypes' (similar to cluster-model prototypes). To date, AA has not been applied to group......-level fMRI; a major limitation is that it does not generalize to multi-subject datasets, which may have significant variations in blood oxygenation-level-dependent signal and heteroscedastic noise. We develop multi-subject AA (MS-AA), which accounts for group-level data by assuming that archetypal...

  14. Altered Dynamics of the fMRI Response to Faces in Individuals with Autism

    Science.gov (United States)

    Kleinhans, Natalia M.; Richards, Todd; Greenson, Jessica; Dawson, Geraldine; Aylward, Elizabeth

    2016-01-01

    Abnormal fMRI habituation in autism spectrum disorders (ASDs) has been proposed as a critical component in social impairment. This study investigated habituation to fearful faces and houses in ASD and whether fMRI measures of brain activity discriminate between ASD and typically developing (TD) controls. Two identical fMRI runs presenting masked…

  15. Neuroethics and fMRI: Mapping a fledgling relationship

    DEFF Research Database (Denmark)

    Garnett, Alex; Whiteley, Louise Emma; Piwowar, Heather;

    2011-01-01

    of moral judgments. It is at the centre of debate surrounding the importance of neuroscience findings for concepts such as personhood and free will, and the extent of their practical consequences. Here, we map the landscape of fMRI and neuroethics, using citation analysis to uncover salient topics. We find...... that this landscape is sparsely populated: despite previous calls for debate, there are few articles that discuss both fMRI and ethical, legal, or social implications (ELSI), and even fewer direct citations between the two literatures. Recognizing that practical barriers exist to integrating ELSI discussion...

  16. Mixed-effects and fMRI studies

    DEFF Research Database (Denmark)

    Friston, K.J; Stephan, K.E; Ellegaard Lund, Torben

    2005-01-01

    This note concerns mixed-effect (MFX) analyses in multisession functional magnetic resonance imaging (fMRI) studies. It clarifies the relationship between mixed-effect analyses and the two-stage 'summary statistics' procedure (Holmes, A.P., Friston, K.J., 1998. Generalisability, random effects...... and population inference. NeuroImage 7, S754) that has been adopted widely for analyses of fMRI data at the group level. We describe a simple procedure, based on restricted maximum likelihood (ReML) estimates of covariance components, that enables full mixed-effects analyses in the context of statistical...

  17. Temporal and Spatial Independent Component Analysis for fMRI Data Sets Embedded in the AnalyzeFMRI R Package

    Directory of Open Access Journals (Sweden)

    Pierre Lafaye de Micheaux

    2011-10-01

    Full Text Available For statistical analysis of functional magnetic resonance imaging (fMRI data sets, we propose a data-driven approach based on independent component analysis (ICA implemented in a new version of the AnalyzeFMRI R package. For fMRI data sets, spatial dimension being much greater than temporal dimension, spatial ICA is the computationally tractable approach generally proposed. However, for some neuroscientific applications, temporal independence of source signals can be assumed and temporal ICA becomes then an attractive exploratory technique. In this work, we use a classical linear algebra result ensuring the tractability of temporal ICA. We report several experiments on synthetic data and real MRI data sets that demonstrate the potential interest of our R package.

  18. Identifying individuals with antisocial personality disorder using resting-state FMRI.

    Directory of Open Access Journals (Sweden)

    Yan Tang

    Full Text Available Antisocial personality disorder (ASPD is closely connected to criminal behavior. A better understanding of functional connectivity in the brains of ASPD patients will help to explain abnormal behavioral syndromes and to perform objective diagnoses of ASPD. In this study we designed an exploratory data-driven classifier based on machine learning to investigate changes in functional connectivity in the brains of patients with ASPD using resting state functional magnetic resonance imaging (fMRI data in 32 subjects with ASPD and 35 controls. The results showed that the classifier achieved satisfactory performance (86.57% accuracy, 77.14% sensitivity and 96.88% specificity and could extract stabile information regarding functional connectivity that could be used to discriminate ASPD individuals from normal controls. More importantly, we found that the greatest change in the ASPD subjects was uncoupling between the default mode network and the attention network. Moreover, the precuneus, superior parietal gyrus and cerebellum exhibited high discriminative power in classification. A voxel-based morphometry analysis was performed and showed that the gray matter volumes in the parietal lobule and white matter volumes in the precuneus were abnormal in ASPD compared to controls. To our knowledge, this study was the first to use resting-state fMRI to identify abnormal functional connectivity in ASPD patients. These results not only demonstrated good performance of the proposed classifier, which can be used to improve the diagnosis of ASPD, but also elucidate the pathological mechanism of ASPD from a resting-state functional integration viewpoint.

  19. Impact of Global Normalization in fMRI Acupuncture Studies

    Directory of Open Access Journals (Sweden)

    Jinbo Sun

    2012-01-01

    Full Text Available Global normalization is often used as a preprocessing step for dispelling the “nuisance effects.” However, it has been shown in cognitive and emotion tasks that this preprocessing step might greatly distort statistical results when the orthogonality assumption of global normalization is violated. The present study examines this issue in fMRI acupuncture studies. Thirty healthy subjects were recruited to evaluate the impacts of the global normalization on the BOLD responses evoked by acupuncture stimulation during De-qi sensation and tactile stimulation during nonpainful sensations. To this end, we compared results by conducting global normalization (PSGS and not conducting global normalization (NO PSGS based on a proportional scaling model. The orthogonality assumption of global normalization was violated, and significant changes between BOLD responses for NO PSGS and PSGS were shown in most subjects. Extensive deactivations of acupuncture in fMRI were the non-specifically pernicious consequences of global normalization. The central responses of acupuncture during De-qi are non-specifically activation-dominant at the somatosensory-related brain network, whose statistical power is specifically enhanced by PSGS. In conclusion, PSGS should be unjustified for acupuncture studies in fMRI. The differences including the global normalization or not may partly contribute to conflicting results and interpretations in previous fMRI acupuncture studies.

  20. Bayesian Modelling of fMRI Time Series

    DEFF Research Database (Denmark)

    Højen-Sørensen, Pedro; Hansen, Lars Kai; Rasmussen, Carl Edward

    2000-01-01

    We present a Hidden Markov Model (HMM) for inferring the hidden psychological state (or neural activity) during single trial fMRI activation experiments with blocked task paradigms. Inference is based on Bayesian methodology, using a combination of analytical and a variety of Markov Chain Monte...

  1. The Effect of Strategy on Problem Solving: An FMRI Study

    Science.gov (United States)

    Newman, Sharlene D.; Pruce, Benjamin; Rusia, Akash; Burns, Thomas, Jr.

    2010-01-01

    fMRI was used to examine the differential effect of two problem-solving strategies. Participants were trained to use both a pictorial/spatial and a symbolic/algebraic strategy to solve word problems. While these two strategies activated similar cortical regions, a number of differences were noted in the level of activation. These differences…

  2. fMRI in Parkinson’s Disease

    DEFF Research Database (Denmark)

    Siebner, Hartwig R.; Herz, Damian

    2013-01-01

    In this chapter we review recent advances in functional magnetic resonance imaging (fMRI) in Parkinson’s disease (PD). Covariance patterns of regional resting-state activity in functional brain networks can be used to distinguish Parkinson patients from healthy controls and might play an important...

  3. Realignment strategies for awake-monkey fMRI data.

    Science.gov (United States)

    Stoewer, Steffen; Goense, Jozien; Keliris, Georgios A; Bartels, Andreas; Logothetis, Nikos K; Duncan, John; Sigala, Natasha

    2011-12-01

    Functional magnetic resonance imaging (fMRI) experiments with awake nonhuman primates (NHPs) have recently seen a surge of applications. However, the standard fMRI analysis tools designed for human experiments are not optimal for NHP data collected at high fields. One major difference is the experimental setup. Although real head movement is impossible for NHPs, MRI image series often contain visible motion artifacts. Animal body movement results in image position changes and geometric distortions. Since conventional realignment methods are not appropriate to address such differences, algorithms tailored specifically for animal scanning become essential. We have implemented a series of high-field NHP specific methods in a software toolbox, fMRI Sandbox (http://kyb.tuebingen.mpg.de/~stoewer/), which allows us to use different realignment strategies. Here we demonstrate the effect of different realignment strategies on the analysis of awake-monkey fMRI data acquired at high field (7 T). We show that the advantage of using a nonstandard realignment algorithm depends on the amount of distortion in the dataset. While the benefits for less distorted datasets are minor, the improvement of statistical maps for heavily distorted datasets is significant.

  4. Linear systems analysis of the fMRI signal.

    Science.gov (United States)

    Boynton, Geoffrey M; Engel, Stephen A; Heeger, David J

    2012-08-15

    In 1995 when we began our investigations of the human visual system using fMRI, little was known about the temporal properties of the fMRI signal. Before we felt comfortable making quantitative estimates of neuronal responses with this new technique, we decided to first conduct a basic study of how the time-course of the fMRI response varied with stimulus timing and strength. The results ended up showing strong evidence that to a first approximation the hemodynamic transformation was linear in time. This was both important and remarkable: important because nearly all fMRI data analysis techniques assume or require linearity, and remarkable because the physiological basis of the hemodynamic transformation is so complex that we still have a far from complete understanding of it. In this paper, we provide highlights of the results of our original paper supporting the linear transform hypothesis. A reanalysis of the original data provides some interesting new insights into the published results. We also provide a detailed appendix describing of the properties and predictions of a linear system in time in the context of the transformation between neuronal responses and the BOLD signal.

  5. Neuroethics and fMRI: mapping a fledgling relationship.

    Science.gov (United States)

    Garnett, Alex; Whiteley, Louise; Piwowar, Heather; Rasmussen, Edie; Illes, Judy

    2011-04-22

    Human functional magnetic resonance imaging (fMRI) informs the understanding of the neural basis of mental function and is a key domain of ethical enquiry. It raises questions about the practice and implications of research, and reflexively informs ethics through the empirical investigation of moral judgments. It is at the centre of debate surrounding the importance of neuroscience findings for concepts such as personhood and free will, and the extent of their practical consequences. Here, we map the landscape of fMRI and neuroethics, using citation analysis to uncover salient topics. We find that this landscape is sparsely populated: despite previous calls for debate, there are few articles that discuss both fMRI and ethical, legal, or social implications (ELSI), and even fewer direct citations between the two literatures. Recognizing that practical barriers exist to integrating ELSI discussion into the research literature, we argue nonetheless that the ethical challenges of fMRI, and controversy over its conceptual and practical implications, make this essential.

  6. Neuroethics and fMRI: mapping a fledgling relationship.

    Directory of Open Access Journals (Sweden)

    Alex Garnett

    Full Text Available Human functional magnetic resonance imaging (fMRI informs the understanding of the neural basis of mental function and is a key domain of ethical enquiry. It raises questions about the practice and implications of research, and reflexively informs ethics through the empirical investigation of moral judgments. It is at the centre of debate surrounding the importance of neuroscience findings for concepts such as personhood and free will, and the extent of their practical consequences. Here, we map the landscape of fMRI and neuroethics, using citation analysis to uncover salient topics. We find that this landscape is sparsely populated: despite previous calls for debate, there are few articles that discuss both fMRI and ethical, legal, or social implications (ELSI, and even fewer direct citations between the two literatures. Recognizing that practical barriers exist to integrating ELSI discussion into the research literature, we argue nonetheless that the ethical challenges of fMRI, and controversy over its conceptual and practical implications, make this essential.

  7. The Effect of Strategy on Problem Solving: An FMRI Study

    Science.gov (United States)

    Newman, Sharlene D.; Pruce, Benjamin; Rusia, Akash; Burns, Thomas, Jr.

    2010-01-01

    fMRI was used to examine the differential effect of two problem-solving strategies. Participants were trained to use both a pictorial/spatial and a symbolic/algebraic strategy to solve word problems. While these two strategies activated similar cortical regions, a number of differences were noted in the level of activation. These differences…

  8. The Effect of fMRI (Noise) on Cognitive Control

    Science.gov (United States)

    Hommel, Bernhard; Fischer, Rico; Colzato, Lorenza S.; van den Wildenberg, Wery P. M.; Cellini, Cristiano

    2012-01-01

    Stressful situations, the aversiveness of events, or increases in task difficulty (e.g., conflict) have repeatedly been shown to be capable of triggering attentional control adjustments. In the present study we tested whether the particularity of an fMRI testing environment (i.e., EPI noise) might result in such increases of the cognitive control…

  9. Energetics of neuronal signaling and fMRI activity.

    Science.gov (United States)

    Maandag, Natasja J G; Coman, Daniel; Sanganahalli, Basavaraju G; Herman, Peter; Smith, Arien J; Blumenfeld, Hal; Shulman, Robert G; Hyder, Fahmeed

    2007-12-18

    Energetics of resting and evoked fMRI signals were related to localized ensemble firing rates (nu) measured by electrophysiology in rats. Two different unstimulated, or baseline, states were established by anesthesia. Halothane and alpha-chloralose established baseline states of high and low energy, respectively, in which forepaw stimulation excited the contralateral primary somatosensory cortex (S1). With alpha-chloralose, forepaw stimulation induced strong and reproducible fMRI activations in the contralateral S1, where the ensemble firing was dominated by slow signaling neurons (SSN; nu range of 1-13 Hz). Under halothane, weaker and less reproducible fMRI activations were observed in the contralateral S1 and elsewhere in the cortex, but ensemble activity in S1 was dominated by rapid signaling neurons (RSN; nu range of 13-40 Hz). For both baseline states, the RSN activity (i.e., higher frequencies, including the gamma band) did not vary upon stimulation, whereas the SSN activity (i.e., alpha band and lower frequencies) did change. In the high energy baseline state, a large majority of total oxidative energy [cerebral metabolic rate of oxygen consumption (CMR(O2))] was devoted to RSN activity, whereas in the low energy baseline state, it was roughly divided between SSN and RSN activities. We hypothesize that in the high energy baseline state, the evoked changes in fMRI activation in areas beyond S1 are supported by rich intracortical interactions represented by RSN. We discuss implications for interpreting fMRI data where stimulus-specific DeltaCMR(O2) is generally small compared with baseline CMR(O2).

  10. Age-Dependent Mesial Temporal Lobe Lateralization in Language FMRI

    Science.gov (United States)

    Sepeta, Leigh N.; Berl, Madison M.; Wilke, Marko; You, Xiaozhen; Mehta, Meera; Xu, Benjamin; Inati, Sara; Dustin, Irene; Khan, Omar; Austermuehle, Alison; Theodore, William H.; Gaillard, William D.

    2015-01-01

    Objective FMRI activation of the mesial temporal lobe (MTL) may be important for epilepsy surgical planning. We examined MTL activation and lateralization during language fMRI in children and adults with focal epilepsy. Methods 142 controls and patients with left hemisphere focal epilepsy (Pediatric: epilepsy, n = 17, mean age = 9.9 ± 2.0; controls, n = 48; mean age = 9.1 ± 2.6; Adult: epilepsy, n = 20, mean age = 26.7 ± 5.8; controls, n = 57, mean age = 26.2 ± 7.5) underwent 3T fMRI using a language task (auditory description decision task). Image processing and analyses were conducted in SPM8; ROIs included MTL, Broca’s area, and Wernicke’s area. We assessed group and individual MTL activation, and examined degree of lateralization. Results Patients and controls (pediatric and adult) demonstrated group and individual MTL activation during language fMRI. MTL activation was left lateralized for adults but less so in children (p’s < 0.005). Patients did not differ from controls in either age group. Stronger left-lateralized MTL activation was related to older age (p = 0.02). Language lateralization (Broca’s and Wernicke’s) predicted 19% of the variance in MTL lateralization for adults (p = 0.001), but not children. Significance Language fMRI may be used to elicit group and individual MTL activation. The developmental difference in MTL lateralization and its association with language lateralization suggests a developmental shift in lateralization of MTL function, with increased left lateralization across the age span. This shift may help explain why children have better memory outcomes following resection compared to adults. PMID:26696589

  11. Volume Entropy

    CERN Document Server

    Astuti, Valerio; Rovelli, Carlo

    2016-01-01

    Building on a technical result by Brunnemann and Rideout on the spectrum of the Volume operator in Loop Quantum Gravity, we show that the dimension of the space of the quadrivalent states --with finite-volume individual nodes-- describing a region with total volume smaller than $V$, has \\emph{finite} dimension, bounded by $V \\log V$. This allows us to introduce the notion of "volume entropy": the von Neumann entropy associated to the measurement of volume.

  12. Automatic physiological waveform processing for FMRI noise correction and analysis.

    Directory of Open Access Journals (Sweden)

    Daniel J Kelley

    Full Text Available Functional MRI resting state and connectivity studies of brain focus on neural fluctuations at low frequencies which share power with physiological fluctuations originating from lung and heart. Due to the lack of automated software to process physiological signals collected at high magnetic fields, a gap exists in the processing pathway between the acquisition of physiological data and its use in fMRI software for both physiological noise correction and functional analyses of brain activation and connectivity. To fill this gap, we developed an open source, physiological signal processing program, called PhysioNoise, in the python language. We tested its automated processing algorithms and dynamic signal visualization on resting monkey cardiac and respiratory waveforms. PhysioNoise consistently identifies physiological fluctuations for fMRI noise correction and also generates covariates for subsequent analyses of brain activation and connectivity.

  13. Variational Bayesian Causal Connectivity Analysis for fMRI

    Directory of Open Access Journals (Sweden)

    Martin eLuessi

    2014-05-01

    Full Text Available The ability to accurately estimate effective connectivity among brain regions from neuroimaging data could help answering many open questions in neuroscience. We propose a method which uses causality to obtain a measure of effective connectivity from fMRI data. The method uses a vector autoregressive model for the latent variables describing neuronal activity in combination with a linear observation model based on a convolution with a hemodynamic response function. Due to the employed modeling, it is possible to efficiently estimate all latent variables of the model using a variational Bayesian inference algorithm. The computational efficiency of the method enables us to apply it to large scale problems with high sampling rates and several hundred regions of interest. We use a comprehensive empirical evaluation with synthetic and real fMRI data to evaluate the performance of our method under various conditions.

  14. Neurophysiologic correlates of fMRI in human motor cortex.

    Science.gov (United States)

    Hermes, Dora; Miller, Kai J; Vansteensel, Mariska J; Aarnoutse, Erik J; Leijten, Frans S S; Ramsey, Nick F

    2012-07-01

    The neurophysiological underpinnings of functional magnetic resonance imaging (fMRI) are not well understood. To understand the relationship between the fMRI blood oxygen level dependent (BOLD) signal and neurophysiology across large areas of cortex, we compared task related BOLD change during simple finger movement to brain surface electric potentials measured on a similar spatial scale using electrocorticography (ECoG). We found that spectral power increases in high frequencies (65-95 Hz), which have been related to local neuronal activity, colocalized with spatially focal BOLD peaks on primary sensorimotor areas. Independent of high frequencies, decreases in low frequency rhythms (neurophysiological mechanisms, one being spatially focal neuronal processing and the other spatially distributed low frequency rhythms. Copyright © 2011 Wiley-Liss, Inc.

  15. Prospective multiaxial motion correction for fMRI.

    Science.gov (United States)

    Ward, H A; Riederer, S J; Grimm, R C; Ehman, R L; Felmlee, J P; Jack, C R

    2000-03-01

    Corruption of the image time series due to interimage head motion limits the clinical utility of functional MRI. This paper presents a method for real-time prospective correction of rotation and translation in all six degrees of rigid body motion. By incorporating an orbital navigator (ONAV) echo for each of the sagittal, axial, and coronal planes into the fMRI pulse sequence, rotation and translation can be measured and the spatial orientation of the image acquisition sequence that follows can be corrected prospectively in as little as 160 msec. Testing of the method using a computerized motion phantom capable of performing complex multiaxial motion showed subdegree rotational and submillimeter translational accuracy over a range of +/-8 degrees and +/-8 mm of motion. In vivo images demonstrate correction of simultaneous through-plane and in-plane motion and improved detection of fMRI activation in the presence of head motion.

  16. fMRI paradigm designing and post-processing tools

    Directory of Open Access Journals (Sweden)

    Jija S James

    2014-01-01

    Full Text Available In this article, we first review some aspects of functional magnetic resonance imaging (fMRI paradigm designing for major cognitive functions by using stimulus delivery systems like Cogent, E-Prime, Presentation, etc., along with their technical aspects. We also review the stimulus presentation possibilities (block, event-related for visual or auditory paradigms and their advantage in both clinical and research setting. The second part mainly focus on various fMRI data post-processing tools such as Statistical Parametric Mapping (SPM and Brain Voyager, and discuss the particulars of various preprocessing steps involved (realignment, co-registration, normalization, smoothing in these software and also the statistical analysis principles of General Linear Modeling for final interpretation of a functional activation result.

  17. Replicability and heterogeneity of awake unrestrained canine FMRI responses.

    Directory of Open Access Journals (Sweden)

    Gregory S Berns

    Full Text Available Previously, we demonstrated the possibility of fMRI in two awake and unrestrained dogs. Here, we determined the replicability and heterogeneity of these results in an additional 11 dogs for a total of 13 subjects. Based on an anatomically placed region-of-interest, we compared the caudate response to a hand signal indicating the imminent availability of a food reward to a hand signal indicating no reward. 8 of 13 dogs had a positive differential caudate response to the signal indicating reward. The mean differential caudate response was 0.09%, which was similar to a comparable human study. These results show that canine fMRI is reliable and can be done with minimal stress to the dogs.

  18. Replicability and heterogeneity of awake unrestrained canine FMRI responses.

    Science.gov (United States)

    Berns, Gregory S; Brooks, Andrew; Spivak, Mark

    2013-01-01

    Previously, we demonstrated the possibility of fMRI in two awake and unrestrained dogs. Here, we determined the replicability and heterogeneity of these results in an additional 11 dogs for a total of 13 subjects. Based on an anatomically placed region-of-interest, we compared the caudate response to a hand signal indicating the imminent availability of a food reward to a hand signal indicating no reward. 8 of 13 dogs had a positive differential caudate response to the signal indicating reward. The mean differential caudate response was 0.09%, which was similar to a comparable human study. These results show that canine fMRI is reliable and can be done with minimal stress to the dogs.

  19. Bayesian Modelling of fMRI Time Series

    DEFF Research Database (Denmark)

    Højen-Sørensen, Pedro; Hansen, Lars Kai; Rasmussen, Carl Edward

    2000-01-01

    We present a Hidden Markov Model (HMM) for inferring the hidden psychological state (or neural activity) during single trial fMRI activation experiments with blocked task paradigms. Inference is based on Bayesian methodology, using a combination of analytical and a variety of Markov Chain Monte C...... Carlo (MCMC) sampling techniques. The advantage of this method is that detection of short time learning effects between repeated trials is possible since inference is based only on single trial experiments....

  20. Contradictory Reasoning Network: An EEG and fMRI Study

    OpenAIRE

    Porcaro, Camillo; Medaglia, Maria Teresa; Thai, Ngoc Jade; Seri, Stefano; Rotshtein, Pia; Tecchio, Franca

    2014-01-01

    Contradiction is a cornerstone of human rationality, essential for everyday life and communication. We investigated electroencephalographic (EEG) and functional magnetic resonance imaging (fMRI) in separate recording sessions during contradictory judgments, using a logical structure based on categorical propositions of the Aristotelian Square of Opposition (ASoO). The use of ASoO propositions, while controlling for potential linguistic or semantic confounds, enabled us to observe the spatial ...

  1. Exploring fMRI Data for Periodic Signal Components

    DEFF Research Database (Denmark)

    Hansen, Lars Kai; Nielsen, Finn Årup; Larsen, Jan

    2002-01-01

    We use a Bayesian framework to detect periodic components in fMRI data. The resulting detector is sensitive to periodic components with a flexible number of harmonics and with arbitrary amplitude and phases of the harmonics. It is possible to detect the correct number of harmonics in periodic sig...... signals even if the fundamental frequency is beyond the Nyquist frequency. We apply the signal detector to locate regions that are highly affected by periodic physiological artifacts, such as cardiac pulsation....

  2. The effect of topiramate on cognitive fMRI

    Science.gov (United States)

    Yasuda, Clarissa Lin; Centeno, Maria; Vollmar, Christian; Stretton, Jason; Symms, Mark; Cendes, Fernando; Mehta, Mitul A.; Thompson, Pamela; Duncan, John S.; Koepp, Matthias J.

    2013-01-01

    Summary Purpose Topiramate (TPM) is known to cause language impairment in healthy volunteers and patients with epilepsy. We assessed the effects of TPM on functional language networks in both patients with focal epilepsies and healthy controls using functional magnetic resonance imaging (fMRI). Methods We obtained fMRI data in 24 controls and 35 patients with frontal lobe epilepsy using a simple verbal fluency (VF) paradigm. Eight of the 35 patients were treated with TPM in polytherapy. We compared cognitive task related activations and de-activations in patients taking TPM with patients taking other AEDs and healthy controls. In a longitudinal pilot study with VF-fMRI paradigm, we studied two patients with focal epilepsies twice, prior to starting and on stable doses of TPM, two patients twice, before and after tapering TPM completely and two healthy controls twice, before and after single doses of 200 mg TPM. Key findings Cross sectional analyses of VF-fMRI showed a reduction in the task-related deactivation of the default mode network (DMN) in patients taking TPM. The longitudinal study corroborated these findings as both chronic administration and a single dose of TPM were associated with impaired categorical verbal fluency and disruption of task-related deactivations. Significance Similar neuropsychological and fMRI findings in patients and healthy controls indicate a specific effect of TPM in default mode network areas that may be essential components of the language network. Our preliminary data suggest a mechanism by which TPM impairs cognitive processing during language function and highlights the sensitivity of fMRI to detect the effects of AEDs on cognitive brain networks. PMID:23333471

  3. Investigation of fMRI activation in the internal capsule

    Directory of Open Access Journals (Sweden)

    Brewer Kimberley D

    2011-06-01

    Full Text Available Abstract Background Functional magnetic resonance imaging (fMRI in white matter has long been considered controversial. Recently, this viewpoint has been challenged by an emerging body of evidence demonstrating white matter activation in the corpus callosum. The current study aimed to determine whether white matter activation could be detected outside of the corpus callosum, in the internal capsule. Data were acquired from a 4 T MRI using a specialized asymmetric spin echo spiral sequence. A motor task was selected to elicit activation in the posterior limb of the internal capsule. Results White matter fMRI activation was examined at the individual and group levels. Analyses revealed that activation was present in the posterior limb of the internal capsule in 80% of participants. These results provide further support for white matter fMRI activation. Conclusions The ability to visualize functionally active tracts has strong implications for the basic scientific study of connectivity and the clinical assessment of white matter disease.

  4. Acoustic FMRI noise: linear time-invariant system model.

    Science.gov (United States)

    Rizzo Sierra, Carlos V; Versluis, Maarten J; Hoogduin, Johannes M; Duifhuis, Hendrikus Diek

    2008-09-01

    Functional magnetic resonance imaging (fMRI) enables sites of brain activation to be localized in human subjects. For auditory system studies, however, the acoustic noise generated by the scanner tends to interfere with the assessments of this activation. Understanding and modeling fMRI acoustic noise is a useful step to its reduction. To study acoustic noise, the MR scanner is modeled as a linear electroacoustical system generating sound pressure signals proportional to the time derivative of the input gradient currents. The transfer function of one MR scanner is determined for two different input specifications: 1) by using the gradient waveform calculated by the scanner software and 2) by using a recording of the gradient current. Up to 4 kHz, the first method is shown as reliable as the second one, and its use is encouraged when direct measurements of gradient currents are not possible. Additionally, the linear order and average damping properties of the gradient coil system are determined by impulse response analysis. Since fMRI is often based on echo planar imaging (EPI) sequences, a useful validation of the transfer function prediction ability can be obtained by calculating the acoustic output for the EPI sequence. We found a predicted sound pressure level (SPL) for the EPI sequence of 104 dB SPL compared to a measured value of 102 dB SPL. As yet, the predicted EPI pressure waveform shows similarity as well as some differences with the directly measured EPI pressure waveform.

  5. Matched-filter acquisition for BOLD fMRI.

    Science.gov (United States)

    Kasper, Lars; Haeberlin, Maximilian; Dietrich, Benjamin E; Gross, Simon; Barmet, Christoph; Wilm, Bertram J; Vannesjo, S Johanna; Brunner, David O; Ruff, Christian C; Stephan, Klaas E; Pruessmann, Klaas P

    2014-10-15

    We introduce matched-filter fMRI, which improves BOLD (blood oxygen level dependent) sensitivity by variable-density image acquisition tailored to subsequent image smoothing. Image smoothing is an established post-processing technique used in the vast majority of fMRI studies. Here we show that the signal-to-noise ratio of the resulting smoothed data can be substantially increased by acquisition weighting with a weighting function that matches the k-space filter imposed by the smoothing operation. We derive the theoretical SNR advantage of this strategy and propose a practical implementation of 2D echo-planar acquisition matched to common Gaussian smoothing. To reliably perform the involved variable-speed trajectories, concurrent magnetic field monitoring with NMR probes is used. Using this technique, phantom and in vivo measurements confirm reliable SNR improvement in the order of 30% in a "resting-state" condition and prove robust in different regimes of physiological noise. Furthermore, a preliminary task-based visual fMRI experiment equally suggests a consistent BOLD sensitivity increase in terms of statistical sensitivity (average t-value increase of about 35%). In summary, our study suggests that matched-filter acquisition is an effective means of improving BOLD SNR in studies that rely on image smoothing at the post-processing level. Copyright © 2014 Elsevier Inc. All rights reserved.

  6. Decreased activation of subcortical brain areas in the motor fatigue state: an fMRI study

    Directory of Open Access Journals (Sweden)

    Lijuan Hou

    2016-08-01

    Full Text Available One aspect of motor fatigue is the exercise-induced reduction of neural activity to voluntarily drive the muscle or muscle group. Functional magnetic resonance imaging provides access to investigate the neural activation on the whole brain level and studies observed changes of activation intensity after exercise-induced motor fatigue in the sensorimotor cortex. However, in human, little evidence exists to demonstrate the role of subcortical brain regions in motor fatigue, which is contradict to abundant researches in rodent indicating that during simple movement, the activity of the basal ganglia is modulated by the state of motor fatigue. Thus, in present study, we explored the effect of motor fatigue on subcortical areas in human. A series of fMRI data were collected from 11 healthy subjects while they were executing simple motor tasks in two conditions: before and under the motor fatigue state. The results showed that in both conditions, movements evoked activation volumes in the sensorimotor areas, SMA, cerebellum, thalamus and basal ganglia. Of primary importance are the results that the intensity and size of activation volumes in the subcortical areas (i.e. thalamus and basal ganglia areas are significantly decreased during the motor fatigue state, implying that motor fatigue disturbs the motor control processing in a way that both sensorimotor areas and subcortical brain areas are less active. Further study is needed to clarify how subcortical areas contribute to the overall decreased activity of CNS during motor fatigue state.

  7. A Non-Parametric Approach for the Activation Detection of Block Design fMRI Simulated Data Using Self-Organizing Maps and Support Vector Machine.

    Science.gov (United States)

    Bahrami, Sheyda; Shamsi, Mousa

    2017-01-01

    Functional magnetic resonance imaging (fMRI) is a popular method to probe the functional organization of the brain using hemodynamic responses. In this method, volume images of the entire brain are obtained with a very good spatial resolution and low temporal resolution. However, they always suffer from high dimensionality in the face of classification algorithms. In this work, we combine a support vector machine (SVM) with a self-organizing map (SOM) for having a feature-based classification by using SVM. Then, a linear kernel SVM is used for detecting the active areas. Here, we use SOM for feature extracting and labeling the datasets. SOM has two major advances: (i) it reduces dimension of data sets for having less computational complexity and (ii) it is useful for identifying brain regions with small onset differences in hemodynamic responses. Our non-parametric model is compared with parametric and non-parametric methods. We use simulated fMRI data sets and block design inputs in this paper and consider the contrast to noise ratio (CNR) value equal to 0.6 for simulated datasets. fMRI simulated dataset has contrast 1-4% in active areas. The accuracy of our proposed method is 93.63% and the error rate is 6.37%.

  8. fMRI reliability in subjects with stroke.

    Science.gov (United States)

    Kimberley, Teresa Jacobson; Khandekar, Gauri; Borich, Michael

    2008-03-01

    Functional MRI (fMRI) has become one of the most commonly used neuroimaging tools to assess the cortical effects associated with rehabilitation, learning, or disease recovery in subjects with stroke. Despite this, there has been no systematic study of the reliability of the fMR signal in this population. The purpose of this study was to examine the within- and between-session reliability of fMRI in cortical and cerebellar structures in subjects with stroke during a complex, continuous visual motor task performed with the less affected hand. Nine subjects with stroke underwent four testing trials during two sessions separated by three weeks. Subjects performed a drawing task using an MRI compatible joystick while in the MRI. Methods of analysis evaluated included: percent signal intensity change, active voxel count and a voxel by voxel stat value analysis within and between testing sessions. Reliability was determined with Interclass correlation coefficients (ICC) in the following regions of interest: primary motor (M1), primary sensory (S1), premotor cortex (PMC), medial cerebellum (MCB), and lateral cerebellum (LCB). Results indicate that intensity change has superior reliability to the other methods of analysis (Average ICC across brain regions and trials: intensity change: 0.73, voxel count: 0.58, voxel by voxel: 0.67) and that generally with any analysis method, within-session reliability was higher than between-session, as indicated by higher ICC values across brain regions. Overall, when comparing between-session results, moderate to good reliability was obtained with intensity change (ICC: M1: 0.52, S1: 0.80, SMA: 0.78, PMC: 0.94, MCB: 0.86, and LCB: 0.59). These results show good reliability in subjects with stroke when performing a continuous motor task. These findings give confidence for interpreting fMRI test/retest research in subjects with stroke.

  9. Modeling Task fMRI Data via Deep Convolutional Autoencoder.

    Science.gov (United States)

    Huang, Heng; Hu, Xintao; Zhao, Yu; Makkie, Milad; Dong, Qinglin; Zhao, Shijie; Guo, Lei; Liu, Tianming

    2017-06-15

    Task-based fMRI (tfMRI) has been widely used to study functional brain networks under task performance. Modeling tfMRI data is challenging due to at least two problems: the lack of the ground truth of underlying neural activity and the highly complex intrinsic structure of tfMRI data. To better understand brain networks based on fMRI data, data-driven approaches have been proposed, for instance, Independent Component Analysis (ICA) and Sparse Dictionary Learning (SDL). However, both ICA and SDL only build shallow models, and they are under the strong assumption that original fMRI signal could be linearly decomposed into time series components with their corresponding spatial maps. As growing evidence shows that human brain function is hierarchically organized, new approaches that can infer and model the hierarchical structure of brain networks are widely called for. Recently, deep convolutional neural network (CNN) has drawn much attention, in that deep CNN has proven to be a powerful method for learning high-level and mid-level abstractions from low-level raw data. Inspired by the power of deep CNN, in this study, we developed a new neural network structure based on CNN, called Deep Convolutional Auto-Encoder (DCAE), in order to take the advantages of both data-driven approach and CNN's hierarchical feature abstraction ability for the purpose of learning mid-level and high-level features from complex, large-scale tfMRI time series in an unsupervised manner. The DCAE has been applied and tested on the publicly available human connectome project (HCP) tfMRI datasets, and promising results are achieved.

  10. Bayesian networks for fMRI: a primer.

    Science.gov (United States)

    Mumford, Jeanette A; Ramsey, Joseph D

    2014-02-01

    Bayesian network analysis is an attractive approach for studying the functional integration of brain networks, as it includes both the locations of connections between regions of the brain (functional connectivity) and more importantly the direction of the causal relationship between the regions (directed functional connectivity). Further, these approaches are more attractive than other functional connectivity analyses in that they can often operate on larger sets of nodes and run searches over a wide range of candidate networks. An important study by Smith et al. (2011) illustrated that many Bayesian network approaches did not perform well in identifying the directionality of connections in simulated single-subject data. Since then, new Bayesian network approaches have been developed that have overcome the failures in the Smith work. Additionally, an important discovery was made that shows a preprocessing step used in the Smith data puts some of the Bayesian network methods at a disadvantage. This work provides a review of Bayesian network analyses, focusing on the methods used in the Smith work as well as methods developed since 2011 that have improved estimation performance. Importantly, only approaches that have been specifically designed for fMRI data perform well, as they have been tailored to meet the challenges of fMRI data. Although this work does not suggest a single best model, it describes the class of models that perform best and highlights the features of these models that allow them to perform well on fMRI data. Specifically, methods that rely on non-Gaussianity to direct causal relationships in the network perform well.

  11. Auditory processing in the brainstem and audiovisual integration in humans studied with fMRI

    NARCIS (Netherlands)

    Slabu, Lavinia Mihaela

    2008-01-01

    Functional magnetic resonance imaging (fMRI) is a powerful technique because of the high spatial resolution and the noninvasiveness. The applications of the fMRI to the auditory pathway remain a challenge due to the intense acoustic scanner noise of approximately 110 dB SPL. The auditory system cons

  12. Fmri, antipsychotics and schizophrenia. influence of different antipsychotics on bold-signal

    NARCIS (Netherlands)

    C. Röder (Constantin); J.M. Hoogendam (Janna Marie); F.M. van der Veen (Frederik)

    2010-01-01

    textabstractIn the last decade, functional Magnetic Resonance Imaging (FMRI) has been increasingly used to investigate the neurobiology of schizophrenia. This technique relies on changes in the blood-oxygen-level-dependent (BOLD) -signal, which changes in response to neural activity. Many FMRI studi

  13. fMRI activation in relation to sound intensity and loudness

    NARCIS (Netherlands)

    Langers, Dave R. M.; van Dijk, Pirn; Schoemaker, Esther S.; Backes, Walter H.

    2007-01-01

    The aim of this fMRI study was to relate cortical fMRI responses to both physical and perceptual sound level characteristics. Besides subjects with normal hearing, subjects with high-frequency sensorineural hearing loss were included, as distortion of loudness perception is a characteristic of such

  14. Acoustic fMRI noise : Linear time-invariant system model

    NARCIS (Netherlands)

    Sierra, Carlos V. Rizzo; Versluis, Maarten J.; Hoogduin, Johannes M.; Duifhuis, Hendrikus (Diek)

    2008-01-01

    Functional magnetic resonance imaging (fMRI) enables sites of brain activation to be localized in human subjects. For auditory system studies, however, the acoustic noise generated by the scanner tends to interfere with the assessments of this activation. Understanding and modeling fMRI acoustic noi

  15. Surface EMG measurements during fMRI at 3T : Accurate EMG recordings after artifact correction

    NARCIS (Netherlands)

    van Duinen, Hiske; Zijdewind, Inge; Hoogduin, H; Maurits, N

    2005-01-01

    In this experiment, we have measured surface EMG of the first dorsal interosseus during predefined submaximal isometric contractions (5, 15, 30, 50, and 70% of maximal force) of the index finger simultaneously with fMRI measurements. Since we have used sparse sampling fMRI (3-s scanning; 2-s non-sca

  16. Convolution power spectrum analysis for FMRI data based on prior image signal.

    Science.gov (United States)

    Zhang, Jiang; Chen, Huafu; Fang, Fang; Liao, Wei

    2010-02-01

    Functional MRI (fMRI) data-processing methods based on changes in the time domain involve, among other things, correlation analysis and use of the general linear model with statistical parametric mapping (SPM). Unlike conventional fMRI data analysis methods, which aim to model the blood-oxygen-level-dependent (BOLD) response of voxels as a function of time, the theory of power spectrum (PS) analysis focuses completely on understanding the dynamic energy change of interacting systems. We propose a new convolution PS (CPS) analysis of fMRI data, based on the theory of matched filtering, to detect brain functional activation for fMRI data. First, convolution signals are computed between the measured fMRI signals and the image signal of prior experimental pattern to suppress noise in the fMRI data. Then, the PS density analysis of the convolution signal is specified as the quantitative analysis energy index of BOLD signal change. The data from simulation studies and in vivo fMRI studies, including block-design experiments, reveal that the CPS method enables a more effective detection of some aspects of brain functional activation, as compared with the canonical PS SPM and the support vector machine methods. Our results demonstrate that the CPS method is useful as a complementary analysis in revealing brain functional information regarding the complex nature of fMRI time series.

  17. Neurovascular and neurometabolic couplings in dynamic calibrated fMRI: transient oxidative neuroenergetics for block-design and event-related paradigms

    Directory of Open Access Journals (Sweden)

    D. S. Fahmeed Hyder

    2010-08-01

    Full Text Available Functional magnetic resonance imaging (fMRI with blood-oxygenation level dependent (BOLD contrast is an important tool for mapping brain activity. Interest in quantitative fMRI has renewed awareness in importance of oxidative neuroenergetics, as reflected by cerebral metabolic rate of oxygen consumption (CMRO2, for supporting brain function. Relationships between BOLD signal and the underlying neurophysiological parameters have been elucidated to allow determination of dynamic changes in CMRO2 by “calibrated fMRI”, which require multi-modal measurements of BOLD signal along with cerebral blood flow (CBF and volume (CBV. But how do CMRO2 changes, steady-state or transient, derived from calibrated fMRI compare with neural activity recordings of local field potential (LFP and/or multi-unit activity (MUA? Here we discuss recent findings primarily from animal studies which allow high magnetic fields studies for superior BOLD sensitivity as well as multi-modal CBV and CBF measurements in conjunction with LFP and MUA recordings from activated sites. A key observation is that while relationships between neural activity and sensory stimulus features range from linear to non-linear, associations between hyperemic components (BOLD, CBF, CBV and neural activity (LFP, MUA are almost always linear. More importantly, the results demonstrate good agreement between the changes in CMRO2 and independent measures of LFP or MUA. The tight neurovascular and neurometabolic couplings, observed from steady-state conditions to events separated by <200 ms, suggest rapid oxygen equilibration between blood and tissue pools and thus calibrated fMRI at high magnetic fields can provide high spatiotemporal mapping of CMRO2 changes.

  18. A novel fMRI paradigm suggests that pedaling-related brain activation is altered after stroke

    Science.gov (United States)

    Promjunyakul, Nutta-on; Schmit, Brian D.; Schindler-Ivens, Sheila M.

    2015-01-01

    The purpose of this study was to examine the feasibility of using functional magnetic resonance imaging (fMRI) to measure pedaling-related brain activation in individuals with stroke and age-matched controls. We also sought to identify stroke-related changes in brain activation associated with pedaling. Fourteen stroke and 12 control subjects were asked to pedal a custom, MRI-compatible device during fMRI. Subjects also performed lower limb tapping to localize brain regions involved in lower limb movement. All stroke and control subjects were able to pedal while positioned for fMRI. Two control subjects were withdrawn due to claustrophobia, and one control data set was excluded from analysis due to an incidental finding. In the stroke group, one subject was unable to enter the gantry due to excess adiposity, and one stroke data set was excluded from analysis due to excessive head motion. Consequently, 81% of subjects (12/14 stroke, 9/12 control) completed all procedures and provided valid pedaling-related fMRI data. In these subjects, head motion was ≤3 mm. In both groups, brain activation localized to the medial aspect of M1, S1, and Brodmann’s area 6 (BA6) and to the cerebellum (vermis, lobules IV, V, VIII). The location of brain activation was consistent with leg areas. Pedaling-related brain activation was apparent on both sides of the brain, with values for laterality index (LI) of –0.06 (0.20) in the stroke cortex, 0.05 (±0.06) in the control cortex, 0.29 (0.33) in the stroke cerebellum, and 0.04 (0.15) in the control cerebellum. In the stroke group, activation in the cerebellum – but not cortex – was significantly lateralized toward the damaged side of the brain (p = 0.01). The volume of pedaling-related brain activation was smaller in stroke as compared to control subjects. Differences reached statistical significance when all active regions were examined together [p = 0.03; 27,694 (9,608) μL stroke; 37,819 (9,169) μL control]. When individual

  19. Compressed Sensing for fMRI: Feasibility Study on the Acceleration of Non-EPI fMRI at 9.4T

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    Paul Kyu Han

    2015-01-01

    Full Text Available Conventional functional magnetic resonance imaging (fMRI technique known as gradient-recalled echo (GRE echo-planar imaging (EPI is sensitive to image distortion and degradation caused by local magnetic field inhomogeneity at high magnetic fields. Non-EPI sequences such as spoiled gradient echo and balanced steady-state free precession (bSSFP have been proposed as an alternative high-resolution fMRI technique; however, the temporal resolution of these sequences is lower than the typically used GRE-EPI fMRI. One potential approach to improve the temporal resolution is to use compressed sensing (CS. In this study, we tested the feasibility of k-t FOCUSS—one of the high performance CS algorithms for dynamic MRI—for non-EPI fMRI at 9.4T using the model of rat somatosensory stimulation. To optimize the performance of CS reconstruction, different sampling patterns and k-t FOCUSS variations were investigated. Experimental results show that an optimized k-t FOCUSS algorithm with acceleration by a factor of 4 works well for non-EPI fMRI at high field under various statistical criteria, which confirms that a combination of CS and a non-EPI sequence may be a good solution for high-resolution fMRI at high fields.

  20. Disrupted thalamocortical connectivity in PSP: a resting-state fMRI, DTI, and VBM study.

    Science.gov (United States)

    Whitwell, Jennifer L; Avula, Ramesh; Master, Ankit; Vemuri, Prashanthi; Senjem, Matthew L; Jones, David T; Jack, Clifford R; Josephs, Keith A

    2011-09-01

    Progressive supranuclear palsy (PSP) is associated with pathological changes along the dentatorubrothalamic tract and in premotor cortex. We aimed to assess whether functional neural connectivity is disrupted along this pathway in PSP, and to determine how functional changes relate to changes in structure and diffusion. Eighteen probable PSP subjects and 18 controls had resting-state (task-free) fMRI, diffusion tensor imaging and structural MRI. Functional connectivity was assessed between thalamus and the rest of the brain, and within the basal ganglia, salience and default mode networks (DMN). Patterns of atrophy were assessed using voxel-based morphometry, and patterns of white matter tract degeneration were assessed using tract-based spatial statistics. Reduced in-phase functional connectivity was observed between the thalamus and premotor cortex including supplemental motor area (SMA), striatum, thalamus and cerebellum in PSP. Reduced connectivity in premotor cortex, striatum and thalamus were observed in the basal ganglia network and DMN, with subcortical salience network reductions. Tract degeneration was observed between cerebellum and thalamus and in superior longitudinal fasciculus, with grey matter loss in frontal lobe, premotor cortex, SMA and caudate nucleus. SMA functional connectivity correlated with SMA volume and measures of cognitive and motor dysfunction, while thalamic connectivity correlated with degeneration of superior cerebellar peduncles. PSP is therefore associated with disrupted thalamocortical connectivity that is associated with degeneration of the dentatorubrothalamic tract and the presence of cortical atrophy.

  1. Selection of a Model of Cerebral Activity for fMRI Group Data Analysis

    CERN Document Server

    Keller, Merlin; Lavielle, Marc

    2010-01-01

    This thesis is dedicated to the statistical analysis of multi-sub ject fMRI data, with the purpose of identifying bain structures involved in certain cognitive or sensori-motor tasks, in a reproducible way across sub jects. To overcome certain limitations of standard voxel-based testing methods, as implemented in the Statistical Parametric Mapping (SPM) software, we introduce a Bayesian model selection approach to this problem, meaning that the most probable model of cerebral activity given the data is selected from a pre-defined collection of possible models. Based on a parcellation of the brain volume into functionally homogeneous regions, each model corresponds to a partition of the regions into those involved in the task under study and those inactive. This allows to incorporate prior information, and avoids the dependence of the SPM-like approach on an arbitrary threshold, called the cluster- forming threshold, to define active regions. By controlling a Bayesian risk, our approach balances false positive...

  2. Brain Activity Associated with Emoticons: An fMRI Study

    Science.gov (United States)

    Yuasa, Masahide; Saito, Keiichi; Mukawa, Naoki

    In this paper, we describe that brain activities associated with emoticons by using fMRI. In communication over a computer network, we use abstract faces such as computer graphics (CG) avatars and emoticons. These faces convey users' emotions and enrich their communications. However, the manner in which these faces influence the mental process is as yet unknown. The human brain may perceive the abstract face in an entirely different manner, depending on its level of reality. We conducted an experiment using fMRI in order to investigate the effects of emoticons. The results show that right inferior frontal gyrus, which associated with nonverbal communication, is activated by emoticons. Since the emoticons were created to reflect the real human facial expressions as accurately as possible, we believed that they would activate the right fusiform gyrus. However, this region was not found to be activated during the experiment. This finding is useful in understanding how abstract faces affect our behaviors and decision-making in communication over a computer network.

  3. Explorations of Object and Location Memory using fMRI

    Directory of Open Access Journals (Sweden)

    Antony D Passaro

    2013-08-01

    Full Text Available Content-specific sub-systems of visual working memory (VWM have been explored in many neuroimaging studies with inconsistent findings and procedures across experiments. The present study employed functional magnetic resonance imaging (fMRI and a change detection task using a high number of trials and matched stimulus displays across object and location change ("what" vs "where" conditions. Furthermore, individual task periods were studied independently across conditions to identify period-specific differences. Importantly, this combination of task controls has not previously been described in the fMRI literature. Composite results revealed differential frontoparietal activation during each task period. A separation of object and location conditions yielded a distributed system of dorsal and ventral streams during the encoding of information corresponding to bilateral inferior parietal lobule (IPL and lingual gyrus activation, respectively. Differential activity was also shown during the maintenance of information in middle frontal structures bilaterally for objects and the right IPL and left insula for locations. Together, these results reflect a domain-specific dissociation spanning several cortices and task periods. Furthermore, differential activations suggest a general caudal-rostral separation corresponding to object and location memory, respectively.

  4. Recovery of directed intracortical connectivity from fMRI data

    Science.gov (United States)

    Gilson, Matthieu; Ritter, Petra; Deco, Gustavo

    2016-06-01

    The brain exhibits complex spatio-temporal patterns of activity. In particular, its baseline activity at rest has a specific structure: imaging techniques (e.g., fMRI, EEG and MEG) show that cortical areas experience correlated fluctuations, which is referred to as functional connectivity (FC). The present study relies on our recently developed model in which intracortical white-matter connections shape noise-driven fluctuations to reproduce FC observed in experimental data (here fMRI BOLD signal). Here noise has a functional role and represents the variability of neural activity. The model also incorporates anatomical information obtained using diffusion tensor imaging (DTI), which estimates the density of white-matter fibers (structural connectivity, SC). After optimization to match empirical FC, the model provides an estimation of the efficacies of these fibers, which we call effective connectivity (EC). EC differs from SC, as EC not only accounts for the density of neural fibers, but also the concentration of synapses formed at their end, the type of neurotransmitters associated and the excitability of target neural populations. In summary, the model combines anatomical SC and activity FC to evaluate what drives the neural dynamics, embodied in EC. EC can then be analyzed using graph theory to understand how it generates FC and to seek for functional communities among cortical areas (parcellation of 68 areas). We find that intracortical connections are not symmetric, which affects the dynamic range of cortical activity (i.e., variety of states it can exhibit).

  5. Examining rhythm and melody processing in young children using FMRI.

    Science.gov (United States)

    Overy, K; Norton, A; Cronin, K; Winner, E; Schlaug, G

    2005-12-01

    While it is often reported that musical experience can have positive effects on cognitive development in young children, the neural basis of such potential effects remains relatively unexplored. Employing functional magnetic resonance imaging (fMRI) for such research presents as many challenges as possibilities, not least of which is the fact that young children can find it difficult to remain still and attentive for long periods of time. Here we describe an fMRI scanning protocol designed specifically for young children using short scanning runs, a sparse temporal sampling data acquisition technique, simple rhythmic and melodic discrimination tasks with a button-press response, and a child-oriented preparation session. Children were recruited as part of a large-scale longitudinal study examining the effects of musical training on cognitive development and the structure and function of the growing brain. Results from an initial analysis of 33 children and from the first five children to be re-scanned after musical training indicate that our scanning protocol is successful and that activation differences can be detected both between conditions and over time.

  6. Increasing fMRI sampling rate improves Granger causality estimates.

    Directory of Open Access Journals (Sweden)

    Fa-Hsuan Lin

    Full Text Available Estimation of causal interactions between brain areas is necessary for elucidating large-scale functional brain networks underlying behavior and cognition. Granger causality analysis of time series data can quantitatively estimate directional information flow between brain regions. Here, we show that such estimates are significantly improved when the temporal sampling rate of functional magnetic resonance imaging (fMRI is increased 20-fold. Specifically, healthy volunteers performed a simple visuomotor task during blood oxygenation level dependent (BOLD contrast based whole-head inverse imaging (InI. Granger causality analysis based on raw InI BOLD data sampled at 100-ms resolution detected the expected causal relations, whereas when the data were downsampled to the temporal resolution of 2 s typically used in echo-planar fMRI, the causality could not be detected. An additional control analysis, in which we SINC interpolated additional data points to the downsampled time series at 0.1-s intervals, confirmed that the improvements achieved with the real InI data were not explainable by the increased time-series length alone. We therefore conclude that the high-temporal resolution of InI improves the Granger causality connectivity analysis of the human brain.

  7. Pycortex: an interactive surface visualizer for fMRI

    Directory of Open Access Journals (Sweden)

    James Shuang Gao

    2015-09-01

    Full Text Available Surface visualizations of fMRI provide a comprehensive view of cortical activity. However, surface visualizations are difficult to generate and most common visualization techniques rely on unnecessary interpolation which limits the fidelity of the resulting maps. Furthermore, it is difficult to understand the relationship between flattened cortical surfaces and the underlying 3D anatomy using tools available currently. To address these problems we have developed pycortex, a Python toolbox for interactive surface mapping and visualization. Pycortex exploits the power of modern graphics cards to sample volumetric data on a per-pixel basis, allowing dense and accurate mapping of the voxel grid across the surface. Anatomical, functional and fiduciary information can be projected onto the cortical surface. The surface can be inflated and flattened interactively, aiding interpretation of the correspondence between the anatomical surface and the flattened cortical sheet. The output of pycortex can be viewed using WebGL, a technology compatible with modern web browsers. This allows complex fMRI surface maps to be distributed broadly online without requiring installation of complex software.

  8. Haptic fMRI: combining functional neuroimaging with haptics for studying the brain's motor control representation.

    Science.gov (United States)

    Menon, Samir; Brantner, Gerald; Aholt, Chris; Kay, Kendrick; Khatib, Oussama

    2013-01-01

    A challenging problem in motor control neuroimaging studies is the inability to perform complex human motor tasks given the Magnetic Resonance Imaging (MRI) scanner's disruptive magnetic fields and confined workspace. In this paper, we propose a novel experimental platform that combines Functional MRI (fMRI) neuroimaging, haptic virtual simulation environments, and an fMRI-compatible haptic device for real-time haptic interaction across the scanner workspace (above torso ∼ .65×.40×.20m(3)). We implement this Haptic fMRI platform with a novel haptic device, the Haptic fMRI Interface (HFI), and demonstrate its suitability for motor neuroimaging studies. HFI has three degrees-of-freedom (DOF), uses electromagnetic motors to enable high-fidelity haptic rendering (>350Hz), integrates radio frequency (RF) shields to prevent electromagnetic interference with fMRI (temporal SNR >100), and is kinematically designed to minimize currents induced by the MRI scanner's magnetic field during motor displacement (Tesla fMRI scanner's baseline noise variation (∼.85±.1%). Finally, HFI is haptically transparent and does not interfere with human motor tasks (tested for .4m reaches). By allowing fMRI experiments involving complex three-dimensional manipulation with haptic interaction, Haptic fMRI enables-for the first time-non-invasive neuroscience experiments involving interactive motor tasks, object manipulation, tactile perception, and visuo-motor integration.

  9. Variable Selection for Functional Logistic Regression in fMRI Data Analysis

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    Nedret BILLOR

    2015-03-01

    Full Text Available This study was motivated by classification problem in Functional Magnetic Resonance Imaging (fMRI, a noninvasive imaging technique which allows an experimenter to take images of a subject's brain over time. As fMRI studies usually have a small number of subjects and we assume that there is a smooth, underlying curve describing the observations in fMRI data, this results in incredibly high-dimensional datasets that are functional in nature. High dimensionality is one of the biggest problems in statistical analysis of fMRI data. There is also a need for the development of better classification methods. One of the best things about fMRI technique is its noninvasiveness. If statistical classification methods are improved, it could aid the advancement of noninvasive diagnostic techniques for mental illness or even degenerative diseases such as Alzheimer's. In this paper, we develop a variable selection technique, which tackles high dimensionality and correlation problems in fMRI data, based on L1 regularization-group lasso for the functional logistic regression model where the response is binary and represent two separate classes; the predictors are functional. We assess our method with a simulation study and an application to a real fMRI dataset.

  10. Sequential Dictionary Learning From Correlated Data: Application to fMRI Data Analysis.

    Science.gov (United States)

    Seghouane, Abd-Krim; Iqbal, Asif

    2017-03-22

    Sequential dictionary learning via the K-SVD algorithm has been revealed as a successful alternative to conventional data driven methods such as independent component analysis (ICA) for functional magnetic resonance imaging (fMRI) data analysis. fMRI datasets are however structured data matrices with notions of spatio-temporal correlation and temporal smoothness. This prior information has not been included in the K-SVD algorithm when applied to fMRI data analysis. In this paper we propose three variants of the K-SVD algorithm dedicated to fMRI data analysis by accounting for this prior information. The proposed algorithms differ from the K-SVD in their sparse coding and dictionary update stages. The first two algorithms account for the known correlation structure in the fMRI data by using the squared Q, R-norm instead of the Frobenius norm for matrix approximation. The third and last algorithm account for both the known correlation structure in the fMRI data and the temporal smoothness. The temporal smoothness is incorporated in the dictionary update stage via regularization of the dictionary atoms obtained with penalization. The performance of the proposed dictionary learning algorithms are illustrated through simulations and applications on real fMRI data.

  11. Improving the Test-Retest Reliability of Resting State fMRI by Removing the Impact of Sleep

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    Jiahui Wang

    2017-05-01

    Full Text Available Resting state functional magnetic resonance imaging (rs-fMRI provides a powerful tool to examine large-scale neural networks in the human brain and their disturbances in neuropsychiatric disorders. Thanks to its low demand and high tolerance, resting state paradigms can be easily acquired from clinical population. However, due to the unconstrained nature, resting state paradigm is associated with excessive head movement and proneness to sleep. Consequently, the test-retest reliability of rs-fMRI measures is moderate at best, falling short of widespread use in the clinic. Here, we characterized the effect of sleep on the test-retest reliability of rs-fMRI. Using measures of heart rate variability (HRV derived from simultaneous electrocardiogram (ECG recording, we identified portions of fMRI data when subjects were more alert or sleepy, and examined their effects on the test-retest reliability of functional connectivity measures. When volumes of sleep were excluded, the reliability of rs-fMRI is significantly improved, and the improvement appears to be general across brain networks. The amount of improvement is robust with the removal of as much as 60% volumes of sleepiness. Therefore, test-retest reliability of rs-fMRI is affected by sleep and could be improved by excluding volumes of sleepiness as indexed by HRV. Our results suggest a novel and practical method to improve test-retest reliability of rs-fMRI measures.

  12. Joint sparse representation of brain activity patterns in multi-task fMRI data.

    Science.gov (United States)

    Ramezani, M; Marble, K; Trang, H; Johnsrude, I S; Abolmaesumi, P

    2015-01-01

    A single-task functional magnetic resonance imaging (fMRI) experiment may only partially highlight alterations to functional brain networks affected by a particular disorder. Multivariate analysis across multiple fMRI tasks may increase the sensitivity of fMRI-based diagnosis. Prior research using multi-task analysis in fMRI, such as those that use joint independent component analysis (jICA), has mainly assumed that brain activity patterns evoked by different tasks are independent. This may not be valid in practice. Here, we use sparsity, which is a natural characteristic of fMRI data in the spatial domain, and propose a joint sparse representation analysis (jSRA) method to identify common information across different functional subtraction (contrast) images in data from a multi-task fMRI experiment. Sparse representation methods do not require independence, or that the brain activity patterns be nonoverlapping. We use functional subtraction images within the joint sparse representation analysis to generate joint activation sources and their corresponding sparse modulation profiles. We evaluate the use of sparse representation analysis to capture individual differences with simulated fMRI data and with experimental fMRI data. The experimental fMRI data was acquired from 16 young (age: 19-26) and 16 older (age: 57-73) adults obtained from multiple speech comprehension tasks within subjects, where an independent measure (namely, age in years) can be used to differentiate between groups. Simulation results show that this method yields greater sensitivity, precision, and higher Jaccard indexes (which measures similarity and diversity of the true and estimated brain activation sources) than does the jICA method. Moreover, superiority of the jSRA method in capturing individual differences was successfully demonstrated using experimental fMRI data.

  13. Analytic programming with FMRI data: a quick-start guide for statisticians using R.

    Science.gov (United States)

    Eloyan, Ani; Li, Shanshan; Muschelli, John; Pekar, Jim J; Mostofsky, Stewart H; Caffo, Brian S

    2014-01-01

    Functional magnetic resonance imaging (fMRI) is a thriving field that plays an important role in medical imaging analysis, biological and neuroscience research and practice. This manuscript gives a didactic introduction to the statistical analysis of fMRI data using the R project, along with the relevant R code. The goal is to give statisticians who would like to pursue research in this area a quick tutorial for programming with fMRI data. References of relevant packages and papers are provided for those interested in more advanced analysis.

  14. Analytic programming with FMRI data: a quick-start guide for statisticians using R.

    Directory of Open Access Journals (Sweden)

    Ani Eloyan

    Full Text Available Functional magnetic resonance imaging (fMRI is a thriving field that plays an important role in medical imaging analysis, biological and neuroscience research and practice. This manuscript gives a didactic introduction to the statistical analysis of fMRI data using the R project, along with the relevant R code. The goal is to give statisticians who would like to pursue research in this area a quick tutorial for programming with fMRI data. References of relevant packages and papers are provided for those interested in more advanced analysis.

  15. Automatic EEG-assisted retrospective motion correction for fMRI (aE-REMCOR).

    Science.gov (United States)

    Wong, Chung-Ki; Zotev, Vadim; Misaki, Masaya; Phillips, Raquel; Luo, Qingfei; Bodurka, Jerzy

    2016-04-01

    Head motions during functional magnetic resonance imaging (fMRI) impair fMRI data quality and introduce systematic artifacts that can affect interpretation of fMRI results. Electroencephalography (EEG) recordings performed simultaneously with fMRI provide high-temporal-resolution information about ongoing brain activity as well as head movements. Recently, an EEG-assisted retrospective motion correction (E-REMCOR) method was introduced. E-REMCOR utilizes EEG motion artifacts to correct the effects of head movements in simultaneously acquired fMRI data on a slice-by-slice basis. While E-REMCOR is an efficient motion correction approach, it involves an independent component analysis (ICA) of the EEG data and identification of motion-related ICs. Here we report an automated implementation of E-REMCOR, referred to as aE-REMCOR, which we developed to facilitate the application of E-REMCOR in large-scale EEG-fMRI studies. The aE-REMCOR algorithm, implemented in MATLAB, enables an automated preprocessing of the EEG data, an ICA decomposition, and, importantly, an automatic identification of motion-related ICs. aE-REMCOR has been used to perform retrospective motion correction for 305 fMRI datasets from 16 subjects, who participated in EEG-fMRI experiments conducted on a 3T MRI scanner. Performance of aE-REMCOR has been evaluated based on improvement in temporal signal-to-noise ratio (TSNR) of the fMRI data, as well as correction efficiency defined in terms of spike reduction in fMRI motion parameters. The results show that aE-REMCOR is capable of substantially reducing head motion artifacts in fMRI data. In particular, when there are significant rapid head movements during the scan, a large TSNR improvement and high correction efficiency can be achieved. Depending on a subject's motion, an average TSNR improvement over the brain upon the application of aE-REMCOR can be as high as 27%, with top ten percent of the TSNR improvement values exceeding 55%. The average

  16. Bootstrapping GEE models for fMRI regional connectivity.

    Science.gov (United States)

    D'Angelo, Gina M; Lazar, Nicole A; Zhou, Gongfu; Eddy, William F; Morris, John C; Sheline, Yvette I

    2012-12-01

    An Alzheimer's fMRI study has motivated us to evaluate inter-regional correlations during rest between groups. We apply generalized estimating equation (GEE) models to test for differences in regional correlations across groups. Both the GEE marginal model and GEE transition model are evaluated and compared to the standard pooling Fisher-z approach using simulation studies. Standard errors of all methods are estimated both theoretically (model-based) and empirically (bootstrap). Of all the methods, we find that the transition models have the best statistical properties. Overall, the model-based standard errors and bootstrap standard errors perform about the same. We also demonstrate the methods with a functional connectivity study in a healthy cognitively normal population of ApoE4+ participants and ApoE4- participants who are recruited from the Adult Children's Study conducted at the Washington University Knight Alzheimer's Disease Research Center.

  17. An fMRI study on sunk cost effect.

    Science.gov (United States)

    Zeng, Jianmin; Zhang, Qinglin; Chen, Changming; Yu, Rongjun; Gong, Qiyong

    2013-06-26

    Sunk cost effect (also called escalation of commitment, etc) is a pervasive, interesting and famous decision bias, which has been intensively discussed in psychology, economics, management, political science, zoology, etc. To date, little has been known about the neural basis of this phenomenon. We investigated it by using functional magnetic resonance imaging (fMRI) to monitor healthy subjects' brain activities when they made decisions in a task wherein sunk cost and incremental cost were systematically manipulated. Higher sunk cost only increased activity of some brain areas (mainly lateral frontal and parietal cortices, which are involved in risk-taking), whereas lower incremental cost mainly increased activity of some brain areas (including striatum and medial prefrontal cortex, which are sensitive to rewards). No overlapping brain areas were found to respond to both sunk cost and incremental cost. These results favor certainty effect over self-justification or diminishing sensitivity as account of sunk cost effect.

  18. Unsupervised segmentation of task activated regions in fmRI

    DEFF Research Database (Denmark)

    Røge, Rasmus; Madsen, Kristoffer Hougaard; Schmidt, Mikkel Nørgaard

    2015-01-01

    Functional Magnetic Resonance Imaging has become a central measuring modality to quantify functional activiation of the brain in both task and rest. Most analysis used to quantify functional activation requires supervised approaches as employed in statistical parametric mapping (SPM) to extract...... of task activated functional units in multi-subject fMRI data that exploits that regions of task activation are consistent across subjects and can be more reliably inferred than regions that are not activated. We develop a non-parametric Gaussian mixture model that apriori assumes activations are smooth...... using a Gaussian Process prior while assuming the segmented functional maps are the same across subjects but having individual time-courses and noise variances. To improve inference we propose an enhanced split-merge procedure. We find that our approach well extracts the induced activity of a finger...

  19. Simple Fully Automated Group Classification on Brain fMRI

    Energy Technology Data Exchange (ETDEWEB)

    Honorio, J.; Goldstein, R.; Honorio, J.; Samaras, D.; Tomasi, D.; Goldstein, R.Z.

    2010-04-14

    We propose a simple, well grounded classification technique which is suited for group classification on brain fMRI data sets that have high dimensionality, small number of subjects, high noise level, high subject variability, imperfect registration and capture subtle cognitive effects. We propose threshold-split region as a new feature selection method and majority voteas the classification technique. Our method does not require a predefined set of regions of interest. We use average acros ssessions, only one feature perexperimental condition, feature independence assumption, and simple classifiers. The seeming counter-intuitive approach of using a simple design is supported by signal processing and statistical theory. Experimental results in two block design data sets that capture brain function under distinct monetary rewards for cocaine addicted and control subjects, show that our method exhibits increased generalization accuracy compared to commonly used feature selection and classification techniques.

  20. Learning and generalization under ambiguity: an fMRI study.

    Directory of Open Access Journals (Sweden)

    J R Chumbley

    2012-01-01

    Full Text Available Adaptive behavior often exploits generalizations from past experience by applying them judiciously in new situations. This requires a means of quantifying the relative importance of prior experience and current information, so they can be balanced optimally. In this study, we ask whether the brain generalizes in an optimal way. Specifically, we used Bayesian learning theory and fMRI to test whether neuronal responses reflect context-sensitive changes in ambiguity or uncertainty about experience-dependent beliefs. We found that the hippocampus expresses clear ambiguity-dependent responses that are associated with an augmented rate of learning. These findings suggest candidate neuronal systems that may be involved in aberrations of generalization, such as over-confidence.

  1. Hand classification of fMRI ICA noise components.

    Science.gov (United States)

    Griffanti, Ludovica; Douaud, Gwenaëlle; Bijsterbosch, Janine; Evangelisti, Stefania; Alfaro-Almagro, Fidel; Glasser, Matthew F; Duff, Eugene P; Fitzgibbon, Sean; Westphal, Robert; Carone, Davide; Beckmann, Christian F; Smith, Stephen M

    2016-12-16

    We present a practical "how-to" guide to help determine whether single-subject fMRI independent components (ICs) characterise structured noise or not. Manual identification of signal and noise after ICA decomposition is required for efficient data denoising: to train supervised algorithms, to check the results of unsupervised ones or to manually clean the data. In this paper we describe the main spatial and temporal features of ICs and provide general guidelines on how to evaluate these. Examples of signal and noise components are provided from a wide range of datasets (3T data, including examples from the UK Biobank and the Human Connectome Project, and 7T data), together with practical guidelines for their identification. Finally, we discuss how the data quality, data type and preprocessing can influence the characteristics of the ICs and present examples of particularly challenging datasets.

  2. Applications of fMRI for Brain Mapping

    Directory of Open Access Journals (Sweden)

    Nivedita Daimiwal

    2012-11-01

    Full Text Available Brain-mapping techniques have proven to be vital in understanding the molecular, cellular, and functional mechanisms of the brain. Normal anatomical imaging can provide structural information on certain abnormalities in the brain. However there are many neurological disorders for which only structure studies are not sufficient. In such cases it is required to investigate the functional organization of the brain. Further it is necessary to study the brain functions under normal as well as diseased conditions. Brain mapping techniques can help in deriving useful and important information on these issues. Brain functions and brain area responsible for the particular activities like motor, sensory speech and memory process could be investigated. The authors provide an overview of various Brain Mapping techniques and fMRI signal processing methods.

  3. Integral calculus problem solving: an fMRI investigation.

    Science.gov (United States)

    Krueger, Frank; Spampinato, Maria Vittoria; Pardini, Matteo; Pajevic, Sinisa; Wood, Jacqueline N; Weiss, George H; Landgraf, Steffen; Grafman, Jordan

    2008-07-16

    Only a subset of adults acquires specific advanced mathematical skills, such as integral calculus. The representation of more sophisticated mathematical concepts probably evolved from basic number systems; however its neuroanatomical basis is still unknown. Using fMRI, we investigated the neural basis of integral calculus while healthy participants were engaged in an integration verification task. Solving integrals activated a left-lateralized cortical network including the horizontal intraparietal sulcus, posterior superior parietal lobe, posterior cingulate gyrus, and dorsolateral prefrontal cortex. Our results indicate that solving of more abstract and sophisticated mathematical facts, such as calculus integrals, elicits a pattern of brain activation similar to the cortical network engaged in basic numeric comparison, quantity manipulation, and arithmetic problem solving.

  4. Optimizing complexity measures for FMRI data: algorithm, artifact, and sensitivity.

    Directory of Open Access Journals (Sweden)

    Denis Rubin

    Full Text Available INTRODUCTION: Complexity in the brain has been well-documented at both neuronal and hemodynamic scales, with increasing evidence supporting its use in sensitively differentiating between mental states and disorders. However, application of complexity measures to fMRI time-series, which are short, sparse, and have low signal/noise, requires careful modality-specific optimization. METHODS: HERE WE USE BOTH SIMULATED AND REAL DATA TO ADDRESS TWO FUNDAMENTAL ISSUES: choice of algorithm and degree/type of signal processing. Methods were evaluated with regard to resilience to acquisition artifacts common to fMRI as well as detection sensitivity. Detection sensitivity was quantified in terms of grey-white matter contrast and overlap with activation. We additionally investigated the variation of complexity with activation and emotional content, optimal task length, and the degree to which results scaled with scanner using the same paradigm with two 3T magnets made by different manufacturers. Methods for evaluating complexity were: power spectrum, structure function, wavelet decomposition, second derivative, rescaled range, Higuchi's estimate of fractal dimension, aggregated variance, and detrended fluctuation analysis. To permit direct comparison across methods, all results were normalized to Hurst exponents. RESULTS: Power-spectrum, Higuchi's fractal dimension, and generalized Hurst exponent based estimates were most successful by all criteria; the poorest-performing measures were wavelet, detrended fluctuation analysis, aggregated variance, and rescaled range. CONCLUSIONS: Functional MRI data have artifacts that interact with complexity calculations in nontrivially distinct ways compared to other physiological data (such as EKG, EEG for which these measures are typically used. Our results clearly demonstrate that decisions regarding choice of algorithm, signal processing, time-series length, and scanner have a significant impact on the reliability and

  5. Renormalized Volume

    Science.gov (United States)

    Gover, A. Rod; Waldron, Andrew

    2017-09-01

    We develop a universal distributional calculus for regulated volumes of metrics that are suitably singular along hypersurfaces. When the hypersurface is a conformal infinity we give simple integrated distribution expressions for the divergences and anomaly of the regulated volume functional valid for any choice of regulator. For closed hypersurfaces or conformally compact geometries, methods from a previously developed boundary calculus for conformally compact manifolds can be applied to give explicit holographic formulæ for the divergences and anomaly expressed as hypersurface integrals over local quantities (the method also extends to non-closed hypersurfaces). The resulting anomaly does not depend on any particular choice of regulator, while the regulator dependence of the divergences is precisely captured by these formulæ. Conformal hypersurface invariants can be studied by demanding that the singular metric obey, smoothly and formally to a suitable order, a Yamabe type problem with boundary data along the conformal infinity. We prove that the volume anomaly for these singular Yamabe solutions is a conformally invariant integral of a local Q-curvature that generalizes the Branson Q-curvature by including data of the embedding. In each dimension this canonically defines a higher dimensional generalization of the Willmore energy/rigid string action. Recently, Graham proved that the first variation of the volume anomaly recovers the density obstructing smooth solutions to this singular Yamabe problem; we give a new proof of this result employing our boundary calculus. Physical applications of our results include studies of quantum corrections to entanglement entropies.

  6. Hypercapnic normalization of BOLD fMRI: comparison across field strengths and pulse sequences

    DEFF Research Database (Denmark)

    Cohen, Eric R.; Rostrup, Egill; Sidaros, Karam

    2004-01-01

    size, as well as experimental, such as pulse sequence and static magnetic field strength (B(0)). Thus, it is difficult to compare task-induced fMRI signals across subjects, field strengths, and pulse sequences. This problem can be overcome by normalizing the neural activity-induced BOLD fMRI response...... by a global hypercapnia-induced BOLD signal. To demonstrate the effectiveness of the BOLD normalization approach, gradient-echo BOLD fMRI at 1.5, 4, and 7 T and spin-echo BOLD fMRI at 4 T were performed in human subjects. For neural stimulation, subjects performed sequential finger movements at 2 Hz, while...... for global stimulation, subjects breathed a 5% CO(2) gas mixture. Under all conditions, voxels containing primarily large veins and those containing primarily active tissue (i.e., capillaries and small veins) showed distinguishable behavior after hypercapnic normalization. This allowed functional activity...

  7. Tensor-based fusion of EEG and FMRI to understand neurological changes in Schizophrenia

    DEFF Research Database (Denmark)

    Evrim, Acar Ataman; Levin-Schwartz, Yuri; Calhoun, Vince D.

    2016-01-01

    Neuroimaging modalities such as functional magnetic resonance imaging (fMRI) and electroencephalography (EEG) provide information about neurological functions in complementary spatiotemporal resolutions; therefore, fusion of these modalities is expected to provide better understanding of brain...

  8. ICA if fMRI based on a convolutive mixture model

    DEFF Research Database (Denmark)

    Hansen, Lars Kai

    2003-01-01

    processing strategies. Global linear dependencies can be probed by independent component analysis (ICA) based on higher order statistics or spatio-temporal properties. With ICA we separate the different sources of the fMRI signal. ICA can be performed assuming either spatial or temporal independency. A major....... The mixing is represented by “mixture coefficient images” quantifying the local response to a given source at a certain time lag. This is the first communication to address this important issue in the context of fMRI ICA. Data: A single slice holding 128x128 pixels and passing through primary visual cortex......Modeling & Analysis Abstract The fMRI signal has many sources: Stimulus induced activation, other brain activations, confounds including several physiological signal components, the most prominent being the cardiac pulsation at about 1 Hz, and breathing induced motion (0.2-1 Hz). Most fMRI data...

  9. Vicarious function within the human primary motor cortex? A longitudinal fMRI stroke study

    National Research Council Canada - National Science Library

    Jaillard, Assia; Martin, Chantal Delon; Garambois, Katia; Lebas, Jean François; Hommel, Marc

    2005-01-01

    .... We examined four patients with one ischaemic stroke limited to M1, and four sex- and age-matched healthy controls in a temporally balanced prospective longitudinal fMRI study over three sessions...

  10. Principal Feature Analysis: A Multivariate Feature Selection Method for fMRI Data

    Directory of Open Access Journals (Sweden)

    Lijun Wang

    2013-01-01

    Full Text Available Brain decoding with functional magnetic resonance imaging (fMRI requires analysis of complex, multivariate data. Multivoxel pattern analysis (MVPA has been widely used in recent years. MVPA treats the activation of multiple voxels from fMRI data as a pattern and decodes brain states using pattern classification methods. Feature selection is a critical procedure of MVPA because it decides which features will be included in the classification analysis of fMRI data, thereby improving the performance of the classifier. Features can be selected by limiting the analysis to specific anatomical regions or by computing univariate (voxel-wise or multivariate statistics. However, these methods either discard some informative features or select features with redundant information. This paper introduces the principal feature analysis as a novel multivariate feature selection method for fMRI data processing. This multivariate approach aims to remove features with redundant information, thereby selecting fewer features, while retaining the most information.

  11. Physiological measurements using ultra-high field fMRI: a review.

    Science.gov (United States)

    Francis, Sue; Panchuelo, Rosa Sanchez

    2014-09-01

    Functional MRI (fMRI) has grown to be the neuroimaging technique of choice for investigating brain function. This topical review provides an outline of fMRI methods and applications, with a particular emphasis on the recent advances provided by ultra-high field (UHF) scanners to allow functional mapping with greater sensitivity and improved spatial specificity. A short outline of the origin of the blood oxygenation level dependent (BOLD) contrast is provided, followed by a review of BOLD fMRI methods based on gradient-echo (GE) and spin-echo (SE) contrast. Phase based fMRI measures, as well as perfusion contrast obtained with the technique of arterial spin labelling (ASL), are also discussed. An overview of 7 T based functional neuroimaging is provided, outlining the potential advances to be made and technical challenges to be addressed.

  12. A fMRI Data Processing Method Using a New Composite ICA Algorithm

    Institute of Scientific and Technical Information of China (English)

    CHENHuafu; ZHOUQun; YAODezhong; ZENGMin

    2004-01-01

    One of the reasons Independent component analysis (ICA) becoming so popular is that ICA is a promising tool for signal process application, such as Functional magnetic resonance imaging (fMRI) data processing. However, there are still some problems to be solved. Most ICA algorithms are not stable in fMRI data processing. This paper presents a novel composite ICA algorithm integrating fixed-point algorithm and natural gradient algorithm for brain activity localization in Functional magnetic resonance imaging (fMRI) data. The new composite ICA algorithm has overcome the drawbacks of the both algorithms, providing more accurate and fast detection of weak fMRI functional signals. Simulations show great performance improvement compared with correlation analysis and Automated functional neuro imaging (AfNI) software.

  13. The impact of "physiological correction" on functional connectivity analysis of pharmacological resting state fMRI

    NARCIS (Netherlands)

    Khalili-Mahani, N.; Chang, C.; Osch, M.J.; Veer, I.M.; Buchem, van M.A.; Dahan, A.; Beckmann, C.F.

    2013-01-01

    Growing interest in pharmacological resting state fMRI (RSfMRI) necessitates developing standardized and robust analytical approaches that are insensitive to spurious correlated physiological signals. However, in pharmacological experiments physiological variations constitute an important aspect of

  14. FMRI effective connectivity and TMS chronometry: complementary accounts of causality in the visuospatial judgment network.

    Directory of Open Access Journals (Sweden)

    Tom A de Graaf

    Full Text Available BACKGROUND: While traditionally quite distinct, functional neuroimaging (e.g. functional magnetic resonance imaging: fMRI and functional interference techniques (e.g. transcranial magnetic stimulation: TMS increasingly address similar questions of functional brain organization, including connectivity, interactions, and causality in the brain. Time-resolved TMS over multiple brain network nodes can elucidate the relative timings of functional relevance for behavior ("TMS chronometry", while fMRI functional or effective connectivity (fMRI EC can map task-specific interactions between brain regions based on the interrelation of measured signals. The current study empirically assessed the relation between these different methods. METHODOLOGY/PRINCIPAL FINDINGS: One group of 15 participants took part in two experiments: one fMRI EC study, and one TMS chronometry study, both of which used an established cognitive paradigm involving one visuospatial judgment task and one color judgment control task. Granger causality mapping (GCM, a data-driven variant of fMRI EC analysis, revealed a frontal-to-parietal flow of information, from inferior/middle frontal gyrus (MFG to posterior parietal cortex (PPC. FMRI EC-guided Neuronavigated TMS had behavioral effects when applied to both PPC and to MFG, but the temporal pattern of these effects was similar for both stimulation sites. At first glance, this would seem in contradiction to the fMRI EC results. However, we discuss how TMS chronometry and fMRI EC are conceptually different and show how they can be complementary and mutually constraining, rather than contradictory, on the basis of our data. CONCLUSIONS/SIGNIFICANCE: The findings that fMRI EC could successfully localize functionally relevant TMS target regions on the single subject level, and conversely, that TMS confirmed an fMRI EC identified functional network to be behaviorally relevant, have important methodological and theoretical implications. Our

  15. Independent Component Analysis for fMRI: What is Signal and What is Noise?

    DEFF Research Database (Denmark)

    McKeown, Martin; Hansen, Lars Kai; Sejnowski, Terrence J.

    2003-01-01

    Many sources of fluctuation contribute to the functional magnetic resonance imaging (fMRI) signal, complicating attempts to infer those changes that are truly related to brain activation. Unlike methods of analysis of fMRI data that test the time course of each voxel against a hypothesized waveform...... that are not time-locked to an easily specified sensory or motor event. These methods can be further improved by incorporating prior knowledge regarding the temporal and spatial extent of brain activation....

  16. Neural correlates of text‐based emoticons: a preliminary fMRI study

    OpenAIRE

    Kim, Ko Woon; Lee, Sang Won; Choi, Jeewook; Kim, Tae Min; Jeong, Bumseok

    2016-01-01

    Abstract Introduction Like nonverbal cues in oral interactions, text‐based emoticons, which are textual portrayals of a writer's facial expressions, are commonly used in electronic device–mediated communication. Little is known, however, about how text‐based emoticons are processed in the human brain. With this study, we investigated whether the text‐based emoticons are processed as face expressions using fMRI. Methods During fMRI scan, subjects were asked to respond by pressing a button, ind...

  17. BOLD fMRI signal characteristics of S1- and S2-SSFP at 7 Tesla

    NARCIS (Netherlands)

    Goa, Pål E; Koopmans, Peter J; Poser, Benedikt A; Barth, Markus; Norris, David G

    2014-01-01

    OBJECT: To compare the BOLD fMRI signal characteristics at in the cortex and on the pial surface for a non-balanced steady-state free precession sequence (nb-SSFP) at 7 T. MATERIALS AND METHODS: A multi-echo nb-SSFP sequence was used for high resolution fMRI at 7 T. Two S1 (S(+)) echoes at different

  18. Collective Correlations of Brodmann Areas fMRI Study with RMT-Denoising

    OpenAIRE

    Burda, Zdzislaw; Kornelsen, Jennifer; Nowak, Maciej A.; Porebski, Bartosz; Sboto-Frankenstein, Uta; Tomanek, Boguslaw; Tyburczyk, Jacek

    2013-01-01

    We study collective behavior of Brodmann regions of human cerebral cortex using functional Magnetic Resonance Imaging (fMRI) and Random Matrix Theory (RMT). The raw fMRI data is mapped onto the cortex regions corresponding to the Brodmann areas with the aid of the Talairach coordinates. Principal Component Analysis (PCA) of the Pearson correlation matrix for 41 different Brodmann regions is carried out to determine their collective activity in the idle state and in the active state stimulated...

  19. Memory fMRI predicts verbal memory decline after anterior temporal lobe resection

    OpenAIRE

    Sidhu, Meneka K; Stretton, Jason; Winston, Gavin P.; Symms, Mark; Thompson, Pamela J; Koepp, Matthias J; Duncan, John S.

    2015-01-01

    Objective: To develop a clinically applicable memory functional MRI (fMRI) method of predicting postsurgical memory outcome in individual patients. Methods: In this prospective cohort study, 50 patients with temporal lobe epilepsy (23 left) and 26 controls underwent an fMRI memory encoding paradigm of words with a subsequent out-of-scanner recognition assessment. Neuropsychological assessment was performed preoperatively and 4 months after anterior temporal lobe resection, and at equal time i...

  20. Spontaneous low frequency BOLD signal variations from resting-state fMRI are decreased in Alzheimer disease.

    Science.gov (United States)

    Kazemifar, Samaneh; Manning, Kathryn Y; Rajakumar, Nagalingam; Gómez, Francisco A; Soddu, Andrea; Borrie, Michael J; Menon, Ravi S; Bartha, Robert

    2017-01-01

    Previous studies have demonstrated altered brain activity in Alzheimer's disease using task based functional MRI (fMRI), network based resting-state fMRI, and glucose metabolism from 18F fluorodeoxyglucose-PET (FDG-PET). Our goal was to define a novel indicator of neuronal activity based on a first-order textural feature of the resting state functional MRI (RS-fMRI) signal. Furthermore, we examined the association between this neuronal activity metric and glucose metabolism from 18F FDG-PET. We studied 15 normal elderly controls (NEC) and 15 probable Alzheimer disease (AD) subjects from the AD Neuroimaging Initiative. An independent component analysis was applied to the RS-fMRI, followed by template matching to identify neuronal components (NC). A regional brain activity measurement was constructed based on the variation of the RS-fMRI signal of these NC. The standardized glucose uptake values of several brain regions relative to the cerebellum (SUVR) were measured from partial volume corrected FDG-PET images. Comparing the AD and NEC groups, the mean brain activity metric was significantly lower in the accumbens, while the glucose SUVR was significantly lower in the amygdala and hippocampus. The RS-fMRI brain activity metric was positively correlated with cognitive measures and amyloid β1-42 cerebral spinal fluid levels; however, these did not remain significant following Bonferroni correction. There was a significant linear correlation between the brain activity metric and the glucose SUVR measurements. This proof of concept study demonstrates that this novel and easy to implement RS-fMRI brain activity metric can differentiate a group of healthy elderly controls from a group of people with AD.

  1. An in vivo MRI template set for morphometry, tissue segmentation and fMRI localization in rats

    Directory of Open Access Journals (Sweden)

    Pedro Antonio Valdes Hernandez

    2011-11-01

    Full Text Available Over the last decade, several papers have focused on the construction of highly detailed mouse high field MRI templates via nonlinear registration to unbiased reference spaces, allowing for a variety of neuroimaging applications such as robust morphometric analyses. However, work in rats has only provided medium field MRI averages based on linear registration to biased spaces with the sole purpose of approximate fMRI localization. This precludes any morphometric analysis in spite of the need of exploring in detail the neuroanatomical substrates of diseases in a recent advent of rat models. In this paper we present a new in vivo rat T2 MRI template set, comprising average images of both intensity and shape, obtained via nonlinear registration. Also, unlike previous rat template sets, we include white and gray matter probabilistic segmentations, expanding its use to those applications demanding prior-based tissue segmentation, e.g. SPM voxel-based morphometry. We also provide a preliminary digitalization of latest Paxinos & Watson atlas for anatomical and functional interpretations within the cerebral cortex. We confirmed that, like with previous templates, forepaw and hindpaw fMRI activations can be correctly localized in the expected atlas structure. To exemplify the use of our new MRI template set, we reported the volumes of brain tissues and cortical structures and probed their relationships with ontogenetic development. Other in vivo applications in the near future can be tensor-, deformation- or voxel-based morphometry, morphological connectivity and diffusion tensor-based anatomical connectivity. Our template set, freely available through the SPM extension website, could be an important tool for future longitudinal and/or functional extensive preclinical studies.

  2. An in vivo MRI Template Set for Morphometry, Tissue Segmentation, and fMRI Localization in Rats.

    Science.gov (United States)

    Valdés-Hernández, Pedro Antonio; Sumiyoshi, Akira; Nonaka, Hiroi; Haga, Risa; Aubert-Vásquez, Eduardo; Ogawa, Takeshi; Iturria-Medina, Yasser; Riera, Jorge J; Kawashima, Ryuta

    2011-01-01

    Over the last decade, several papers have focused on the construction of highly detailed mouse high field magnetic resonance image (MRI) templates via non-linear registration to unbiased reference spaces, allowing for a variety of neuroimaging applications such as robust morphometric analyses. However, work in rats has only provided medium field MRI averages based on linear registration to biased spaces with the sole purpose of approximate functional MRI (fMRI) localization. This precludes any morphometric analysis in spite of the need of exploring in detail the neuroanatomical substrates of diseases in a recent advent of rat models. In this paper we present a new in vivo rat T2 MRI template set, comprising average images of both intensity and shape, obtained via non-linear registration. Also, unlike previous rat template sets, we include white and gray matter probabilistic segmentations, expanding its use to those applications demanding prior-based tissue segmentation, e.g., statistical parametric mapping (SPM) voxel-based morphometry. We also provide a preliminary digitalization of latest Paxinos and Watson atlas for anatomical and functional interpretations within the cerebral cortex. We confirmed that, like with previous templates, forepaw and hindpaw fMRI activations can be correctly localized in the expected atlas structure. To exemplify the use of our new MRI template set, were reported the volumes of brain tissues and cortical structures and probed their relationships with ontogenetic development. Other in vivo applications in the near future can be tensor-, deformation-, or voxel-based morphometry, morphological connectivity, and diffusion tensor-based anatomical connectivity. Our template set, freely available through the SPM extension website, could be an important tool for future longitudinal and/or functional extensive preclinical studies.

  3. Paradigm-free mapping with morphological component analysis: getting most out of fMRI data

    Science.gov (United States)

    Caballero Gaudes, César; Van De Ville, Dimitri; Petridou, Natalia; Lazeyras, François; Gowland, Penny

    2011-09-01

    Functional magnetic resonance imaging (fMRI) is a non-invasive imaging technique that maps the brain's response to neuronal activity based on the blood oxygenation level dependent (BOLD) effect. This work proposes a novel method for fMRI data analysis that enables the decomposition of the fMRI signal in its sources based on morphological descriptors. Beyond traditional fMRI hypothesis-based or blind data-driven exploratory approaches, this method allows the detection of BOLD responses without prior timing information. It is based on the deconvolution of the neuronal-related haemodynamic component of the fMRI signal with paradigm free mapping and also furnishes estimates of the movement-related effects, instrumental drifts and physiological fluctuations. Our algorithm is based on an overcomplete representation of the fMRI voxel time series with an additive linear model that is recovered by means of a L1-norm regularized least-squares estimators and an adapted block coordinate relaxation procedure. The performance of the technique is evaluated with simulated data and real experimental data acquired at 3T.

  4. The neuroscience of investing: fMRI of the reward system.

    Science.gov (United States)

    Peterson, Richard L

    2005-11-15

    Functional magnetic resonance imaging (fMRI) has proven a useful tool for observing neural BOLD signal changes during complex cognitive and emotional tasks. Yet the meaning and applicability of the fMRI data being gathered is still largely unknown. The brain's reward system underlies the fundamental neural processes of goal evaluation, preference formation, positive motivation, and choice behavior. fMRI technology allows researchers to dynamically visualize reward system processes. Experimenters can then correlate reward system BOLD activations with experimental behavior from carefully controlled experiments. In the SPAN lab at Stanford University, directed by Brian Knutson Ph.D., researchers have been using financial tasks during fMRI scanning to correlate emotion, behavior, and cognition with the reward system's fundamental neural activations. One goal of the SPAN lab is the development of predictive models of behavior. In this paper we extrapolate our fMRI results toward understanding and predicting individual behavior in the uncertain and high-risk environment of the financial markets. The financial market price anomalies of "value versus glamour" and "momentum" may be real-world examples of reward system activation biasing collective behavior. On the individual level, the investor's bias of overconfidence may similarly be related to reward system activation. We attempt to understand selected "irrational" investor behaviors and anomalous financial market price patterns through correlations with findings from fMRI research of the reward system.

  5. Cerebrovascular reactivity among native-raised high altitude residents: an fMRI study

    Directory of Open Access Journals (Sweden)

    Zhang Jiaxing

    2011-09-01

    Full Text Available Abstract Background The impact of long term residence on high altitude (HA on human brain has raised concern among researchers in recent years. This study investigated the cerebrovascular reactivity among native-born high altitude (HA residents as compared to native sea level (SL residents. The two groups were matched on the ancestral line, ages, gender ratios, and education levels. A visual cue guided maximum inspiration task with brief breath holding was performed by all the subjects while Blood-Oxygenation-Level-Dependent (BOLD functional Magnetic Resonance Imaging (fMRI data were acquired from them. Results Compared to SL controls, the HA group showed generally decreased cerebrovascular reactivity and longer delay in hemodynamic response. Clusters showing significant differences in the former aspect were located at the bilateral primary motor cortex, the right somatosensory association cortex, the right thalamus and the right caudate, the bilateral precuneus, the right cingulate gyrus and the right posterior cingulate cortex, as well as the left fusiform gyrus and the right lingual cortex; clusters showing significant differences in the latter aspect were located at the precuneus, the insula, the superior frontal and temporal gyrus, the somatosensory cortex (the postcentral gyrus and the cerebellar tonsil. Inspiratory reserve volume (IRV, which is an important aspect of pulmonary function, demonstrated significant correlation with the amount of BOLD signal change in multiple brain regions, particularly at the bilateral insula among the HA group. Conclusions Native-born HA residents generally showed reduced cerebrovascular reactivity as demonstrated in the hemodynamic response during a visual cue guided maximum inspiration task conducted with BOLD-fMRI. This effect was particularly manifested among brain regions that are typically involved in cerebral modulation of respiration.

  6. Hemispheric asymmetry for affective stimulus processing in healthy subjects--a fMRI study.

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    Esther Beraha

    Full Text Available BACKGROUND: While hemispheric specialization of language processing is well established, lateralization of emotion processing is still under debate. Several conflicting hypotheses have been proposed, including right hemisphere hypothesis, valence asymmetry hypothesis and region-specific lateralization hypothesis. However, experimental evidence for these hypotheses remains inconclusive, partly because direct comparisons between hemispheres are scarce. METHODS: The present fMRI study systematically investigated functional lateralization during affective stimulus processing in 36 healthy participants. We normalized our functional data on a symmetrical template to avoid confounding effects of anatomical asymmetries. Direct comparison of BOLD responses between hemispheres was accomplished taking two approaches: a hypothesis-driven region of interest analysis focusing on brain areas most frequently reported in earlier neuroimaging studies of emotion; and an exploratory whole volume analysis contrasting non-flipped with flipped functional data using paired t-test. RESULTS: The region of interest analysis revealed lateralization towards the left in the medial prefrontal cortex (BA 10 during positive stimulus processing; while negative stimulus processing was lateralized towards the right in the dorsolateral prefrontal cortex (BA 9 & 46 and towards the left in the amygdala and uncus. The whole brain analysis yielded similar results and, in addition, revealed lateralization towards the right in the premotor cortex (BA 6 and the temporo-occipital junction (BA 19 & 37 during positive stimulus processing; while negative stimulus processing showed lateralization towards the right in the temporo-parietal junction (BA 37,39,42 and towards the left in the middle temporal gyrus (BA 21. CONCLUSION: Our data suggests region-specific functional lateralization of emotion processing. Findings show valence asymmetry for prefrontal cortical areas and left

  7. A comparison between EEG source localization and fMRI during the processing of emotional visual stimuli

    Science.gov (United States)

    Hu, Jin; Tian, Jie; Pan, Xiaohong; Liu, Jiangang

    2007-03-01

    The purpose of this paper is to compare between EEG source localization and fMRI during emotional processing. 108 pictures for EEG (categorized as positive, negative and neutral) and 72 pictures for fMRI were presented to 24 healthy, right-handed subjects. The fMRI data were analyzed using statistical parametric mapping with SPM2. LORETA was applied to grand averaged ERP data to localize intracranial sources. Statistical analysis was implemented to compare spatiotemporal activation of fMRI and EEG. The fMRI results are in accordance with EEG source localization to some extent, while part of mismatch in localization between the two methods was also observed. In the future we should apply the method for simultaneous recording of EEG and fMRI to our study.

  8. A multifunctional method (ERP and fMRI of analysis on facial expression. Three pilot studies

    Directory of Open Access Journals (Sweden)

    Galit Yovel

    2007-04-01

    Full Text Available As social primates, one of the most important cognitive tasks we conduct, dozens of times a day, is to look at a face and extract the person's identity. During the last decade, the neural basis of face processing has been extensively investigated in humans with event-related potential (ERP and functional MRI (fMRI. These two methods provide complementary information about the temporal and spatial aspects of the neural response, with ERPs allowing high temporal resolution of milliseconds but low spatial resolution of the neural generator and fMRI displaying a slow hemodynamic response but better spatial localization of the activated regions. Despite the extensive fMRI and ERP research of faces, only a few studies have assessed the relationship between the two methods and no study to date have collected simultaneous ERP and fMRI responses to face stimuli. In the current paper we will try to assess the spatial and temporal aspects of the neural response to faces by simultaneously collecting functional MRI and event-related potentials (ERP to face stimuli. Our goals are twofold: 1 ERP and fMRI show a robust selective response to faces. In particular, two well-established face-specific phenomena, the RH superiority and the inversion effect are robustly found with both ERP and fMRI. Despite the extensive research of these effects with ERP and fMRI, it is still unknown to what extent their spatial (fMRI and temporal (ERP aspects are associated. In Study 1 we will employ an individual differences approach, to assess the relationship between these ERP and fMRI face-specific responses. 2 Face processing involves several stages starting from structural encoding of the face image through identity processing to storage for later retrieval. This representation undergoes several manipulations that take place at different time points and in different brain regions before the final percept is generated. By simultaneously recording ERP and fMRI we hope to gain a

  9. Assessment of biofeedback rehabilitation in post-stroke patients combining fMRI and gait analysis: a case study

    OpenAIRE

    Del Din, Silvia; Bertoldo, Alessandra; Sawacha, Zimi; Jonsdottir, Johanna; Rabuffetti, Marco; Cobelli, Claudio; Ferrarin, Maurizio

    2014-01-01

    Background The ability to walk independently is a primary goal for rehabilitation after stroke. Gait analysis provides a great amount of valuable information, while functional magnetic resonance imaging (fMRI) offers a powerful approach to define networks involved in motor control. The present study reports a new methodology based on both fMRI and gait analysis outcomes in order to investigate the ability of fMRI to reflect the phases of motor learning before/after electromyographic biofeedba...

  10. Letter representations in writing: An fMRI adaptation approach

    Directory of Open Access Journals (Sweden)

    Olivier eDufor

    2013-10-01

    Full Text Available Abstract:Behavioral and neuropsychological research in reading and spelling has provided evidence for the role of the following types of orthographic representations in letter writing: letter forms, letter case, and abstract letter identities. We report on the results of an fMRI investigation designed to identify the neural substrates of these different representational types. Using a neural adaptation paradigm we examined the neural distribution of inhibition and release from inhibition in a letter-writing task in which, on every trial, participants produced three repetitions of the same letter and a fourth letter that was either identical to (no-change trial or different from the previous three (change trial. Change trials involved a change in the shape, case and/or identity of the letter. After delineating the general letter writing network by identifying areas that exhibited significant neural adaptation effects on no-change trials, we used deconvolution analysis to examine this network for effects of release from inhibition on change trials. In this way we identified regions specifically associated with the representation of letter shape (left SFS and SFG/pre-CG and letter identity (left fusiform gyrus or both (cerebellum, post-central gyrus and middle frontal gyrus. No regions were associated with the representation of letter case. This study showcases an investigational approach that allows for the differentiation of the neurotopography of the representational types that are key to our ability to produce written language.

  11. An fMRI study of processing novel metaphoric sentences.

    Science.gov (United States)

    Mashal, N; Faust, M; Hendler, T; Jung-Beeman, M

    2009-01-01

    Due to inconsistent findings, the role of the two cerebral hemispheres in processing metaphoric language is controversial. The present study examined the possibility that these inconsistent findings may be due, at least partly, to differences in the type (i.e., words vs sentences) or the familiarity of the linguistic material. Previous research has shown that novel two-word metaphoric expressions showed stronger activation in the right homologue of Wernicke's area for the novel metaphors than for both literal expressions and unrelated word pairs. In the present study fMRI was used to identify the left (LH) and the right hemisphere (RH) neural networks associated with processing unfamiliar, novel metaphoric sentences taken from poetry, as compared to those involved in processing familiar literal sentences and unfamiliar nonsensical sentences. Across participants, several left lateralised brain regions showed stronger activation for novel metaphoric sentences than for the nonsensical sentences although both types of sentence represent unfamiliar linguistic expressions. Moreover, the metaphoric sentences elicited more activation in the left dorsolateral prefrontal cortex and the posterior middle temporal gyri than did both the literal sentences and the nonsensical sentences. The increased activation in these brain regions might reflect the enhanced demand on the episodic and semantic memory systems in order to generate de-novo verbal semantic associations. The involvement of the left posterior middle temporal gyri could reflect extra reliance on classical brain structures devoted to sentence comprehension.

  12. Impaired working memory in geriatric depression: an FMRI study.

    Science.gov (United States)

    Dumas, Julie A; Newhouse, Paul A

    2015-04-01

    Older adults with major depressive disorder (MDD) experience poor cognitive and behavioral outcomes as MDD occurs in the context of other age-related brain changes. Patients with depression often have impairments on measures of frontal lobe functioning such as working memory. Understanding the effects of depression on cognitive functioning in older adults is important for the development of treatment strategies that focus on cognitive changes as well as mood. Eleven older adults with current MDD and 12 nondepressed comparison participants (all aged 60 years and older) performed the N-back test of working memory during fMRI. Depressed older adults performed worse than nondepressed participants on the N-back task. Depressed older adults had decreased lateral frontal and parietal activation during the most difficult working memory load condition on the N-back compared with nondepressed older adults. Cognitive dysfunction in geriatric depression may be related to reorganization of brain networks involved in working memory. Copyright © 2015 American Association for Geriatric Psychiatry. Published by Elsevier Inc. All rights reserved.

  13. Walking indoors, walking outdoors: an fMRI study

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    Riccardo eDalla Volta

    2015-10-01

    Full Text Available An observation/execution matching system for walking has not been assessed yet. The present fMRI study was aimed at assessing whether, as for object-directed actions, an observation/execution matching system is active for walking and whether the spatial context of walking (open or narrow space recruits different neural correlates. Two experimental conditions were employed. In the execution condition, while being scanned, participants performed walking on a rolling cylinder located just outside the scanner. The same action was performed also while observing a video presenting either an open space (a country field or a narrow space (a corridor. In the observation condition, participants observed a video presenting an individual walking on the same cylinder on which the actual action was executed, the open space video and the narrow space video, respectively. Results showed common bilateral activations in the dorsal premotor/supplementary motor areas and in the posterior parietal lobe for both execution and observation of walking, thus supporting a matching system for this action. Moreover, specific sectors of the occipital-temporal cortex and the middle temporal gyrus were consistently active when processing a narrow space versus an open one, thus suggesting their involvement in the visuo-motor transformation required when walking in a narrow space. We forward that the present findings may have implications for rehabilitation of gait and sport training.

  14. Normalized cut group clustering of resting-state FMRI data.

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    Martijn van den Heuvel

    Full Text Available BACKGROUND: Functional brain imaging studies have indicated that distinct anatomical brain regions can show coherent spontaneous neuronal activity during rest. Regions that show such correlated behavior are said to form resting-state networks (RSNs. RSNs have been investigated using seed-dependent functional connectivity maps and by using a number of model-free methods. However, examining RSNs across a group of subjects is still a complex task and often involves human input in selecting meaningful networks. METHODOLOGY/PRINCIPAL FINDINGS: We report on a voxel based model-free normalized cut graph clustering approach with whole brain coverage for group analysis of resting-state data, in which the number of RSNs is computed as an optimal clustering fit of the data. Inter-voxel correlations of time-series are grouped at the individual level and the consistency of the resulting networks across subjects is clustered at the group level, defining the group RSNs. We scanned a group of 26 subjects at rest with a fast BOLD sensitive fMRI scanning protocol on a 3 Tesla MR scanner. CONCLUSIONS/SIGNIFICANCE: An optimal group clustering fit revealed 7 RSNs. The 7 RSNs included motor/visual, auditory and attention networks and the frequently reported default mode network. The found RSNs showed large overlap with recently reported resting-state results and support the idea of the formation of spatially distinct RSNs during rest in the human brain.

  15. Simulating fiction: individual differences in literature comprehension revealed with FMRI.

    Directory of Open Access Journals (Sweden)

    Annabel D Nijhof

    Full Text Available When we read literary fiction, we are transported to fictional places, and we feel and think along with the characters. Despite the importance of narrative in adult life and during development, the neurocognitive mechanisms underlying fiction comprehension are unclear. We used functional magnetic resonance imaging (fMRI to investigate how individuals differently employ neural networks important for understanding others' beliefs and intentions (mentalizing, and for sensori-motor simulation while listening to excerpts from literary novels. Localizer tasks were used to localize both the cortical motor network and the mentalizing network in participants after they listened to excerpts from literary novels. Results show that participants who had high activation in anterior medial prefrontal cortex (aMPFC; part of the mentalizing network when listening to mentalizing content of literary fiction, had lower motor cortex activity when they listened to action-related content of the story, and vice versa. This qualifies how people differ in their engagement with fiction: some people are mostly drawn into a story by mentalizing about the thoughts and beliefs of others, whereas others engage in literature by simulating more concrete events such as actions. This study provides on-line neural evidence for the existence of qualitatively different styles of moving into literary worlds, and adds to a growing body of literature showing the potential to study narrative comprehension with neuroimaging methods.

  16. FMRI supports the sensorimotor theory of motor resonance.

    Science.gov (United States)

    Landmann, Claire; Landi, Sofia M; Grafton, Scott T; Della-Maggiore, Valeria

    2011-01-01

    The neural mechanisms mediating the activation of the motor system during action observation, also known as motor resonance, are of major interest to the field of motor control. It has been proposed that motor resonance develops in infants through Hebbian plasticity of pathways connecting sensory and motor regions that fire simultaneously during imitation or self movement observation. A fundamental problem when testing this theory in adults is that most experimental paradigms involve actions that have been overpracticed throughout life. Here, we directly tested the sensorimotor theory of motor resonance by creating new visuomotor representations using abstract stimuli (motor symbols) and identifying the neural networks recruited through fMRI. We predicted that the network recruited during action observation and execution would overlap with that recruited during observation of new motor symbols. Our results indicate that a network consisting of premotor and posterior parietal cortex, the supplementary motor area, the inferior frontal gyrus and cerebellum was activated both by new motor symbols and by direct observation of the corresponding action. This tight spatial overlap underscores the importance of sensorimotor learning for motor resonance and further indicates that the physical characteristics of the perceived stimulus are irrelevant to the evoked response in the observer.

  17. Contradictory reasoning network: an EEG and FMRI study.

    Science.gov (United States)

    Porcaro, Camillo; Medaglia, Maria Teresa; Thai, Ngoc Jade; Seri, Stefano; Rotshtein, Pia; Tecchio, Franca

    2014-01-01

    Contradiction is a cornerstone of human rationality, essential for everyday life and communication. We investigated electroencephalographic (EEG) and functional magnetic resonance imaging (fMRI) in separate recording sessions during contradictory judgments, using a logical structure based on categorical propositions of the Aristotelian Square of Opposition (ASoO). The use of ASoO propositions, while controlling for potential linguistic or semantic confounds, enabled us to observe the spatial temporal unfolding of this contradictory reasoning. The processing started with the inversion of the logical operators corresponding to right middle frontal gyrus (rMFG-BA11) activation, followed by identification of contradictory statement associated with in the right inferior frontal gyrus (rIFG-BA47) activation. Right medial frontal gyrus (rMeFG, BA10) and anterior cingulate cortex (ACC, BA32) contributed to the later stages of process. We observed a correlation between the delayed latency of rBA11 response and the reaction time delay during inductive vs. deductive reasoning. This supports the notion that rBA11 is crucial for manipulating the logical operators. Slower processing time and stronger brain responses for inductive logic suggested that examples are easier to process than general principles and are more likely to simplify communication.

  18. Contradictory reasoning network: an EEG and FMRI study.

    Directory of Open Access Journals (Sweden)

    Camillo Porcaro

    Full Text Available Contradiction is a cornerstone of human rationality, essential for everyday life and communication. We investigated electroencephalographic (EEG and functional magnetic resonance imaging (fMRI in separate recording sessions during contradictory judgments, using a logical structure based on categorical propositions of the Aristotelian Square of Opposition (ASoO. The use of ASoO propositions, while controlling for potential linguistic or semantic confounds, enabled us to observe the spatial temporal unfolding of this contradictory reasoning. The processing started with the inversion of the logical operators corresponding to right middle frontal gyrus (rMFG-BA11 activation, followed by identification of contradictory statement associated with in the right inferior frontal gyrus (rIFG-BA47 activation. Right medial frontal gyrus (rMeFG, BA10 and anterior cingulate cortex (ACC, BA32 contributed to the later stages of process. We observed a correlation between the delayed latency of rBA11 response and the reaction time delay during inductive vs. deductive reasoning. This supports the notion that rBA11 is crucial for manipulating the logical operators. Slower processing time and stronger brain responses for inductive logic suggested that examples are easier to process than general principles and are more likely to simplify communication.

  19. A Sensitivity Analysis of fMRI Balloon Model

    KAUST Repository

    Zayane, Chadia

    2015-04-22

    Functional magnetic resonance imaging (fMRI) allows the mapping of the brain activation through measurements of the Blood Oxygenation Level Dependent (BOLD) contrast. The characterization of the pathway from the input stimulus to the output BOLD signal requires the selection of an adequate hemodynamic model and the satisfaction of some specific conditions while conducting the experiment and calibrating the model. This paper, focuses on the identifiability of the Balloon hemodynamic model. By identifiability, we mean the ability to estimate accurately the model parameters given the input and the output measurement. Previous studies of the Balloon model have somehow added knowledge either by choosing prior distributions for the parameters, freezing some of them, or looking for the solution as a projection on a natural basis of some vector space. In these studies, the identification was generally assessed using event-related paradigms. This paper justifies the reasons behind the need of adding knowledge, choosing certain paradigms, and completing the few existing identifiability studies through a global sensitivity analysis of the Balloon model in the case of blocked design experiment.

  20. Simulating fiction: individual differences in literature comprehension revealed with FMRI.

    Science.gov (United States)

    Nijhof, Annabel D; Willems, Roel M

    2015-01-01

    When we read literary fiction, we are transported to fictional places, and we feel and think along with the characters. Despite the importance of narrative in adult life and during development, the neurocognitive mechanisms underlying fiction comprehension are unclear. We used functional magnetic resonance imaging (fMRI) to investigate how individuals differently employ neural networks important for understanding others' beliefs and intentions (mentalizing), and for sensori-motor simulation while listening to excerpts from literary novels. Localizer tasks were used to localize both the cortical motor network and the mentalizing network in participants after they listened to excerpts from literary novels. Results show that participants who had high activation in anterior medial prefrontal cortex (aMPFC; part of the mentalizing network) when listening to mentalizing content of literary fiction, had lower motor cortex activity when they listened to action-related content of the story, and vice versa. This qualifies how people differ in their engagement with fiction: some people are mostly drawn into a story by mentalizing about the thoughts and beliefs of others, whereas others engage in literature by simulating more concrete events such as actions. This study provides on-line neural evidence for the existence of qualitatively different styles of moving into literary worlds, and adds to a growing body of literature showing the potential to study narrative comprehension with neuroimaging methods.

  1. The dynamics of deductive reasoning: an fMRI investigation.

    Science.gov (United States)

    Rodriguez-Moreno, Diana; Hirsch, Joy

    2009-03-01

    Although the basis for deductive reasoning has been a traditional focus of philosophical discussion, the neural correlates and mechanisms that underlie deductive reasoning have only recently become the focus of scientific investigation. In syllogistic deductive reasoning information presented in two related sequential premises leads to a subsequent conclusion. While previous imaging studies have identified frontal, parietal, temporal, and occipital complexes that are activated during these reasoning events, there are substantive differences among the findings with respect to the specific regions engaged in reasoning and the contribution of language areas. Further, little is known about the various stages of information processing during reasoning. Using event-related fMRI and an auditory and visual conjunction technique, we identified a long-range supramodal network active during reasoning processes including areas in the left frontal and parietal regions as well as the bilateral caudate nucleus. Time courses of activation for each of these regions suggest that reasoning processes emerge during the presentation of the second premise, and remain active until the validation of the conclusion. Thus, areas within the frontal and parietal regions are differentially engaged at different time points in the reasoning process consistent with coordinated intra-network interactions.

  2. Emotion-motion interactions in conversion disorder: an FMRI study.

    Directory of Open Access Journals (Sweden)

    Selma Aybek

    Full Text Available To evaluate the neural correlates of implicit processing of negative emotions in motor conversion disorder (CD patients.An event related fMRI task was completed by 12 motor CD patients and 14 matched healthy controls using standardised stimuli of faces with fearful and sad emotional expressions in comparison to faces with neutral expressions. Temporal changes in the sensitivity to stimuli were also modelled and tested in the two groups.We found increased amygdala activation to negative emotions in CD compared to healthy controls in region of interest analyses, which persisted over time consistent with previous findings using emotional paradigms. Furthermore during whole brain analyses we found significantly increased activation in CD patients in areas involved in the 'freeze response' to fear (periaqueductal grey matter, and areas involved in self-awareness and motor control (cingulate gyrus and supplementary motor area.In contrast to healthy controls, CD patients exhibited increased response amplitude to fearful stimuli over time, suggesting abnormal emotional regulation (failure of habituation / sensitization. Patients with CD also activated midbrain and frontal structures that could reflect an abnormal behavioral-motor response to negative including threatening stimuli. This suggests a mechanism linking emotions to motor dysfunction in CD.

  3. FMRI supports the sensorimotor theory of motor resonance.

    Directory of Open Access Journals (Sweden)

    Claire Landmann

    Full Text Available The neural mechanisms mediating the activation of the motor system during action observation, also known as motor resonance, are of major interest to the field of motor control. It has been proposed that motor resonance develops in infants through Hebbian plasticity of pathways connecting sensory and motor regions that fire simultaneously during imitation or self movement observation. A fundamental problem when testing this theory in adults is that most experimental paradigms involve actions that have been overpracticed throughout life. Here, we directly tested the sensorimotor theory of motor resonance by creating new visuomotor representations using abstract stimuli (motor symbols and identifying the neural networks recruited through fMRI. We predicted that the network recruited during action observation and execution would overlap with that recruited during observation of new motor symbols. Our results indicate that a network consisting of premotor and posterior parietal cortex, the supplementary motor area, the inferior frontal gyrus and cerebellum was activated both by new motor symbols and by direct observation of the corresponding action. This tight spatial overlap underscores the importance of sensorimotor learning for motor resonance and further indicates that the physical characteristics of the perceived stimulus are irrelevant to the evoked response in the observer.

  4. Emotion-motion interactions in conversion disorder: an FMRI study.

    Science.gov (United States)

    Aybek, Selma; Nicholson, Timothy R; O'Daly, Owen; Zelaya, Fernando; Kanaan, Richard A; David, Anthony S

    2015-01-01

    To evaluate the neural correlates of implicit processing of negative emotions in motor conversion disorder (CD) patients. An event related fMRI task was completed by 12 motor CD patients and 14 matched healthy controls using standardised stimuli of faces with fearful and sad emotional expressions in comparison to faces with neutral expressions. Temporal changes in the sensitivity to stimuli were also modelled and tested in the two groups. We found increased amygdala activation to negative emotions in CD compared to healthy controls in region of interest analyses, which persisted over time consistent with previous findings using emotional paradigms. Furthermore during whole brain analyses we found significantly increased activation in CD patients in areas involved in the 'freeze response' to fear (periaqueductal grey matter), and areas involved in self-awareness and motor control (cingulate gyrus and supplementary motor area). In contrast to healthy controls, CD patients exhibited increased response amplitude to fearful stimuli over time, suggesting abnormal emotional regulation (failure of habituation / sensitization). Patients with CD also activated midbrain and frontal structures that could reflect an abnormal behavioral-motor response to negative including threatening stimuli. This suggests a mechanism linking emotions to motor dysfunction in CD.

  5. Investigating emotion with music: an fMRI study.

    Science.gov (United States)

    Koelsch, Stefan; Fritz, Thomas; V Cramon, D Yves; Müller, Karsten; Friederici, Angela D

    2006-03-01

    The present study used pleasant and unpleasant music to evoke emotion and functional magnetic resonance imaging (fMRI) to determine neural correlates of emotion processing. Unpleasant (permanently dissonant) music contrasted with pleasant (consonant) music showed activations of amygdala, hippocampus, parahippocampal gyrus, and temporal poles. These structures have previously been implicated in the emotional processing of stimuli with (negative) emotional valence; the present data show that a cerebral network comprising these structures can be activated during the perception of auditory (musical) information. Pleasant (contrasted to unpleasant) music showed activations of the inferior frontal gyrus (IFG, inferior Brodmann's area (BA) 44, BA 45, and BA 46), the anterior superior insula, the ventral striatum, Heschl's gyrus, and the Rolandic operculum. IFG activations appear to reflect processes of music-syntactic analysis and working memory operations. Activations of Rolandic opercular areas possibly reflect the activation of mirror-function mechanisms during the perception of the pleasant tunes. Rolandic operculum, anterior superior insula, and ventral striatum may form a motor-related circuitry that serves the formation of (premotor) representations for vocal sound production during the perception of pleasant auditory information. In all of the mentioned structures, except the hippocampus, activations increased over time during the presentation of the musical stimuli, indicating that the effects of emotion processing have temporal dynamics; the temporal dynamics of emotion have so far mainly been neglected in the functional imaging literature. Copyright 2005 Wiley-Liss, Inc.

  6. FMRI evidence of 'mirror' responses to geometric shapes.

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    Clare Press

    Full Text Available Mirror neurons may be a genetic adaptation for social interaction. Alternatively, the associative hypothesis proposes that the development of mirror neurons is driven by sensorimotor learning, and that, given suitable experience, mirror neurons will respond to any stimulus. This hypothesis was tested using fMRI adaptation to index populations of cells with mirror properties. After sensorimotor training, where geometric shapes were paired with hand actions, BOLD response was measured while human participants experienced runs of events in which shape observation alternated with action execution or observation. Adaptation from shapes to action execution, and critically, observation, occurred in ventral premotor cortex (PMv and inferior parietal lobule (IPL. Adaptation from shapes to execution indicates that neuronal populations responding to the shapes had motor properties, while adaptation to observation demonstrates that these populations had mirror properties. These results indicate that sensorimotor training induced populations of cells with mirror properties in PMv and IPL to respond to the observation of arbitrary shapes. They suggest that the mirror system has not been shaped by evolution to respond in a mirror fashion to biological actions; instead, its development is mediated by stimulus-general processes of learning within a system adapted for visuomotor control.

  7. A Computational Model for the Automatic Diagnosis of Attention Deficit Hyperactivity Disorder Based on Functional Brain Volume

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    Lirong Tan

    2017-09-01

    Full Text Available In this paper, we investigated the problem of computer-aided diagnosis of Attention Deficit Hyperactivity Disorder (ADHD using machine learning techniques. With the ADHD-200 dataset, we developed a Support Vector Machine (SVM model to classify ADHD patients from typically developing controls (TDCs, using the regional brain volumes as predictors. Conventionally, the volume of a brain region was considered to be an anatomical feature and quantified using structural magnetic resonance images. One major contribution of the present study was that we had initially proposed to measure the regional brain volumes using fMRI images. Brain volumes measured from fMRI images were denoted as functional volumes, which quantified the volumes of brain regions that were actually functioning during fMRI imaging. We compared the predictive power of functional volumes with that of regional brain volumes measured from anatomical images, which were denoted as anatomical volumes. The former demonstrated higher discriminative power than the latter for the classification of ADHD patients vs. TDCs. Combined with our two-step feature selection approach which integrated prior knowledge with the recursive feature elimination (RFE algorithm, our SVM classification model combining functional volumes and demographic characteristics achieved a balanced accuracy of 67.7%, which was 16.1% higher than that of a relevant model published previously in the work of Sato et al. Furthermore, our classifier highlighted 10 brain regions that were most discriminative in distinguishing between ADHD patients and TDCs. These 10 regions were mainly located in occipital lobe, cerebellum posterior lobe, parietal lobe, frontal lobe, and temporal lobe. Our present study using functional images will likely provide new perspectives about the brain regions affected by ADHD.

  8. Renormalized Volume

    CERN Document Server

    Gover, A Rod

    2016-01-01

    For any conformally compact manifold with hypersurface boundary we define a canonical renormalized volume functional and compute an explicit, holographic formula for the corresponding anomaly. For the special case of asymptotically Einstein manifolds, our method recovers the known results. The anomaly does not depend on any particular choice of regulator, but the coefficients of divergences do. We give explicit formulae for these divergences valid for any choice of regulating hypersurface; these should be relevant to recent studies of quantum corrections to entanglement entropies. The anomaly is expressed as a conformally invariant integral of a local Q-curvature that generalizes the Branson Q-curvature by including data of the embedding. In each dimension this canonically defines a higher dimensional generalization of the Willmore energy/rigid string action. We show that the variation of these energy functionals is exactly the obstruction to solving a singular Yamabe type problem with boundary data along the...

  9. Extending Local Canonical Correlation Analysis to Handle General Linear Contrasts for fMRI Data

    Directory of Open Access Journals (Sweden)

    Mingwu Jin

    2012-01-01

    Full Text Available Local canonical correlation analysis (CCA is a multivariate method that has been proposed to more accurately determine activation patterns in fMRI data. In its conventional formulation, CCA has several drawbacks that limit its usefulness in fMRI. A major drawback is that, unlike the general linear model (GLM, a test of general linear contrasts of the temporal regressors has not been incorporated into the CCA formalism. To overcome this drawback, a novel directional test statistic was derived using the equivalence of multivariate multiple regression (MVMR and CCA. This extension will allow CCA to be used for inference of general linear contrasts in more complicated fMRI designs without reparameterization of the design matrix and without reestimating the CCA solutions for each particular contrast of interest. With the proper constraints on the spatial coefficients of CCA, this test statistic can yield a more powerful test on the inference of evoked brain regional activations from noisy fMRI data than the conventional t-test in the GLM. The quantitative results from simulated and pseudoreal data and activation maps from fMRI data were used to demonstrate the advantage of this novel test statistic.

  10. EEG-assisted retrospective motion correction for fMRI: E-REMCOR

    CERN Document Server

    Zotev, Vadim; Phillips, Raquel; Bodurka, Jerzy

    2012-01-01

    We propose a method for retrospective motion correction of fMRI data in simultaneous EEG-fMRI that employs the EEG array as a sensitive motion detector. EEG motion artifacts are used to generate motion regressors describing rotational head movements with millisecond temporal resolution. These regressors are employed for slice-specific motion correction of unprocessed fMRI data. The method does not require any specialized equipment beyond the standard EEG-fMRI instrumentation. Its performance is demonstrated by correction of fMRI data from four patients with major depressive disorder, who exhibited random head movements by 1-3 mm during a resting EEG-fMRI run. The fMRI datasets, corrected using eight EEG-based motion regressors, show significant improvements in temporal SNR (tSNR) of fMRI time series, particularly in the frontal brain regions and near the surface of the brain. The tSNR improvements are as high as 50% for large brain areas in single-subject analysis and as high as 25% when the results are avera...

  11. Sparse representation of whole-brain fMRI signals for identification of functional networks.

    Science.gov (United States)

    Lv, Jinglei; Jiang, Xi; Li, Xiang; Zhu, Dajiang; Chen, Hanbo; Zhang, Tuo; Zhang, Shu; Hu, Xintao; Han, Junwei; Huang, Heng; Zhang, Jing; Guo, Lei; Liu, Tianming

    2015-02-01

    There have been several recent studies that used sparse representation for fMRI signal analysis and activation detection based on the assumption that each voxel's fMRI signal is linearly composed of sparse components. Previous studies have employed sparse coding to model functional networks in various modalities and scales. These prior contributions inspired the exploration of whether/how sparse representation can be used to identify functional networks in a voxel-wise way and on the whole brain scale. This paper presents a novel, alternative methodology of identifying multiple functional networks via sparse representation of whole-brain task-based fMRI signals. Our basic idea is that all fMRI signals within the whole brain of one subject are aggregated into a big data matrix, which is then factorized into an over-complete dictionary basis matrix and a reference weight matrix via an effective online dictionary learning algorithm. Our extensive experimental results have shown that this novel methodology can uncover multiple functional networks that can be well characterized and interpreted in spatial, temporal and frequency domains based on current brain science knowledge. Importantly, these well-characterized functional network components are quite reproducible in different brains. In general, our methods offer a novel, effective and unified solution to multiple fMRI data analysis tasks including activation detection, de-activation detection, and functional network identification. Copyright © 2014 Elsevier B.V. All rights reserved.

  12. Mapping between fMRI responses to movies and their natural language annotations.

    Science.gov (United States)

    Vodrahalli, Kiran; Chen, Po-Hsuan; Liang, Yingyu; Baldassano, Christopher; Chen, Janice; Yong, Esther; Honey, Christopher; Hasson, Uri; Ramadge, Peter; Norman, Kenneth A; Arora, Sanjeev

    2017-06-23

    Several research groups have shown how to map fMRI responses to the meanings of presented stimuli. This paper presents new methods for doing so when only a natural language annotation is available as the description of the stimulus. We study fMRI data gathered from subjects watching an episode of BBCs Sherlock (Chen et al., 2017), and learn bidirectional mappings between fMRI responses and natural language representations. By leveraging data from multiple subjects watching the same movie, we were able to perform scene classification with 72% accuracy (random guessing would give 4%) and scene ranking with average rank in the top 4% (random guessing would give 50%). The key ingredients underlying this high level of performance are (a) the use of the Shared Response Model (SRM) and its variant SRM-ICA (Chen et al., 2015; Zhang et al., 2016) to aggregate fMRI data from multiple subjects, both of which are shown to be superior to standard PCA in producing low-dimensional representations for the tasks in this paper; (b) a sentence embedding technique adapted from the natural language processing (NLP) literature (Arora et al., 2017) that produces semantic vector representation of the annotations; (c) using previous timestep information in the featurization of the predictor data. These optimizations in how we featurize the fMRI data and text annotations provide a substantial improvement in classification performance, relative to standard approaches. Copyright © 2017 The Authors. Published by Elsevier Inc. All rights reserved.

  13. A new vibrator to stimulate muscle proprioceptors in fMRI.

    Science.gov (United States)

    Montant, Marie; Romaiguère, Patricia; Roll, Jean-Pierre

    2009-03-01

    Studying cognitive brain functions by functional magnetic resonance imaging (fMRI) requires appropriate stimulation devices that do not interfere with the magnetic fields. Since the emergence of fMRI in the 90s, a number of stimulation devices have been developed for the visual and auditory modalities. Only few devices, however, have been developed for the somesthesic modality. Here, we present a vibration device for studying somesthesia that is compatible with high magnetic field environments and that can be used in fMRI machines. This device consists of a poly vinyl chloride (PVC) vibrator containing a wind turbine and of a pneumatic apparatus that controls 1-6 vibrators simultaneously. Just like classical electromagnetic vibrators, our device stimulates muscle mechanoreceptors (muscle spindles) and generates reliable illusions of movement. We provide the fMRI compatibility data (phantom test), the calibration curve (vibration frequency as a function of air flow), as well as the results of a kinesthetic test (perceived speed of the illusory movement as a function of vibration frequency). This device was used successfully in several brain imaging studies using both fMRI and magnetoencephalography.

  14. A SVM-based quantitative fMRI method for resting-state functional network detection.

    Science.gov (United States)

    Song, Xiaomu; Chen, Nan-kuei

    2014-09-01

    Resting-state functional magnetic resonance imaging (fMRI) aims to measure baseline neuronal connectivity independent of specific functional tasks and to capture changes in the connectivity due to neurological diseases. Most existing network detection methods rely on a fixed threshold to identify functionally connected voxels under the resting state. Due to fMRI non-stationarity, the threshold cannot adapt to variation of data characteristics across sessions and subjects, and generates unreliable mapping results. In this study, a new method is presented for resting-state fMRI data analysis. Specifically, the resting-state network mapping is formulated as an outlier detection process that is implemented using one-class support vector machine (SVM). The results are refined by using a spatial-feature domain prototype selection method and two-class SVM reclassification. The final decision on each voxel is made by comparing its probabilities of functionally connected and unconnected instead of a threshold. Multiple features for resting-state analysis were extracted and examined using an SVM-based feature selection method, and the most representative features were identified. The proposed method was evaluated using synthetic and experimental fMRI data. A comparison study was also performed with independent component analysis (ICA) and correlation analysis. The experimental results show that the proposed method can provide comparable or better network detection performance than ICA and correlation analysis. The method is potentially applicable to various resting-state quantitative fMRI studies.

  15. Characterizing response to elemental unit of acoustic imaging noise: an FMRI study.

    Science.gov (United States)

    Tamer, Gregory G; Luh, Wen-Ming; Talavage, Thomas M

    2009-07-01

    Acoustic imaging noise produced during functional magnetic resonance imaging (fMRI) studies can hinder auditory fMRI research analysis by altering the properties of the acquired time-series data. Acoustic imaging noise can be especially confounding when estimating the time course of the hemodynamic response (HDR) in auditory event-related fMRI (fMRI) experiments. This study is motivated by the desire to establish a baseline function that can serve not only as a comparison to other quantities of acoustic imaging noise for determining how detrimental is one's experimental noise, but also as a foundation for a model that compensates for the response to acoustic imaging noise. Therefore, the amplitude and spatial extent of the HDR to the elemental unit of acoustic imaging noise (i.e., a single ping) associated with echoplanar acquisition were characterized and modeled. Results from this fMRI study at 1.5 T indicate that the group-averaged HDR in left and right auditory cortex to acoustic imaging noise (duration of 46 ms) has an estimated peak magnitude of 0.29% (right) to 0.48% (left) signal change from baseline, peaks between 3 and 5 s after stimulus presentation, and returns to baseline and remains within the noise range approximately 8 s after stimulus presentation.

  16. Functional Topography of Human Corpus Callosum: An fMRI Mapping Study

    Directory of Open Access Journals (Sweden)

    Mara Fabri

    2013-01-01

    Full Text Available The concept of a topographical map of the corpus callosum (CC has emerged from human lesion studies and from electrophysiological and anatomical tracing investigations in other mammals. Over the last few years a rising number of researchers have been reporting functional magnetic resonance imaging (fMRI activation in white matter, particularly the CC. In this study the scope for describing CC topography with fMRI was explored by evoking activation through simple sensory stimulation and motor tasks. We reviewed our published and unpublished fMRI and diffusion tensor imaging data on the cortical representation of tactile, gustatory, auditory, and visual sensitivity and of motor activation, obtained in 36 normal volunteers and in 6 patients with partial callosotomy. Activation foci were consistently detected in discrete CC regions: anterior (taste stimuli, central (motor tasks, central and posterior (tactile stimuli, and splenium (auditory and visual stimuli. Reconstruction of callosal fibers connecting activated primary gustatory, motor, somatosensory, auditory, and visual cortices by diffusion tensor tracking showed bundles crossing, respectively, through the genu, anterior and posterior body, and splenium, at sites harboring fMRI foci. These data confirm that the CC commissure has a topographical organization and demonstrate that its functional topography can be explored with fMRI.

  17. fMRI investigation of monocular pattern rivalry.

    Science.gov (United States)

    Mendola, Janine D; Buckthought, Athena

    2013-01-01

    In monocular pattern rivalry, a composite image is shown to both eyes. The patient experiences perceptual alternations in which the two stimulus components alternate in clarity or salience. We used fMRI at 3T to image brain activity while participants perceived monocular rivalry passively or indicated their percepts with a task. The stimulus patterns were left/right oblique gratings, face/house composites, or a nonrivalrous control stimulus that did not support the perception of transparency or image segmentation. All stimuli were matched for luminance, contrast, and color. Compared with the control stimulus, the cortical activation for passive viewing of grating rivalry included dorsal and ventral extrastriate cortex, superior and inferior parietal regions, and multiple sites in frontal cortex. When the BOLD signal for the object rivalry task was compared with the grating rivalry task, a similar whole-brain network was engaged, but with significantly greater activity in extrastriate regions, including V3, V3A, fusiform face area (FFA), and parahippocampal place area (PPA). In addition, for the object rivalry task, FFA activity was significantly greater during face-dominant periods whereas parahippocampal place area activity was greater during house-dominant periods. Our results demonstrate that slight stimulus changes that trigger monocular rivalry recruit a large whole-brain network, as previously identified for other forms of bistability. Moreover, the results indicate that rivalry for complex object stimuli preferentially engages extrastriate cortex. We also establish that even with natural viewing conditions, endogenous attentional fluctuations in monocular pattern rivalry will differentially drive object-category-specific cortex, similar to binocular rivalry, but without complete suppression of the nondominant image.

  18. fMRI characterization of visual working memory recognition.

    Science.gov (United States)

    Rahm, Benjamin; Kaiser, Jochen; Unterrainer, Josef M; Simon, Juliane; Bledowski, Christoph

    2014-04-15

    Encoding and maintenance of information in visual working memory have been extensively studied, highlighting the crucial and capacity-limiting role of fronto-parietal regions. In contrast, the neural basis of recognition in visual working memory has remained largely unspecified. Cognitive models suggest that recognition relies on a matching process that compares sensory information with the mental representations held in memory. To characterize the neural basis of recognition we varied both the need for recognition and the degree of similarity between the probe item and the memory contents, while independently manipulating memory load to produce load-related fronto-parietal activations. fMRI revealed a fractionation of working memory functions across four distributed networks. First, fronto-parietal regions were activated independent of the need for recognition. Second, anterior parts of load-related parietal regions contributed to recognition but their activations were independent of the difficulty of matching in terms of sample-probe similarity. These results argue against a key role of the fronto-parietal attention network in recognition. Rather the third group of regions including bilateral temporo-parietal junction, posterior cingulate cortex and superior frontal sulcus reflected demands on matching both in terms of sample-probe-similarity and the number of items to be compared. Also, fourth, bilateral motor regions and right superior parietal cortex showed higher activation when matching provided clear evidence for a decision. Together, the segregation between the well-known fronto-parietal activations attributed to attentional operations in working memory from those regions involved in matching supports the theoretical view of separable attentional and mnemonic contributions to working memory. Yet, the close theoretical and empirical correspondence to perceptual decision making may call for an explicit consideration of decision making mechanisms in

  19. Brain network dynamics underlying visuospatial judgment: an FMRI connectivity study.

    Science.gov (United States)

    de Graaf, Tom A; Roebroeck, Alard; Goebel, Rainer; Sack, Alexander T

    2010-09-01

    Previous functional imaging research has consistently indicated involvement of bilateral fronto-parietal networks during the execution of visuospatial tasks. Studies with TMS have suggested that the right hemispheric network, but not the left, is functionally relevant for visuospatial judgments. However, very little is still known about the interactions within these fronto-parietal networks underlying visuospatial processing. In the current study, we investigated task modulation of functional connectivity (instantaneous correlations of regional time courses), and task-specific effective connectivity (direction of influences), within the right fronto-parietal network activated during visuospatial judgments. Ten healthy volunteers performed a behaviorally controlled visuospatial judgment task (ANGLE) or a control task (COLOR) in an fMRI experiment. Visuospatial task-specific activations were found in posterior parietal cortex (PPC) and middle/inferior frontal gyrus (MFG). Functional connectivity within this network was task-modulated, with significantly higher connectivity between PPC and MFG during ANGLE than during COLOR. Effective connectivity analysis for directed influence revealed that visuospatial task-specific projections within this network were predominantly in a frontal-to-parietal direction. Moreover, ANGLE-specific influences from thalamic nuclei to PPC were identified. Exploratory effective connectivity analysis revealed that closely neighboring clusters, within visuospatial regions, were differentially involved in the network. These neighboring clusters had opposite effective connectivity patterns to other nodes of the fronto-parietal network. Our data thus reveal that visuospatial judgments are supported by massive fronto-parietal backprojections, thalamo-parietal influence, and multiple stages, or loops, of information flow within the visuospatial network. We speculate on possible functional contributions of the various network nodes and

  20. Reproducibility of graph metrics in fMRI networks

    Directory of Open Access Journals (Sweden)

    Qawi K Telesford

    2010-12-01

    Full Text Available The reliability of graph metrics calculated in network analysis is essential to the interpretation of complex network organization. These graph metrics are used to deduce the small-world properties in networks. In this study, we investigated the test-retest reliability of graph metrics from functional magnetic resonance imaging (fMRI data collected for two runs in 45 healthy older adults. Graph metrics were calculated on data for both runs and compared using intraclass correlation coefficient (ICC statistics and Bland-Altman (BA plots. ICC scores describe the level of absolute agreement between two measurements and provide a measure of reproducibility. For mean graph metrics, ICC scores were high for clustering coefficient (ICC=0.86, global efficiency (ICC=0.83, path length (ICC=0.79, and local efficiency (ICC=0.75; the ICC score for degree was found to be low (ICC=0.29. ICC scores were also used to generate reproducibility maps in brain space to test voxel-wise reproducibility for unsmoothed and smoothed data. Reproducibility was uniform across the brain for global efficiency and path length, but was only high in network hubs for clustering coefficient, local efficiency and degree. BA plots were used to test the measurement repeatability of all graph metrics. All graph metrics fell within the limits for repeatability. Together, these results suggest that with exception of degree, mean graph metrics are reproducible and suitable for clinical studies. Further exploration is warranted to better understand reproducibility across the brain on a voxel-wise basis.

  1. Visual Cortex Plasticity Following Peripheral Damage To The Visual System: fMRI Evidence.

    Science.gov (United States)

    Lemos, João; Pereira, Daniela; Castelo-Branco, Miguel

    2016-10-01

    Over the last two decades, functional magnetic resonance imaging (fMRI) has become a powerful research method to investigate cortical visual plasticity. Abnormal fMRI response patterns have been occasionally detected in the visually deprived cortex of patients with bilateral retinal diseases. Controversy remains whether these observations indicate structural reorganization of the visual cortex or unmasking of previously silent cortico-cortical connections. In optic nerve diseases, there is weak evidence showing that early visual cortex seems to lack reorganization, while higher-order visual areas undergo plastic changes which may contribute to optimise visual function. There is however accumulating imaging evidence demonstrating trans-synaptic degeneration of the visual cortex in patients with disease of the anterior visual pathways. This may preclude the use of restorative treatments in these patients. Here, we review and update the body of fMRI evidence on visual cortical plasticity.

  2. Independent vector analysis for capturing common components in fMRI group analysis

    DEFF Research Database (Denmark)

    Engberg, Astrid M. E.; Andersen, Kasper W.; Mørup, Morten;

    2016-01-01

    -subject studies. Independent vector analysis (IVA) is a promising alternative approach to perform group fMRI analysis, which has been shown to better capture components with high inter-subject variability. The most widely applied IVA method is based on the multivariate Laplace distribution (IVA-GL), which assumes...... independence within subject components coupled across subjects only through shared scaling. In this study, we propose a more natural formulation of IVA based on a Normal-Inverse-Gamma distribution (IVA-NIG), in which the components can be directly interpreted as realizations of a common mean component...... with individual subject variability. We evaluate the performance of IVA-NIG compared to IVA-GL and similar decomposition methods, through the application of two types of simulated data and on real task fMRI data. The results show that IVA-NIG offers superior detection of components in simulated fMRI data. On real...

  3. Feature-space clustering for fMRI meta-analysis

    DEFF Research Database (Denmark)

    Goutte, C.; Hansen, L.K.; Liptrot, Matthew George

    2001-01-01

    Clustering functional magnetic resonance imaging (fMRI) time series has emerged in recent years as a possible alternative to parametric modeling approaches. Most of the work so far has been concerned with clustering raw time series. In this contribution we investigate the applicability...... of a clustering method applied to features extracted from the data. This approach is extremely versatile and encompasses previously published results [Goutte et al., 1999] as special cases. A typical application is in data reduction: as the increase in temporal resolution of fMRI experiments routinely yields f......-voxel analyses. In particular this allows the checking of the differences and agreements between different methods of analysis. Both approaches are illustrated on a fMRI data set involving visual stimulation, and we show that the feature space clustering approach yields nontrivial results and, in particular...

  4. Human memory retention and recall processes. A review of EEG and fMRI studies.

    Science.gov (United States)

    Amin, Hafeezullah; Malik, Aamir S

    2013-10-01

    Human memory is an important concept in cognitive psychology and neuroscience. Our brain is actively engaged in functions of learning and memorization. Generally, human memory has been classified into 2 groups: short-term/working memory, and long-term memory. Using different memory paradigms and brain mapping techniques, psychologists and neuroscientists have identified 3 memory processes: encoding, retention, and recall. These processes have been studied using EEG and functional MRI (fMRI) in cognitive and neuroscience research. This study reviews previous research reported for human memory processes, particularly brain behavior in memory retention and recall processes with the use of EEG and fMRI. We discuss issues and challenges related to memory research with EEG and fMRI techniques.

  5. A comparison of Gamma and Gaussian dynamic convolution models of the fMRI BOLD response.

    Science.gov (United States)

    Chen, Huafu; Yao, Dezhong; Liu, Zuxiang

    2005-01-01

    Blood oxygenation level-dependent (BOLD) contrast-based functional magnetic resonance imaging (fMRI) has been widely utilized to detect brain neural activities and great efforts are now stressed on the hemodynamic processes of different brain regions activated by a stimulus. The focus of this paper is the comparison of Gamma and Gaussian dynamic convolution models of the fMRI BOLD response. The convolutions are between the perfusion function of the neural response to a stimulus and a Gaussian or Gamma function. The parameters of the two models are estimated by a nonlinear least-squares optimal algorithm for the fMRI data of eight subjects collected in a visual stimulus experiment. The results show that the Gaussian model is better than the Gamma model in fitting the data. The model parameters are different in the left and right occipital regions, which indicate that the dynamic processes seem different in various cerebral functional regions.

  6. Feature-space clustering for fMRI meta-analysis

    DEFF Research Database (Denmark)

    Goutte, C.; Hansen, L.K.; Liptrot, Matthew George

    2001-01-01

    Clustering functional magnetic resonance imaging (fMRI) time series has emerged in recent years as a possible alternative to parametric modeling approaches. Most of the work so far has been concerned with clustering raw time series. In this contribution we investigate the applicability...... of a clustering method applied to features extracted from the data. This approach is extremely versatile and encompasses previously published results [Goutte et al., 1999] as special cases. A typical application is in data reduction: as the increase in temporal resolution of fMRI experiments routinely yields f......-voxel analyses. In particular this allows the checking of the differences and agreements between different methods of analysis. Both approaches are illustrated on a fMRI data set involving visual stimulation, and we show that the feature space clustering approach yields nontrivial results and, in particular...

  7. Modelling the neurovascular habituation effect on fMRI time series

    Energy Technology Data Exchange (ETDEWEB)

    Ciuciu, Ph.; Sockeel, S.; Vincent, T. [NeuroSpin/CEA, F-91191 Gif-sur-Yvette (France); Idier, J. [IRCCyN/CNRS, 1 rue de la Noe 44300 Nantes (France)

    2009-07-01

    In this paper, a novel non-stationary model of functional Magnetic Resonance Imaging (fMRI) time series is proposed. It allows us to account for some putative habituation effect arising in event-related fMRI paradigms that involves the so-called repetition-suppression phenomenon and induces decreasing magnitude responses over successive trials. Akin, this model is defined over functionally homogeneous regions-of-interest (ROIs) and embedded in a joint detection-estimation approach of brain activity. Importantly, its non-stationarity character is embodied in the trial-varying nature of the BOLD response magnitude. Habituation and activation maps are then estimated within the Bayesian framework in a fully unsupervised MCMC procedure. On artificial fMRI datasets, we show that habituation effects can be accurately recovered in activating voxels. (authors)

  8. ICA if fMRI based on a convolutive mixture model

    DEFF Research Database (Denmark)

    Hansen, Lars Kai

    2003-01-01

    mixing relevant for spatial ICA. Convolutive ICA has many computational problems and no standard solution is available. In this study a new predictive estimation method is used for finding the mixing coefficients and the source signals of a convolutive mixture and it is applied in temporal mode...... challenge with previous independent component analyses is the convolutive nature of the mixing process in fMRI. In temporal ICA we assume that the measured fMRI response is an instantaneous, spatially varying, mixture of independent time functions. However, the convolutive structure of the hemodynamic....... The mixing is represented by “mixture coefficient images” quantifying the local response to a given source at a certain time lag. This is the first communication to address this important issue in the context of fMRI ICA. Data: A single slice holding 128x128 pixels and passing through primary visual cortex...

  9. The effect of ageing on fMRI: Correction for the confounding effects of vascular reactivity evaluated by joint fMRI and MEG in 335 adults

    Science.gov (United States)

    Henson, Richard N. A.; Tyler, Lorraine K.; Davis, Simon W.; Shafto, Meredith A.; Taylor, Jason R.; Williams, Nitin; Cam‐CAN; Rowe, James B.

    2015-01-01

    Abstract In functional magnetic resonance imaging (fMRI) research one is typically interested in neural activity. However, the blood‐oxygenation level‐dependent (BOLD) signal is a composite of both neural and vascular activity. As factors such as age or medication may alter vascular function, it is essential to account for changes in neurovascular coupling when investigating neurocognitive functioning with fMRI. The resting‐state fluctuation amplitude (RSFA) in the fMRI signal (rsfMRI) has been proposed as an index of vascular reactivity. The RSFA compares favourably with other techniques such as breath‐hold and hypercapnia, but the latter are more difficult to perform in some populations, such as older adults. The RSFA is therefore a candidate for use in adjusting for age‐related changes in vascular reactivity in fMRI studies. The use of RSFA is predicated on its sensitivity to vascular rather than neural factors; however, the extent to which each of these factors contributes to RSFA remains to be characterized. The present work addressed these issues by comparing RSFA (i.e., rsfMRI variability) to proxy measures of (i) cardiovascular function in terms of heart rate (HR) and heart rate variability (HRV) and (ii) neural activity in terms of resting state magnetoencephalography (rsMEG). We derived summary scores of RSFA, a sensorimotor task BOLD activation, cardiovascular function and rsMEG variability for 335 healthy older adults in the population‐based Cambridge Centre for Ageing and Neuroscience cohort (Cam‐CAN; www.cam-can.com). Mediation analysis revealed that the effects of ageing on RSFA were significantly mediated by vascular factors, but importantly not by the variability in neuronal activity. Furthermore, the converse effects of ageing on the rsMEG variability were not mediated by vascular factors. We then examined the effect of RSFA scaling of task‐based BOLD in the sensorimotor task. The scaling analysis revealed that much of the effects

  10. Blood Flow and Brain Function: Investigations of neurovascular coupling using BOLD fMRI at 7 tesla

    NARCIS (Netherlands)

    Siero, J.C.W.

    2013-01-01

    The advent of ultra high field (7 tesla) MRI systems has opened the possibility to probe biological processes of the human body in great detail. Especially for studying brain function using BOLD fMRI there is a large benefit from the increased magnetic field strength. BOLD fMRI is the working horse

  11. Cortical response variation with different sound pressure levels : a combined event-related potentials and FMRI study

    NARCIS (Netherlands)

    Neuner, Irene; Kawohl, Wolfram; Arrubla, Jorge; Warbrick, Tracy; Hitz, Konrad; Wyss, Christine; Boers, Frank; Shah, N Jon

    2014-01-01

    INTRODUCTION: Simultaneous recording of electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) provides high spatial and temporal resolution. In this study we combined EEG and fMRI to investigate the structures involved in the processing of different sound pressure levels (SPL

  12. fMRI of retina-originated phosphenes experienced by patients with Leber congenital amaurosis.

    Directory of Open Access Journals (Sweden)

    Manzar Ashtari

    Full Text Available A phenomenon characterized by the experience of seeing light without any light actually entering the eye is called phosphenes or photopsias. Phosphenes can occur spontaneously or via induction by external stimuli. Previous reports regarding phosphenes have primarily focused on externally induced phosphenes such as by applying alternating or direct current to the cortex. A few of these reports used functional magnetic resonance (fMRI to study activations induced by cortical phosphenes. However, there are no fMRI reports on spontaneous phosphenes originating from the retina and the resulting pattern of cortical activations. We performed fMRI during a reversing checkerboard paradigm in three LCA patients who underwent unilateral gene therapy and reported experiencing frequent phosphene on a daily basis. We observed bilateral cortical activation covering the entire visual cortices when patients reported experiencing phosphenes. In contrast, in the absence of phosphenes, activation was regulated by patient's visual ability and demonstrated improved cortical activation due to gene therapy. These fMRI results illustrate the potential impact of phosphene perception on visual function and they may explain some of the variability that clinicians find in visual function testing in retinal degeneration. Although we did not perform correlations between visual function and phosphenes, we hope data presented here raises awareness of this phenomenon and its potential effect on visual function and the implications for clinical testing. We recommend a thorough history for phosphene experiences be taken in patients with retinal disease who are candidates for gene or molecular therapy. Lastly, these data illustrate the potential power of fMRI as an outcome measure of gene therapy and the negative impact phosphenes may have on vision testing. fMRI has proven to be a sensitive, non-invasive, and reproducible test paradigm for these purposes and can complement

  13. Is Granger causality a viable technique for analyzing fMRI data?

    Directory of Open Access Journals (Sweden)

    Xiaotong Wen

    Full Text Available Multivariate neural data provide the basis for assessing interactions in brain networks. Among myriad connectivity measures, Granger causality (GC has proven to be statistically intuitive, easy to implement, and generate meaningful results. Although its application to functional MRI (fMRI data is increasing, several factors have been identified that appear to hinder its neural interpretability: (a latency differences in hemodynamic response function (HRF across different brain regions, (b low-sampling rates, and (c noise. Recognizing that in basic and clinical neuroscience, it is often the change of a dependent variable (e.g., GC between experimental conditions and between normal and pathology that is of interest, we address the question of whether there exist systematic relationships between GC at the fMRI level and that at the neural level. Simulated neural signals were convolved with a canonical HRF, down-sampled, and noise-added to generate simulated fMRI data. As the coupling parameters in the model were varied, fMRI GC and neural GC were calculated, and their relationship examined. Three main results were found: (1 GC following HRF convolution is a monotonically increasing function of neural GC; (2 this monotonicity can be reliably detected as a positive correlation when realistic fMRI temporal resolution and noise level were used; and (3 although the detectability of monotonicity declined due to the presence of HRF latency differences, substantial recovery of detectability occurred after correcting for latency differences. These results suggest that Granger causality is a viable technique for analyzing fMRI data when the questions are appropriately formulated.

  14. Two pitfalls of BOLD fMRI magnitude-based neuroimage analysis: non-negativity and edge effect.

    Science.gov (United States)

    Chen, Zikuan; Calhoun, Vince D

    2011-08-15

    BOLD fMRI is accepted as a noninvasive imaging modality for neuroimaging and brain mapping. A BOLD fMRI dataset consists of magnitude and phase components. Currently, only the magnitude is used for neuroimage analysis. In this paper, we show that the fMRI-magnitude-based neuroimage analysis may suffer two pitfalls: one is that the magnitude is non-negative and cannot differentiate positive from negative BOLD activity; the other is an edge effect that may manifest as an edge enhancement or a spatial interior dip artifact at a local uniform BOLD region. We demonstrate these pitfalls via numeric simulations using a BOLD fMRI model and also via a phantom experiment. We also propose a solution by making use of the fMRI phase image, the counterpart of the fMRI magnitude.

  15. Brain activation in complex partial seizures during switching from a the goal-directed task to a resting state: comparison of fMRI maps to the default mode network.

    Science.gov (United States)

    Karmonik, Christof; Dulay, Mario; Verma, Amit; Yen, Christopher; Grossman, Robert G

    2010-01-01

    The default mode network (DMN) has been previously identified as a set of brain regions activated during internally directed cognition. The objective of this study was to investigate patterns of brain activation during switching between a goal-directed task and a rest period obtained from clinical functional magnetic resonance imaging (fMRI) paradigms in complex partial seizures (CPS) and age-matched controls. As part of pre-surgical evaluation with fMRI, a visually presented block-design language task was performed by eight subjects (4 CPS, 4 age-matched controls). Single subject fMRI maps were calculated and transferred into Talairach space for an atlas-based analysis. For the rest state, total volumes of activation, brain regions with largest volume of activation and regions commonly activated in the CPS and the control group were identified. A voxel-by-voxel comparison was conducted to reveal inter-group statistically significant differences. Average volume of activation in the CPS group was significantly higher (32,080 mm(3)) than in the control group (7,915 mm(3), p-value 〈 0.03). In both groups, most of the common activation volume (81% in the CPS group and 98 % in the control group) was located in cognitive regions of the frontal lobe and temporal lobes as well as anterior cingulate cortex, precuneus and cuneus. The remaining 19% in the CPS group included regions in the precentral gyrus, the superior and medial occipital gyrus, the parahippocampal gyrus, the inferior parietal lobule and the angular gyrus. The voxel-by-voxel comparison showed larger areas of activation mostly in the frontal and temporal lobes in the CPS group (as well as in the cuneus and precuneus), while regions with larger activation in the control group were found mostly in the parietal lobe. Our findings implicate that switching from goal-directed behavior to the default mode in CPS patients is impaired. Information contained in clinical fMRI block-design image data can be used to

  16. Multi-Subject fMRI Generalization with| Independent Component Representation

    DEFF Research Database (Denmark)

    Madsen, Rasmus Elsborg

    2003-01-01

    by cross-validation, forming the so-called learning curves. The fMRI case story is a motor-control study, involving multiple applied static force levels. Despite the relative complexity of this case study, the classification of the 'stimulus' shows good generalizability, measured by the test set error rate......Generalizability in a multi-subject fMRI study is investigated. The analysis is based on principal and independent component representations. Subsequent supervised learning and classification is carried out by canonical variates analysis and clustering methods. The generalization error is estimated...

  17. Non-white noise in fMRI: Does modelling have an impact?

    DEFF Research Database (Denmark)

    Lund, Torben Ellegaard; Madsen, Kristoffer Hougaard; Sidaros, Karam;

    2006-01-01

    are typically modelled as an autoregressive (AR) process. In this paper, we propose an alternative approach: Nuisance Variable Regression (NVR). By inclusion of confounding effects in a general linear model (GLM), we first confirm that the spatial distribution of the various fMRI noise sources is similar......The sources of non-white noise in Blood Oxygenation Level Dependent (BOLD) functional magnetic resonance imaging (fMRI) are many. Familiar sources include low-frequency drift due to hardware imperfections, oscillatory noise due to respiration and cardiac pulsation and residual movement artefacts...

  18. Intersession reliability of fMRI activation for heat pain and motor tasks

    Directory of Open Access Journals (Sweden)

    Raimi L. Quiton

    2014-01-01

    Full Text Available As the practice of conducting longitudinal fMRI studies to assess mechanisms of pain-reducing interventions becomes more common, there is a great need to assess the test–retest reliability of the pain-related BOLD fMRI signal across repeated sessions. This study quantitatively evaluated the reliability of heat pain-related BOLD fMRI brain responses in healthy volunteers across 3 sessions conducted on separate days using two measures: (1 intraclass correlation coefficients (ICC calculated based on signal amplitude and (2 spatial overlap. The ICC analysis of pain-related BOLD fMRI responses showed fair-to-moderate intersession reliability in brain areas regarded as part of the cortical pain network. Areas with the highest intersession reliability based on the ICC analysis included the anterior midcingulate cortex, anterior insula, and second somatosensory cortex. Areas with the lowest intersession reliability based on the ICC analysis also showed low spatial reliability; these regions included pregenual anterior cingulate cortex, primary somatosensory cortex, and posterior insula. Thus, this study found regional differences in pain-related BOLD fMRI response reliability, which may provide useful information to guide longitudinal pain studies. A simple motor task (finger-thumb opposition was performed by the same subjects in the same sessions as the painful heat stimuli were delivered. Intersession reliability of fMRI activation in cortical motor areas was comparable to previously published findings for both spatial overlap and ICC measures, providing support for the validity of the analytical approach used to assess intersession reliability of pain-related fMRI activation. A secondary finding of this study is that the use of standard ICC alone as a measure of reliability may not be sufficient, as the underlying variance structure of an fMRI dataset can result in inappropriately high ICC values; a method to eliminate these false positive results

  19. Decoding Complex Cognitive States Online by Manifold Regularization in Real-Time fMRI

    DEFF Research Database (Denmark)

    Hansen, Toke Jansen; Hansen, Lars Kai; Madsen, Kristoffer Hougaard

    2011-01-01

    Human decision making is complex and influenced by many factors on multiple time scales, reflected in the numerous brain networks and connectivity patterns involved as revealed by fMRI. We address mislabeling issues in paradigms involving complex cognition, by considering a manifold regularizing...... prior for modeling a sequence of neural events leading to a decision. The method is directly applicable for online learning in the context of real-time fMRI, and our experimental results show that the method can efficiently avoid model degeneracy caused by mislabeling....

  20. 11.74T fMRI of cortical and subcortical visual networks in the rat

    DEFF Research Database (Denmark)

    Bailey, Christopher; Sanganahalli, Basavaraju G.; Siefert, Alyssa;

    Though a predominantly nocturnal animal, the rat has a functional visual system, albeit of low acuity, and has at least a basic form of color vision extending into the UV range. Our aim here was to develop methods to probe this system with both high field fMRI and electrophysiological techniques...... and reproducible delivery of visual stimuli in fMRI as well as neurophysiology environments. It has the advantage of allowing variation of the stimulus source (e.g. colour of LED) without the need for manipulating the subject in the bore....

  1. fMRI data from Korean, Chinese and English subjects in a word rhyming judgment task

    Directory of Open Access Journals (Sweden)

    Fan Cao

    2016-06-01

    Full Text Available This article includes the description of data information from a visual word rhyming judgment task in native Korean, native Chinese and native English speakers. You will find fMRI data information including experimental design, MRI protocol, and brain activation results from a conjunction analysis of the three groups of subjects. Other results from the same study were published in “How does language distance between L1 and L2 affect the L2 brain network? An fMRI study of Korean–Chinese–English trilinguals” (Kim et al., 2015 [1].

  2. [fMRI functional connectivity analysis of anxiety disease patients based on spatiotemporal Lyapunov exponent method].

    Science.gov (United States)

    Wang, Zhikang; Lou, Haifang; Sun, Jianzhong

    2011-07-01

    Attempting to use nonlinear spatiotemporal Lyapunov exponent to characterize fMRI brain functional connectivity of anxiety disease patients, we adopted the methods of nonlinear spatiotemporal Lyapunov exponent and linear correlation coefficients to analyses fMRI datum of 11 anxiety disease patients and 11 healthy volunteers, respectively. The results show that there are significant normalized variance exponent (NVE) differences in Inferior Frontal Gyrus (rIFG) and Medial Frontal Gyrus (MFG) between the two groups (PLyapunov exponent method had higher sensitivity than the correlation coefficient method in the characterization of functional connectivity; Anxiety disease patients have abnormal functional connectivity in rIFG and MFG during our experiment.

  3. Perception of matching and conflicting audiovisual speech in dyslexic and fluent readers: an fMRI study at 3 T.

    Science.gov (United States)

    Pekkola, Johanna; Laasonen, Marja; Ojanen, Ville; Autti, Taina; Jääskeläinen, Iiro P; Kujala, Teija; Sams, Mikko

    2006-02-01

    We presented phonetically matching and conflicting audiovisual vowels to 10 dyslexic and 10 fluent-reading young adults during "clustered volume acquisition" functional magnetic resonance imaging (fMRI) at 3 T. We further assessed co-variation between the dyslexic readers' phonological processing abilities, as indexed by neuropsychological test scores, and BOLD signal change within the visual cortex, auditory cortex, and Broca's area. Both dyslexic and fluent readers showed increased activation during observation of phonetically conflicting compared to matching vowels within the classical motor speech regions (Broca's area and the left premotor cortex), this activation difference being more extensive and bilateral in the dyslexic group. The between-group activation difference in conflicting > matching contrast reached significance in the motor speech regions and in the left inferior parietal lobule, with dyslexic readers exhibiting stronger activation than fluent readers. The dyslexic readers' BOLD signal change co-varied with their phonological processing abilities within the visual cortex and Broca's area, and to a lesser extent within the auditory cortex. We suggest these findings as reflecting dyslexic readers' greater use of motor-articulatory and visual strategies during phonetic processing of audiovisual speech, possibly to compensate for their difficulties in auditory speech perception.

  4. FMRI 3D registration based on Fourier space subsets using neural networks.

    Science.gov (United States)

    Freire, Luis C; Gouveia, Ana R; Godinho, Fernando M

    2010-01-01

    In this work, we present a neural network (NN) based method designed for 3D rigid-body registration of FMRI time series, which relies on a limited number of Fourier coefficients of the images to be aligned. These coefficients, which are comprised in a small cubic neighborhood located at the first octant of a 3D Fourier space (including the DC component), are then fed into six NN during the learning stage. Each NN yields the estimates of a registration parameter. The proposed method was assessed for 3D rigid-body transformations, using DC neighborhoods of different sizes. The mean absolute registration errors are of approximately 0.030 mm in translations and 0.030 deg in rotations, for the typical motion amplitudes encountered in FMRI studies. The construction of the training set and the learning stage are fast requiring, respectively, 90 s and 1 to 12 s, depending on the number of input and hidden units of the NN. We believe that NN-based approaches to the problem of FMRI registration can be of great interest in the future. For instance, NN relying on limited K-space data (possibly in navigation echoes) can be a valid solution to the problem of prospective (in frame) FMRI registration.

  5. Visual Detection and Identification Are Not the Same: Evidence from Psychophysics and fMRI

    Science.gov (United States)

    Straube, Sirko; Fahle, Manfred

    2011-01-01

    Sometimes object detection as opposed to identification is sufficient to initiate the appropriate action. To explore the neural origin of behavioural differences between the two tasks, we combine psychophysical measurements and fMRI, specifically contrasting shape detection versus identification of a figure. This figure consisted of Gabor elements…

  6. Perceiving Age and Gender in Unfamiliar Faces: An fMRI Study on Face Categorization

    Science.gov (United States)

    Wiese, Holger; Kloth, Nadine; Gullmar, Daniel; Reichenbach, Jurgen R.; Schweinberger, Stefan R.

    2012-01-01

    Efficient processing of unfamiliar faces typically involves their categorization (e.g., into old vs. young or male vs. female). However, age and gender categorization may pose different perceptual demands. In the present study, we employed functional magnetic resonance imaging (fMRI) to compare the activity evoked during age vs. gender…

  7. Seeing Chinese Characters in Action: An fMRI Study of the Perception of Writing Sequences

    Science.gov (United States)

    Yu, Hongbo; Gong, Lanyun; Qiu, Yinchen; Zhou, Xiaolin

    2011-01-01

    The Chinese character is composed of a finite set of strokes whose order in writing follows consensual principles and is learnt through school education. Using functional magnetic resonance imaging (fMRI), this study investigates the neural activity associated with the perception of writing sequences by asking participants to observe…

  8. Neural Changes after Phonological Treatment for Anomia: An fMRI Study

    Science.gov (United States)

    Rochon, Elizabeth; Leonard, Carol; Burianova, Hana; Laird, Laura; Soros, Peter; Graham, Simon; Grady, Cheryl

    2010-01-01

    Functional magnetic resonance imaging (fMRI) was used to investigate the neural processing characteristics associated with word retrieval abilities after a phonologically-based treatment for anomia in two stroke patients with aphasia. Neural activity associated with a phonological and a semantic task was compared before and after treatment with…

  9. Hemispheric preference in visuospatial processing: a complementary approach with fMRI and lesion studies.

    Science.gov (United States)

    Ng, V W; Eslinger, P J; Williams, S C; Brammer, M J; Bullmore, E T; Andrew, C M; Suckling, J; Morris, R G; Benton, A L

    2000-06-01

    Historically, the left cerebral hemisphere has been considered specialized for language, whereas the right cerebral hemisphere is aligned with spatial processes. However, studies have called into question adherence to this model and suggested that both hemispheres participate in language and spatial cognition. Using functional Magnetic Resonance Imaging (fMRI) and human brain lesion studies, we determined whether these complementary techniques could clarify issues of hemispheric dominance. Using a modified Benton Judgement of Line Orientation (JLO) test, considered a relatively pure spatial processing task, we found robust and significant (p < 0.0005) bilateral superior parietal lobe activation on fMRI in ten right-handed male adult volunteers. This was corroborated by lesion data in a cohort of 17 patients who showed significant JLO impairments after either right or left parietal lobe damage, with right parietal damage associated with somewhat more severe deficit. Detailed wavelet analysis of the fMRI time-series did, however, reveal a more dominant role of the right parietal lobe in "kick-starting" the task. To our knowledge, this is a novel way of using fMRI to address functional hemispheric differences in a cognitive task that is known to have bilateral representation.

  10. How mood challenges emotional memory formation: An fMRI investigation

    NARCIS (Netherlands)

    Fitzgerald, D.A.; Arnold, J.F.; Becker, E.S.; Speckens, A.E.M.; Rinck, M.; Rijpkema, M.J.P.; Fernandez, G.S.E.; Tendolkar, I.

    2011-01-01

    Experimental mood manipulations and functional magnetic resonance imaging (fMRI) provide a unique opportunity for examining the neural correlates of mood-congruent memory formation. While prior studies in mood-disorder patients point to the medial temporal lobe in the genesis of mood-congruent memor

  11. How mood challenges emotional memory formation: an fMRI investigation

    NARCIS (Netherlands)

    Fitzgerald, D.A.; Arnold, J.F.; Becker, E.S.; Speckens, A.E.M.; Rinck, M.; Rijpkema, M.J.P.; Fernandez, G.S.E.; Tendolkar, I.

    2011-01-01

    Experimental mood manipulations and functional magnetic resonance imaging (fMRI) provide a unique opportunity for examining the neural correlates of mood-congruent memory formation. While prior studies in mood-disorder patients point to the medial temporal lobe in the genesis of mood-congruent memor

  12. 78 FR 73895 - In the Matter of FMRI, Inc., Muskogee, Oklahoma Facility

    Science.gov (United States)

    2013-12-09

    ... Order Modifying License II. FMRI (or Licensee) is the holder of the NRC License No. SMB-911 (License...-1). On October 2, 2012, the NRC issued Amendment 14 to License No. SMB-911 authorizing the change of... License No. SMB-911 is modified as follows: A. Amendment 14, which changed license conditions 1, 3, 10, 26...

  13. Finding multivariate outliers in fMRI time-series data.

    Science.gov (United States)

    Magnotti, John F; Billor, Nedret

    2014-10-01

    A fundamental challenge for researchers studying the brain is to explain how distributed patterns of brain activity relate to a specific representation or computation. Multivariate techniques are therefore becoming increasingly popular for pattern localization of functional magnetic resonance imaging (fMRI) data. The increased power of these techniques can be offset by their susceptibility to multivariate outliers, a problem not directly encountered when fMRI data are analyzed in more common univariate analysis techniques. We test how two algorithms, High Dimensional Blocked Adaptive Computationally Efficient Outlier Nominators (HD BACON) and Principal Component based Outlier detection (PCOut), can detect multivariate outliers in high-dimensional fMRI data, in which the number of variables is larger than the number of observations. We show how these methods can be applied to individual, voxel time-series to identify outlying voxels within a region of interest. Finally, we compare these methods with simulated data to identify which aspects of the data each method is most sensitive to. Voxels identified by both algorithms were primarily on the edges of univariate activation clusters and near the boundaries between different tissue types. Simulation results showed the PCOut outperformed HD BACON, maintaining both high sensitivity and specificity across a wide range of outlier contamination percentages. Our results suggest that multivariate analysis of fMRI can benefit from including multivariate outlier detection as a routine data quality check prior to model fitting. Copyright © 2014 Elsevier Ltd. All rights reserved.

  14. Studying the neural bases of prism adaptation using fMRI: A technical and design challenge.

    Science.gov (United States)

    Bultitude, Janet H; Farnè, Alessandro; Salemme, Romeo; Ibarrola, Danielle; Urquizar, Christian; O'Shea, Jacinta; Luauté, Jacques

    2016-12-30

    Prism adaptation induces rapid recalibration of visuomotor coordination. The neural mechanisms of prism adaptation have come under scrutiny since the observations that the technique can alleviate hemispatial neglect following stroke, and can alter spatial cognition in healthy controls. Relative to non-imaging behavioral studies, fMRI investigations of prism adaptation face several challenges arising from the confined physical environment of the scanner and the supine position of the participants. Any researcher who wishes to administer prism adaptation in an fMRI environment must adjust their procedures enough to enable the experiment to be performed, but not so much that the behavioral task departs too much from true prism adaptation. Furthermore, the specific temporal dynamics of behavioral components of prism adaptation present additional challenges for measuring their neural correlates. We developed a system for measuring the key features of prism adaptation behavior within an fMRI environment. To validate our configuration, we present behavioral (pointing) and head movement data from 11 right-hemisphere lesioned patients and 17 older controls who underwent sham and real prism adaptation in an MRI scanner. Most participants could adapt to prismatic displacement with minimal head movements, and the procedure was well tolerated. We propose recommendations for fMRI studies of prism adaptation based on the design-specific constraints and our results.

  15. Implicit Structured Sequence Learning: An FMRI Study of the Structural Mere-Exposure Effect

    Directory of Open Access Journals (Sweden)

    Vasiliki eFolia

    2014-02-01

    Full Text Available In this event-related FMRI study we investigated the effect of five days of implicit acquisition on preference classification by means of an artificial grammar learning (AGL paradigm based on the structural mere-exposure effect and preference classification using a simple right-linear unification grammar. This allowed us to investigate implicit AGL in a proper learning design by including baseline measurements prior to grammar exposure. After 5 days of implicit acquisition, the FMRI results showed activations in a network of brain regions including the inferior frontal (centered on BA 44/45 and the medial prefrontal regions (centered on BA 8/32. Importantly, and central to this study, the inclusion of a naive preference FMRI baseline measurement allowed us to conclude that these FMRI findings were the intrinsic outcomes of the learning process itself and not a reflection of a preexisting functionality recruited during classification, independent of acquisition. Support for the implicit nature of the knowledge utilized during preference classification on day 5 come from the fact that the basal ganglia, associated with implicit procedural learning, were activated during classification, while the medial temporal lobe system, associated with explicit declarative memory, was consistently deactivated. Thus, preference classification in combination with structural mere-exposure can be used to investigate structural sequence processing (syntax in unsupervised AGL paradigms with proper learning designs.

  16. Analysis of group ICA-based connectivity measures from fMRI: application to Alzheimer's disease.

    Directory of Open Access Journals (Sweden)

    Shanshan Li

    Full Text Available Functional magnetic resonance imaging (fMRI is a powerful tool for the in vivo study of the pathophysiology of brain disorders and disease. In this manuscript, we propose an analysis stream for fMRI functional connectivity data and apply it to a novel study of Alzheimer's disease. In the first stage, spatial independent component analysis is applied to group fMRI data to obtain common brain networks (spatial maps and subject-specific mixing matrices (time courses. In the second stage, functional principal component analysis is utilized to decompose the mixing matrices into population-level eigenvectors and subject-specific loadings. Inference is performed using permutation-based exact logistic regression for matched pairs data. The method is applied to a novel fMRI study of Alzheimer's disease risk under a verbal paired associates task. We found empirical evidence of alternative ICA-based metrics of connectivity when comparing subjects evidencing mild cognitive impairment relative to carefully matched controls.

  17. Puzzlingly High Correlations in fMRI Studies of Emotion, Personality, and Social Cognition

    Science.gov (United States)

    Vul, Edward; Harris, Christine; Winkielman, Piotr; Pashler, Harold

    2009-01-01

    Functional Magnetic Resonance Imaging (fMRI) studies of emotion, personality, and social cognition have drawn much attention in recent years, with high-profile studies frequently reporting extremely high (e.g., > 8) correlations between behavioral and self-report measures of personality or emotion and measures of brain activation. We show that…

  18. Abnormal fMRI Activation Pattern during Story Listening in Individuals with Down Syndrome

    Science.gov (United States)

    Reynolds Losin, Elizabeth A.; Rivera, Susan M.; O'Hare, Elizabeth D.; Sowell, Elizabeth R.; Pinter, Joseph D.

    2009-01-01

    Down syndrome is characterized by disproportionately severe impairments of speech and language, yet little is known about the neural underpinnings of these deficits. We compared fMRI activation patterns during passive story listening in 9 young adults with Down syndrome and 9 approximately age-matched, typically developing controls. The typically…

  19. Using fMRI to Investigate Memory in Young Children Born Small for Gestational Age.

    Directory of Open Access Journals (Sweden)

    Henrica M A de Bie

    Full Text Available Intrauterine growth restriction (IUGR can lead to infants being born small for gestational age (SGA. SGA is associated with differences in brain anatomy and impaired cognition. We investigated learning and memory in children born SGA using neuropsychological testing and functional Magnetic Resonance Imaging (fMRI.18 children born appropriate for gestational age (AGA and 34 SGA born children (18 with and 16 without postnatal catch-up growth participated in this study. All children were between 4 and 7 years old. Cognitive functioning was assessed by IQ and memory testing (Digit/Word Span and Location Learning. A newly developed fMRI picture encoding task was completed by all children in order to assess brain regions involved in memory processes.Neuropsychological testing demonstrated that SGA children had IQ's within the normal range but lower than in AGA and poorer performances across measures of memory. Using fMRI, we observed memory related activity in posterior parahippocampal gyrus as well as the hippocampus proper. Additionally, activation was seen bilaterally in the prefrontal gyrus. Children born SGA showed less activation in the left parahippocampal region compared to AGA.This is the first fMRI study demonstrating different brain activation patterns in 4-7 year old children born SGA, suggesting that intrauterine growth restriction continues to affect neural functioning in children later-on.

  20. Utility of resting fMRI and connectivity in patients with brain tumor

    Directory of Open Access Journals (Sweden)

    Sandhya Manglore

    2013-01-01

    Full Text Available Background: Resting state (task independent Functional Magnetic Resonance Imaging (fMRI has opened a new avenue in cognitive studies and has found practical clinical applications. Materials and Methods: Resting fMRI analysis was performed in six patients with brain tumor in the motor cortex. For comparison, task-related mapping of the motor cortex was done. Connectivity analysis to study the connections and strength of the connections between the primary motor cortex, premotor cortex, and primary somatosensory cortex on the affected side was also performed and compared with the contralateral normal side and the controls. Results: Resting fMRI in patients with brain tumor in the motor cortex mapped the motor cortex in a task-free state and the results were comparable to the motor task paradigm. Decreased connectivity on the tumor-affected side was observed, as compared to the unaffected side. Conclusion: Resting fMRI and connectivity analysis are useful in the presurgical evaluation of patients with brain tumors and may help in uncooperative or pediatric patients. They can also prognosticate the postoperative outcome. This method also has significant applications due to the ease of image acquisition.

  1. Functional brain activation differences in stuttering identified with a rapid fMRI sequence

    Science.gov (United States)

    Kraft, Shelly Jo; Choo, Ai Leen; Sharma, Harish; Ambrose, Nicoline G.

    2011-01-01

    The purpose of this study was to investigate whether brain activity related to the presence of stuttering can be identified with rapid functional MRI (fMRI) sequences that involved overt and covert speech processing tasks. The long-term goal is to develop sensitive fMRI approaches with developmentally appropriate tasks to identify deviant speech motor and auditory brain activity in children who stutter closer to the age at which recovery from stuttering is documented. Rapid sequences may be preferred for individuals or populations who do not tolerate long scanning sessions. In this report, we document the application of a picture naming and phoneme monitoring task in three minute fMRI sequences with adults who stutter (AWS). If relevant brain differences are found in AWS with these approaches that conform to previous reports, then these approaches can be extended to younger populations. Pairwise contrasts of brain BOLD activity between AWS and normally fluent adults indicated the AWS showed higher BOLD activity in the right inferior frontal gyrus (IFG), right temporal lobe and sensorimotor cortices during picture naming and and higher activity in the right IFG during phoneme monitoring. The right lateralized pattern of BOLD activity together with higher activity in sensorimotor cortices is consistent with previous reports, which indicates rapid fMRI sequences can be considered for investigating stuttering in younger participants. PMID:22133409

  2. Item Memory, Context Memory and the Hippocampus: fMRI Evidence

    Science.gov (United States)

    Rugg, Michael D.; Vilberg, Kaia L.; Mattson, Julia T.; Yu, Sarah S.; Johnson, Jeffrey D.; Suzuki, Maki

    2012-01-01

    Dual-process models of recognition memory distinguish between the retrieval of qualitative information about a prior event (recollection), and judgments of prior occurrence based on an acontextual sense of familiarity. fMRI studies investigating the neural correlates of memory encoding and retrieval conducted within the dual-process framework have…

  3. Dual-Tasking Alleviated Sleep Deprivation Disruption in Visuomotor Tracking: An fMRI Study

    Science.gov (United States)

    Gazes, Yunglin; Rakitin, Brian C.; Steffener, Jason; Habeck, Christian; Butterfield, Brady; Basner, Robert C.; Ghez, Claude; Stern, Yaakov

    2012-01-01

    Effects of dual-responding on tracking performance after 49-h of sleep deprivation (SD) were evaluated behaviorally and with functional magnetic resonance imaging (fMRI). Continuous visuomotor tracking was performed simultaneously with an intermittent color-matching visual detection task in which a pair of color-matched stimuli constituted a…

  4. Decoding Overlapping Memories in the Medial Temporal Lobes Using High-Resolution fMRI

    Science.gov (United States)

    Chadwick, Martin J.; Hassabis, Demis; Maguire, Eleanor A.

    2011-01-01

    The hippocampus is proposed to process overlapping episodes as discrete memory traces, although direct evidence for this in human episodic memory is scarce. Using green-screen technology we created four highly overlapping movies of everyday events. Participants were scanned using high-resolution fMRI while recalling the movies. Multivariate…

  5. Non-white noise in fMRI: Does modelling have an impact?

    DEFF Research Database (Denmark)

    Lund, Torben Ellegaard; Madsen, Kristoffer Hougaard; Sidaros, Karam

    2006-01-01

    The sources of non-white noise in Blood Oxygenation Level Dependent (BOLD) functional magnetic resonance imaging (fMRI) are many. Familiar sources include low-frequency drift due to hardware imperfections, oscillatory noise due to respiration and cardiac pulsation and residual movement artefacts...

  6. Abnormal fMRI Activation Pattern during Story Listening in Individuals with Down Syndrome

    Science.gov (United States)

    Reynolds Losin, Elizabeth A.; Rivera, Susan M.; O'Hare, Elizabeth D.; Sowell, Elizabeth R.; Pinter, Joseph D.

    2009-01-01

    Down syndrome is characterized by disproportionately severe impairments of speech and language, yet little is known about the neural underpinnings of these deficits. We compared fMRI activation patterns during passive story listening in 9 young adults with Down syndrome and 9 approximately age-matched, typically developing controls. The typically…

  7. Variational group-PCA for intrinsic dimensionality determination in fMRI data

    DEFF Research Database (Denmark)

    Hinrich, Jesper Løve; Nielsen, Søren Føns Vind; Madsen, Kristoffer Hougaard;

    2016-01-01

    implementation using a graphical processing unit (GPU). We test the model on fMRI data from 29 healthy subjects performing a block-design fingertapping experiment. The model identified 10 active components. Neither variational Bayesian PCA on temporally concatenated data nor Group-BPCA, where uncertainties...

  8. Select and Cluster: A Method for Finding Functional Networks of Clustered Voxels in fMRI

    Science.gov (United States)

    DonGiovanni, Danilo

    2016-01-01

    Extracting functional connectivity patterns among cortical regions in fMRI datasets is a challenge stimulating the development of effective data-driven or model based techniques. Here, we present a novel data-driven method for the extraction of significantly connected functional ROIs directly from the preprocessed fMRI data without relying on a priori knowledge of the expected activations. This method finds spatially compact groups of voxels which show a homogeneous pattern of significant connectivity with other regions in the brain. The method, called Select and Cluster (S&C), consists of two steps: first, a dimensionality reduction step based on a blind multiresolution pairwise correlation by which the subset of all cortical voxels with significant mutual correlation is selected and the second step in which the selected voxels are grouped into spatially compact and functionally homogeneous ROIs by means of a Support Vector Clustering (SVC) algorithm. The S&C method is described in detail. Its performance assessed on simulated and experimental fMRI data is compared to other methods commonly used in functional connectivity analyses, such as Independent Component Analysis (ICA) or clustering. S&C method simplifies the extraction of functional networks in fMRI by identifying automatically spatially compact groups of voxels (ROIs) involved in whole brain scale activation networks. PMID:27656202

  9. Effects of motor fatigue on human brain activity, an fMRI study

    NARCIS (Netherlands)

    van Duinen, Hiske; Renken, Remco; Maurits, Natasha; Zijdewind, Inge

    2007-01-01

    The main purpose of this study was to investigate effects of motor fatigue on brain activation in humans, using fMRI. First, we assessed brain activation that correlated with muscle activity during brief contractions at different force levels (force modulation). Second, a similar analysis was done f

  10. Functional Imaging of Broca’s Area in Native Persian Speakers: An fMRI Study

    Directory of Open Access Journals (Sweden)

    A Mahdavi

    2008-12-01

    Full Text Available Background/Objective: The problem of localization of speech associated cortices using noninvasive methods has been of utmost importance in many neuroimaging studies, but the results are difficult to resolve for specific neurosurgical applications. In this study, we used fMRI to delineate language-related brain activation patterns with emphasis on the Broca's area during the execution of two Persian language tasks."nPatients and Methods: The subjects comprised of nine healthy right-handed men who participated voluntarily in this study. They performed two consequent fMRI paradigms namely; "Word Production" and "Reverse Word Reading". The fMRI data were collected and analyzed. Then, functional images were registered to anatomical images using FSL software. The laterality indices were also calculated in regions of interest with different threshold levels."nResults: The results indicate that Broca's area, as the classical language-production center, was robustly activated while performing these two tasks. In eight out of nine subjects, the left hemisphere dominancy and Broca's area activation were observed and in one case activation was prominent in the homologous area in the right hemisphere."nConclusion: Similar pattern of cortical activation during Persian word production and Anglophone languages such as English was revealed. fMRI is a valuable means for brain mapping in language studies.

  11. Dual-Tasking Alleviated Sleep Deprivation Disruption in Visuomotor Tracking: An fMRI Study

    Science.gov (United States)

    Gazes, Yunglin; Rakitin, Brian C.; Steffener, Jason; Habeck, Christian; Butterfield, Brady; Basner, Robert C.; Ghez, Claude; Stern, Yaakov

    2012-01-01

    Effects of dual-responding on tracking performance after 49-h of sleep deprivation (SD) were evaluated behaviorally and with functional magnetic resonance imaging (fMRI). Continuous visuomotor tracking was performed simultaneously with an intermittent color-matching visual detection task in which a pair of color-matched stimuli constituted a…

  12. Bayesian Inference for Functional Dynamics Exploring in fMRI Data.

    Science.gov (United States)

    Guo, Xuan; Liu, Bing; Chen, Le; Chen, Guantao; Pan, Yi; Zhang, Jing

    2016-01-01

    This paper aims to review state-of-the-art Bayesian-inference-based methods applied to functional magnetic resonance imaging (fMRI) data. Particularly, we focus on one specific long-standing challenge in the computational modeling of fMRI datasets: how to effectively explore typical functional interactions from fMRI time series and the corresponding boundaries of temporal segments. Bayesian inference is a method of statistical inference which has been shown to be a powerful tool to encode dependence relationships among the variables with uncertainty. Here we provide an introduction to a group of Bayesian-inference-based methods for fMRI data analysis, which were designed to detect magnitude or functional connectivity change points and to infer their functional interaction patterns based on corresponding temporal boundaries. We also provide a comparison of three popular Bayesian models, that is, Bayesian Magnitude Change Point Model (BMCPM), Bayesian Connectivity Change Point Model (BCCPM), and Dynamic Bayesian Variable Partition Model (DBVPM), and give a summary of their applications. We envision that more delicate Bayesian inference models will be emerging and play increasingly important roles in modeling brain functions in the years to come.

  13. Blind source separation of fMRI data by means of factor analytic transformations

    NARCIS (Netherlands)

    Langers, Dave R. M.

    2009-01-01

    In this study, the application of factor analytic (FA) rotation methods in the context of neuroimaging data analysis was explored. Three FA algorithms (ProMax, QuartiMax, and VariMax) were employed to carry out blind source separation in a functional magnetic resonance imaging (fMRI) experiment that

  14. Auditory Verb Perception Recruits Motor Systems in the Developing Brain: An fMRI Investigation

    Science.gov (United States)

    James, Karin Harman; Maouene, Josita

    2009-01-01

    This study investigated neural activation patterns during verb processing in children, using fMRI (functional Magnetic Resonance Imaging). Preschool children (aged 4-6) passively listened to lists of verbs and adjectives while neural activation was measured. Findings indicated that verbs were processed differently than adjectives, as the verbs…

  15. Are Errors Differentiable from Deceptive Responses when Feigning Memory Impairment? An fMRI Study

    Science.gov (United States)

    Lee, Tatia M. C.; Au, Ricky K. C.; Liu, Ho-Ling; Ting, K. H.; Huang, Chih-Mao; Chan, Chetwyn C. H.

    2009-01-01

    Previous neuroimaging studies have suggested that the neural activity associated with truthful recall, with false memory, and with feigned memory impairment are different from one another. Here, we report a functional magnetic resonance imaging (fMRI) study that addressed an important but yet unanswered question: Is the neural activity associated…

  16. Diffusion fMRI detects white-matter dysfunction in mice with acute optic neuritis.

    Science.gov (United States)

    Lin, Tsen-Hsuan; Spees, William M; Chiang, Chia-Wen; Trinkaus, Kathryn; Cross, Anne H; Song, Sheng-Kwei

    2014-07-01

    Optic neuritis is a frequent and early symptom of multiple sclerosis (MS). Conventional magnetic resonance (MR) techniques provide means to assess multiple MS-related pathologies, including axonal injury, demyelination, and inflammation. A method to directly and non-invasively probe white-matter function could further elucidate the interplay of underlying pathologies and functional impairments. Previously, we demonstrated a significant 27% activation-associated decrease in the apparent diffusion coefficient of water perpendicular to the axonal fibers (ADC⊥) in normal C57BL/6 mouse optic nerve with visual stimulation using diffusion fMRI. Here we apply this approach to explore the relationship between visual acuity, optic nerve pathology, and diffusion fMRI in the experimental autoimmune encephalomyelitis (EAE) mouse model of optic neuritis. Visual stimulation produced a significant 25% (vs. baseline) ADC⊥ decrease in sham EAE optic nerves, while only a 7% (vs. baseline) ADC⊥ decrease was seen in EAE mice with acute optic neuritis. The reduced activation-associated ADC⊥ response correlated with post-MRI immunohistochemistry determined pathologies (including inflammation, demyelination, and axonal injury). The negative correlation between activation-associated ADC⊥ response and visual acuity was also found when pooling EAE-affected and sham groups under our experimental criteria. Results suggest that reduction in diffusion fMRI directly reflects impaired axonal-activation in EAE mice with optic neuritis. Diffusion fMRI holds promise for directly gauging in vivo white-matter dysfunction or therapeutic responses in MS patients.

  17. Using fMRI to Study Conceptual Change: Why and How?

    Science.gov (United States)

    Masson, Steve; Potvin, Patrice; Riopel, Martin; Foisy, Lorie-Marlene Brault; Lafortune, Stephanie

    2012-01-01

    Although the use of brain imaging techniques, such as functional magnetic resonance imaging (fMRI) is increasingly common in educational research, only a few studies regarding science learning have so far taken advantage of this technology. This paper aims to facilitate the design and implementation of brain imaging studies relating to science…

  18. Clustering of Dependent Components: A New Paradigm for fMRI Signal Detection

    Directory of Open Access Journals (Sweden)

    Hurdal Monica K

    2005-01-01

    Full Text Available Exploratory data-driven methods such as unsupervised clustering and independent component analysis (ICA are considered to be hypothesis-generating procedures and are complementary to the hypothesis-led statistical inferential methods in functional magnetic resonance imaging (fMRI. Recently, a new paradigm in ICA emerged, that of finding "clusters" of dependent components. This intriguing idea found its implementation into two new ICA algorithms: tree-dependent and topographic ICA. For fMRI, this represents the unifying paradigm of combining two powerful exploratory data analysis methods, ICA and unsupervised clustering techniques. For the fMRI data, a comparative quantitative evaluation between the two methods, tree-dependent and topographic ICA, was performed. The comparative results were evaluated by (1 task-related activation maps, (2 associated time courses, and (3 ROC study. The most important findings in this paper are that (1 both tree-dependent and topographic ICA are able to identify signal components with high correlation to the fMRI stimulus, and that (2 topographic ICA outperforms all other ICA methods including tree-dependent ICA for 8 and 9 ICs. However for 16 ICs, topographic ICA is outperformed by tree-dependent ICA (KGV using as an approximation of the mutual information the kernel generalized variance. The applicability of the new algorithm is demonstrated on experimental data.

  19. Assessment of abstract reasoning abilities in alcohol-dependent subjects: an fMRI study

    Energy Technology Data Exchange (ETDEWEB)

    Bagga, Deepika; Singh, Namita; Singh, Sadhana; Modi, Shilpi; Kumar, Pawan [Institute of Nuclear Medicine and Allied Sciences (INMAS), NMR Research Centre, Delhi (India); Bhattacharya, D. [Base Hospital, Department of Psychiatry, Delhi Cantt (India); Garg, Mohan L. [Panjab University, Department of Biophysics, Chandigarh (India); Khushu, Subash [Institute of Nuclear Medicine and Allied Sciences (INMAS), NMR Research Centre, Delhi (India); INMAS, DRDO, NMR Research Centre, Delhi (India)

    2014-01-15

    Chronic alcohol abuse has been traditionally associated with impaired cognitive abilities. The deficits are most evident in higher order cognitive functions, such as abstract reasoning, problem solving and visuospatial processing. The present study sought to increase current understanding of the neuropsychological basis of poor abstract reasoning abilities in alcohol-dependent subjects using functional magnetic resonance imaging (fMRI). An abstract reasoning task-based fMRI study was carried out on alcohol-dependent subjects (n = 18) and healthy controls (n = 18) to examine neural activation pattern. The study was carried out using a 3-T whole-body magnetic resonance scanner. Preprocessing and post processing was performed using SPM 8 software. Behavioral data indicated that alcohol-dependent subjects took more time than controls for performing the task but there was no significant difference in their response accuracy. Analysis of the fMRI data indicated that for solving abstract reasoning-based problems, alcohol-dependent subjects showed enhanced right frontoparietal neural activation involving inferior frontal gyrus, post central gyrus, superior parietal lobule, and occipito-temporal gyrus. The extensive activation observed in alcohol dependents as compared to controls suggests that alcohol dependents recruit additional brain areas to meet the behavioral demands for equivalent task performance. The results are consistent with previous fMRI studies suggesting decreased neural efficiency of relevant brain networks or compensatory mechanisms for the execution of task for showing an equivalent performance. (orig.)

  20. Ready...go: Amplitude of the FMRI signal encodes expectation of cue arrival time.

    Directory of Open Access Journals (Sweden)

    Xu Cui

    2009-08-01

    Full Text Available What happens when the brain awaits a signal of uncertain arrival time, as when a sprinter waits for the starting pistol? And what happens just after the starting pistol fires? Using functional magnetic resonance imaging (fMRI, we have discovered a novel correlate of temporal expectations in several brain regions, most prominently in the supplementary motor area (SMA. Contrary to expectations, we found little fMRI activity during the waiting period; however, a large signal appears after the "go" signal, the amplitude of which reflects learned expectations about the distribution of possible waiting times. Specifically, the amplitude of the fMRI signal appears to encode a cumulative conditional probability, also known as the cumulative hazard function. The fMRI signal loses its dependence on waiting time in a "countdown" condition in which the arrival time of the go cue is known in advance, suggesting that the signal encodes temporal probabilities rather than simply elapsed time. The dependence of the signal on temporal expectation is present in "no-go" conditions, demonstrating that the effect is not a consequence of motor output. Finally, the encoding is not dependent on modality, operating in the same manner with auditory or visual signals. This finding extends our understanding of the relationship between temporal expectancy and measurable neural signals.

  1. Improved Detection of Time Windows of Brain Responses in Fmri Using Modified Temporal Clustering Analysis

    Institute of Scientific and Technical Information of China (English)

    2002-01-01

    @@ Temporal clustering analysis (TCA) has been proposed recently as a method to detect time windows of brain responses in functional MRI (fMRI) studies when the timing and location of the activation are completely unknown. Modifications to the TCA technique are introduced in this report to further improve the sensitivity in detecting brain activation.

  2. Modelling large motion events in fMRI studies of patients with epilepsy

    DEFF Research Database (Denmark)

    Lemieux, Louis; Salek-Haddadi, Afraim; Lund, Torben E;

    2007-01-01

    % of cases, there was a significant effect of motion in 50% of the brain or greater; for the scan nulling effect, the proportion was 36%; this effect was predominantly in the neocortex. We conclude that careful consideration of the motion-related effects in fMRI studies of patients with epilepsy is essential...

  3. Bayesian Inference for Functional Dynamics Exploring in fMRI Data

    Directory of Open Access Journals (Sweden)

    Xuan Guo

    2016-01-01

    Full Text Available This paper aims to review state-of-the-art Bayesian-inference-based methods applied to functional magnetic resonance imaging (fMRI data. Particularly, we focus on one specific long-standing challenge in the computational modeling of fMRI datasets: how to effectively explore typical functional interactions from fMRI time series and the corresponding boundaries of temporal segments. Bayesian inference is a method of statistical inference which has been shown to be a powerful tool to encode dependence relationships among the variables with uncertainty. Here we provide an introduction to a group of Bayesian-inference-based methods for fMRI data analysis, which were designed to detect magnitude or functional connectivity change points and to infer their functional interaction patterns based on corresponding temporal boundaries. We also provide a comparison of three popular Bayesian models, that is, Bayesian Magnitude Change Point Model (BMCPM, Bayesian Connectivity Change Point Model (BCCPM, and Dynamic Bayesian Variable Partition Model (DBVPM, and give a summary of their applications. We envision that more delicate Bayesian inference models will be emerging and play increasingly important roles in modeling brain functions in the years to come.

  4. Ready...go: Amplitude of the FMRI signal encodes expectation of cue arrival time.

    Science.gov (United States)

    Cui, Xu; Stetson, Chess; Montague, P Read; Eagleman, David M

    2009-08-01

    What happens when the brain awaits a signal of uncertain arrival time, as when a sprinter waits for the starting pistol? And what happens just after the starting pistol fires? Using functional magnetic resonance imaging (fMRI), we have discovered a novel correlate of temporal expectations in several brain regions, most prominently in the supplementary motor area (SMA). Contrary to expectations, we found little fMRI activity during the waiting period; however, a large signal appears after the "go" signal, the amplitude of which reflects learned expectations about the distribution of possible waiting times. Specifically, the amplitude of the fMRI signal appears to encode a cumulative conditional probability, also known as the cumulative hazard function. The fMRI signal loses its dependence on waiting time in a "countdown" condition in which the arrival time of the go cue is known in advance, suggesting that the signal encodes temporal probabilities rather than simply elapsed time. The dependence of the signal on temporal expectation is present in "no-go" conditions, demonstrating that the effect is not a consequence of motor output. Finally, the encoding is not dependent on modality, operating in the same manner with auditory or visual signals. This finding extends our understanding of the relationship between temporal expectancy and measurable neural signals.

  5. Voluntary enhancement of neural signatures of affiliative emotion using FMRI neurofeedback.

    Directory of Open Access Journals (Sweden)

    Jorge Moll

    Full Text Available In Ridley Scott's film "Blade Runner", empathy-detection devices are employed to measure affiliative emotions. Despite recent neurocomputational advances, it is unknown whether brain signatures of affiliative emotions, such as tenderness/affection, can be decoded and voluntarily modulated. Here, we employed multivariate voxel pattern analysis and real-time fMRI to address this question. We found that participants were able to use visual feedback based on decoded fMRI patterns as a neurofeedback signal to increase brain activation characteristic of tenderness/affection relative to pride, an equally complex control emotion. Such improvement was not observed in a control group performing the same fMRI task without neurofeedback. Furthermore, the neurofeedback-driven enhancement of tenderness/affection-related distributed patterns was associated with local fMRI responses in the septohypothalamic area and frontopolar cortex, regions previously implicated in affiliative emotion. This demonstrates that humans can voluntarily enhance brain signatures of tenderness/affection, unlocking new possibilities for promoting prosocial emotions and countering antisocial behavior.

  6. Voluntary enhancement of neural signatures of affiliative emotion using FMRI neurofeedback.

    Science.gov (United States)

    Moll, Jorge; Weingartner, Julie H; Bado, Patricia; Basilio, Rodrigo; Sato, João R; Melo, Bruno R; Bramati, Ivanei E; de Oliveira-Souza, Ricardo; Zahn, Roland

    2014-01-01

    In Ridley Scott's film "Blade Runner", empathy-detection devices are employed to measure affiliative emotions. Despite recent neurocomputational advances, it is unknown whether brain signatures of affiliative emotions, such as tenderness/affection, can be decoded and voluntarily modulated. Here, we employed multivariate voxel pattern analysis and real-time fMRI to address this question. We found that participants were able to use visual feedback based on decoded fMRI patterns as a neurofeedback signal to increase brain activation characteristic of tenderness/affection relative to pride, an equally complex control emotion. Such improvement was not observed in a control group performing the same fMRI task without neurofeedback. Furthermore, the neurofeedback-driven enhancement of tenderness/affection-related distributed patterns was associated with local fMRI responses in the septohypothalamic area and frontopolar cortex, regions previously implicated in affiliative emotion. This demonstrates that humans can voluntarily enhance brain signatures of tenderness/affection, unlocking new possibilities for promoting prosocial emotions and countering antisocial behavior.

  7. Motion or activity: their role in intra- and inter-subject variation in fMRI

    DEFF Research Database (Denmark)

    Lund, Torben E; Nørgaard, Minna D; Rostrup, Egill

    2005-01-01

    Functional MRI (fMRI) carries the potential for non-invasive measurements of brain activity. Typically, what are referred to as activation images are actually thresholded statistical parametric maps. These maps possess large inter-session variability. This is especially problematic when applying ...

  8. PHYCAA: Data-driven measurement and removal of physiological noise in BOLD fMRI

    DEFF Research Database (Denmark)

    Churchill, Nathan W.; Yourganov, Grigori; Spring, Robyn

    2012-01-01

    challenge for identifying and removing such artifact. This paper presents a multivariate, data-driven method for the characterization and removal of physiological noise in fMRI data, termed PHYCAA (PHYsiological correction using Canonical Autocorrelation Analysis). The method identifies high frequency...

  9. Non-white noise in fMRI: does modelling have an impact?

    DEFF Research Database (Denmark)

    Lund, Torben E; Madsen, Kristoffer H; Sidaros, Karam

    2006-01-01

    The sources of non-white noise in Blood Oxygenation Level Dependent (BOLD) functional magnetic resonance imaging (fMRI) are many. Familiar sources include low-frequency drift due to hardware imperfections, oscillatory noise due to respiration and cardiac pulsation and residual movement artefacts ...

  10. Neural Changes after Phonological Treatment for Anomia: An fMRI Study

    Science.gov (United States)

    Rochon, Elizabeth; Leonard, Carol; Burianova, Hana; Laird, Laura; Soros, Peter; Graham, Simon; Grady, Cheryl

    2010-01-01

    Functional magnetic resonance imaging (fMRI) was used to investigate the neural processing characteristics associated with word retrieval abilities after a phonologically-based treatment for anomia in two stroke patients with aphasia. Neural activity associated with a phonological and a semantic task was compared before and after treatment with…

  11. Preoperative Evaluation with fMRI of Patients with Intracranial Gliomas

    Directory of Open Access Journals (Sweden)

    Ioannis Z. Kapsalakis

    2012-01-01

    Full Text Available Introduction. Aggressive surgical resection constitutes the optimal treatment for intracranial gliomas. However, the proximity of a tumor to eloquent areas requires exact knowledge of its anatomic relationships to functional cortex. The purpose of our study was to evaluate fMRI’s accuracy by comparing it to intraoperative cortical stimulation (DCS mapping. Material and Methods. Eighty-seven patients, with presumed glioma diagnosis, underwent preoperative fMRI and intraoperative DCS for cortical mapping during tumor resection. Findings of fMRI and DCS were considered concordant if the identified cortical centers were less than 5 mm apart. Pre and postoperative Karnofsky Performance Scale and Spitzer scores were recorded. A postoperative MRI was obtained for assessing the extent of resection. Results. The areas of interest were identified by fMRI and DCS in all participants. The concordance between fMRI and DCS was 91.9% regarding sensory-motor cortex, 100% for visual cortex, and 85.4% for language. Data analysis showed that patients with better functional condition demonstrated higher concordance rates, while there also was a weak association between tumor grade and concordance rate. The mean extent of tumor resection was 96.7%. Conclusions. Functional MRI is a highly accurate preoperative methodology for sensory-motor mapping. However, in language mapping, DCS remains necessary for accurate localization.

  12. Neural Substrates of the Topology Test to Measure Fluid Reasoning: An fMRI Study

    Science.gov (United States)

    Masunaga, Hiromi; Kawashima, Ryuta; Horn, John L.; Sassa, Yuko; Sekiguchi, Atsushi

    2008-01-01

    In our prior study the negative correlation between Topology, a behavioral measure of fluid reasoning, and adult age diminished with the increase in the level of expertise in a cognitively-demanding domain of expertise in the game of GO. The present fMRI study was designed to investigate neural substrates of Topology. The modified topology…

  13. Blind source separation of fMRI data by means of factor analytic transformations

    NARCIS (Netherlands)

    Langers, Dave R. M.

    2009-01-01

    In this study, the application of factor analytic (FA) rotation methods in the context of neuroimaging data analysis was explored. Three FA algorithms (ProMax, QuartiMax, and VariMax) were employed to carry out blind source separation in a functional magnetic resonance imaging (fMRI) experiment that

  14. A Study of Long-Term fMRI Reproducibility Using Data-Driven Analysis Methods.

    Science.gov (United States)

    Song, Xiaomu; Panych, Lawrence P; Chou, Ying-Hui; Chen, Nan-Kuei

    2014-12-01

    The reproducibility of functional magnetic resonance imaging (fMRI) is important for fMRI-based neuroscience research and clinical applications. Previous studies show considerable variation in amplitude and spatial extent of fMRI activation across repeated sessions on individual subjects even using identical experimental paradigms and imaging conditions. Most existing fMRI reproducibility studies were typically limited by time duration and data analysis techniques. Particularly, the assessment of reproducibility is complicated by a fact that fMRI results may depend on data analysis techniques used in reproducibility studies. In this work, the long-term fMRI reproducibility was investigated with a focus on the data analysis methods. Two spatial smoothing techniques, including a wavelet-domain Bayesian method and the Gaussian smoothing, were evaluated in terms of their effects on the long-term reproducibility. A multivariate support vector machine (SVM)-based method was used to identify active voxels, and compared to a widely used general linear model (GLM)-based method at the group level. The reproducibility study was performed using multisession fMRI data acquired from eight healthy adults over 1.5 years' period of time. Three regions-of-interest (ROI) related to a motor task were defined based upon which the long-term reproducibility were examined. Experimental results indicate that different spatial smoothing techniques may lead to different reproducibility measures, and the wavelet-based spatial smoothing and SVM-based activation detection is a good combination for reproducibility studies. On the basis of the ROIs and multiple numerical criteria, we observed a moderate to substantial within-subject long-term reproducibility. A reasonable long-term reproducibility was also observed from the inter-subject study. It was found that the short-term reproducibility is usually higher than the long-term reproducibility. Furthermore, the results indicate that brain

  15. Flexible adaptive paradigms for fMRI using a novel software package 'Brain Analysis in Real-Time' (BART.

    Directory of Open Access Journals (Sweden)

    Lydia Hellrung

    Full Text Available In this work we present a new open source software package offering a unified framework for the real-time adaptation of fMRI stimulation procedures. The software provides a straightforward setup and highly flexible approach to adapt fMRI paradigms while the experiment is running. The general framework comprises the inclusion of parameters from subject's compliance, such as directing gaze to visually presented stimuli and physiological fluctuations, like blood pressure or pulse. Additionally, this approach yields possibilities to investigate complex scientific questions, for example the influence of EEG rhythms or fMRI signals results themselves. To prove the concept of this approach, we used our software in a usability example for an fMRI experiment where the presentation of emotional pictures was dependent on the subject's gaze position. This can have a significant impact on the results. So far, if this is taken into account during fMRI data analysis, it is commonly done by the post-hoc removal of erroneous trials. Here, we propose an a priori adaptation of the paradigm during the experiment's runtime. Our fMRI findings clearly show the benefits of an adapted paradigm in terms of statistical power and higher effect sizes in emotion-related brain regions. This can be of special interest for all experiments with low statistical power due to a limited number of subjects, a limited amount of time, costs or available data to analyze, as is the case with real-time fMRI.

  16. Language dominance in children with epilepsy: concordance of fMRI with intracarotid amytal testing and cortical stimulation.

    Science.gov (United States)

    Rodin, Danielle; Bar-Yosef, Omer; Smith, Mary Lou; Kerr, Elizabeth; Morris, Drew; Donner, Elizabeth J

    2013-10-01

    Accurate localization of language function is critical in children undergoing epilepsy surgery. Functional magnetic resonance imaging (fMRI) is a noninvasive mapping method that has begun to replace electrocortical stimulation mapping (ESM) and the intracarotid amytal test (IAT). We used both quantitative and qualitative methods to evaluate the concordance of fMRI with ESM and IAT in 20 children using a panel of language tasks. In no cases did fMRI assessment of language hemisphere dominance identify the opposite hemisphere from assessment by IAT or ESM. Concordance with IAT and ESM was higher using fMRI visual inspection than an fMRI laterality index, which failed to lateralize language in a number of the subjects. We have demonstrated that fMRI has good concordance with more traditional methods of language mapping. When fMRI demonstrates bilateral language activations, however, we continue to recommend confirmatory testing by either IAT or ESM prior to resection in classic language regions.

  17. On the Relationship Between fMRI and Theories of Cognition: The Arrow Points in Both Directions.

    Science.gov (United States)

    Wixted, John T; Mickes, Laura

    2013-01-01

    In this article, we ask about the contribution of fMRI data to our understanding of theories of cognition and about the contribution of theories of cognition to our understanding of fMRI data. Experiments using fMRI can contribute to our understanding of cognition when they are designed to test the predictions of a particular cognitive theory. Although not all cognitive theories make clear predictions about patterns of activity in the brain fMRI experiments are often well suited to testing the predictions of those that do. However, many fMRI studies that are concerned with cognitive functional neuroanatomy are not designed to test predictions of cognitive theories but are instead designed to investigate the role played by different regions of the brain in cognitive activity. These fMRI studies do not shed light on cognitive theories but instead depend on cognitive theories to interpret the data-an interpretation that is only as valid as the cognitive theory on which it is based. These considerations suggest that the relationship between fMRI and theories of cognition is a two-way street.

  18. A hierarchical model for probabilistic independent component analysis of multi-subject fMRI studies.

    Science.gov (United States)

    Guo, Ying; Tang, Li

    2013-12-01

    An important goal in fMRI studies is to decompose the observed series of brain images to identify and characterize underlying brain functional networks. Independent component analysis (ICA) has been shown to be a powerful computational tool for this purpose. Classic ICA has been successfully applied to single-subject fMRI data. The extension of ICA to group inferences in neuroimaging studies, however, is challenging due to the unavailability of a pre-specified group design matrix. Existing group ICA methods generally concatenate observed fMRI data across subjects on the temporal domain and then decompose multi-subject data in a similar manner to single-subject ICA. The major limitation of existing methods is that they ignore between-subject variability in spatial distributions of brain functional networks in group ICA. In this article, we propose a new hierarchical probabilistic group ICA method to formally model subject-specific effects in both temporal and spatial domains when decomposing multi-subject fMRI data. The proposed method provides model-based estimation of brain functional networks at both the population and subject level. An important advantage of the hierarchical model is that it provides a formal statistical framework to investigate similarities and differences in brain functional networks across subjects, for example, subjects with mental disorders or neurodegenerative diseases such as Parkinson's as compared to normal subjects. We develop an EM algorithm for model estimation where both the E-step and M-step have explicit forms. We compare the performance of the proposed hierarchical model with that of two popular group ICA methods via simulation studies. We illustrate our method with application to an fMRI study of Zen meditation.

  19. BROCCOLI: Software for Fast fMRI Analysis on Many-Core CPUs and GPUs

    Directory of Open Access Journals (Sweden)

    Anders eEklund

    2014-03-01

    Full Text Available Analysis of functional magnetic resonance imaging (fMRI data is becoming ever more computationally demanding as temporal and spatial resolutions improve, and large, publicly available data sets proliferate. Moreover, methodological improvements in the neuroimaging pipeline, such as non-linear spatial normalization, non-parametric permutation tests and Bayesian Markov Chain Monte Carlo approaches, can dramatically increase the computational burden. Despite these challenges, there do not yet exist any fMRI software packages which leverage inexpensive and powerful graphics processing units (GPUs to perform these analyses. Here, we therefore present BROCCOLI, a free software package written in OpenCL (Open Computing Language that can be used for parallel analysis of fMRI data on a large variety of hardware configurations. BROCCOLI has, for example, been tested with an Intel CPU, an Nvidia GPU and an AMD GPU. These tests show that parallel processing of fMRI data can lead to significantly faster analysis pipelines. This speedup can be achieved on relatively standard hardware, but further, dramatic speed improvements require only a modest investment in GPU hardware. BROCCOLI (running on a GPU can perform non-linear spatial normalization to a 1 mm3 brain template in 4-6 seconds, and run a second level permutation test with 10,000 permutations in about a minute. These non-parametric tests are generally more robust than their parametric counterparts, and can also enable more sophisticated analyses by estimating complicated null distributions. Additionally, BROCCOLI includes support for Bayesian first-level fMRI analysis using a Gibbs sampler. The new software is freely available under GNU GPL3 and can be downloaded from github (https://github.com/wanderine/BROCCOLI/.

  20. BROCCOLI: Software for fast fMRI analysis on many-core CPUs and GPUs.

    Science.gov (United States)

    Eklund, Anders; Dufort, Paul; Villani, Mattias; Laconte, Stephen

    2014-01-01

    Analysis of functional magnetic resonance imaging (fMRI) data is becoming ever more computationally demanding as temporal and spatial resolutions improve, and large, publicly available data sets proliferate. Moreover, methodological improvements in the neuroimaging pipeline, such as non-linear spatial normalization, non-parametric permutation tests and Bayesian Markov Chain Monte Carlo approaches, can dramatically increase the computational burden. Despite these challenges, there do not yet exist any fMRI software packages which leverage inexpensive and powerful graphics processing units (GPUs) to perform these analyses. Here, we therefore present BROCCOLI, a free software package written in OpenCL (Open Computing Language) that can be used for parallel analysis of fMRI data on a large variety of hardware configurations. BROCCOLI has, for example, been tested with an Intel CPU, an Nvidia GPU, and an AMD GPU. These tests show that parallel processing of fMRI data can lead to significantly faster analysis pipelines. This speedup can be achieved on relatively standard hardware, but further, dramatic speed improvements require only a modest investment in GPU hardware. BROCCOLI (running on a GPU) can perform non-linear spatial normalization to a 1 mm(3) brain template in 4-6 s, and run a second level permutation test with 10,000 permutations in about a minute. These non-parametric tests are generally more robust than their parametric counterparts, and can also enable more sophisticated analyses by estimating complicated null distributions. Additionally, BROCCOLI includes support for Bayesian first-level fMRI analysis using a Gibbs sampler. The new software is freely available under GNU GPL3 and can be downloaded from github (https://github.com/wanderine/BROCCOLI/).

  1. Electrodermal Recording and fMRI to Inform Sensorimotor Recovery in Stroke Patients

    Science.gov (United States)

    MacIntosh, Bradley J.; McIlroy, William E.; Mraz, Richard; Staines, W. Richard; Black, Sandra E.; Graham, Simon J.

    2016-01-01

    Background Functional magnetic resonance imaging (fMRI) appears to be useful for investigating motor recovery after stroke. Some of the potential confounders of brain activation studies, however, could be mitigated through complementary physiological monitoring. Objective To investigate a sensorimotor fMRI battery that included simultaneous measurement of electrodermal activity in subjects with hemiparetic stroke to provide a measure related to the sense of effort during motor performance. Methods Bilateral hand and ankle tasks were performed by 6 patients with stroke (2 subacute, 4 chronic) during imaging with blood oxygen level-dependent (BOLD) fMRI using an event-related design. BOLD percent changes, peak activation, and laterality index values were calculated in the sensorimotor cortex. Electrodermal recordings were made concurrently and used as a regressor. Results Sensorimotor BOLD time series and percent change values provided evidence of an intact motor network in each of these well-recovered patients. During tasks involving the hemiparetic limb, electrodermal activity changes were variable in amplitude, and electrodermal activity time-series data showed significant correlations with fMRI in 3 of 6 patients. No such correlations were observed for control tasks involving the unaffected lower limb. Conclusions Electrodermal activity activation maps implicated the contralesional over the ipsilesional hemisphere, supporting the notion that stroke patients may require higher order motor processing to perform simple tasks. Electrodermal activity recordings may be useful as a physiological marker of differences in effort required during movements of a subject’s hemiparetic compared with the unaffected limb during fMRI studies. PMID:18784267

  2. Combining EEG Microstates with fMRI Structural Features for Modeling Brain Activity.

    Science.gov (United States)

    Michalopoulos, Kostas; Bourbakis, Nikolaos

    2015-12-01

    Combining information from Electroencephalography (EEG) and Functional Magnetic Resonance Imaging (fMRI) has been a topic of increased interest recently. The main advantage of the EEG is its high temporal resolution, in the scale of milliseconds, while the main advantage of fMRI is the detection of functional activity with good spatial resolution. The advantages of each modality seem to complement each other, providing better insight in the neuronal activity of the brain. The main goal of combining information from both modalities is to increase the spatial and the temporal localization of the underlying neuronal activity captured by each modality. This paper presents a novel technique based on the combination of these two modalities (EEG, fMRI) that allow a better representation and understanding of brain activities in time. EEG is modeled as a sequence of topographies, based on the notion of microstates. Hidden Markov Models (HMMs) were used to model the temporal evolution of the topography of the average Event Related Potential (ERP). For each model the Fisher score of the sequence is calculated by taking the gradient of the trained model parameters. The Fisher score describes how this sequence deviates from the learned HMM. Canonical Partial Least Squares (CPLS) were used to decompose the two datasets and fuse the EEG and fMRI features. In order to test the effectiveness of this method, the results of this methodology were compared with the results of CPLS using the average ERP signal of a single channel. The presented methodology was able to derive components that co-vary between EEG and fMRI and present significant differences between the two tasks.

  3. Using concurrent EEG and fMRI to probe the state of the brain in schizophrenia.

    Science.gov (United States)

    Ford, Judith M; Roach, Brian J; Palzes, Vanessa A; Mathalon, Daniel H

    2016-01-01

    Perceptional abnormalities in schizophrenia are associated with hallucinations and delusions, but also with negative symptoms and poor functional outcome. Perception can be studied using EEG-derived event related potentials (ERPs). Because of their excellent temporal resolution, ERPs have been used to ask when perception is affected by schizophrenia. Because of its excellent spatial resolution, functional magnetic resonance imaging (fMRI) has been used to ask where in the brain these effects are seen. We acquired EEG and fMRI data simultaneously to explore when and where auditory perception is affected by schizophrenia. Thirty schizophrenia (SZ) patients and 23 healthy comparison subjects (HC) listened to 1000 Hz tones occurring about every second. We used joint independent components analysis (jICA) to combine EEG-based event-related potential (ERP) and fMRI responses to tones. Five ERP-fMRI joint independent components (JIC) were extracted. The "N100" JIC had temporal weights during N100 (peaking at 100 ms post-tone onset) and fMRI spatial weights in superior and middle temporal gyri (STG/MTG); however, it did not differ between groups. The "P200" JIC had temporal weights during P200 and positive fMRI spatial weights in STG/MTG and frontal areas, and negative spatial weights in the nodes of the default mode network (DMN) and visual cortex. Groups differed on the "P200" JIC: SZ had smaller "P200" JIC, especially those with more severe avolition/apathy. This is consistent with negative symptoms being related to perceptual deficits, and suggests patients with avolition/apathy may allocate too few resources to processing external auditory events and too many to processing internal events.

  4. Real time fMRI: a tool for the routine presurgical localisation of the motor cortex

    Energy Technology Data Exchange (ETDEWEB)

    Moeller, M.; Freund, M.; Schwindt, W.; Gaus, C.; Heindel, W. [University of Muenster, Department of Clinical Radiology, Munster (Germany); Greiner, C. [University of Muenster, Department of Neurosurgery, Munster (Germany)

    2005-02-01

    In patients with brain lesions adjacent to the central area, exact preoperative knowledge of the spatial relation of the tumour to the motor cortex is of major importance. Many studies have shown that functional magnetic resonance imaging (fMRI) is a reliable tool to identify the motor cortex. However, fMRI data acquisition and data processing are time-consuming procedures, and this prevents general routine clinical application. We report a new application of real time fMRI that allows immediate access to fMRI results by automatic on-line data processing. Prior to surgery we examined ten patients with a brain tumour adjacent to the central area. Three measurements were performed at a 1.5-T Magnetom Vision Scanner (Siemens, Forchheim, Germany) on seven patients and at a 1.5-T Intera Scanner (Philips, Best, The Netherlands) on three patients using a sequential finger-tapping paradigm for motor cortex activation versus at rest condition. Blood oxygen level-dependant (BOLD) images were acquired using a multislice EPI sequence (16 slices, TE 60, TR 6000, FOV 210 x 210, matrix 64 x 64). The central sulcus of the left hemisphere could be clearly identified by a maximum of cortical activity after finger tapping of the right hand in all investigated patients. In eight of ten patients the right central sulcus was localised by a signal maximum, whereas in two patients the central sulcus could not be identified due to a hemiparesis in one and strong motion artefacts in the second patient. Finger tapping with one side versus rest condition seems to result in more motion artefacts, while finger tapping of the right versus the left hand yielded the strongest signal in the central area. Real time fMRI is a quick and reliable method to identify the central sulcus and has the potential to become a clinical tool to assess patients non-invasively before neurosurgical treatment. (orig.)

  5. FMRI orientation decoding in V1 does not require global maps or globally coherent orientation stimuli

    Directory of Open Access Journals (Sweden)

    Arjen eAlink

    2013-08-01

    Full Text Available The orientation of a large grating can be decoded from V1 functional magnetic resonance imaging (fMRI data, even at low resolution (3-mm isotropic voxels. This finding has suggested that columnar-level neuronal information might be accessible to fMRI at 3T. However, orientation decodability might alternatively arise from global orientation-bias maps. Such global maps across V1 could result from bottom-up processing, if the preferences of V1 neurons were biased toward particular orientations (e.g. radial from fixation, or cardinal, i.e. vertical or horizontal. Global maps could also arise from local recurrent or top-down processing, reflecting pre-attentive perceptual grouping, attention spreading, or predictive coding of global form. Here we investigate whether fMRI orientation decoding with 2-mm voxels requires (a globally coherent orientation stimuli and/or (b global-scale patterns of V1 activity. We used opposite-orientation gratings (balanced about the cardinal orientations and spirals (balanced about the radial orientation, along with novel patch-swapped variants of these stimuli. The two stimuli of a patch-swapped pair have opposite orientations everywhere (like their globally coherent parent stimuli. However, the two stimuli appear globally similar, a patchwork of opposite orientations. We find that all stimulus pairs are robustly decodable, demonstrating that fMRI orientation decoding does not require globally coherent orientation stimuli. Furthermore, decoding remained robust after spatial high-pass filtering for all stimuli, showing that fine-grained components of the fMRI patterns reflect visual orientations. Consistent with previous studies, we found evidence for global radial and vertical bias maps in V1. However, these were weak or absent for patch-swapped stimuli, suggesting that global bias maps depend on globally coherent orientations and might arise through recurrent or top-down processes related to the perception of global

  6. Is Model Fitting Necessary for Model-Based fMRI?

    Directory of Open Access Journals (Sweden)

    Robert C Wilson

    2015-06-01

    Full Text Available Model-based analysis of fMRI data is an important tool for investigating the computational role of different brain regions. With this method, theoretical models of behavior can be leveraged to find the brain structures underlying variables from specific algorithms, such as prediction errors in reinforcement learning. One potential weakness with this approach is that models often have free parameters and thus the results of the analysis may depend on how these free parameters are set. In this work we asked whether this hypothetical weakness is a problem in practice. We first developed general closed-form expressions for the relationship between results of fMRI analyses using different regressors, e.g., one corresponding to the true process underlying the measured data and one a model-derived approximation of the true generative regressor. Then, as a specific test case, we examined the sensitivity of model-based fMRI to the learning rate parameter in reinforcement learning, both in theory and in two previously-published datasets. We found that even gross errors in the learning rate lead to only minute changes in the neural results. Our findings thus suggest that precise model fitting is not always necessary for model-based fMRI. They also highlight the difficulty in using fMRI data for arbitrating between different models or model parameters. While these specific results pertain only to the effect of learning rate in simple reinforcement learning models, we provide a template for testing for effects of different parameters in other models.

  7. Language dominance assessment in a bilingual population: validity of fMRI in the second language.

    Science.gov (United States)

    Centeno, Maria; Koepp, Matthias J; Vollmar, Christian; Stretton, Jason; Sidhu, Meneka; Michallef, Caroline; Symms, Mark R; Thompson, Pamela J; Duncan, John S

    2014-10-01

    Assessment of language dominance using functional magnetic resonance imaging (fMRI) is a standard tool to estimate the risk of language function decline after epilepsy surgery. Although there has been considerable research in the characterization of language networks in bilingual individuals; little is known about the clinical usefulness of language mapping in a secondary language in patients with epilepsy, and how language lateralization assessed by fMRI may differ by the use of native or a secondary language paradigms. In this study we investigate language representation in a population of nonnative English speakers to assess differences in fMRI language lateralization between the first (native) and second language (English). Sixteen nonnative English-speaking patients with focal drug-resistant epilepsy underwent language fMRI in their first (native) language (L1) and in English (L2). Differences between language maps using L1 and L2 paradigms were examined at the single subject level by comparing within-subject lateralization indexes obtained for each language. Differences at the group level were examined for each of the tasks and languages. Group maps for the second language (English) showed overlapping areas of activation with the native language, but with larger clusters, and more bilaterally distributed than for the first language. However, at the individual level, lateralization indexes were concordant between the two languages, except for one patient with bilateral hippocampal sclerosis who was left dominant in English and showed bilateral dominance for verb generation and right dominance for verbal fluency in his native tongue. Language lateralization can generally be reliably derived from fMRI tasks in a second language provided that the subject can follow the task. Subjects with greater likelihood of atypical language representation should be evaluated more carefully, using more than one language paradigm. Wiley Periodicals, Inc. © 2014 International

  8. BROCCOLI: Software for fast fMRI analysis on many-core CPUs and GPUs

    Science.gov (United States)

    Eklund, Anders; Dufort, Paul; Villani, Mattias; LaConte, Stephen

    2014-01-01

    Analysis of functional magnetic resonance imaging (fMRI) data is becoming ever more computationally demanding as temporal and spatial resolutions improve, and large, publicly available data sets proliferate. Moreover, methodological improvements in the neuroimaging pipeline, such as non-linear spatial normalization, non-parametric permutation tests and Bayesian Markov Chain Monte Carlo approaches, can dramatically increase the computational burden. Despite these challenges, there do not yet exist any fMRI software packages which leverage inexpensive and powerful graphics processing units (GPUs) to perform these analyses. Here, we therefore present BROCCOLI, a free software package written in OpenCL (Open Computing Language) that can be used for parallel analysis of fMRI data on a large variety of hardware configurations. BROCCOLI has, for example, been tested with an Intel CPU, an Nvidia GPU, and an AMD GPU. These tests show that parallel processing of fMRI data can lead to significantly faster analysis pipelines. This speedup can be achieved on relatively standard hardware, but further, dramatic speed improvements require only a modest investment in GPU hardware. BROCCOLI (running on a GPU) can perform non-linear spatial normalization to a 1 mm3 brain template in 4–6 s, and run a second level permutation test with 10,000 permutations in about a minute. These non-parametric tests are generally more robust than their parametric counterparts, and can also enable more sophisticated analyses by estimating complicated null distributions. Additionally, BROCCOLI includes support for Bayesian first-level fMRI analysis using a Gibbs sampler. The new software is freely available under GNU GPL3 and can be downloaded from github (https://github.com/wanderine/BROCCOLI/). PMID:24672471

  9. Subject specific BOLD fMRI respiratory and cardiac response functions obtained from global signal.

    Science.gov (United States)

    Falahpour, Maryam; Refai, Hazem; Bodurka, Jerzy

    2013-05-15

    Subtle changes in either breathing pattern or cardiac pulse rate alter blood oxygen level dependent functional magnetic resonance imaging signal (BOLD fMRI). This is problematic because such fluctuations could possibly not be related to underlying neuronal activations of interest but instead the source of physiological noise. Several methods have been proposed to eliminate physiological noise in BOLD fMRI data. One such method is to derive a template based on average multi-subject data for respiratory response function (RRF) and cardiac response function (CRF) by simultaneously utilizing an external recording of cardiac and respiratory waveforms with the fMRI. Standard templates can then be used to model, map, and remove respiration and cardiac fluctuations from fMRI data. Utilizing these does not, however, account for intra-subject variations in physiological response. Thus, performing a more individualized approach for single subject physiological noise correction becomes more desirable, especially for clinical purposes. Here we propose a novel approach that employs subject-specific RRF and CRF response functions obtained from the whole brain or brain tissue-specific global signals (GS). Averaging multiple voxels in global signal computation ensures physiological noise dominance over thermal and system noise in even high-spatial-resolution fMRI data, making the GS suitable for deriving robust estimations of both RRF and CRF for individual subjects. Using these individualized response functions instead of standard templates based on multi-subject averages judiciously removes physiological noise from the data, assuming that there is minimal neuronal contribution in the derived individualized filters. Subject-specific physiological response functions obtained from the GS better maps individuals' physiological characteristics.

  10. Is Model Fitting Necessary for Model-Based fMRI?

    Science.gov (United States)

    Wilson, Robert C; Niv, Yael

    2015-06-01

    Model-based analysis of fMRI data is an important tool for investigating the computational role of different brain regions. With this method, theoretical models of behavior can be leveraged to find the brain structures underlying variables from specific algorithms, such as prediction errors in reinforcement learning. One potential weakness with this approach is that models often have free parameters and thus the results of the analysis may depend on how these free parameters are set. In this work we asked whether this hypothetical weakness is a problem in practice. We first developed general closed-form expressions for the relationship between results of fMRI analyses using different regressors, e.g., one corresponding to the true process underlying the measured data and one a model-derived approximation of the true generative regressor. Then, as a specific test case, we examined the sensitivity of model-based fMRI to the learning rate parameter in reinforcement learning, both in theory and in two previously-published datasets. We found that even gross errors in the learning rate lead to only minute changes in the neural results. Our findings thus suggest that precise model fitting is not always necessary for model-based fMRI. They also highlight the difficulty in using fMRI data for arbitrating between different models or model parameters. While these specific results pertain only to the effect of learning rate in simple reinforcement learning models, we provide a template for testing for effects of different parameters in other models.

  11. Language lateralization of a bilingual person with epilepsy using a combination of fMRI and neuropsychological assessment findings.

    Science.gov (United States)

    O'Grady, Christopher; Omisade, Antonina; Sadler, R Mark

    2016-10-01

    This report describes the findings of language functional magnetic resonance imaging (fMRI) in a left-handed Urdu and English speaker with right hemisphere-originating epilepsy and unclear language dominance. fMRI is a reliable method for determining hemispheric language dominance in presurgical planning. However, the effects of bilingualism on language activation depend on many factors including age of acquisition and proficiency in the tested language, and morphological properties of the language itself. This case demonstrates that completing fMRI in both spoken languages and interpreting the results within the context of a neuropsychological assessment are essential in arriving at accurate conclusions about language distribution in bilingual patients.

  12. Best current practice for obtaining high quality EEG data during simultaneous FMRI.

    Science.gov (United States)

    Mullinger, Karen J; Castellone, Pierluigi; Bowtell, Richard

    2013-06-03

    Simultaneous EEG-fMRI allows the excellent temporal resolution of EEG to be combined with the high spatial accuracy of fMRI. The data from these two modalities can be combined in a number of ways, but all rely on the acquisition of high quality EEG and fMRI data. EEG data acquired during simultaneous fMRI are affected by several artifacts, including the gradient artefact (due to the changing magnetic field gradients required for fMRI), the pulse artefact (linked to the cardiac cycle) and movement artifacts (resulting from movements in the strong magnetic field of the scanner, and muscle activity). Post-processing methods for successfully correcting the gradient and pulse artifacts require a number of criteria to be satisfied during data acquisition. Minimizing head motion during EEG-fMRI is also imperative for limiting the generation of artifacts. Interactions between the radio frequency (RF) pulses required for MRI and the EEG hardware may occur and can cause heating. This is only a significant risk if safety guidelines are not satisfied. Hardware design and set-up, as well as careful selection of which MR sequences are run with the EEG hardware present must therefore be considered. The above issues highlight the importance of the choice of the experimental protocol employed when performing a simultaneous EEG-fMRI experiment. Based on previous research we describe an optimal experimental set-up. This provides high quality EEG data during simultaneous fMRI when using commercial EEG and fMRI systems, with safety risks to the subject minimized. We demonstrate this set-up in an EEG-fMRI experiment using a simple visual stimulus. However, much more complex stimuli can be used. Here we show the EEG-fMRI set-up using a Brain Products GmbH (Gilching, Germany) MRplus, 32 channel EEG system in conjunction with a Philips Achieva (Best, Netherlands) 3T MR scanner, although many of the techniques are transferable to other systems.

  13. Inner experience in the scanner: Can high fidelity apprehensions of inner experience be integrated with fMRI?

    Directory of Open Access Journals (Sweden)

    Simone eKühn

    2014-12-01

    Full Text Available To provide full accounts of human experience and behavior, research in cognitive neuroscience must be linked to inner experience, but introspective reports of inner experience have often been found to be unreliable. The present case study aimed at providing proof of principle that introspection using one method, Descriptive Experience Sampling (DES, can be reliably integrated with fMRI. A participant was trained in the DES method, followed by nine sessions of sampling within an MRI scanner. During moments where the DES interview revealed ongoing inner speaking, fMRI data reliably showed activation in classic speech processing areas including left inferior frontal gyrus. Further, the fMRI data validated the participant’s DES observations of the experiential distinction between inner speaking and innerly hearing her own voice. These results highlight the precision and validity of the DES method as a technique of exploring inner experience and the utility of combining such methods with fMRI.

  14. Lateralisation of cerebral response to active acupuncture in patients with unilateral ischaemic stroke: an fMRI study

    National Research Council Canada - National Science Library

    Huang, Yong; Chen, Jun-Qi; Lai, Xin-Sheng; Tang, Chun-Zhi; Yang, Jun-Jun; Chen, Hua; Wu, Jun-Xian; Xiao, Hui-Ling; Qu, Shan-Shan; Zhang, Yi-Dan; Zhang, Zhang-Jin

    2013-01-01

    Acupuncture is beneficial in treating stroke neuropsychiatric symptoms. The present study aimed to identify functional brain response to active acupuncture in patients with unilateral ischaemic stroke using functional MRI (fMRI...

  15. Regional Coherence Alterations Revealed by Resting-State fMRI in Post-Stroke Patients with Cognitive Dysfunction

    National Research Council Canada - National Science Library

    Peng, Cheng-Yu; Chen, Yu-Chen; Cui, Ying; Zhao, Deng-Ling; Jiao, Yun; Tang, Tian-Yu; Ju, Shenghong; Teng, Gao-Jun

    2016-01-01

    ...) to investigate the alterations in regional coherence in patients after subcortical stroke. Resting-state fMRI measurements were acquired from 16 post-stroke patients with poor cognitive function (PSPC...

  16. Understanding the Pathophysiology of Alzheimer's Disease and Mild Cognitive Impairment: A Mini Review on fMRI and ERP Studies

    Directory of Open Access Journals (Sweden)

    Takao Yamasaki

    2012-01-01

    Full Text Available The prevalence of Alzheimer's disease (AD is predicted to increase rapidly in the coming decade, highlighting the importance of early detection and intervention in patients with AD and mild cognitive impairment (MCI. Recently, remarkable advances have been made in the application of neuroimaging techniques in investigations of AD and MCI. Among the various neuroimaging techniques, functional magnetic resonance imaging (fMRI has many potential advantages, noninvasively detecting alterations in brain function that may be present very early in the course of AD and MCI. In this paper, we first review task-related and resting-state fMRI studies on AD and MCI. We then present our recent fMRI studies with additional event-related potential (ERP experiments during a motion perception task in MCI. Our results indicate that fMRI, especially when combined with ERP recording, can be useful for detecting spatiotemporal functional changes in AD and MCI patients.

  17. The Role of the Amygdala in Facial Trustworthiness Processing: A Systematic Review and Meta-Analyses of fMRI Studies.

    Science.gov (United States)

    Santos, Sara; Almeida, Inês; Oliveiros, Bárbara; Castelo-Branco, Miguel

    2016-01-01

    Faces play a key role in signaling social cues such as signals of trustworthiness. Although several studies identify the amygdala as a core brain region in social cognition, quantitative approaches evaluating its role are scarce. This review aimed to assess the role of the amygdala in the processing of facial trustworthiness, by analyzing its amplitude BOLD response polarity to untrustworthy versus trustworthy facial signals under fMRI tasks through a Meta-analysis of effect sizes (MA). Activation Likelihood Estimation (ALE) analyses were also conducted. Articles were retrieved from MEDLINE, ScienceDirect and Web-of-Science in January 2016. Following the PRISMA statement guidelines, a systematic review of original research articles in English language using the search string "(face OR facial) AND (trustworthiness OR trustworthy OR untrustworthy OR trustee) AND fMRI" was conducted. The MA concerned amygdala responses to facial trustworthiness for the contrast Untrustworthy vs. trustworthy faces, and included whole-brain and ROI studies. To prevent potential bias, results were considered even when at the single study level they did not survive correction for multiple comparisons or provided non-significant results. ALE considered whole-brain studies, using the same methodology to prevent bias. A summary of the methodological options (design and analysis) described in the articles was finally used to get further insight into the characteristics of the studies and to perform a subgroup analysis. Data were extracted by two authors and checked independently. Twenty fMRI studies were considered for systematic review. An MA of effect sizes with 11 articles (12 studies) showed high heterogeneity between studies [Q(11) = 265.68, p trustworthiness. Six articles/studies showed that posterior cingulate and medial frontal gyrus present positive correlations with increasing facial trustworthiness levels. Significant effects considering subgroup analysis based on methodological

  18. Enhanced Thalamic Functional Connectivity with No fMRI Responses to Affected Forelimb Stimulation in Stroke-Recovered Rats

    OpenAIRE

    Shim, Woo H.; Suh, Ji-Yeon; Kim, Jeong K.; Jeong, Jaeseung; Kim, Young R.

    2017-01-01

    Neurological recovery after stroke has been extensively investigated to provide better understanding of neurobiological mechanism, therapy, and patient management. Recent advances in neuroimaging techniques, particularly functional MRI (fMRI), have widely contributed to unravel the relationship between the altered neural function and stroke-affected brain areas. As results of previous investigations, the plastic reorganization and/or gradual restoration of the hemodynamic fMRI responses to ne...

  19. ICA-based artifact removal diminishes scan site differences in multi-center resting-state fMRI

    OpenAIRE

    2015-01-01

    Resting-state fMRI (R-fMRI) has shown considerable promise in providing potential biomarkers for diagnosis, prognosis and drug response across a range of diseases. Incorporating R-fMRI into multi-center studies is becoming increasingly popular, imposing technical challenges on data acquisition and analysis, as fMRI data is particularly sensitive to structured noise resulting from hardware, software, and environmental differences. Here, we investigated whether a novel clean up tool for structu...

  20. ICA-based artifact removal diminishes scan site differences in multi-center resting-state fMRI.

    OpenAIRE

    2015-01-01

    Resting-state fMRI (R-fMRI) has shown considerable promise in providing potential biomarkers for diagnosis, prognosis and drug response across a range of diseases. Incorporating R-fMRI into multi-center studies is becoming increasingly popular, imposing technical challenges on data acquisition and analysis, as fMRI data is particularly sensitive to structured noise resulting from hardware, software and environmental differences. Here, we investigated whether a novel clean up tool for structur...

  1. A 4-channel 3 Tesla phased array receive coil for awake rhesus monkey fMRI and diffusion MRI experiments

    OpenAIRE

    Khachaturian, Mark Haig

    2010-01-01

    Awake monkey fMRI and diffusion MRI combined with conventional neuroscience techniques has the potential to study the structural and functional neural network. The majority of monkey fMRI and diffusion MRI experiments are performed with single coils which suffer from severe EPI distortions which limit resolution. By constructing phased array coils for monkey MRI studies, gains in SNR and anatomical accuracy (i.e., reduction of EPI distortions) can be achieved using parallel imaging. The major...

  2. On the characterization of single-event related brain activity from functional Magnetic Resonance Imaging (fMRI) measurements

    KAUST Repository

    Khoram, Nafiseh

    2014-08-01

    We propose an efficient numerical technique for calibrating the mathematical model that describes the singleevent related brain response when fMRI measurements are given. This method employs a regularized Newton technique in conjunction with a Kalman filtering procedure. We have applied this method to estimate the biophysiological parameters of the Balloon model that describes the hemodynamic brain responses. Illustrative results obtained with both synthetic and real fMRI measurements are presented. © 2014 IEEE.

  3. Multimodal classification of schizophrenia patients with MEG and fMRI data using static and dynamic connectivity measures

    Directory of Open Access Journals (Sweden)

    Mustafa Sinan Cetin

    2016-10-01

    Full Text Available Mental disorders like schizophrenia are currently diagnosed by physicians/psychiatrists through clinical assessment and their evaluation of patient’s self-reported experiences as the illness emerges. There is great interest in identifying biological markers of prognosis at the onset of illness, rather than relying on the evolution of symptoms across time. Functional network connectivity, which indicates a subject's overall level of 'synchronicity' of activity between brain regions, demonstrates promise in providing individual subject predictive power. Many previous studies reported functional connectivity changes during resting-state using only functional magnetic resonance imaging (fMRI. Nevertheless, exclusive reliance on fMRI to generate such networks may limit the inference of the underlying dysfunctional connectivity, which is hypothesized to be a factor in patient symptoms, as fMRI measures connectivity via hemodynamics. Therefore, combination of connectivity assessments using fMRI and magnetoencephalography (MEG, which more directly measures neuronal activity, may provide improved classification of schizophrenia than either modality alone. Moreover, recent evidence indicates that metrics of dynamic connectivity may also be critical for understanding pathology in schizophrenia. In this work, we propose a new framework for extraction of important disease related features and classification of patients with schizophrenia based on using both fMRI and MEG to investigate functional network components in the resting state. Results of this study show that the integration of fMRI and MEG provides important information that captures fundamental characteristics of functional network connectivity in schizophrenia and is helpful for prediction of schizophrenia patient group membership. Combined fMRI/MEG methods, using static functional network connectivity analyses, improved classification accuracy relative to use of fMRI or MEG methods alone (by 15

  4. Assessing hippocampal functional reserve in temporal lobe epilepsy: A multi-voxel pattern analysis of fMRI data

    OpenAIRE

    Bonnici, Heidi M; Sidhu, Meneka; Chadwick, Martin J.; Duncan, John S.; Maguire, Eleanor A.

    2013-01-01

    Summary Assessing the functional reserve of key memory structures in the medial temporal lobes (MTL) of pre-surgical patients with intractable temporal lobe epilepsy (TLE) remains a challenge. Conventional functional MRI (fMRI) memory paradigms have yet to fully convince of their ability to confidently assess the risk of a post-surgical amnesia. An alternative fMRI analysis method, multi-voxel pattern analysis (MVPA), focuses on the patterns of activity across voxels in specific brain regions...

  5. Sub-Millimeter T2 Weighted fMRI at 7 T: Comparison of 3D-GRASE and 2D SE-EPI

    Directory of Open Access Journals (Sweden)

    Valentin G. Kemper

    2015-05-01

    Full Text Available Functional magnetic resonance imaging (fMRI allows studying human brain function non-invasively up to the spatial resolution of cortical columns and layers. Most fMRI acquisitions rely on the blood oxygenation level dependent (BOLD contrast employing T2* weighted 2D multi-slice echo-planar imaging (EPI. At ultra-high magnetic field (i.e. 7 T and above, it has been shown experimentally and by simulation, that T2 weighted acquisitions yield a signal that is spatially more specific to the site of neuronal activity at the cost of functional sensitivity. This study compared two T2 weighted imaging sequences, inner-volume 3D Gradient-and-Spin-Echo (3D-GRASE and 2D Spin-Echo EPI (SE-EPI, with evaluation of their imaging point-spread function, functional specificity, and functional sensitivity at sub-millimeter resolution. Simulations and measurements of the imaging point-spread function revealed that the strongest anisotropic blurring in 3D-GRASE (along the second phase-encoding direction was about 60 % higher than the strongest anisotropic blurring in 2D SE-EPI (along the phase-encoding direction In a visual paradigm, the BOLD sensitivity of 3D-GRASE was found to be superior due to its higher temporal signal-to-noise ratio. High resolution cortical depth profiles suggested that the contrast mechanisms are similar between the two sequences, however, 2D SE-EPI had a higher surface bias owing to the higher T2* contribution of the longer in-plane EPI echo-train for full field of view compared to the reduced field of view of zoomed 3D-GRASE.

  6. Posterior cingulate cortex-related co-activation patterns: a resting state FMRI study in propofol-induced loss of consciousness.

    Directory of Open Access Journals (Sweden)

    Enrico Amico

    Full Text Available BACKGROUND: Recent studies have been shown that functional connectivity of cerebral areas is not a static phenomenon, but exhibits spontaneous fluctuations over time. There is evidence that fluctuating connectivity is an intrinsic phenomenon of brain dynamics that persists during anesthesia. Lately, point process analysis applied on functional data has revealed that much of the information regarding brain connectivity is contained in a fraction of critical time points of a resting state dataset. In the present study we want to extend this methodology for the investigation of resting state fMRI spatial pattern changes during propofol-induced modulation of consciousness, with the aim of extracting new insights on brain networks consciousness-dependent fluctuations. METHODS: Resting-state fMRI volumes on 18 healthy subjects were acquired in four clinical states during propofol injection: wakefulness, sedation, unconsciousness, and recovery. The dataset was reduced to a spatio-temporal point process by selecting time points in the Posterior Cingulate Cortex (PCC at which the signal is higher than a given threshold (i.e., BOLD intensity above 1 standard deviation. Spatial clustering on the PCC time frames extracted was then performed (number of clusters = 8, to obtain 8 different PCC co-activation patterns (CAPs for each level of consciousness. RESULTS: The current analysis shows that the core of the PCC-CAPs throughout consciousness modulation seems to be preserved. Nonetheless, this methodology enables to differentiate region-specific propofol-induced reductions in PCC-CAPs, some of them already present in the functional connectivity literature (e.g., disconnections of the prefrontal cortex, thalamus, auditory cortex, some others new (e.g., reduced co-activation in motor cortex and visual area. CONCLUSION: In conclusion, our results indicate that the employed methodology can help in improving and refining the characterization of local

  7. Let's Not Waste Time: Using Temporal Information in Clustered Activity Estimation with Spatial Adjacency Restrictions (CAESAR) for Parcellating FMRI Data.

    Science.gov (United States)

    Janssen, Ronald J; Jylänki, Pasi; van Gerven, Marcel A J

    2016-01-01

    We have proposed a Bayesian approach for functional parcellation of whole-brain FMRI measurements which we call Clustered Activity Estimation with Spatial Adjacency Restrictions (CAESAR). We use distance-dependent Chinese restaurant processes (dd-CRPs) to define a flexible prior which partitions the voxel measurements into clusters whose number and shapes are unknown a priori. With dd-CRPs we can conveniently implement spatial constraints to ensure that our parcellations remain spatially contiguous and thereby physiologically meaningful. In the present work, we extend CAESAR by using Gaussian process (GP) priors to model the temporally smooth haemodynamic signals that give rise to the measured FMRI data. A challenge for GP inference in our setting is the cubic scaling with respect to the number of time points, which can become computationally prohibitive with FMRI measurements, potentially consisting of long time series. As a solution we describe an efficient implementation that is practically as fast as the corresponding time-independent non-GP model with typically-sized FMRI data sets. We also employ a population Monte-Carlo algorithm that can significantly speed up convergence compared to traditional single-chain methods. First we illustrate the benefits of CAESAR and the GP priors with simulated experiments. Next, we demonstrate our approach by parcellating resting state FMRI data measured from twenty participants as taken from the Human Connectome Project data repository. Results show that CAESAR affords highly robust and scalable whole-brain clustering of FMRI timecourses.

  8. Diagnostic benefits of presurgical fMRI in patients with brain tumours in the primary sensorimotor cortex

    Energy Technology Data Exchange (ETDEWEB)

    Wengenroth, Martina; Blatow, M.; Guenther, J. [University of Heidelberg Medical School, Department of Neuroradiology, Heidelberg (Germany); Akbar, M. [University of Heidelberg Medical School, Department of Orthopaedics, Heidelberg (Germany); Tronnier, V.M. [University of Schleswig-Holstein, Department of Neurosurgery, Luebeck (Germany); Stippich, C. [University Hospital Basle, Department of Diagnostic and Interventional Neuroradiology, Basle (Switzerland)

    2011-07-15

    Reliable imaging of eloquent tumour-adjacent brain areas is necessary for planning function-preserving neurosurgery. This study evaluates the potential diagnostic benefits of presurgical functional magnetic resonance imaging (fMRI) in comparison to a detailed analysis of morphological MRI data. Standardised preoperative functional and structural neuroimaging was performed on 77 patients with rolandic mass lesions at 1.5 Tesla. The central region of both hemispheres was allocated using six morphological and three functional landmarks. fMRI enabled localisation of the motor hand area in 76/77 patients, which was significantly superior to analysis of structural MRI (confident localisation of motor hand area in 66/77 patients; p < 0.002). FMRI provided additional diagnostic information in 96% (tongue representation) and 97% (foot representation) of patients. FMRI-based presurgical risk assessment correlated in 88% with a positive postoperative clinical outcome. Routine presurgical FMRI allows for superior assessment of the spatial relationship between brain tumour and motor cortex compared with a very detailed analysis of structural 3D MRI, thus significantly facilitating the preoperative risk-benefit assessment and function-preserving surgery. The additional imaging time seems justified. FMRI has the potential to reduce postoperative morbidity and therefore hospitalisation time. (orig.)

  9. The impact of fMRI on multimodal navigation in surgery of cerebral lesions: four years clinical experience

    Energy Technology Data Exchange (ETDEWEB)

    Wurm, Gabriele [Landesnervenklinik Wagner Jauregg, Neurosurgical Department, Linz (Austria); Schnizer, Mathilde [Landesnervenklinik Wagner Jauregg, Neurological Department, Linz (Austria); Landesnervenklinik Wagner Jauregg, Neuroradiological Department, Linz (Austria); Fellner, Claudia [Landesnervenklinik Wagner Jauregg, Neuroradiological Department, Linz (Austria); University of Regensburg, Institute of Radiology, Regensburg (Germany)

    2008-09-15

    Neuronavigation with display of intraoperative structures, instrument locations, orientation and relationships to nearby structures can increase anatomic precision while enhancing the surgeon's confidence and his/her perception of safety. Combination of neuronavigation with functional imaging provides multimodal guidance for surgery of cerebral lesions. We evaluated the impact of functional MRI (fMRI) on surgical decision making and outcome. A neuronavigational device (StealthStation (tm), Medtronic Inc.) was used as platform to merge fMRI data with anatomic images, and to implement intraoperative multimodal guidance. In a 52-month period, where 977 surgical procedures were performed with the aid of neuronavigation, 88 patients underwent image-guided procedures using multimodal guidance. Patient, surgical and outcome data of this series was prospectively collected. Evaluation of 88 procedures on cerebral lesions in complex regions where fMRI data were integrated using the navigation system demonstrated that the additional information was presented in a user-friendly way. Computer assisted fMRI integration was found to be especially helpful in planning the best approach, in assessing alternative approaches, and in defining the extent of the surgical exposure. Furthermore, the surgeons found it more effective to interpret fMRI information when shown in a navigation system as compared to the traditional display on a light board or monitor. Multimodal navigation enhanced by fMRI was judged useful for optimization of surgery of cerebral lesions, especially in and around eloquent regions by experienced neurosurgeons. (orig.)

  10. fMRI mapping of the visual system in the mouse brain with interleaved snapshot GE-EPI.

    Science.gov (United States)

    Niranjan, Arun; Christie, Isabel N; Solomon, Samuel G; Wells, Jack A; Lythgoe, Mark F

    2016-06-10

    The use of functional magnetic resonance imaging (fMRI) in mice is increasingly prevalent, providing a means to non-invasively characterise functional abnormalities associated with genetic models of human diseases. The predominant stimulus used in task-based fMRI in the mouse is electrical stimulation of the paw. Task-based fMRI in mice using visual stimuli remains underexplored, despite visual stimuli being common in human fMRI studies. In this study, we map the mouse brain visual system with BOLD measurements at 9.4T using flashing light stimuli with medetomidine anaesthesia. BOLD responses were observed in the lateral geniculate nucleus, the superior colliculus and the primary visual area of the cortex, and were modulated by the flashing frequency, diffuse vs focussed light and stimulus context. Negative BOLD responses were measured in the visual cortex at 10Hz flashing frequency; but turned positive below 5Hz. In addition, the use of interleaved snapshot GE-EPI improved fMRI image quality without diminishing the temporal contrast-noise-ratio. Taken together, this work demonstrates a novel methodological protocol in which the mouse brain visual system can be non-invasively investigated using BOLD fMRI.

  11. Hemodynamic modelling of BOLD fMRI - A machine learning approach

    DEFF Research Database (Denmark)

    Jacobsen, Danjal Jakup

    2007-01-01

    This Ph.D. thesis concerns the application of machine learning methods to hemodynamic models for BOLD fMRI data. Several such models have been proposed by different researchers, and they have in common a basis in physiological knowledge of the hemodynamic processes involved in the generation...... of the BOLD signal. The BOLD signal is modelled as a non-linear function of underlying, hidden (non-measurable) hemodynamic state variables. The focus of this thesis work has been to develop methods for learning the parameters of such models, both in their traditional formulation, and in a state space...... formulation. In the latter, noise enters at the level of the hidden states, as well as in the BOLD measurements themselves. A framework has been developed to allow approximate posterior distributions of model parameters to be learned from real fMRI data. This is accomplished with Markov chain Monte Carlo...

  12. Resting-state fMRI study of patients with fragile X syndrome

    Science.gov (United States)

    Isanova, E.; Petrovskiy, E.; Savelov, A.; Yudkin, D.; Tulupov, A.

    2017-08-01

    The study aimed to assess the neural activity of different brain regions in patients with fragile X syndrome (FXS) and the healthy volunteers by resting-state functional magnetic resonance imaging (fMRI) on a 1.5 T MRI Achieva scanner (Philips). Results: The fMRI study showed a DMN of brain function in patients with FXS, as well as in the healthy volunteers. Furthermore, it was found that a default mode network of the brain in patients with FXS and healthy volunteers does not have statistically significant differences (p>0.05), which may indicate that the basal activity of neurons in patients with FXS is not reduced. In addition, we have found a significant (pfunctional status of the brain in patients with FXS were received. The significant increase in the resting state functional connectivity within the right inferior parietal and right angular gyrus (p<0.001) in patients with FXS was found.

  13. Serial changes of humor comprehension for four-frame comic Manga: an fMRI study.

    Science.gov (United States)

    Osaka, Mariko; Yaoi, Ken; Minamoto, Takehiro; Osaka, Naoyuki

    2014-07-25

    Serial changes of humor comprehension evoked by a well organized four-frame comic Manga were investigated by fMRI in each step of humor comprehension. The neural substrates underlying the amusing effects in response to funny and mixed order manga were compared. In accordance with the time course of the four frames, fMRI activations changed serially. Beginning with the second frame (development scene), activation of the temporo-parietal junction (TPJ) was observed, followed by activations in the temporal and frontal areas during viewing of the third frame (turn scene). For the fourth frame (punch line), strong increased activations were confirmed in the medial prefrontal cortex (MPFC) and cerebellum. Interestingly, distinguishable activation differences in the cerebellum between funny and non-funny conditions were also found for the fourth frame. These findings suggest that humor comprehension evokes activation that initiates in the TPJ and expands to the MPFC and cerebellum at the convergence level.

  14. Preoperative functional magnetic resonance imaging (fMRI) and transcranial magnetic stimulation (TMS)

    DEFF Research Database (Denmark)

    Hartwigsen, G.; Siebner, Hartwig R.; Stippich, C.

    2010-01-01

    motor as well as higher cognitive functions (i.e. language). Pre-operative fMRI can be used to identify the brain regions that are activated during specific sensorimotor or language tasks. TMS is able to disrupt neuronal processing in the targeted brain area which in turn may affect task performance......Neurosurgical resection of brain lesions aims to maximize excision while minimizing the risk of permanent injury to the surrounding intact brain tissue and resulting neurological deficits. While direct electrical cortical stimulation at the time of surgery allows the precise identification......MRI is well established although the number of studies on presurgical language fMRI is still limited. In contrast, the reliability and accuracy of preoperative TMS remains to be determined....

  15. Relationship between saccadic eye movements and cortical activity as measured by fMRI

    DEFF Research Database (Denmark)

    Kimmig, H.; Greenlee, M.W.; Gondan, Matthias;

    2001-01-01

    quantitative changes in cortical activity associated with qualitative changes in the saccade task for comparable levels of saccadic activity. All experiments required the simultaneous acquisition of eye movement and fMRI data. For this purpose we used a new high-resolution limbus-tracking technique......We investigated the quantitative relationship between saccadic activity (as reflected in frequency of occurrence and amplitude of saccades) and blood oxygenation level dependent (BOLD) changes in the cerebral cortex using functional magnetic resonance imaging (fMRI). Furthermore, we investigated....... The latter finding is taken to indicate a more demanding cortical processing in the "anti" task than the "pro" task, which could explain the observed difference in BOLD activation. We hold that a quantitative analysis of saccade parameters (especially saccade frequency and latency) is important...

  16. Using concurrent EEG and fMRI to probe the state of the brain in schizophrenia

    Directory of Open Access Journals (Sweden)

    Judith M. Ford

    2016-01-01

    Five ERP-fMRI joint independent components (JIC were extracted. The “N100” JIC had temporal weights during N100 (peaking at 100 ms post-tone onset and fMRI spatial weights in superior and middle temporal gyri (STG/MTG; however, it did not differ between groups. The “P200” JIC had temporal weights during P200 and positive fMRI spatial weights in STG/MTG and frontal areas, and negative spatial weights in the nodes of the default mode network (DMN and visual cortex. Groups differed on the “P200” JIC: SZ had smaller “P200” JIC, especially those with more severe avolition/apathy. This is consistent with negative symptoms being related to perceptual deficits, and suggests patients with avolition/apathy may allocate too few resources to processing external auditory events and too many to processing internal events.

  17. Why more is better: Simultaneous modeling of EEG, fMRI, and behavioral data.

    Science.gov (United States)

    Turner, Brandon M; Rodriguez, Christian A; Norcia, Tony M; McClure, Samuel M; Steyvers, Mark

    2016-03-01

    The need to test a growing number of theories in cognitive science has led to increased interest in inferential methods that integrate multiple data modalities. In this manuscript, we show how a method for integrating three data modalities within a single framework provides (1) more detailed descriptions of cognitive processes and (2) more accurate predictions of unobserved data than less integrative methods. Specifically, we show how combining either EEG and fMRI with a behavioral model can perform substantially better than a behavioral-data-only model in both generative and predictive modeling analyses. We then show how a trivariate model - a model including EEG, fMRI, and behavioral data - outperforms bivariate models in both generative and predictive modeling analyses. Together, these results suggest that within an appropriate modeling framework, more data can be used to better constrain cognitive theory, and to generate more accurate predictions for behavioral and neural data.

  18. Discriminating between brain rest and attention states using fMRI connectivity graphs and subtree SVM

    Science.gov (United States)

    Mokhtari, Fatemeh; Bakhtiari, Shahab K.; Hossein-Zadeh, Gholam Ali; Soltanian-Zadeh, Hamid

    2012-02-01

    Decoding techniques have opened new windows to explore the brain function and information encoding in brain activity. In the current study, we design a recursive support vector machine which is enriched by a subtree graph kernel. We apply the classifier to discriminate between attentional cueing task and resting state from a block design fMRI dataset. The classifier is trained using weighted fMRI graphs constructed from activated regions during the two mentioned states. The proposed method leads to classification accuracy of 1. It is also able to elicit discriminative regions and connectivities between the two states using a backward edge elimination algorithm. This algorithm shows the importance of regions including cerebellum, insula, left middle superior frontal gyrus, post cingulate cortex, and connectivities between them to enhance the correct classification rate.

  19. Pooling fMRI data: meta-analysis, mega-analysis and multi-center studies

    Directory of Open Access Journals (Sweden)

    Sergi G Costafreda

    2009-09-01

    Full Text Available The quantitative analysis of pooled data from related fMRI experiments has the potential to significantly accelerate progress in brain mapping. Such data-pooling can be achieved through meta-analysis (the pooled analysis of published results, mega-analysis (the pooled analysis of raw data or multi-site studies which can be seen as designed mega-analyses. Current limitations in function-location brain mapping and how data-pooling can be used to remediate them are reviewed, with particular attention to power aggregation and mitigation of false positive results. Some recently developed analysis tools for meta- and mega-analysis are also presented, and recommendations for the conduct of valid fMRI data pooling are formulated.

  20. Parametric analysis of fMRI data using linear systems methods.

    Science.gov (United States)

    Cohen, M S

    1997-08-01

    Using a model of the functional MRI (fMRI) impulse response based on published data, we have demonstrated that the form of the fMRI response to stimuli of freely varied timing can be modeled well by convolution of the impulse response with the behavioral stimulus. The amplitudes of the responses as a function of parametrically varied behavioral conditions are fitted well using a piecewise linear approximation. Use of the combined model, in conjunction with correlation analysis, results in an increase in sensitivity for the MRI study. This approach, based on the well-established methods of linear systems analysis, also allows a quantitative comparison of the response amplitudes across subjects to a broad range of behavioral conditions. Fit parameters, derived from the amplitude data, are relatively insensitive to a variety of MRI-related artifacts and yield results that are compared readily across subjects.

  1. Collective Correlations of Brodmann Areas fMRI Study with RMT-Denoising

    Science.gov (United States)

    Burda, Z.; Kornelsen, J.; Nowak, M. A.; Porebski, B.; Sboto-Frankenstein, U.; Tomanek, B.; Tyburczyk, J.

    We study collective behavior of Brodmann regions of human cerebral cortex using functional Magnetic Resonance Imaging (fMRI) and Random Matrix Theory (RMT). The raw fMRI data is mapped onto the cortex regions corresponding to the Brodmann areas with the aid of the Talairach coordinates. Principal Component Analysis (PCA) of the Pearson correlation matrix for 41 different Brodmann regions is carried out to determine their collective activity in the idle state and in the active state stimulated by tapping. The collective brain activity is identified through the statistical analysis of the eigenvectors to the largest eigenvalues of the Pearson correlation matrix. The leading eigenvectors have a large participation ratio. This indicates that several Broadmann regions collectively give rise to the brain activity associated with these eigenvectors. We apply random matrix theory to interpret the underlying multivariate data.

  2. Correlation between MEG and BOLD fMRI signals induced by visual flicker stimuli

    Institute of Scientific and Technical Information of China (English)

    Chu Renxin; Holroyd Tom; Duyn Jeff

    2007-01-01

    The goal of this work was to investigate how the MEG signal amplitude correlates with that of BOLD fMRI.To investigate the correlation between fMRI and macroscopic electrical activity, BOLD fMRI and MEG was performed on the same subjects (n =5). A visual flicker stimulus of varying temporal frequency was used to elicit neural responses in early visual areas. A strong similarity was observed in frequency tuning curves between both modalities.Although, averaged over subjects, the BOLD tuning curve was somewhat broader than MEG, both BOLD and MEG had maxima at a flicker frequency of 10 Hz. Also, we measured the first and second harmonic components as the stimuli frequency by MEG. In the low stimuli frequency (less than 6 Hz), the second harmonic has comparable amplitude with the first harmonic, which implies that neural frequency response is nonlinear and has more nonlinear components in low frequency than in high frequency.

  3. fMRI activation patterns in an analytic reasoning task: consistency with EEG source localization

    Science.gov (United States)

    Li, Bian; Vasanta, Kalyana C.; O'Boyle, Michael; Baker, Mary C.; Nutter, Brian; Mitra, Sunanda

    2010-03-01

    Functional magnetic resonance imaging (fMRI) is used to model brain activation patterns associated with various perceptual and cognitive processes as reflected by the hemodynamic (BOLD) response. While many sensory and motor tasks are associated with relatively simple activation patterns in localized regions, higher-order cognitive tasks may produce activity in many different brain areas involving complex neural circuitry. We applied a recently proposed probabilistic independent component analysis technique (PICA) to determine the true dimensionality of the fMRI data and used EEG localization to identify the common activated patterns (mapped as Brodmann areas) associated with a complex cognitive task like analytic reasoning. Our preliminary study suggests that a hybrid GLM/PICA analysis may reveal additional regions of activation (beyond simple GLM) that are consistent with electroencephalography (EEG) source localization patterns.

  4. 11.74T fMRI of cortical and subcortical visual networks in the rat

    DEFF Research Database (Denmark)

    Bailey, Christopher; Sanganahalli, Basavaraju G.; Siefert, Alyssa;

    Though a predominantly nocturnal animal, the rat has a functional visual system, albeit of low acuity, and has at least a basic form of color vision extending into the UV range. Our aim here was to develop methods to probe this system with both high field fMRI and electrophysiological techniques....... Experimental setups in an imaging spectrometer are not ideally suited for studying the visual pathway of the rodent due to stringent physical constraints imposed by the imaging bore size and strong magnetic field. Here we report a method, applicable to both data acquisition scenarios, for specific...... and reproducible delivery of visual stimuli in fMRI as well as neurophysiology environments. It has the advantage of allowing variation of the stimulus source (e.g. colour of LED) without the need for manipulating the subject in the bore....

  5. Neural activity during production of rorschach responses: An fMRI study.

    Science.gov (United States)

    Giromini, Luciano; Viglione, Donald J; Zennaro, Alessandro; Cauda, Franco

    2017-02-10

    Recently, a lot of effort has been made to ground Rorschach interpretations to their evidence base. To date, however, no studies have yet described, via fMRI, what brain areas get involved when one takes the Rorschach. To fill this gap in the literature, we administered the ten-inkblot stimuli to 26 healthy volunteers during fMRI. Analysis of BOLD signals revealed that, compared to fixating a cross, looking at the Rorschach inkblots while thinking of what they might be associated with higher temporo-occipital and fronto-parietal activations, and with greater activity in some small, sub-cortical regions included in the limbic system. These findings are in line with the traditional conceptualization of the test, as they suggest that taking the Rorschach involves (a) high-level visual processing, (b) top-down as well as bottom-up attentional processes, and (c) perception and processing of emotions and emotional memories.

  6. A novel method for spatio-temporal pattern analysis of brain fMRI data

    Institute of Scientific and Technical Information of China (English)

    LIU Yadong; ZHOU Zongtan; HU Dewen; YAN Lirong; TAN Changlian; WU Daxing; YAO Shuqiao

    2005-01-01

    A novel data processing procedure for fMRI was suggested in this paper, by which spatial and temporal characteristics of stimuli-induced signal dynamic responses can be investigated simultaneously. First the multitaper spectral estimation was utilized to estimate the spectrum of each voxel; the significance of the line frequency components at the interested frequency was tested to detect the task-related cortex areas; the temporal independent component analysis (tICA) was then applied to the activated voxels to obtain stimuli-induced signal dynamic responses. The advantages of this procedure are: few assumptions are needed for the cerebral hemodynamics and spatial distribution of task-related areas, problems which often appear in tICA analysis of fMRI data, such as the lack of stability, reliability and robustness, are overcome by the suggested method.

  7. A New General Linear Convolution Model for fMRI Data Process

    Institute of Scientific and Technical Information of China (English)

    YUAN Hong; CHEN Hua-fu; YAO De-zhong

    2005-01-01

    General linear model (GLM) is the most popular method for functional magnetic resource imaging (fMRI) data analysis. However, its theory is imperfect. The key of this model is how to constitute the design-matrix to model the interesting effects better and separate noises better. For the purpose of detecting brain function activation, according to the principle of GLM, a new convolution model is presented by a new dynamic function convolving with design-matrix, which combining with t-test can be used to detect brain active signal. The fMRI imaging result of visual stimulus experiment indicates that brain activities mainly concentrate among vland v2 areas of visual cortex, and also verified the validity of this technique.

  8. Time Slice Analysis Method Based on OTCA Used in fMRI Weak Signal Function Extraction

    Institute of Scientific and Technical Information of China (English)

    LUO Sen-lin; LI Li; ZHANG Xin-li; ZHANG Tie-mei

    2007-01-01

    The original temporal clustering analysis (OTCA) is an effective technique for obtaining brain activation maps when the timing and location of the activation are completely unknown, but its deficiency of sensitivity is exposed in processing brain activation signal which is relatively weak. The time slice analysis method based on OTCA is proposed considering the weakness of the functional magnetic resonance imaging (fMRI) signal of the rat model. By dividing the stimulation period into several time slices and analyzing each slice to detect the activated pixels respectively after the background removal, the sensitivity is significantly improved. The inhibitory response in the hypothalamus after glucose loading is detected successfully with this method in the experiment on rat. Combined with the OTCA method, the time slice analysis method based on OTCA is effective on detecting when, where and which type of response will happen after stimulation, even if the fMRI signal is weak.

  9. Analysis of short single rest/activation epoch fMRI by self-organizing map neural network

    Science.gov (United States)

    Erberich, Stephan G.; Dietrich, Thomas; Kemeny, Stefan; Krings, Timo; Willmes, Klaus; Thron, Armin; Oberschelp, Walter

    2000-04-01

    Functional magnet resonance imaging (fMRI) has become a standard non invasive brain imaging technique delivering high spatial resolution. Brain activation is determined by magnetic susceptibility of the blood oxygen level (BOLD effect) during an activation task, e.g. motor, auditory and visual tasks. Usually box-car paradigms have 2 - 4 rest/activation epochs with at least an overall of 50 volumes per scan in the time domain. Statistical test based analysis methods need a large amount of repetitively acquired brain volumes to gain statistical power, like Student's t-test. The introduced technique based on a self-organizing neural network (SOM) makes use of the intrinsic features of the condition change between rest and activation epoch and demonstrated to differentiate between the conditions with less time points having only one rest and one activation epoch. The method reduces scan and analysis time and the probability of possible motion artifacts from the relaxation of the patients head. Functional magnet resonance imaging (fMRI) of patients for pre-surgical evaluation and volunteers were acquired with motor (hand clenching and finger tapping), sensory (ice application), auditory (phonological and semantic word recognition task) and visual paradigms (mental rotation). For imaging we used different BOLD contrast sensitive Gradient Echo Planar Imaging (GE-EPI) single-shot pulse sequences (TR 2000 and 4000, 64 X 64 and 128 X 128, 15 - 40 slices) on a Philips Gyroscan NT 1.5 Tesla MR imager. All paradigms were RARARA (R equals rest, A equals activation) with an epoch width of 11 time points each. We used the self-organizing neural network implementation described by T. Kohonen with a 4 X 2 2D neuron map. The presented time course vectors were clustered by similar features in the 2D neuron map. Three neural networks were trained and used for labeling with the time course vectors of one, two and all three on/off epochs. The results were also compared by using a

  10. Interplay between functional connectivity and scale-free dynamics in intrinsic fMRI networks.

    Science.gov (United States)

    Ciuciu, Philippe; Abry, Patrice; He, Biyu J

    2014-07-15

    Studies employing functional connectivity-type analyses have established that spontaneous fluctuations in functional magnetic resonance imaging (fMRI) signals are organized within large-scale brain networks. Meanwhile, fMRI signals have been shown to exhibit 1/f-type power spectra - a hallmark of scale-free dynamics. We studied the interplay between functional connectivity and scale-free dynamics in fMRI signals, utilizing the fractal connectivity framework - a multivariate extension of the univariate fractional Gaussian noise model, which relies on a wavelet formulation for robust parameter estimation. We applied this framework to fMRI data acquired from healthy young adults at rest and while performing a visual detection task. First, we found that scale-invariance existed beyond univariate dynamics, being present also in bivariate cross-temporal dynamics. Second, we observed that frequencies within the scale-free range do not contribute evenly to inter-regional connectivity, with a systematically stronger contribution of the lowest frequencies, both at rest and during task. Third, in addition to a decrease of the Hurst exponent and inter-regional correlations, task performance modified cross-temporal dynamics, inducing a larger contribution of the highest frequencies within the scale-free range to global correlation. Lastly, we found that across individuals, a weaker task modulation of the frequency contribution to inter-regional connectivity was associated with better task performance manifesting as shorter and less variable reaction times. These findings bring together two related fields that have hitherto been studied separately - resting-state networks and scale-free dynamics, and show that scale-free dynamics of human brain activity manifest in cross-regional interactions as well.

  11. Preserving Subject Variability in Group fMRI Analysis: Performance Evaluation of GICA versus IVA

    Directory of Open Access Journals (Sweden)

    Andrew eMichael

    2014-06-01

    Full Text Available Independent component analysis (ICA is a widely applied technique to derive functionally connected brain networks from fMRI data. Group ICA (GICA and Independent Vector Analysis (IVA are extensions of ICA that enable users to perform group fMRI analyses; however a full comparison of the performance limits of GICA and IVA has not been investigated. Recent interest in resting state fMRI data with potentially higher degree of subject variability makes the evaluation of the above techniques important. In this paper we compare component estimation accuracies of GICA and an improved version of IVA using simulated fMRI datasets. We systematically change the degree of component spatial variability and evaluate estimation accuracy over all spatial maps (SMs and time courses (TCs of the decomposition. Our results indicate the following: (1 at low levels of SM variability or when just one SM is varied, both GICA and IVA perform well, (2 at higher levels of SM variability or when more than one SMs are varied, IVA continues to perform well but GICA yields SM estimates that are composites of other SMs with errors in TCs, (3 both GICA and IVA remove spatial correlations of overlapping SMs and introduce artificial correlations in their TCs, (4 if number of SMs is over estimated, IVA continues to perform well but GICA introduces artifacts in the varying and extra SMs with artificial correlations in the TCs of extra components, and (5 in the absence or presence of SMs unique to one subject, GICA produces errors in TCs and IVA estimates are accurate. In summary, our simulation experiments (both simplistic and realistic and our holistic analyses approach indicate that IVA produces results that are closer to ground truth and thereby better preserves subject variability. The improved version of IVA is now packaged into the GIFT toolbox (http://mialab.mrn.org/software/gift.

  12. fMRI and MEG in the study of typical and atypical cognitive development.

    Science.gov (United States)

    Taylor, M J; Donner, E J; Pang, E W

    2012-01-01

    The tremendous changes in brain structure over childhood are critical to the development of cognitive functions. Neuroimaging provides a means of linking these brain-behaviour relations, as task protocols can be adapted for use with young children to assess the development of cognitive functions in both typical and atypical populations. This paper reviews some of our research using magnetoencephalography (MEG) and functional MRI (fMRI) in the study of cognitive development, with a focus on frontal lobe functions. Working memory for complex abstract patterns showed clear development in terms of the recruitment of frontal regions, seen with fMRI, with indications of strategy differences across the age range, from 6 to 35 years of age. Right hippocampal involvement was also evident in these n-back tasks, demonstrating its involvement in recognition in simple working memory protocols. Children born very preterm (7 to 9 years of age) showed reduced fMRI activation particularly in the precuneus and right hippocampal regions relative to control children. In a large normative n-back study (n=90) with upright and inverted faces, MEG data also showed right hippocampal activation that was present across the age range; frontal sources were evident only from 10 years of age. Other studies have investigated the development of set shifting, an executive function that is often deficit in atypical populations. fMRI showed recruitment of frontal areas, including the insula, that have significantly different patterns in children (7 to 14 years of age) with autism spectrum disorder compared to typically developing children, indicating that successful performance implicated differing strategies in these two groups of children. These types of studies will help our understanding of both normal brain-behaviour development and cognitive dysfunction in atypically developing populations. Copyright © 2011 Elsevier Masson SAS. All rights reserved.

  13. An investigation of fMRI time series stationarity during motor sequence learning foot tapping tasks.

    Science.gov (United States)

    Muhei-aldin, Othman; VanSwearingen, Jessie; Karim, Helmet; Huppert, Theodore; Sparto, Patrick J; Erickson, Kirk I; Sejdić, Ervin

    2014-04-30

    Understanding complex brain networks using functional magnetic resonance imaging (fMRI) is of great interest to clinical and scientific communities. To utilize advanced analysis methods such as graph theory for these investigations, the stationarity of fMRI time series needs to be understood as it has important implications on the choice of appropriate approaches for the analysis of complex brain networks. In this paper, we investigated the stationarity of fMRI time series acquired from twelve healthy participants while they performed a motor (foot tapping sequence) learning task. Since prior studies have documented that learning is associated with systematic changes in brain activation, a sequence learning task is an optimal paradigm to assess the degree of non-stationarity in fMRI time-series in clinically relevant brain areas. We predicted that brain regions involved in a "learning network" would demonstrate non-stationarity and may violate assumptions associated with some advanced analysis approaches. Six blocks of learning, and six control blocks of a foot tapping sequence were performed in a fixed order. The reverse arrangement test was utilized to investigate the time series stationarity. Our analysis showed some non-stationary signals with a time varying first moment as a major source of non-stationarity. We also demonstrated a decreased number of non-stationarities in the third block as a result of priming and repetition. Most of the current literature does not examine stationarity prior to processing. The implication of our findings is that future investigations analyzing complex brain networks should utilize approaches robust to non-stationarities, as graph-theoretical approaches can be sensitive to non-stationarities present in data. Copyright © 2014 Elsevier B.V. All rights reserved.

  14. Gaussian Process Based Independent Analysis for Temporal Source Separation in fMRI.

    Science.gov (United States)

    Hald, Ditte Høvenhoff; Henao, Ricardo; Winther, Ole

    2017-02-26

    Functional Magnetic Resonance Imaging (fMRI) gives us a unique insight into the processes of the brain, and opens up for analyzing the functional activation patterns of the underlying sources. Task-inferred supervised learning with restrictive assumptions in the regression set-up, restricts the exploratory nature of the analysis. Fully unsupervised independent component analysis (ICA) algorithms, on the other hand, can struggle to detect clear classifiable components on single-subject data. We attribute this shortcoming to inadequate modeling of the fMRI source signals by failing to incorporate its temporal nature. fMRI source signals, biological stimuli and non-stimuli-related artifacts are all smooth over a time-scale compatible with the sampling time (TR). We therefore propose Gaussian process ICA (GPICA), which facilitates temporal dependency by the use of Gaussian process source priors. On two fMRI data sets with different sampling frequency, we show that the GPICA-inferred temporal components and associated spatial maps allow for a more definite interpretation than standard temporal ICA methods. The temporal structures of the sources are controlled by the covariance of the Gaussian process, specified by a kernel function with an interpretable and controllable temporal length scale parameter. We propose a hierarchical model specification, considering both instantaneous and convolutive mixing, and we infer source spatial maps, temporal patterns and temporal length scale parameters by Markov Chain Monte Carlo. A companion implementation made as a plug-in for SPM can be downloaded from https://github.com/dittehald/GPICA.

  15. A new trigemino-nociceptive stimulation model for event-related fMRI.

    Science.gov (United States)

    Stankewitz, A; Voit, H L; Bingel, U; Peschke, C; May, A

    2010-04-01

    Functional imaging of human trigemino-nociceptive processing provides meaningful insights into altered pain processing in head and face pain diseases. Although functional magnetic resonance imaging (fMRI) offers high temporal and spatial resolution, most studies available were done with radioligand-positron emission tomography, as fMRI requires non-magnetic stimulus equipment and fast on-off conditions. We developed a new approach for painful stimulation of the trigeminal nerve that can be implemented within an event-related design using fMRI and aimed to detect increased blood-oxygen-level-dependent (BOLD) signals as surrogate markers of trigeminal pain processing. Using an olfactometer, 20 healthy volunteers received intranasally standardized trigeminal nociceptive stimuli (ammonia gas) as well as olfactory (rose odour) and odorless control stimuli (air puffs). Imaging revealed robust BOLD responses to the trigeminal nociceptive stimulation in cortical and subcortical brain areas known to be involved in pain processing. Focusing on the trigeminal pain pathway, significant activations were observed bilaterally in brainstem areas at the trigeminal nerve entry zone, which are agreeable with the principal trigeminal nuclei. Furthermore, increased signal changes could be detected ipsilaterally at anatomical localization of the trigeminal ganglion and bilaterally in the rostral medulla, which probably represents the spinal trigeminal nuclei. However, brainstem areas involved in the endogenous pain control system that are close to this anatomical localization, such as raphe nuclei, have to be discussed. Our findings suggest that mapping trigeminal pain processing using fMRI with this non-invasive experimental design is feasible and capable of evoking specific activations in the trigeminal nociceptive system. This method will provide an ideal opportunity to study the trigeminal pain system in both health and pathological conditions such as idiopathic headache disorders.

  16. Brain imaging correlates of recovered swallowing after dysphagic stroke: A fMRI and DWI study

    OpenAIRE

    Paul Glad Mihai; Mareile Otto; Martin Domin; Thomas Platz; Shaheen Hamdy; Martin Lotze

    2016-01-01

    Neurogenic dysphagia frequently occurs after stroke and deglutitive aspiration is one of the main reasons for subacute death after stroke. Although promising therapeutic interventions for neurogenic dysphagia are being developed, the functional neuroanatomy of recovered swallowing in this population remains uncertain. Here, we investigated 18 patients post-stroke who recovered from dysphagia using an event related functional magnetic resonance imaging (fMRI) study of swallowing. Patients were...

  17. Identifying Subgroups of Tinnitus Using Novel Resting State fMRI Biomarkers and Cluster Analysis

    Science.gov (United States)

    2016-10-01

    differences. The immediate outcome of the study is furthering our knowledge of neural correlates of tinnitus and of resting-state fMRI as an objective tool to...regularly to UIUC o Ongoing data processing and analysis to monitor data quality o For year 1 (at end of first 12 months) details see next section... poster was presented at the Society for Neurosicence conference in 2016. Schmidt, S. A., Schubel, M., Hirani, A. N., Baryshnikov, Husain, F.T

  18. Classification of autistic individuals and controls using cross-task characterization of fMRI activity

    Directory of Open Access Journals (Sweden)

    Guillaume Chanel

    2016-01-01

    Full Text Available Multivariate pattern analysis (MVPA has been applied successfully to task-based and resting-based fMRI recordings to investigate which neural markers distinguish individuals with autistic spectrum disorders (ASD from controls. While most studies have focused on brain connectivity during resting state episodes and regions of interest approaches (ROI, a wealth of task-based fMRI datasets have been acquired in these populations in the last decade. This calls for techniques that can leverage information not only from a single dataset, but from several existing datasets that might share some common features and biomarkers. We propose a fully data-driven (voxel-based approach that we apply to two different fMRI experiments with social stimuli (faces and bodies. The method, based on Support Vector Machines (SVMs and Recursive Feature Elimination (RFE, is first trained for each experiment independently and each output is then combined to obtain a final classification output. Second, this RFE output is used to determine which voxels are most often selected for classification to generate maps of significant discriminative activity. Finally, to further explore the clinical validity of the approach, we correlate phenotypic information with obtained classifier scores. The results reveal good classification accuracy (range between 69% and 92.3%. Moreover, we were able to identify discriminative activity patterns pertaining to the social brain without relying on a priori ROI definitions. Finally, social motivation was the only dimension which correlated with classifier scores, suggesting that it is the main dimension captured by the classifiers. Altogether, we believe that the present RFE method proves to be efficient and may help identifying relevant biomarkers by taking advantage of acquired task-based fMRI datasets in psychiatric populations.

  19. Decreased sleep duration is associated with increased fMRI responses to emotional faces in children.

    Science.gov (United States)

    Reidy, Brooke L; Hamann, Stephan; Inman, Cory; Johnson, Katrina C; Brennan, Patricia A

    2016-04-01

    In adults and children, sleep loss is associated with affective dysregulation and increased responsivity to negative stimuli. Adult functional neuroimaging (fMRI) studies have demonstrated associations between restricted sleep and neural alterations in the amygdala and reward circuitry when viewing emotional picture and face stimuli. Despite this, few studies have examined the associations between short sleep duration and emotional responsivity in typically developing children, and no studies have investigated this relationship using fMRI. The current study examined the relationship between sleep duration and fMRI activation to emotional facial expressions in 15 male children (ages 7-11 years). During fMRI scanning, subjects viewed and made perceptual judgments regarding negative, neutral, and positive emotional faces. Maternal reported child sleep duration was negatively associated with (a) activation in the bilateral amygdala, left insula, and left temporal pole activation when viewing negative (i.e., fearful, disgust) vs. neutral faces, (b) right orbitofrontal and bilateral prefrontal activation when viewing disgust vs. neutral faces, and (c) bilateral orbitofrontal, right anterior cingulate, and left amygdala activation when viewing happy vs. neutral faces. Consistent with our prediction, we also noted that emotion-dependent functional connectivity between the bilateral amygdala and prefrontal cortex, cingulate, fusiform, and occipital cortex was positively associated with sleep duration. Paralleling similar studies in adults, these findings collectively suggest that decreased sleep duration in school-aged children may contribute to enhanced reactivity of brain regions involved in emotion and reward processing, as well as decreased emotion-dependent functional connectivity between the amygdala and brain regions associated with emotion regulation.

  20. The spinning dancer illusion and spontaneous brain fluctuations: an fMRI study.

    Science.gov (United States)

    Bernal, Byron; Guillen, Magno; Marquez, Juan Camilo

    2014-01-01

    The brain activation associated with the Spinning Dancer Illusion, a cognitive visual illusion, is not entirely known. Inferences from other study modalities point to the involvement of the dorso-parieto-occipital areas in the spontaneous switchings of perception in other bistable non-kinetic illusions. fMRI is a mature technique used to investigate the brain responses associated with mental changes. Resting-state fMRI is a novel technique that may help ascertain the effects of spontaneous brain changes in the top-down regulation of visual perception. The purpose of this report is to describe the brain activation associated with the subjective illusory changes of perception of a kinetic bistable stimulus. We hypothesize that there is a relationship between the perception phases with the very slow cortical spontaneous fluctuations, recently described. A single normal subject who was trained to produce voluntarily perception phase switches underwent a series of fMRI studies whose blocks were either defined post-hoc or accordingly with a predefined timeline to assess spontaneous and voluntarily evoked visual perception switches, respectively. Correlation of findings with resting-state fMRI and independent component analysis of the task series was sought. Phases of the rotation direction were found associated with right parietal activity. Independent component analysis of the task series and their comparison with basal resting-state components suggest that this activity is related to one of the very slow spontaneous brain fluctuations. The spontaneous fluctuations of the cortical activity may explain the subjective changes in perception of direction of the Spinning Dancer Illusion. This observation is a proof-of-principle, suggesting that the spontaneous brain oscillations may influence top-down sensory regulation.

  1. Knowing Who’s Boss: fMRI and ERP Investigations of Social Dominance Perception

    OpenAIRE

    Chiao, Joan Y.; Adams, Reginald B.; Tse, Peter U.; Lowenthal, Lowenthal; Richeson, Jennifer A.; Ambady, Nalini

    2008-01-01

    Humans use facial cues to convey social dominance and submission. Despite the evolutionary importance of this social ability, how the brain recognizes social dominance from the face is unknown. We used event-related brain potentials (ERP) and functional magnetic resonance imaging (fMRI) to examine the neural mechanisms underlying social dominance perception from facial cues. Participants made gender judgments while viewing aggression-related facial expressions as well as facial postures conve...

  2. Residual fMRI sensitivity for identity changes in acquired prosopagnosia

    OpenAIRE

    Fox, Christopher J; Giuseppe eIaria; Duchaine, Bradley C.; Barton, Jason J.S.

    2013-01-01

    While a network of cortical regions contribute to face processing, the lesions in acquired prosopagnosia are highly variable, and likely result in different combinations of spared and affected regions of this network. To assess the residual functional sensitivities of spared regions in prosopagnosia, we designed a rapid event-related functional magnetic resonance imaging (fMRI) experiment that included pairs of faces with same or different identities and same or different expressions. By meas...

  3. Stability of fMRI striatal response to alcohol cues: a hierarchical linear modeling approach.

    Science.gov (United States)

    Schacht, Joseph P; Anton, Raymond F; Randall, Patrick K; Li, Xingbao; Henderson, Scott; Myrick, Hugh

    2011-05-01

    In functional magnetic resonance imaging (fMRI) studies of alcohol-dependent individuals, alcohol cues elicit activation of the ventral and dorsal aspects of the striatum (VS and DS), which are believed to underlie aspects of reward learning critical to the initiation and maintenance of alcohol dependence. Cue-elicited striatal activation may represent a biological substrate through which treatment efficacy may be measured. However, to be useful for this purpose, VS or DS activation must first demonstrate stability across time. Using hierarchical linear modeling (HLM), this study tested the stability of cue-elicited activation in anatomically and functionally defined regions of interest in bilateral VS and DS. Nine non-treatment-seeking alcohol-dependent participants twice completed an alcohol cue reactivity task during two fMRI scans separated by 14 days. HLM analyses demonstrated that, across all participants, alcohol cues elicited significant activation in each of the regions of interest. At the group level, these activations attenuated slightly between scans, but session-wise differences were not significant. Within-participants stability was best in the anatomically defined right VS and DS and in a functionally defined region that encompassed right caudate and putamen (intraclass correlation coefficients of .75, .81, and .76, respectively). Thus, within this small sample, alcohol cue-elicited fMRI activation had good reliability in the right striatum, though a larger sample is necessary to ensure generalizability and further evaluate stability. This study also demonstrates the utility of HLM analytic techniques for serial fMRI studies, in which separating within-participants variance (individual changes in activation) from between-participants factors (time or treatment) is critical. Copyright © 2011 Elsevier Inc. All rights reserved.

  4. Neural Correlates of the Poggendorff Illusion Driven by Illusory Contour: An fMRI Study

    OpenAIRE

    Qi Chen; Li Li

    2011-01-01

    The Poggendorff illusion is a well-documented geometric illusion that involves the brain's perception of the interaction between oblique lines and object contours: an oblique line is apparently misaligned once interrupted by two parallel contours. This illusion occurs even when the parallel contours are defined subjectively or illusorily. In this fMRI study, we adopted a 4 (type of stimuli: Poggendorff illusion under real contour and its corresponding control condition; Poggendorff illusion u...

  5. Effective Connectivity Modeling for fMRI: Six Issues and Possible Solutions Using Linear Dynamic Systems

    Directory of Open Access Journals (Sweden)

    Jason Fitzgerald Smith

    2012-01-01

    Full Text Available Analysis of directionally specific or causal interactions between regions in functional magnetic resonance imaging (fMRI data has proliferated. Here we identify six issues with existing effective connectivity methods that need addressed. The issues are discussed within the framework of Linear Dynamic Systems for fMRI (LDSf. The first concerns the use of deterministic models to identify inter-regional effective connectivity. We show that deterministic dynamics are incapable of identifying the trial-to-trial variability typically investigated as the marker of connectivity while stochastic models can capture this variability. The second concerns the simplistic (constant connectivity modeled by most methods. Connectivity parameters of the LDSf model can vary at the same timescale as the input data. Further, extending LDSf to mixtures of multiple models provides more robust connectivity variation. The third concerns the correct identification of the network itself including the number and anatomical origin of the network nodes. Augmentation of the LDSf state space can identify additional nodes of a network. The fourth concerns the locus of the signal used as a node in a network. A novel extension LDSf incorporating sparse canonical correlations can select most relevant voxels from an anatomically defined region based on connectivity. The fifth concerns connection interpretation. Individual parameter differences have received most attention. We present alternative network descriptors of connectivity changes which consider the whole network. The sixth concerns the temporal resolution of fMRI data relative to the timescale of the inter-regional interactions in the brain. LDSf includes an instantaneous connection term to capture connectivity occurring at timescales faster than the data resolution. The LDS framework can also be extended to statistically combine fMRI and EEG data. The LDSf framework is a promising foundation for effective connectivity

  6. Infraslow LFP correlates to resting-state fMRI BOLD signals

    OpenAIRE

    2013-01-01

    The slow fluctuations of the blood-oxygenation-level dependent (BOLD) signal in resting-state fMRI are widely utilized as a surrogate marker of ongoing neural activity. Spontaneous neural activity includes a broad range of frequencies, from infraslow (< 0.5 Hz) fluctuations to fast action potentials. Recent studies have demonstrated a correlative relationship between the BOLD fluctuations and power modulations of the local field potential (LFP), particularly in the gamma band. However, the re...

  7. Support Vector Machine Learning-based fMRI Data Group Analysis*

    OpenAIRE

    Wang, Ze; Childress, Anna R.; Wang, Jiongjiong; Detre, John A.

    2007-01-01

    To explore the multivariate nature of fMRI data and to consider the inter-subject brain response discrepancies, a multivariate and brain response model-free method is fundamentally required. Two such methods are presented in this paper by integrating a machine learning algorithm, the support vector machine (SVM), and the random effect model. Without any brain response modeling, SVM was used to extract a whole brain spatial discriminance map (SDM), representing the brain response difference be...

  8. Relating resting-state fMRI and EEG whole-brain connectomes across frequency bands

    Science.gov (United States)

    Deligianni, Fani; Centeno, Maria; Carmichael, David W.; Clayden, Jonathan D.

    2014-01-01

    Whole brain functional connectomes hold promise for understanding human brain activity across a range of cognitive, developmental and pathological states. So called resting-state (rs) functional MRI studies have contributed to the brain being considered at a macroscopic scale as a set of interacting regions. Interactions are defined as correlation-based signal measurements driven by blood oxygenation level dependent (BOLD) contrast. Understanding the neurophysiological basis of these measurements is important in conveying useful information about brain function. Local coupling between BOLD fMRI and neurophysiological measurements is relatively well defined, with evidence that gamma (range) frequency EEG signals are the closest correlate of BOLD fMRI changes during cognitive processing. However, it is less clear how whole-brain network interactions relate during rest where lower frequency signals have been suggested to play a key role. Simultaneous EEG-fMRI offers the opportunity to observe brain network dynamics with high spatio-temporal resolution. We utilize these measurements to compare the connectomes derived from rs-fMRI and EEG band limited power (BLP). Merging this multi-modal information requires the development of an appropriate statistical framework. We relate the covariance matrices of the Hilbert envelope of the source localized EEG signal across bands to the covariance matrices derived from rs-fMRI with the means of statistical prediction based on sparse Canonical Correlation Analysis (sCCA). Subsequently, we identify the most prominent connections that contribute to this relationship. We compare whole-brain functional connectomes based on their geodesic distance to reliably estimate the performance of the prediction. The performance of predicting fMRI from EEG connectomes is considerably better than predicting EEG from fMRI across all bands, whereas the connectomes derived in low frequency EEG bands resemble best rs-fMRI connectivity. PMID:25221467

  9. Classification of autistic individuals and controls using cross-task characterization of fMRI activity.

    Science.gov (United States)

    Chanel, Guillaume; Pichon, Swann; Conty, Laurence; Berthoz, Sylvie; Chevallier, Coralie; Grèzes, Julie

    2016-01-01

    Multivariate pattern analysis (MVPA) has been applied successfully to task-based and resting-based fMRI recordings to investigate which neural markers distinguish individuals with autistic spectrum disorders (ASD) from controls. While most studies have focused on brain connectivity during resting state episodes and regions of interest approaches (ROI), a wealth of task-based fMRI datasets have been acquired in these populations in the last decade. This calls for techniques that can leverage information not only from a single dataset, but from several existing datasets that might share some common features and biomarkers. We propose a fully data-driven (voxel-based) approach that we apply to two different fMRI experiments with social stimuli (faces and bodies). The method, based on Support Vector Machines (SVMs) and Recursive Feature Elimination (RFE), is first trained for each experiment independently and each output is then combined to obtain a final classification output. Second, this RFE output is used to determine which voxels are most often selected for classification to generate maps of significant discriminative activity. Finally, to further explore the clinical validity of the approach, we correlate phenotypic information with obtained classifier scores. The results reveal good classification accuracy (range between 69% and 92.3%). Moreover, we were able to identify discriminative activity patterns pertaining to the social brain without relying on a priori ROI definitions. Finally, social motivation was the only dimension which correlated with classifier scores, suggesting that it is the main dimension captured by the classifiers. Altogether, we believe that the present RFE method proves to be efficient and may help identifying relevant biomarkers by taking advantage of acquired task-based fMRI datasets in psychiatric populations.

  10. Relating resting-state fMRI and EEG whole-brain connectomes across frequency bands

    Directory of Open Access Journals (Sweden)

    Fani eDeligianni

    2014-08-01

    Full Text Available Whole brain functional connectomes hold promise for understanding human brain activity across a range of cognitive, developmental and pathological states. So called ‘resting-state’ (rs functional MRI studies have contributed to the brain being considered at a macroscopic scale as a set of interacting regions. Interactions are defined as correlation-based signal measurements driven by blood oxygenation level dependent (BOLD contrast. Understanding the neurophysiological basis of these measurements is important in conveying useful information about brain function. Local coupling between BOLD fMRI and neurophysiological measurements is relatively well defined, with evidence that gamma (range frequency EEG signals are the closest correlate of BOLD fMRI changes during cognitive processing. However, it is less clear how whole-brain network interactions relate during rest where lower frequency signals have been suggested to play a key role. Simultaneous EEG-fMRI offers the opportunity to observe brain network dynamics with high spatio-temporal resolution. We utilize these measurements to compare the connectomes derived from rs-fMRI and EEG band limited power (BLP. Merging this multi-modal information requires the development of an appropriate statistical framework. We relate the covariance matrices of the Hilbert envelope of the source localised EEG signal across bands to the covariance matrices derived from rs-fMRI with the means of statistical prediction based on sparse Canonical Correlation Analysis (sCCA. Subsequently, we identify the most prominent connections that contribute to this relationship. We compare whole-brain functional connectomes based on their geodesic distance to reliably estimate the performance of the prediction. The performance of predicting fMRI from EEG connectomes is considerably better than predicting EEG from fMRI across all bands, whereas the connectomes derived in low frequency EEG bands resemble best rs

  11. Enhanced emotional reactivity after selective REM sleep deprivation in humans: an fMRI study

    OpenAIRE

    Rosales-Lagarde, Alejandra; Jorge L Armony; del Río-Portilla, Yolanda; Trejo-Martínez, David; Conde, Ruben; Corsi-Cabrera, Maria

    2012-01-01

    Converging evidence from animal and human studies suggest that rapid eye movement (REM) sleep modulates emotional processing. The aim of the present study was to explore the effects of selective REM sleep deprivation (REM-D) on emotional responses to threatening visual stimuli and their brain correlates using functional magnetic resonance imaging (fMRI). Twenty healthy subjects were randomly assigned to two groups: selective REM-D, by awakening them at each REM sleep onset, or non-rapid eye m...

  12. Enhanced emotional reactivity after selective REM sleep deprivation in humans: an fMRI study

    OpenAIRE

    Alejandra eRosales-Lagarde; Jorge L Armony; Yolanda edel Río-Portilla; David eTrejo-Martínez; Ruben eConde; Maria eCorsi-Cabrera

    2012-01-01

    Converging evidence from animal and human studies suggest that REM sleep modulates emotional processing. The aim of the present study was to explore the effects of selective REM sleep deprivation on emotional responses to threatening visual stimuli and their brain correlates using functional magnetic resonance imaging (fMRI). Twenty healthy subjects were randomly assigned to two groups: selective REM sleep deprivation (REM-D), by awakening them at each REM sleep onset, or NREM sleep interrupt...

  13. Eye Dominance Predicts fMRI Signals in Human Retinotopic Cortex

    OpenAIRE

    Mendola, Janine D; Conner, Ian P.

    2006-01-01

    There have been many attempts to define eye dominance in normal subjects, but limited consensus exists, and relevant physiological data is scarce. In this study, we consider two different behavioral methods for assignment of eye dominance, and how well they predict fMRI signals evoked by monocular stimulation. Sighting eye dominance was assessed with two standard tests, the Porta Test, and a ‘hole in hand’ variation of the Miles Test. Acuity dominance was tested with a standard eye chart and ...

  14. Evaluation of Multiband EPI Acquisitions for Resting State fMRI.

    Directory of Open Access Journals (Sweden)

    Christine Preibisch

    Full Text Available Functional magnetic resonance imaging (fMRI and particularly resting state fMRI (rs-fMRI is widely used to investigate resting state brain networks (RSNs on the systems level. Echo planar imaging (EPI is the state-of-the-art imaging technique for most fMRI studies. Therefore, improvements of EPI might lead to increased sensitivity for a large amount of studies performed every day. A number of developments to shorten acquisition time have been recently proposed and the multiband technique, allowing the simultaneous acquisition of multiple slices yielding an equivalent reduction of measurement time, is the most promising among them. While the prospect to significantly reduce acquisition time by means of high multiband acceleration factors (M appears tempting, signal quality parameters and the sensitivity to detect common RSNs with increasing M-factor have only been partially investigated up to now. In this study, we therefore acquired rs-fMRI data from 20 healthy volunteers to systematically investigate signal characteristics and sensitivity for brain network activity in datasets with increasing M-factor, M = 2 - 4. Combined with an inplane, sensitivity encoding (SENSE, acceleration factor, S = 2, we applied a maximal acceleration factor of 8 (S2×M4. Our results suggest that an M-factor of 2 (total acceleration of 4 only causes negligible SNR decrease but reveals common RSN with increased sensitivity and stability. Further M-factor increase produced random artifacts as revealed by signal quality measures that may affect interpretation of RSNs under common scanning conditions. Given appropriate hardware, a mb-EPI sequence with a total acceleration of 4 significantly reduces overall scanning time and clearly increases sensitivity to detect common RSNs. Together, our results suggest mb-EPI at moderate acceleration factors as a novel standard for fMRI that might increase our understanding of network dynamics in healthy and diseased brains.

  15. Prioritizing spatial accuracy in high-resolution fMRI data using multivariate feature weight mapping

    Directory of Open Access Journals (Sweden)

    Johannes eStelzer

    2014-04-01

    Full Text Available Although ultra-high-field fMRI at field strengths of 7T or above provides substantial gains in BOLD contrast-to-noise ratio, when very high-resolution fMRI is required such gains are inevitably reduced. The improvement in sensitivity provided by multivariate analysis techniques, as compared with univariate methods, then becomes especially welcome. Information mapping approaches are commonly used, such as the searchlight technique, which take into account the spatially distributed patterns of activation in order to predict stimulus conditions. However, the popular searchlight decoding technique, in particular, has been found to be prone to spatial inaccuracies. For instance, the spatial extent of informative areas is generally exaggerated, and their spatial configuration is distorted. We propose the combination of a nonparametric and permutation-based statistical framework with linear classifiers. We term this new combined method Feature Weight Mapping (FWM. The main goal of the proposed method is to map the specific contribution of each voxel to the classification decision while including a correction for the multiple comparisons problem. Next, we compare this new method to the searchlight approach using a simulation and ultra-high-field 7T experimental data. We found that the searchlight method led to spatial inaccuracies that are especially noticeable in high-resolution fMRI data. In contrast, FWM was more spatially precise, revealing both informative anatomical structures as well as the direction by which voxels contribute to the classification. By maximizing the spatial accuracy of ultra-high-field fMRI results, global multivariate methods provide a substantial improvement for characterizing structure-function relationships.

  16. Preserving subject variability in group fMRI analysis: performance evaluation of GICA vs. IVA.

    Science.gov (United States)

    Michael, Andrew M; Anderson, Mathew; Miller, Robyn L; Adalı, Tülay; Calhoun, Vince D

    2014-01-01

    Independent component analysis (ICA) is a widely applied technique to derive functionally connected brain networks from fMRI data. Group ICA (GICA) and Independent Vector Analysis (IVA) are extensions of ICA that enable users to perform group fMRI analyses; however a full comparison of the performance limits of GICA and IVA has not been investigated. Recent interest in resting state fMRI data with potentially higher degree of subject variability makes the evaluation of the above techniques important. In this paper we compare component estimation accuracies of GICA and an improved version of IVA using simulated fMRI datasets. We systematically change the degree of inter-subject spatial variability of components and evaluate estimation accuracy over all spatial maps (SMs) and time courses (TCs) of the decomposition. Our results indicate the following: (1) at low levels of SM variability or when just one SM is varied, both GICA and IVA perform well, (2) at higher levels of SM variability or when more than one SMs are varied, IVA continues to perform well but GICA yields SM estimates that are composites of other SMs with errors in TCs, (3) both GICA and IVA remove spatial correlations of overlapping SMs and introduce artificial correlations in their TCs, (4) if number of SMs is over estimated, IVA continues to perform well but GICA introduces artifacts in the varying and extra SMs with artificial correlations in the TCs of extra components, and (5) in the absence or presence of SMs unique to one subject, GICA produces errors in TCs and IVA estimates are accurate. In summary, our simulation experiments (both simplistic and realistic) and our holistic analyses approach indicate that IVA produces results that are closer to ground truth and thereby better preserves subject variability. The improved version of IVA is now packaged into the GIFT toolbox (http://mialab.mrn.org/software/gift).

  17. A Hybrid LDA+gCCA Model for fMRI Data Classification and Visualization.

    Science.gov (United States)

    Afshin-Pour, Babak; Shams, Seyed-Mohammad; Strother, Stephen

    2015-05-01

    Linear predictive models are applied to functional MRI (fMRI) data to estimate boundaries that predict experimental task states for scans. These boundaries are visualized as statistical parametric maps (SPMs) and range from low to high spatial reproducibility across subjects (e.g., Strother , 2004; LaConte , 2003). Such inter-subject pattern reproducibility is an essential characteristic of interpretable SPMs that generalize across subjects. Therefore, we introduce a flexible hybrid model that optimizes reproducibility by simultaneously enhancing the prediction power and reproducibility. This hybrid model is formed by a weighted summation of the optimization functions of a linear discriminate analysis (LDA) model and a generalized canonical correlation (gCCA) model (Afshin-Pour , 2012). LDA preserves the model's ability to discriminate the fMRI scans of multiple brain states while gCCA finds a linear combination for each subject's scans such that the estimated boundary map is reproducible. The hybrid model is implemented in a split-half resampling framework (Strother , 2010) which provides reproducibility (r) and prediction (p) quality metrics. Then the model was compared with LDA, and Gaussian Naive Bayes (GNB). For simulated fMRI data, the hybrid model outperforms the other two techniques in terms of receiver operating characteristic (ROC) curves, particularly for detecting less predictable but spatially reproducible networks. These techniques were applied to real fMRI data to estimate the maps for two task contrasts. Our results indicate that compared to LDA and GNB, the hybrid model can provide maps with large increases in reproducibility for small reductions in prediction, which are jointly closer to the ideal performance point of (p=1, r=1).

  18. Brain structural deficits and working memory fMRI dysfunction in young adults who were diagnosed with ADHD in adolescence.

    Science.gov (United States)

    Roman-Urrestarazu, Andres; Lindholm, Päivi; Moilanen, Irma; Kiviniemi, Vesa; Miettunen, Jouko; Jääskeläinen, Erika; Mäki, Pirjo; Hurtig, Tuula; Ebeling, Hanna; Barnett, Jennifer H; Nikkinen, Juha; Suckling, John; Jones, Peter B; Veijola, Juha; Murray, Graham K

    2016-05-01

    When adolescents with ADHD enter adulthood, some no longer meet disorder diagnostic criteria but it is unknown if biological and cognitive abnorma lities persist. We tested the hypothesis that people diagnosed with ADHD during adolescence present residual brain abnormalities both in brain structure and in working memory brain function. 83 young adults (aged 20-24 years) from the Northern Finland 1986 Birth Cohort were classified as diagnosed with ADHD in adolescence (adolescence ADHD, n = 49) or a control group (n = 34). Only one patient had received medication for ADHD. T1-weighted brain scans were acquired and processed in a voxel-based analysis using permutation-based statistics. A sub-sample of both groups (ADHD, n = 21; controls n = 23) also performed a Sternberg working memory task whilst acquiring fMRI data. Areas of structural difference were used as a region of interest to evaluate the implications that structural abnormalities found in the ADHD group might have on working memory function. There was lower grey matter volume bilaterally in adolescence ADHD participants in the caudate (p adolescence ADHD participants, with associated failure to show normal load-dependent caudate activation. Young adults diagnosed with ADHD in adolescence have structural and functional deficits in the caudate associated with abnormal working memory function. These findings are not secondary to stimulant treatment, and emphasise the importance of taking a wider perspective on ADHD outcomes than simply whether or not a particular patient meets diagnostic criteria at any given point in time.

  19. Approaches to capture variance differences in rest fMRI networks in the spatial geometric features: Application to schizophrenia

    Directory of Open Access Journals (Sweden)

    Shruti eGopal

    2016-03-01

    Full Text Available Identification of functionally connected regions while at rest has been at the forefront of research focusing on understanding interactions between different brain regions. Studies have utilized a variety of approaches including seed based as well as data-driven approaches to identifying such networks. Most such techniques involve differentiating groups based on group mean measures. There has been little work focused on differences in spatial characteristics of resting fMRI data. We present a method to identify between group differences in the variability in the cluster characteristics of network regions within components estimated via independent vector analysis (IVA. IVA is a blind source separation approach shown to perform well in capturing individual subject variability within a group model. We evaluate performance of the approach using simulations and then apply to a relatively large schizophrenia data set (82 schizophrenia patients and 89 healthy controls. We postulate that group differences in the intra-network distributional characteristics of resting state network voxel intensities might indirectly capture important distinctions between the brain function of healthy and clinical populations. Results demonstrate that specific areas of the brain, superior and middle temporal gyrus that are involved in language and recognition of emotions, show greater component level variance in amplitude weights for schizophrenia patients than healthy controls. Statistically significant correlation between component level spatial variance and component volume was observed in 19 of the 27 non-artifactual components implying an evident relationship between the two parameters. Additionally, the greater spread in the distance of the cluster peak of a component from the centroid in schizophrenia patients compared to healthy controls was observed for seven components. These results indicate that there is hidden potential in exploring variance and possibly

  20. Mapping informative clusters in a hierarchical [corrected] framework of FMRI multivariate analysis.

    Directory of Open Access Journals (Sweden)

    Rui Xu

    Full Text Available Pattern recognition methods have become increasingly popular in fMRI data analysis, which are powerful in discriminating between multi-voxel patterns of brain activities associated with different mental states. However, when they are used in functional brain mapping, the location of discriminative voxels varies significantly, raising difficulties in interpreting the locus of the effect. Here we proposed a hierarchical framework of multivariate approach that maps informative clusters rather than voxels to achieve reliable functional brain mapping without compromising the discriminative power. In particular, we first searched for local homogeneous clusters that consisted of voxels with similar response profiles. Then, a multi-voxel classifier was built for each cluster to extract discriminative information from the multi-voxel patterns. Finally, through multivariate ranking, outputs from the classifiers were served as a multi-cluster pattern to identify informative clusters by examining interactions among clusters. Results from both simulated and real fMRI data demonstrated that this hierarchical approach showed better performance in the robustness of functional brain mapping than traditional voxel-based multivariate methods. In addition, the mapped clusters were highly overlapped for two perceptually equivalent object categories, further confirming the validity of our approach. In short, the hierarchical framework of multivariate approach is suitable for both pattern classification and brain mapping in fMRI studies.

  1. Towards open sharing of task-based fMRI data: The OpenfMRI project

    Directory of Open Access Journals (Sweden)

    Russell A Poldrack

    2013-07-01

    Full Text Available The large-scale sharing of task-based functional neuroimaging data has the potential to allow novel insights into the organization of mental function in the brain, but the field of neuroimaging has lagged behind other areas of bioscience in the development of data sharing resources. This paper describes the OpenFMRI project (accessible online at http://www.openfmri.org, which aims to provide the neuroimaging community with a resource to support open sharing of task-based fMRI studies. We describe the motivation behind the project, focusing particularly on how this project addresses some of the well-known challenges to sharing of task-based fMRI data. Results from a preliminary analysis of the current database are presented, which demonstrate the ability to classify between task contrasts with high generalization accuracy across subjects, and the ability to identify individual subjects from their activation maps with moderately high accuracy. Clustering analyses show that the similarity relations between statistical maps have a somewhat orderly relation to the mental functions engaged by the relevant tasks. These results highlight the potential of the project to support large-scale multivariate analyses of the relation between mental processes and brain function.

  2. A robust independent component analysis (ICA) model for functional magnetic resonance imaging (fMRI) data

    Science.gov (United States)

    Ao, Jingqi; Mitra, Sunanda; Liu, Zheng; Nutter, Brian

    2011-03-01

    The coupling of carefully designed experiments with proper analysis of functional magnetic resonance imaging (fMRI) data provides us with a powerful as well as noninvasive tool to help us understand cognitive processes associated with specific brain regions and hence could be used to detect abnormalities induced by a diseased state. The hypothesisdriven General Linear Model (GLM) and the data-driven Independent Component Analysis (ICA) model are the two most commonly used models for fMRI data analysis. A hybrid ICA-GLM model combines the two models to take advantages of benefits from both models to achieve more accurate mapping of the stimulus-induced activated brain regions. We propose a modified hybrid ICA-GLM model with probabilistic ICA that includes a noise model. In this modified hybrid model, a probabilistic principle component analysis (PPCA)-based component number estimation is used in the ICA stage to extract the intrinsic number of original time courses. In addition, frequency matching is introduced into the time course selection stage, along with temporal correlation, F-test based model fitting estimation, and time course combination, to produce a more accurate design matrix for GLM. A standard fMRI dataset is used to compare the results of applying GLM and the proposed hybrid ICA-GLM in generating activation maps.

  3. A novel approach to analyzing fMRI and SNP data via parallel independent component analysis

    Science.gov (United States)

    Liu, Jingyu; Pearlson, Godfrey; Calhoun, Vince; Windemuth, Andreas

    2007-03-01

    There is current interest in understanding genetic influences on brain function in both the healthy and the disordered brain. Parallel independent component analysis, a new method for analyzing multimodal data, is proposed in this paper and applied to functional magnetic resonance imaging (fMRI) and a single nucleotide polymorphism (SNP) array. The method aims to identify the independent components of each modality and the relationship between the two modalities. We analyzed 92 participants, including 29 schizophrenia (SZ) patients, 13 unaffected SZ relatives, and 50 healthy controls. We found a correlation of 0.79 between one fMRI component and one SNP component. The fMRI component consists of activations in cingulate gyrus, multiple frontal gyri, and superior temporal gyrus. The related SNP component is contributed to significantly by 9 SNPs located in sets of genes, including those coding for apolipoprotein A-I, and C-III, malate dehydrogenase 1 and the gamma-aminobutyric acid alpha-2 receptor. A significant difference in the presences of this SNP component is found between the SZ group (SZ patients and their relatives) and the control group. In summary, we constructed a framework to identify the interactions between brain functional and genetic information; our findings provide new insight into understanding genetic influences on brain function in a common mental disorder.

  4. Brain imaging correlates of recovered swallowing after dysphagic stroke: A fMRI and DWI study

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    Paul Glad Mihai

    2016-01-01

    Full Text Available Neurogenic dysphagia frequently occurs after stroke and deglutitive aspiration is one of the main reasons for subacute death after stroke. Although promising therapeutic interventions for neurogenic dysphagia are being developed, the functional neuroanatomy of recovered swallowing in this population remains uncertain. Here, we investigated 18 patients post-stroke who recovered from dysphagia using an event related functional magnetic resonance imaging (fMRI study of swallowing. Patients were characterized by initial dysphagia score (mild to severe, lesion mapping, white matter fractional anisotropy (FA of the pyramidal tracts, and swallowing performance measurement during fMRI scanning. Eighteen age matched healthy participants served as a control group. Overall, patients showed decreased fMRI-activation in the entire swallowing network apart from an increase of activation in the contralesional primary somatosensory cortex (S1. Moreover, fMRI activation in contralesional S1 correlated with initial dysphagia score. Finally, when lesions of the pyramidal tract were more severe, recovered swallowing appeared to be associated with asymmetric activation of the ipsilesional anterior cerebellum. Taken together, our data support a role for increased contralesional somatosensory resources and ipsilesional anterior cerebellum feed forward loops for recovered swallowing after dysphagia following stroke.

  5. FMRI, antipsychotics and schizophrenia. Influence of different antipsychotics on BOLD-signal.

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    Röder, Christian H; Hoogendam, Janna Marie; van der Veen, Frederik M

    2010-01-01

    In the last decade, functional Magnetic Resonance Imaging (FMRI) has been increasingly used to investigate the neurobiology of schizophrenia. This technique relies on changes in the blood-oxygen-level-dependent (BOLD) - signal, which changes in response to neural activity. Many FMRI studies on schizophrenia have examined medicated patients, but little is known about the effects of antipsychotic medication on the BOLD-signal. In this review we investigated to what extent studies in patients with schizophrenia (SC), who were treated with different antipsychotics, could give insight in the effects of antipsychotics on the BOLD-signal. A PubMed search was performed using the search items "schizophrenia", "FMRI", "antipsychotics" and "schizophrenia", "BOLD", "antipsychotics". Only articles in which there were at least two groups of patients with different treatments or in which patients were scanned twice with different treatments were selected. 18 articles, published between 1999 and 2009, fulfilled these criteria. Paradigms and results of these studies were compared regarding differences induced by the administered antipsychotics. This analysis showed no general effect of antipsychotics on the BOLD-signal. However, there is some evidence that the extent of blockade of the dopamine (DA) D(2) receptor does influence the BOLD-signal. Higher affinity to the dopamine D2 receptor, as expressed by a higher/lower inhibition constant (Ki) seems to cause a decrease in BOLD-signal.

  6. MEG and fMRI fusion for nonlinear estimation of neural and BOLD signal changes

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    Sergey M Plis

    2010-11-01

    Full Text Available The combined analysis of MEG/EEG and functional MRI measurements can lead to improvement in the description of the dynamical and spatial properties of brain activity. In this paper we empirically demonstrate this improvement using simulated and recorded task related MEG and fMRI activity. Neural activity estimates were derived using a dynamic Bayesian network with continuous real valued parameters by means of a sequential Monte Carlo technique. In synthetic data, we show that MEG and fMRI fusion improves estimation of the indirectly observed neural activity and smooths tracking of the BOLD response. In recordings of task related neural activity the combination of MEG and fMRI produces a result with greater SNR, that confirms the expectation arising from the nature of the experiment. The highly nonlinear model of the BOLD response poses a difficult inference problem for neural activity estimation; computational requirements are also high due to the time and space complexity. We show that joint analysis of the data improves the system's behavior by stabilizing the differential equations system and by requiring fewer computational resources.

  7. A 10-year longitudinal fMRI study of narrative comprehension in children and adolescents.

    Science.gov (United States)

    Szaflarski, Jerzy P; Altaye, Mekibib; Rajagopal, Akila; Eaton, Kenneth; Meng, Xiangxiang; Plante, Elena; Holland, Scott K

    2012-11-15

    Comprehension of spoken narratives requires coordination of multiple language skills. As such, for normal children narrative skills develop well into the school years and, during this period, are particularly vulnerable in the face of brain injury or developmental disorder. For these reasons, we sought to determine the developmental trajectory of narrative processing using longitudinal fMRI scanning. 30 healthy children between the ages of 5 and 18 enrolled at ages 5, 6, or 7, were examined annually for up to 10 years. At each fMRI session, children were presented with a set of five, 30s-long, stories containing 9, 10, or 11 sentences designed to be understood by a 5 year old child. fMRI data analysis was conducted based on a hierarchical linear model (HLM) that was modified to investigate developmental changes while accounting for missing data and controlling for factors such as age, linguistic performance and IQ. Performance testing conducted after each scan indicated well above the chance (pdevelopment of this area throughout childhood and teenage years with no apparent plateau, indicates that full maturation of narrative processing skills has not yet occurred and that it may be delayed to early adulthood.

  8. Enhanced sympathetic arousal in response to FMRI scanning correlates with task induced activations and deactivations.

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    Markus Muehlhan

    Full Text Available It has been repeatedly shown that functional magnetic resonance imaging (fMRI triggers distress and neuroendocrine response systems. Prior studies have revealed that sympathetic arousal increases, particularly at the beginning of the examination. Against this background it appears likely that those stress reactions during the scanning procedure may influence task performance and neural correlates. However, the question how sympathetic arousal elicited by the scanning procedure itself may act as a potential confounder of fMRI data remains unresolved today. Thirty-seven scanner naive healthy subjects performed a simple cued target detection task. Levels of salivary alpha amylase (sAA, as a biomarker for sympathetic activity, were assessed in samples obtained at several time points during the lab visit. SAA increased two times, immediately prior to scanning and at the end of the scanning procedure. Neural activation related to motor preparation and timing as well as task performance was positively correlated with the first increase. Furthermore, the first sAA increase was associated with task induced deactivation (TID in frontal and parietal regions. However, these effects were restricted to the first part of the experiment. Consequently, this bias of scanner related sympathetic activation should be considered in future fMRI investigations. It is of particular importance for pharmacological investigations studying adrenergic agents and the comparison of groups with different stress vulnerabilities like patients and controls or adolescents and adults.

  9. POBE: A Computer Program for Optimal Design of Multi-Subject Blocked fMRI Experiments

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    Bärbel Maus

    2014-01-01

    Full Text Available For functional magnetic resonance imaging (fMRI studies, researchers can use multi-subject blocked designs to identify active brain regions for a certain stimulus type of interest. Before performing such an experiment, careful planning is necessary to obtain efficient stimulus effect estimators within the available financial resources. The optimal number of subjects and the optimal scanning time for a multi-subject blocked design with fixed experimental costs can be determined using optimal design methods. In this paper, the user-friendly computer program POBE 1.2 (program for optimal design of blocked experiments, version 1.2 is presented. POBE provides a graphical user interface for fMRI researchers to easily and efficiently design their experiments. The computer program POBE calculates the optimal number of subjects and the optimal scanning time for user specified experimental factors and model parameters so that the statistical efficiency is maximised for a given study budget. POBE can also be used to determine the minimum budget for a given power. Furthermore, a maximin design can be determined as efficient design for a possible range of values for the unknown model parameters. In this paper, the computer program is described and illustrated with typical experimental factors for a blocked fMRI experiment.

  10. An fMRI study on the neural mechanisms of hyperalgesic nocebo effect

    Science.gov (United States)

    Kong, Jian; Gollub, Randy L.; Polich, Ginger; Kirsch, Irving; LaViolette, Peter; Vangel, Mark; Rosen, Bruce; Kaptchuk, Ted J

    2008-01-01

    Summary Previous studies suggest that nocebo effects, sometimes termed “negative placebo effects,” can contribute appreciably to a variety of medical symptoms and adverse events in clinical trials and medical care. In this study, using a within-subject design, we combined fMRI and an expectation / conditioning manipulation model to investigate the neural substrates of nocebo hyperalgesia using heat pain on the right forearm. Thirteen subjects completed the study. Results showed that after administering inert treatment, subjective pain intensity ratings increased significantly more on nocebo regions as compared with the control regions where no expectancy / conditioning manipulation was performed. fMRI analysis of hyperalgesic nocebo responses to identical calibrated noxious stimuli showed signal increases in brain regions including bilateral dorsal ACC, insula, superior temporal gyrus; left frontal and parietal operculum, medial frontal gyrus, orbital prefrontal cortex, superior parietal lobule and hippocampus; right claustrum / putamen, lateral prefrontal gyrus and middle temporal gyrus. Functional connectivity analysis of spontaneous resting-state fMRI data from the same cohort of subjects showed a correlation between two seed regions (frontal operculum and left hippocampus) pain network including bilateral insula, operculum, ACC, and left S1 / M1. In conclusion, we found evidence that nocebo hyperalgesia may be predominantly produced through an affective-cognitive pain pathway (medial pain system) and the left hippocampus may play an important role in this process. PMID:19052227

  11. Cognitive dissonance induction in everyday life: An fMRI study.

    Science.gov (United States)

    de Vries, Jan; Byrne, Mark; Kehoe, Elizabeth

    2015-01-01

    This functional magnetic resonance imaging (fMRI) study explored the neural substrates of cognitive dissonance during dissonance "induction." A novel task was developed based on the results of a separate item selection study (n = 125). Items were designed to generate dissonance by prompting participants to reflect on everyday personal experiences that were inconsistent with values they had expressed support for. One experimental condition (dissonance) and three control conditions (justification, consonance, and non-self-related inconsistency) were used for comparison. Items of all four types were presented to each participant (n = 14) in a randomized design. The fMRI analysis used a whole-brain approach focusing on the moments dissonance was induced. Results showed that in comparison with the control conditions the dissonance experience led to higher levels of activation in several brain regions. Specifically dissonance was associated with increased neural activation in key brain regions including the anterior cingulate cortex (ACC), anterior insula, inferior frontal gyrus, and precuneus. This supports current perspectives that emphasize the role of anterior cingulate and insula in dissonance processing. Less extensive activation in the prefrontal cortex than in some previous studies is consistent with this study's emphasis on dissonance induction, rather than reduction. This article also contains a short review and comparison with other fMRI studies of cognitive dissonance.

  12. PyMVPA: A Python toolbox for multivariate pattern analysis of fMRI data

    Science.gov (United States)

    Hanke, Michael; Halchenko, Yaroslav O.; Sederberg, Per B.; Hanson, Stephen José; Haxby, James V.; Pollmann, Stefan

    2009-01-01

    Decoding patterns of neural activity onto cognitive states is one of the central goals of functional brain imaging. Standard univariate fMRI analysis methods, which correlate cognitive and perceptual function with the blood oxygenation-level dependent (BOLD) signal, have proven successful in identifying anatomical regions based on signal increases during cognitive and perceptual tasks. Recently, researchers have begun to explore new multivariate techniques that have proven to be more flexible, more reliable, and more sensitive than standard univariate analysis. Drawing on the field of statistical learning theory, these new classifier-based analysis techniques possess explanatory power that could provide new insights into the functional properties of the brain. However, unlike the wealth of software packages for univariate analyses, there are few packages that facilitate multivariate pattern classification analyses of fMRI data. Here we introduce a Python-based, cross-platform, and open-source software toolbox, called PyMVPA, for the application of classifier-based analysis techniques to fMRI datasets. PyMVPA makes use of Python's ability to access libraries written in a large variety of programming languages and computing environments to interface with the wealth of existing machine-learning packages. We present the framework in this paper and provide illustrative examples on its usage, features, and programmability. PMID:19184561

  13. Area summation in human visual system: psychophysics, fMRI, and modeling.

    Science.gov (United States)

    Nurminen, Lauri; Kilpeläinen, Markku; Laurinen, Pentti; Vanni, Simo

    2009-11-01

    Contextual modulation is a fundamental feature of sensory processing, both on perceptual and on single-neuron level. When the diameter of a visual stimulus is increased, the firing rate of a cell typically first increases (summation field) and then decreases (surround field). Such an area summation function draws a comprehensive profile of the receptive field structure of a neuron, including areas outside the classical receptive field. We investigated area summation in human vision with psychophysics and functional magnetic resonance imaging (fMRI). The stimuli were drifting sine wave gratings similar to those used in previous macaque single-cell area summation studies [corrected]. A model was developed to facilitate comparison of area summation in fMRI to area summation in psychophysics and single cells. The model consisted of units with an antagonistic receptive field structure found in single cells in the primary visual cortex. The receptive field centers of the model neurons were distributed in the region of the visual field covered by a single voxel. The measured area summation functions were qualitatively similar to earlier single-cell data. The model with parameters derived from psychophysics captured the spatial structure of the summation field in the primary visual cortex as measured with fMRI. The model also generalized to a novel situation in which the neural population was displaced from the stimulus center. The current study shows that contextual modulation arises from similar spatially antagonistic and overlapping excitatory and inhibitory mechanisms, both in single cells and in human vision.

  14. Probing the Interoceptive Network by Listening to Heartbeats: An fMRI Study.

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    Nina I Kleint

    Full Text Available Exposure to cues of homeostatic relevance (i.e. heartbeats is supposed to increase the allocation of attentional resources towards the cue, due to its importance for self-regulatory, interoceptive processes. This functional magnetic resonance imaging (fMRI study aimed at determining whether listening to heartbeats is accompanied by activation in brain areas associated with interoception, particularly the insular cortex. Brain activity was measured with fMRI during cue-exposure in 36 subjects while listening to heartbeats vs. sinus tones. Autonomic markers (skin conductance and subjective measures of state and trait anxiety were assessed. Stimulation with heartbeat sounds triggered activation in brain areas commonly associated with the processing of interoceptive information, including bilateral insular cortices, the inferior frontal operculum, and the middle frontal gyrus. A psychophysiological interaction analysis indicated a functional connectivity between the middle frontal gyrus (seed region and bilateral insular cortices, the left amygdala and the supplementary motor area. The magnitude of neural activation in the right anterior insular cortex was positively associated with autonomic arousal. The present findings indicate that listening to heartbeats induced activity in areas of the interoception network as well as changes in psychophysiological arousal and subjective emotional experience. As this approach constitutes a promising method for studying interoception in the fMRI environment, a clinical application in anxiety prone populations should be addressed by future studies.

  15. Probing the Interoceptive Network by Listening to Heartbeats: An fMRI Study.

    Science.gov (United States)

    Kleint, Nina I; Wittchen, Hans-Ulrich; Lueken, Ulrike

    2015-01-01

    Exposure to cues of homeostatic relevance (i.e. heartbeats) is supposed to increase the allocation of attentional resources towards the cue, due to its importance for self-regulatory, interoceptive processes. This functional magnetic resonance imaging (fMRI) study aimed at determining whether listening to heartbeats is accompanied by activation in brain areas associated with interoception, particularly the insular cortex. Brain activity was measured with fMRI during cue-exposure in 36 subjects while listening to heartbeats vs. sinus tones. Autonomic markers (skin conductance) and subjective measures of state and trait anxiety were assessed. Stimulation with heartbeat sounds triggered activation in brain areas commonly associated with the processing of interoceptive information, including bilateral insular cortices, the inferior frontal operculum, and the middle frontal gyrus. A psychophysiological interaction analysis indicated a functional connectivity between the middle frontal gyrus (seed region) and bilateral insular cortices, the left amygdala and the supplementary motor area. The magnitude of neural activation in the right anterior insular cortex was positively associated with autonomic arousal. The present findings indicate that listening to heartbeats induced activity in areas of the interoception network as well as changes in psychophysiological arousal and subjective emotional experience. As this approach constitutes a promising method for studying interoception in the fMRI environment, a clinical application in anxiety prone populations should be addressed by future studies.

  16. Four-dimensional compression of fMRI using JPEG2000

    Science.gov (United States)

    Lalgudi, Hariharan G.; Bilgin, Ali; Marcellin, Michael W.; Tabesh, Ali; Nadar, Mariappan S.; Trouard, Theodore P.

    2005-04-01

    Many medical imaging techniques available today generate 4D data sets. One such technique is functional magnetic resonance imaging (fMRI) which aims to determine regions of the brain that are activated due to various cognitive and/or motor functions or sensory stimuli. These data sets often require substantial resources for storage and transmission and hence call for efficient compression algorithms. fMRI data can be seen as a time-series of 3D images of the brain. Many different strategies can be employed for compressing such data. One possibility is to treat each 2D slice independently. Alternatively, it is also possible to compress each 3D image independently. Such methods do not fully exploit the redundancy present in 4D data. In this work, methods using 4D wavelet transforms are proposed. They are compared to different 2D and 3D methods. The proposed schemes are based on JPEG2000, which is included in the DICOM standard as a transfer syntax. Methodologies to test the effects of lossy compression on the end result of fMRI analysis are introduced and used to compare different compression algorithms.

  17. The effect of fMRI task combinations on determining the hemispheric dominance of language functions

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    Niskanen, Eini [University of Eastern Finland, Department of Applied Physics, Kuopio (Finland); Kuopio University Hospital, Department of Clinical Radiology, Kuopio (Finland); Koenoenen, Mervi [Kuopio University Hospital, Department of Clinical Radiology, Kuopio (Finland); Kuopio University Hospital, Department of Clinical Neurophysiology, Kuopio (Finland); Villberg, Ville; Aeikiae, Marja [Kuopio University Hospital, Department of Neurology, Kuopio (Finland); Nissi, Mikko; Ranta-aho, Perttu; Karjalainen, Pasi [University of Eastern Finland, Department of Applied Physics, Kuopio (Finland); Saeisaenen, Laura; Mervaala, Esa [Kuopio University Hospital, Department of Clinical Neurophysiology, Kuopio (Finland); University of Eastern Finland, Institute of Clinical Medicine, Clinical Neurophysiology, Kuopio (Finland); Kaelviaeinen, Reetta [Kuopio University Hospital, Department of Neurology, Kuopio (Finland); University of Eastern Finland, Institute of Clinical Medicine, Neurology, Kuopio (Finland); Vanninen, Ritva [Kuopio University Hospital, Department of Clinical Radiology, Kuopio (Finland); University of Eastern Finland, Institute of Clinical Medicine, Clinical Radiology, Kuopio (Finland)

    2012-04-15

    The purpose of this study is to establish the most suitable combination of functional magnetic resonance imaging (fMRI) language tasks for clinical use in determining language dominance and to define the variability in laterality index (LI) and activation power between different combinations of language tasks. Activation patterns of different fMRI analyses of five language tasks (word generation, responsive naming, letter task, sentence comprehension, and word pair) were defined for 20 healthy volunteers (16 right-handed). LIs and sums of T values were calculated for each task separately and for four combinations of tasks in predefined regions of interest. Variability in terms of activation power and lateralization was defined in each analysis. In addition, the visual assessment of lateralization of language functions based on the individual fMRI activation maps was conducted by an experienced neuroradiologist. A combination analysis of word generation, responsive naming, and sentence comprehension was the most suitable in terms of activation power, robustness to detect essential language areas, and scanning time. In general, combination analyses of the tasks provided higher overall activation levels than single tasks and reduced the number of outlier voxels disturbing the calculation of LI. A combination of auditory and visually presented tasks that activate different aspects of language functions with sufficient activation power may be a useful task battery for determining language dominance in patients. (orig.)

  18. Adaptive thresholding for reliable topological inference in single subject fMRI analysis

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    Krzysztof eGorgolewski

    2012-08-01

    Full Text Available Single subject fMRI has proved to be a useful tool for mapping functional areas in clinical procedures such as tumour resection. Using fMRI data, clinicians assess the risk, plan and execute such procedures based on thresholded statistical maps. However, because current thresholding methods were developed mainly in the context of cognitive neuroscience group studies, most single subject fMRI maps are thresholded manually to satisfy specific criteria related to single subject analyses. Here, we propose a new adaptive thresholding method which combines Gamma-Gaussian mixture modelling with topological thresholding to improve cluster delineation. In a series of simulations we show that by adapting to the signal and noise properties, the new method performs well in terms of the trade-off between false negative and positive cluster error rates as well as in terms of over and underestimation of the true activation border. We also show through simulations and a motor test-retest study on ten volunteer subjects that adaptive thresholding improves reliability, mainly by accounting for the global signal variance. This in turn increases the likelihood that the true activation pattern can be determined.

  19. Parcellation of fMRI Datasets with ICA and PLS-A Data Driven Approach

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    Ji, Yongnan; Aickelin, Uwe; Pitiot, Alain

    2010-01-01

    Inter-subject parcellation of functional Magnetic Resonance Imaging (fMRI) data based on a standard General Linear Model (GLM)and spectral clustering was recently proposed as a means to alleviate the issues associated with spatial normalization in fMRI. However, for all its appeal, a GLM-based parcellation approach introduces its own biases, in the form of a priori knowledge about the shape of Hemodynamic Response Function (HRF) and task-related signal changes, or about the subject behaviour during the task. In this paper, we introduce a data-driven version of the spectral clustering parcellation, based on Independent Component Analysis (ICA) and Partial Least Squares (PLS) instead of the GLM. First, a number of independent components are automatically selected. Seed voxels are then obtained from the associated ICA maps and we compute the PLS latent variables between the fMRI signal of the seed voxels (which covers regional variations of the HRF) and the principal components of the signal across all voxels. F...

  20. Modality Specific Cerebro-Cerebellar Activations in Verbal Working Memory: An fMRI Study

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    Matthew P. Kirschen

    2010-01-01

    Full Text Available Verbal working memory (VWM engages frontal and temporal/parietal circuits subserving the phonological loop, as well as, superior and inferior cerebellar regions which have projections from these neocortical areas. Different cerebro-cerebellar circuits may be engaged for integrating aurally- and visually-presented information for VWM. The present fMRI study investigated load (2, 4, or 6 letters and modality (auditory and visual dependent cerebro-cerebellar VWM activation using a Sternberg task. FMRI revealed modality-independent activations in left frontal (BA 6/9/44, insular, cingulate (BA 32, and bilateral inferior parietal/supramarginal (BA 40 regions, as well as in bilateral superior (HVI and right inferior (HVIII cerebellar regions. Visual presentation evoked prominent activations in right superior (HVI/CrusI cerebellum, bilateral occipital (BA19 and left parietal (BA7/40 cortex while auditory presentation showed robust activations predominately in bilateral temporal regions (BA21/22. In the cerebellum, we noted a visual to auditory emphasis of function progressing from superior to inferior and from lateral to medial regions. These results extend our previous findings of fMRI activation in cerebro-cerebellar networks during VWM, and demonstrate both modality dependent commonalities and differences in activations with increasing memory load.

  1. Language lateralization in left-handed and ambidextrous people: fMRI data.

    Science.gov (United States)

    Szaflarski, J P; Binder, J R; Possing, E T; McKiernan, K A; Ward, B D; Hammeke, T A

    2002-07-23

    It is generally accepted that most people have left-hemispheric language dominance, though the actual incidence of atypical language distribution in non-right-handed subjects has not been extensively studied. The authors examined language distribution in these subjects and evaluated the relationships between personal handedness, family history of sinistrality, and a language laterality index (LI) measured with fMRI. The authors used whole-brain fMRI to examine 50 healthy, non-right-handed subjects (Edinburgh Handedness Inventory quotient between -100 and 52) while they performed language activation and nonlinguistic control tasks. Counts of active voxels (p left hemispheric in 78% (39/50) of the subjects. Lateralization patterns were similar for all hemispheric ROI. Associations were observed between personal handedness and LI (r = 0.28, p = 0.046), family history of sinistrality and LI (p = 0.031), and age and LI (r = -0.49, p left-handed and ambidextrous subjects is higher than in normal right-handed subjects (22% vs 4-6%). These whole-brain results confirm previous findings in a left-handed cohort studied with fMRI of the lateral frontal lobe. Associations observed between personal handedness and LI and family history of handedness and LI may indicate a common genetic factor underlying the inheritance of handedness and language lateralization.

  2. Women's clitoris, vagina, and cervix mapped on the sensory cortex: fMRI evidence.

    Science.gov (United States)

    Komisaruk, Barry R; Wise, Nan; Frangos, Eleni; Liu, Wen-Ching; Allen, Kachina; Brody, Stuart

    2011-10-01

    The projection of vagina, uterine cervix, and nipple to the sensory cortex in humans has not been reported. The aim of this study was to map the sensory cortical fields of the clitoris, vagina, cervix, and nipple, toward an elucidation of the neural systems underlying sexual response. Using functional magnetic resonance imaging (fMRI), we mapped sensory cortical responses to clitoral, vaginal, cervical, and nipple self-stimulation. For points of reference on the homunculus, we also mapped responses to the thumb and great toe (hallux) stimulation. The main outcome measures used for this study were the fMRI of brain regions activated by the various sensory stimuli. Clitoral, vaginal, and cervical self-stimulation activated differentiable sensory cortical regions, all clustered in the medial cortex (medial paracentral lobule). Nipple self-stimulation activated the genital sensory cortex (as well as the thoracic) region of the homuncular map. The genital sensory cortex, identified in the classical Penfield homunculus based on electrical stimulation of the brain only in men, was confirmed for the first time in the literature by the present study in women applying clitoral, vaginal, and cervical self-stimulation, and observing their regional brain responses using fMRI. Vaginal, clitoral, and cervical regions of activation were differentiable, consistent with innervation by different afferent nerves and different behavioral correlates. Activation of the genital sensory cortex by nipple self-stimulation was unexpected, but suggests a neurological basis for women's reports of its erotogenic quality. © 2011 International Society for Sexual Medicine.

  3. Statistical Analysis of fMRI Time-Series: A Critical Review of the GLM Approach

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    Martin M Monti

    2011-03-01

    Full Text Available Functional Magnetic Resonance Imaging (fMRI is one of the most widely used tools to study the neural underpinnings of human cognition. Standard analysis of fMRI data relies on a General Linear Model (GLM approach to separate stimulus induced signals from noise. Crucially, this approach relies on a number of assumptions about the data which, for inferences to be valid, must be met. The current paper reviews the GLM approach to analysis of fMRI time-series, focusing in particular on the degree to which such data abides by the assumptions of the GLM framework, and on the methods that have been developed to correct for any violation of those assumptions. Rather than biasing estimates of effect size, the major consequence of non-conformity to the assumptions is to introduce bias into estimates of the variance, thus affecting test statistics, power and false positive rates. Furthermore, this bias can have pervasive effects on both individual subject and group-level statistics, potentially yielding qualitatively different results across replications, especially after the thresholding procedures commonly used for inference-making.

  4. Statistical Analysis of fMRI Time-Series: A Critical Review of the GLM Approach.

    Science.gov (United States)

    Monti, Martin M

    2011-01-01

    Functional magnetic resonance imaging (fMRI) is one of the most widely used tools to study the neural underpinnings of human cognition. Standard analysis of fMRI data relies on a general linear model (GLM) approach to separate stimulus induced signals from noise. Crucially, this approach relies on a number of assumptions about the data which, for inferences to be valid, must be met. The current paper reviews the GLM approach to analysis of fMRI time-series, focusing in particular on the degree to which such data abides by the assumptions of the GLM framework, and on the methods that have been developed to correct for any violation of those assumptions. Rather than biasing estimates of effect size, the major consequence of non-conformity to the assumptions is to introduce bias into estimates of the variance, thus affecting test statistics, power, and false positive rates. Furthermore, this bias can have pervasive effects on both individual subject and group-level statistics, potentially yielding qualitatively different results across replications, especially after the thresholding procedures commonly used for inference-making.

  5. Mapping of cognitive functions in chronic intractable epilepsy: Role of fMRI

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    Kapil Chaudhary

    2014-01-01

    Full Text Available Background: Functional magnetic resonance imaging (fMRI, a non-invasive technique with high spatial resolution and blood oxygen level dependent (BOLD contrast, has been applied to localize and map cognitive functions in the clinical condition of chronic intractable epilepsy. Purpose: fMRI was used to map the language and memory network in patients of chronic intractable epilepsy pre- and post-surgery. Materials and Methods: After obtaining approval from the institutional ethics committee, six patients with intractable epilepsy with an equal number of age-matched controls were recruited in the study. A 1.5 T MR scanner with 12-channel head coil, integrated with audio-visual fMRI accessories was used. Echo planar imaging sequence was used for BOLD studies. There were two sessions in TLE (pre- and post-surgery. Results: In TLE patients, BOLD activation increased post-surgery in comparison of pre-surgery in inferior frontal gyrus (IFG, middle frontal gyrus (MFG, and superior temporal gyrus (STG, during semantic lexical, judgment, comprehension, and semantic memory tasks. Conclusion: Functional MRI is useful to study the basic concepts related to language and memory lateralization in TLE and guide surgeons for preservation of important brain areas during ATLR. This will help in understanding future directions for the diagnosis and treatment of such disease.

  6. Memory Deficits in Schizophrenia: A Selective Review of Functional Magnetic Resonance Imaging (fMRI Studies

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    Adrienne C. Lahti

    2013-06-01

    Full Text Available Schizophrenia is a complex chronic mental illness that is characterized by positive, negative and cognitive symptoms. Cognitive deficits are most predictive of long-term outcomes, with abnormalities in memory being the most robust finding. The advent of functional magnetic resonance imaging (fMRI has allowed exploring neural correlates of memory deficits in vivo. In this article, we will give a selective review of fMRI studies probing brain regions and functional networks that are thought to be related to abnormal memory performance in two memory systems prominently affected in schizophrenia; working memory and episodic memory. We revisit the classic “hypofrontality” hypothesis of working memory deficits and explore evidence for frontotemporal dysconnectivity underlying episodic memory abnormalities. We conclude that fMRI studies of memory deficits in schizophrenia are far from universal. However, the current literature does suggest that alterations are not isolated to a few brain regions, but are characterized by abnormalities within large-scale brain networks.

  7. Infraslow LFP correlates to resting-state fMRI BOLD signals.

    Science.gov (United States)

    Pan, Wen-Ju; Thompson, Garth John; Magnuson, Matthew Evan; Jaeger, Dieter; Keilholz, Shella

    2013-07-01

    The slow fluctuations of the blood-oxygenation-level dependent (BOLD) signal in resting-state fMRI are widely utilized as a surrogate marker of ongoing neural activity. Spontaneous neural activity includes a broad range of frequencies, from infraslow (<0.5 Hz) fluctuations to fast action potentials. Recent studies have demonstrated a correlative relationship between the BOLD fluctuations and power modulations of the local field potential (LFP), particularly in the gamma band. However, the relationship between the BOLD signal and the infraslow components of the LFP, which are directly comparable in frequency to the BOLD fluctuations, has not been directly investigated. Here we report a first examination of the temporal relation between the resting-state BOLD signal and infraslow LFPs using simultaneous fMRI and full-band LFP recording in rat. The spontaneous BOLD signal at the recording sites exhibited significant localized correlation with the infraslow LFP signals as well as with the slow power modulations of higher-frequency LFPs (1-100 Hz) at a delay comparable to the hemodynamic response time under anesthesia. Infraslow electrical activity has been postulated to play a role in attentional processes, and the findings reported here suggest that infraslow LFP coordination may share a mechanism with the large-scale BOLD-based networks previously implicated in task performance, providing new insight into the mechanisms contributing to the resting state fMRI signal.

  8. Effect of scanner acoustic background noise on strict resting-state fMRI

    Directory of Open Access Journals (Sweden)

    C. Rondinoni

    2013-04-01

    Full Text Available Functional MRI (fMRI resting-state experiments are aimed at identifying brain networks that support basal brain function. Although most investigators consider a ‘resting-state' fMRI experiment with no specific external stimulation, subjects are unavoidably under heavy acoustic noise produced by the equipment. In the present study, we evaluated the influence of auditory input on the resting-state networks (RSNs. Twenty-two healthy subjects were scanned using two similar echo-planar imaging sequences in the same 3T MRI scanner: a default pulse sequence and a reduced “silent” pulse sequence. Experimental sessions consisted of two consecutive 7-min runs with noise conditions (default or silent counterbalanced across subjects. A self-organizing group independent component analysis was applied to fMRI data in order to recognize the RSNs. The insula, left middle frontal gyrus and right precentral and left inferior parietal lobules showed significant differences in the voxel-wise comparison between RSNs depending on noise condition. In the presence of low-level noise, these areas Granger-cause oscillations in RSNs with cognitive implications (dorsal attention and entorhinal, while during high noise acquisition, these connectivities are reduced or inverted. Applying low noise MR acquisitions in research may allow the detection of subtle differences of the RSNs, with implications in experimental planning for resting-state studies, data analysis, and ergonomic factors.

  9. Independent component analysis in the presence of noise in fMRI.

    Science.gov (United States)

    Cordes, Dietmar; Nandy, Rajesh

    2007-11-01

    A noisy version of independent component analysis (noisy ICA) is applied to simulated and real functional magnetic resonance imaging (fMRI) data. The noise covariance is explicitly modeled by an autoregressive (AR) model of order 1. The unmixing matrix of the data is determined using a variant of the FastICA algorithm based on Gaussian moments. The sources are estimated using the principle of maximum likelihood by modeling the source densities as asymmetric exponential functions. Effect of dimensionality reduction on the effective noise covariance used, accuracy of the obtained mixing matrix and degree of improvement in estimating fMRI sources are investigated. The primary conclusions after using this method of evaluation are as follows: (a) weighting matrix estimates are similar for noisy and conventional ICA in the realm of typical fMRI data, and (b) source estimates are improved by 5% (as measured by the correlation coefficient) in realistic simulated data by explicitly modeling the source densities and the noise, even when just a simple white noise model is used.

  10. Probing the mysterious underpinnings of multi-voxel fMRI analyses.

    Science.gov (United States)

    Op de Beeck, Hans P

    2010-04-01

    Various arguments have been proposed for or against sub-voxel sensitivity or hyperacuity in functional magnetic resonance imaging (fMRI) at standard resolution. Sub-voxel sensitivity might exist, but nevertheless the performance of multi-voxel fMRI analyses is very likely to be dominated by a larger-scale organization, even if this organization is very weak. Up to now, most arguments are indirect in nature: they do not in themselves proof or contradict sub-voxel sensitivity, but they are suggestive, seem consistent or not with sub-voxel sensitivity, or show that the principle might or might not work. Here the previously proposed smoothing argument against hyperacuity is extended with simulations that include more realistic signal, noise, and analysis properties than any of the simulations presented before. These simulations confirm the relevance of the smoothing approach to find out the scale of the functional maps that underlie the outcome of multi-voxel analyses, at least in relative terms (differences in the scale of different maps). However, image smoothing, like most other arguments in the literature, is an indirect argument, and at the end of the day such arguments are not sufficient to decide the issue on whether and how much sub-voxel maps contribute. A few suggestions are made about the type of evidence that is needed to help us understand the as yet mysterious underpinnings of multi-voxel fMRI analyses.

  11. Motor function deficits in schizophrenia: an fMRI and VBM study

    Energy Technology Data Exchange (ETDEWEB)

    Singh, Sadhana; Modi, Shilpi; Kumar, Pawan; Singh, Namita; Khushu, Subash [Institute of Nuclear Medicine and Allied Sciences (INMAS), NMR Research Center, Delhi (India); Goyal, Satnam; Bhatia, Triptish; Deshpande, Smita N. [RML Hospital, PGIMER, New Delhi (India)

    2014-05-15

    To investigate whether the motor functional alterations in schizophrenia (SZ) are also associated with structural changes in the related brain areas using functional magnetic resonance imaging (fMRI) and voxel-based morphometry (VBM). A sample of 14 right-handed SZ patients and 14 right-handed healthy control subjects matched for age, sex, and education were examined with structural high-resolution T1-weighted MRI; fMRI images were obtained during right index finger-tapping task in the same session. fMRI results showed reduced functional activation in the motor areas (contralateral precentral and postcentral gyrus) and ipsilateral cerebellum in SZ subjects as compared to healthy controls (n = 14). VBM analysis also revealed reduced grey matter in motor areas and white matter reduction in cerebellum of SZ subjects as compared to controls. The present study provides an evidence for a possible association between structural alterations in the motor cortex and disturbed functional activation in the motor areas in persons affected with SZ during a simple finger-tapping task. (orig.)

  12. Modality specific cerebro-cerebellar activations in verbal working memory: an fMRI study.

    Science.gov (United States)

    Kirschen, Matthew P; Chen, S H Annabel; Desmond, John E

    2010-01-01

    Verbal working memory (VWM) engages frontal and temporal/parietal circuits subserving the phonological loop, as well as, superior and inferior cerebellar regions which have projections from these neocortical areas. Different cerebro-cerebellar circuits may be engaged for integrating aurally- and visually-presented information for VWM. The present fMRI study investigated load (2, 4, or 6 letters) and modality (auditory and visual) dependent cerebro-cerebellar VWM activation using a Sternberg task. FMRI revealed modality-independent activations in left frontal (BA 6/9/44), insular, cingulate (BA 32), and bilateral inferior parietal/supramarginal (BA 40) regions, as well as in bilateral superior (HVI) and right inferior (HVIII) cerebellar regions. Visual presentation evoked prominent activations in right superior (HVI/CrusI) cerebellum, bilateral occipital (BA19) and left parietal (BA7/40) cortex while auditory presentation showed robust activations predominantly in bilateral temporal regions (BA21/22). In the cerebellum, we noted a visual to auditory emphasis of function progressing from superior to inferior and from lateral to medial regions. These results extend our previous findings of fMRI activation in cerebro-cerebellar networks during VWM, and demonstrate both modality dependent commonalities and differences in activations with increasing memory load.

  13. Graph-based inter-subject pattern analysis of FMRI data.

    Directory of Open Access Journals (Sweden)

    Sylvain Takerkart

    Full Text Available In brain imaging, solving learning problems in multi-subjects settings is difficult because of the differences that exist across individuals. Here we introduce a novel classification framework based on group-invariant graphical representations, allowing to overcome the inter-subject variability present in functional magnetic resonance imaging (fMRI data and to perform multivariate pattern analysis across subjects. Our contribution is twofold: first, we propose an unsupervised representation learning scheme that encodes all relevant characteristics of distributed fMRI patterns into attributed graphs; second, we introduce a custom-designed graph kernel that exploits all these characteristics and makes it possible to perform supervised learning (here, classification directly in graph space. The well-foundedness of our technique and the robustness of the performance to the parameter setting are demonstrated through inter-subject classification experiments conducted on both artificial data and a real fMRI experiment aimed at characterizing local cortical representations. Our results show that our framework produces accurate inter-subject predictions and that it outperforms a wide range of state-of-the-art vector- and parcel-based classification methods. Moreover, the genericity of our method makes it is easily adaptable to a wide range of potential applications. The dataset used in this study and an implementation of our framework are available at http://dx.doi.org/10.6084/m9.figshare.1086317.

  14. Modeling inter-subject variability in fMRI activation location: A Bayesian hierarchical spatial model

    Science.gov (United States)

    Xu, Lei; Johnson, Timothy D.; Nichols, Thomas E.; Nee, Derek E.

    2010-01-01

    Summary The aim of this work is to develop a spatial model for multi-subject fMRI data. There has been extensive work on univariate modeling of each voxel for single and multi-subject data, some work on spatial modeling of single-subject data, and some recent work on spatial modeling of multi-subject data. However, there has been no work on spatial models that explicitly account for inter-subject variability in activation locations. In this work, we use the idea of activation centers and model the inter-subject variability in activation locations directly. Our model is specified in a Bayesian hierarchical frame work which allows us to draw inferences at all levels: the population level, the individual level and the voxel level. We use Gaussian mixtures for the probability that an individual has a particular activation. This helps answer an important question which is not addressed by any of the previous methods: What proportion of subjects had a significant activity in a given region. Our approach incorporates the unknown number of mixture components into the model as a parameter whose posterior distribution is estimated by reversible jump Markov Chain Monte Carlo. We demonstrate our method with a fMRI study of resolving proactive interference and show dramatically better precision of localization with our method relative to the standard mass-univariate method. Although we are motivated by fMRI data, this model could easily be modified to handle other types of imaging data. PMID:19210732

  15. Preoperative therapeutic neuroscience education for lumbar radiculopathy: a single-case fMRI report.

    Science.gov (United States)

    Louw, Adriaan; Puentedura, Emilio J; Diener, Ina; Peoples, Randal R

    2015-01-01

    Therapeutic neuroscience education (TNE) has been shown to be effective in the treatment of mainly chronic musculoskeletal pain conditions. This case study aims to describe the changes in brain activation on functional magnetic resonance imaging (fMRI) scanning, before and after the application of a newly-designed preoperative TNE program. A 30-year-old female with a current acute episode of low back pain (LBP) and radiculopathy participated in a single preoperative TNE session. She completed pre- and post-education measures including visual analog scale (VAS) for LBP and leg pain; Oswestry Disability Index (ODI); Fear Avoidance Beliefs Questionnaire (FABQ); Pain Catastrophizing Scale (PCS) and a series of Likert-scale questions regarding beliefs and attitudes to lumbar surgery (LS). After a 30-minute TNE session, ODI decreased by 10%, PCS decreased by 10 points and her beliefs and attitudes shifted positively regarding LS. Immediately following TNE straight leg raise increased by 7° and forward flexion by 8 cm. fMRI testing following TNE revealed 3 marked differences compared to pre-education scanning: (1) deactivation of the periaqueductal gray area; (2) deactivation of the cerebellum; and (3) increased activation of the motor cortex. The immediate positive fMRI, psychometric and physical movement changes may indicate a cortical mechanism of TNE for patients scheduled for LS.

  16. Imaging artifacts induced by electrical stimulation during conventional fMRI of the brain.

    Science.gov (United States)

    Antal, Andrea; Bikson, Marom; Datta, Abhishek; Lafon, Belen; Dechent, Peter; Parra, Lucas C; Paulus, Walter

    2014-01-15

    Functional magnetic resonance imaging (fMRI) of brain activation during transcranial electrical stimulation is used to provide insight into the mechanisms of neuromodulation and targeting of particular brain structures. However, the passage of current through the body may interfere with the concurrent detection of blood oxygen level-dependent (BOLD) signal, which is sensitive to local magnetic fields. To test whether these currents can affect concurrent fMRI recordings we performed conventional gradient echo-planar imaging (EPI) during transcranial direct current (tDCS) and alternating current stimulation (tACS) on two post-mortem subjects. tDCS induced signals in both superficial and deep structures. The signal was specific to the electrode montage, with the strongest signal near cerebrospinal fluid (CSF) and scalp. The direction of change relative to non-stimulation reversed with tDCS stimulation polarity. For tACS there was no net effect of the MRI signal. High-resolution individualized modeling of current flow and induced static magnetic fields suggested a strong coincidence of the change EPI signal with regions of large current density and magnetic fields. These initial results indicate that (1) fMRI studies of tDCS must consider this potentially confounding interference from current flow and (2) conventional MRI imaging protocols can be potentially used to measure current flow during transcranial electrical stimulation. The optimization of current measurement and artifact correction techniques, including consideration of the underlying physics, remains to be addressed.

  17. PyMVPA: A python toolbox for multivariate pattern analysis of fMRI data.

    Science.gov (United States)

    Hanke, Michael; Halchenko, Yaroslav O; Sederberg, Per B; Hanson, Stephen José; Haxby, James V; Pollmann, Stefan

    2009-01-01

    Decoding patterns of neural activity onto cognitive states is one of the central goals of functional brain imaging. Standard univariate fMRI analysis methods, which correlate cognitive and perceptual function with the blood oxygenation-level dependent (BOLD) signal, have proven successful in identifying anatomical regions based on signal increases during cognitive and perceptual tasks. Recently, researchers have begun to explore new multivariate techniques that have proven to be more flexible, more reliable, and more sensitive than standard univariate analysis. Drawing on the field of statistical learning theory, these new classifier-based analysis techniques possess explanatory power that could provide new insights into the functional properties of the brain. However, unlike the wealth of software packages for univariate analyses, there are few packages that facilitate multivariate pattern classification analyses of fMRI data. Here we introduce a Python-based, cross-platform, and open-source software toolbox, called PyMVPA, for the application of classifier-based analysis techniques to fMRI datasets. PyMVPA makes use of Python's ability to access libraries written in a large variety of programming languages and computing environments to interface with the wealth of existing machine learning packages. We present the framework in this paper and provide illustrative examples on its usage, features, and programmability.

  18. Automated selection of brain regions for real-time fMRI brain-computer interfaces

    Science.gov (United States)

    Lührs, Michael; Sorger, Bettina; Goebel, Rainer; Esposito, Fabrizio

    2017-02-01

    Objective. Brain-computer interfaces (BCIs) implemented with real-time functional magnetic resonance imaging (rt-fMRI) use fMRI time-courses from predefined regions of interest (ROIs). To reach best performances, localizer experiments and on-site expert supervision are required for ROI definition. To automate this step, we developed two unsupervised computational techniques based on the general linear model (GLM) and independent component analysis (ICA) of rt-fMRI data, and compared their performances on a communication BCI. Approach. 3 T fMRI data of six volunteers were re-analyzed in simulated real-time. During a localizer run, participants performed three mental tasks following visual cues. During two communication runs, a letter-spelling display guided the subjects to freely encode letters by performing one of the mental tasks with a specific timing. GLM- and ICA-based procedures were used to decode each letter, respectively using compact ROIs and whole-brain distributed spatio-temporal patterns of fMRI activity, automatically defined from subject-specific or group-level maps. Main results. Letter-decoding performances were comparable to supervised methods. In combination with a similarity-based criterion, GLM- and ICA-based approaches successfully decoded more than 80% (average) of the letters. Subject-specific maps yielded optimal performances. Significance. Automated solutions for ROI selection may help accelerating the translation of rt-fMRI BCIs from research to clinical applications.

  19. Ovarian volume throughout life

    DEFF Research Database (Denmark)

    Kelsey, Thomas W; Dodwell, Sarah K; Wilkinson, A Graham

    2013-01-01

    cancer. To date there is no normative model of ovarian volume throughout life. By searching the published literature for ovarian volume in healthy females, and using our own data from multiple sources (combined n=59,994) we have generated and robustly validated the first model of ovarian volume from...... to about 2.8 mL (95% CI 2.7-2.9 mL) at the menopause and smaller volumes thereafter. Our model allows us to generate normal values and ranges for ovarian volume throughout life. This is the first validated normative model of ovarian volume from conception to old age; it will be of use in the diagnosis...

  20. Which fMRI clustering gives good brain parcellations?

    Directory of Open Access Journals (Sweden)

    Bertrand eThirion

    2014-07-01

    Full Text Available Analysis and interpretation of neuroimaging data often require one to divide the brain into a number of regions, or parcels, with homogeneous characteristics, be these regions defined in the brain volume or on on the cortical surface. While predefined brain atlases do not adapt to the signal in the individual subjects images, parcellation approaches use brain activity (e.g. found in some functional contrasts of interest and clustering techniques to define regions with some degree of signal homogeneity. In this work, we address the question of which clustering technique is appropriate and how to optimize the corresponding model. We use two principled criteria: goodness of fit (accuracy, and reproducibility of the parcellation across bootstrap samples. We study these criteria on both simulated and two task-based functional Magnetic Resonance Imaging datasets for the Ward, spectral and K-means clustering algorithms. We show that in general Ward’s clustering performs better than alternative methods with regards to reproducibility and accuracy and that the two criteria diverge regarding the preferred models (reproducibility leading to more conservative solutions, thus deferring the practical decision to a higher level alternative, namely the choice of a trade-off between accuracy and stability.

  1. A unified framework for group independent component analysis for multi-subject fMRI data.

    Science.gov (United States)

    Guo, Ying; Pagnoni, Giuseppe

    2008-09-01

    Independent component analysis (ICA) is becoming increasingly popular for analyzing functional magnetic resonance imaging (fMRI) data. While ICA has been successfully applied to single-subject analysis, the extension of ICA to group inferences is not straightforward and remains an active topic of research. Current group ICA models, such as the GIFT [Calhoun, V.D., Adali, T., Pearlson, G.D., Pekar, J.J., 2001. A method for making group inferences from functional MRI data using independent component analysis. Hum. Brain Mapp. 14, 140-151.] and tensor PICA [Beckmann, C.F., Smith, S.M., 2005. Tensorial extensions of independent component analysis for multisubject FMRI analysis. Neuroimage 25, 294-311.], make different assumptions about the underlying structure of the group spatio-temporal processes and are thus estimated using algorithms tailored for the assumed structure, potentially leading to diverging results. To our knowledge, there are currently no methods for assessing the validity of different model structures in real fMRI data and selecting the most appropriate one among various choices. In this paper, we propose a unified framework for estimating and comparing group ICA models with varying spatio-temporal structures. We consider a class of group ICA models that can accommodate different group structures and include existing models, such as the GIFT and tensor PICA, as special cases. We propose a maximum likelihood (ML) approach with a modified Expectation-Maximization (EM) algorithm for the estimation of the proposed class of models. Likelihood ratio tests (LRT) are presented to compare between different group ICA models. The LRT can be used to perform model comparison and selection, to assess the goodness-of-fit of a model in a particular data set, and to test group differences in the fMRI signal time courses between subject subgroups. Simulation studies are conducted to evaluate the performance of the proposed method under varying structures of group spatio

  2. Persistency of priors-induced bias in decision behavior and the fMRI signal

    Directory of Open Access Journals (Sweden)

    Kathleen eHansen

    2011-03-01

    Full Text Available It is well known that people take advantage of prior knowledge to bias decisions. To investigate this phenomenon behaviorally and in the brain, we acquired fMRI data while human subjects viewed ambiguous abstract shapes and decided whether a shape was of Category A (smoother or B (bumpier. The decision was made in the context of one of two prior knowledge cues, 80/20 and 50/50. The 80/20 cue indicated that upcoming shapes had an 80% probability of being of one category, e.g. B, and a 20% probability of being of the other. The 50/50 cue indicated that upcoming shapes had an equal probability of being of either category. The ideal observer would bias decisions in favor of the indicated alternative at 80/20 and show zero bias at 50/50. We found that subjects did bias their decisions in the predicted direction at 80/20 but did not show zero bias at 50/50. Instead, at 50/50 the subjects retained biases of the same sign as their 80/20 biases, though of diminished magnitude. The signature of a persistent though diminished bias at 50/50 was also evident in fMRI data from frontal and parietal regions previously implicated in decision-making. As a control, we acquired fMRI data from naïve subjects who experienced only the 50/50 stimulus distributions during both the prescan training and the fMRI experiment. The behavioral and fMRI data from the naïve subjects reflected decision biases closer to those of the ideal observer than those of the prior knowledge subjects at 50/50. The results indicate that practice making decisions in the context of non-equal prior probabilities biases decisions made later when prior probabilities are equal. This finding may be related to the anchoring and adjustment strategy described in the psychology, economics and marketing literatures, in which subjects adjust a first approximation response – the anchor – based on additional information, typically applying insufficient adjustment relative to the ideal observer.

  3. Meta-Analysis of fMRI Studies of Disruptive Behavior Disorders.

    Science.gov (United States)

    Alegria, Analucia A; Radua, Joaquim; Rubia, Katya

    2016-11-01

    Functional magnetic resonance imaging (fMRI) studies in conduct disorder and in oppositional defiant disorder have shown inconsistencies. The aim of this meta-analysis of fMRI studies in disruptive behavior disorders was to establish the most consistent brain dysfunctions and to address task- and subtype-related heterogeneity. Web-based publication databases were searched to conduct a meta-analysis of all whole-brain fMRI studies of youths with disruptive behavior disorder or conduct problems up to August 2015. Sub-meta-analyses were conducted in functional subdomains of emotion processing; in cool and hot executive functions, which refer to goal-directed higher cognitive functions with and without motivational and affective significance; and in a subgroup of youths with additional psychopathic traits. The authors performed a meta-analysis of voxel-based group differences in functional activation using the anisotropic effect-size version of seed-based d mapping. Across 24 studies, 338 youths with disruptive behavior disorder or conduct problems relative to 298 typically developing youths had consistent underactivation in the rostral and dorsal anterior cingulate and in the medial prefrontal cortex and ventral caudate. Sub-meta-analyses of fMRI studies showed that medial fronto-cingulate dysfunction was driven by hot executive function. The sub-meta-analysis of emotion processing fMRI studies showed the most consistent underactivation in the dorsolateral prefrontal cortex and temporal pole, while cool executive functions were associated with temporal abnormalities. Youths with disruptive behavior disorder with psychopathic traits showed reduced ventromedial prefrontal-hypothalamic-limbic activation, but they also showed hyperactivation in cognitive control mediating dorsolateral prefrontal-dorsal and striatal regions. The findings show that the most consistent dysfunction in youths with disruptive behavior disorder is in the rostro-dorsomedial, fronto-cingulate, and

  4. Mean nuclear volume

    DEFF Research Database (Denmark)

    Mogensen, O.; Sørensen, Flemming Brandt; Bichel, P.

    1999-01-01

    We evaluated the following nine parameters with respect to their prognostic value in females with endometrial cancer: four stereologic parameters [mean nuclear volume (MNV), nuclear volume fraction, nuclear index and mitotic index], the immunohistochemical expression of cancer antigen (CA125...

  5. Population Receptive Field Properties from fMRI and Electrocorticography in Striate and Extrastriate Cortex of the Same Subject

    Directory of Open Access Journals (Sweden)

    Ben Mark Harvey

    2012-05-01

    Full Text Available Population receptive field (pRF modelling reconstructs the properties of visually responsive neuronal populations, typically using fMRI in humans. However, fMRI is an indirect measure of neural activity. Electrocorticography (ECoG measures electrical activity directly in humans using subdural electrodes. Here, we model pRF properties using both fMRI and ECoG data from the same subject. Prior to clinical intervention, we recorded fMRI responses to visual field mapping stimuli to determine pRF properties and visual area layout. The same subject subsequently underwent surgery to implant subdural ECoG electrodes and was shown the same visual field mapping stimuli while recording ECoG signals. ECoG data were filtered into different spectral bands, which were analysed separately. ECoG electrodes were localised to V1, MT, LO2, and IPS visual areas. Gamma-band responses allowed pRF modelling in all electrodes, and beta-band responses could also be fit in V1. pRF sizes were similar between ECoG and fMRI models. V1 alpha-band amplitude was highest when the stimulus was in the inhibitory surround of the neural population, although this did not reduce the gamma signal below baseline. IPS, MT, and LO2 alpha amplitude was highest when a blank screen was displayed, which was also found in the IPS beta-band. ECoG recording produces comparable results to fMRI for pRF modelling, providing useful validation and extension of fMRI-based reconstruction of neural pRF properties. The fMRI signal cannot be explained by one ECoG spectral density band alone. Alpha band amplitudes reflect inhibitory signals in V1 and resting-state in extra-striate cortex. The same spectral band can reflect different functional processing depending on cortical location.

  6. Age-related differences in memory-encoding fMRI responses after accounting for decline in vascular reactivity.

    Science.gov (United States)

    Liu, Peiying; Hebrank, Andrew C; Rodrigue, Karen M; Kennedy, Kristen M; Section, Jarren; Park, Denise C; Lu, Hanzhang

    2013-09-01

    BOLD fMRI has provided a wealth of information about the aging brain. A common finding is that posterior regions of the brain manifest an age-related decrease in activation while the anterior regions show an age-related increase. Several neurocognitive models have been proposed to interpret these findings. However, one issue that has not been sufficiently considered to date is that the BOLD signal is based on vascular responses secondary to neural activity. Thus the above findings could be in part due to a vascular change, especially in view of the expected decline of vascular health with age. In the present study, we aim to examine age-related differences in memory-encoding fMRI response in the context of vascular aging. One hundred and thirty healthy subjects ranging from 20 to 89 years old underwent a scene-viewing fMRI task and, in the same session, cerebrovascular reactivity (CVR) was measured in each subject using a CO2-inhalation task. Without accounting for the influence of vascular changes, the task-activated fMRI signal showed the typical age-related decrease in visual cortex and medial temporal lobe (MTL), but manifested an increase in the right inferior frontal gyrus (IFG). In the same individuals, an age-related CVR reduction was observed in all of these regions. We then used a previously proposed normalization approach to calculate a CVR-corrected fMRI signal, which was defined as the uncorrected signal divided by CVR. Based on the CVR-corrected fMRI signal, an age-related increase is now seen in both the left and right sides of IFG; and no brain regions showed a signal decrease with age. We additionally used a model-based approach to examine the fMRI data in the context of CVR, which again suggested an age-related change in the two frontal regions, but not in the visual and MTL regions.

  7. Presurgical motor, somatosensory and language fMRI: Technical feasibility and limitations in 491 patients over 13 years.

    Science.gov (United States)

    Tyndall, Anthony J; Reinhardt, Julia; Tronnier, Volker; Mariani, Luigi; Stippich, Christoph

    2017-01-01

    To analyse the long-term feasibility and limitations of presurgical fMRI in a cohort of tumour and epilepsy patients with different MR-scanners at 1.5 and 3.0 T. Four hundred and ninety-one consecutive patients undergoing presurgical fMRI between 2000 and 2012 on five different MR-scanners using established paradigms and semi-automated data processing were included. Success rates of task performance and BOLD-activation were determined for motor and somatosensory somatotopic mapping and language localisation. Procedural success, failures and imaging artifacts were analysed. MR-field strengths were compared. Two thousand three hundred fifteen of 2348 (98.6 %) attempted paradigms (1033 motor, 1220 speech, 95 somatosensory) were successfully performed. 100 paradigms (4.3 %) were repetition runs. 23 speech, 6 motor and 2 sensory paradigms failed for non-compliance and technical issues. Most language paradigm failures were noted in overt sentence generation. Average significant BOLD-activation was higher for motor than language paradigms (95.8 vs. 81.6 %). Most language paradigms showed significantly higher activation rates at 3 T compared to 1.5 T, whereas no significant difference was found for motor paradigms. fMRI proved very robust for the presurgical localisation of the different motor and somatosensory body representations, as well as Broca's and Wernicke's language areas across different MR-scanners at 1.5 and 3.0 T over 13 years. • Standardised presurgical motor and language fMRI is robust across various MRI platforms. • Motor fMRI is less dependent on field strength than language fMRI. • fMRI task failures are relatively low and are reduced by paradigm repetition.

  8. The effects of orientation and attention during surround suppression of small image features: A 7 Tesla fMRI study.

    Science.gov (United States)

    Schallmo, Michael-Paul; Grant, Andrea N; Burton, Philip C; Olman, Cheryl A

    2016-08-01

    Although V1 responses are driven primarily by elements within a neuron's receptive field, which subtends about 1° visual angle in parafoveal regions, previous work has shown that localized fMRI responses to visual elements reflect not only local feature encoding but also long-range pattern attributes. However, separating the response to an image feature from the response to the surrounding stimulus and studying the interactions between these two responses demands both spatial precision and signal independence, which may be challenging to attain with fMRI. The present study used 7 Tesla fMRI with 1.2-mm resolution to measure the interactions between small sinusoidal grating patches (targets) at 3° eccentricity and surrounds of various sizes and orientations to test the conditions under which localized, context-dependent fMRI responses could be predicted from either psychophysical or electrophysiological data. Targets were presented at 8%, 16%, and 32% contrast while manipulating (a) spatial extent of parallel (strongly suppressive) or orthogonal (weakly suppressive) surrounds, (b) locus of attention, (c) stimulus onset asynchrony between target and surround, and (d) blocked versus event-related design. In all experiments, the V1 fMRI signal was lower when target stimuli were flanked by parallel versus orthogonal context. Attention amplified fMRI responses to all stimuli but did not show a selective effect on central target responses or a measurable effect on orientation-dependent surround suppression. Suppression of the V1 fMRI response by parallel surrounds was stronger than predicted from psychophysics but showed a better match to previous electrophysiological reports.

  9. Topiramate and its effect on fMRI of language in patients with right or left temporal lobe epilepsy.

    Science.gov (United States)

    Szaflarski, Jerzy P; Allendorfer, Jane B

    2012-05-01

    Topiramate (TPM) is well recognized for its negative effects on cognition, language performance and lateralization results on the intracarotid amobarbital procedure (IAP). But, the effects of TPM on functional MRI (fMRI) of language and the fMRI signals are less clear. Functional MRI is increasingly used for presurgical evaluation of epilepsy patients in place of IAP for language lateralization. Thus, the goal of this study was to assess the effects of TPM on fMRI signals. In this study, we included 8 patients with right temporal lobe epilepsy (RTLE) and 8 with left temporal lobe epilepsy (LTLE) taking TPM (+TPM). Matched to them for age, handedness and side of seizure onset were 8 patients with RTLE and 8 with LTLE not taking TPM (-TPM). Matched for age and handedness to the patients with TLE were 32 healthy controls. The fMRI paradigm involved semantic decision/tone decision task (in-scanner behavioral data were collected). All epilepsy patients received a standard neuropsychological language battery. One sample t-tests were performed within each group to assess task-specific activations. Functional MRI data random-effects analysis was performed to determine significant group activation differences and to assess the effect of TPM dose on task activation. Direct group comparisons of fMRI, language and demographic data between patients with R/L TLE +TPM vs. -TPM and the analysis of the effects of TPM on blood oxygenation level-dependent (BOLD) signal were performed. Groups were matched for age, handedness and, within the R/L TLE groups, for the age of epilepsy onset/duration and the number of AEDs/TPM dose. The in-scanner language performance of patients was worse when compared to healthy controls - all pTPM vs. -TPM showed significant fMRI signal differences between groups (increases in left cingulate gyrus and decreases in left superior temporal gyrus in the patients with LTLE +TPM; increases in the right BA 10 and left visual cortex and decreases in the left BA

  10. How does an fMRI voxel sample the neuronal activity pattern: compact-kernel or complex spatiotemporal filter?

    Science.gov (United States)

    Kriegeskorte, Nikolaus; Cusack, Rhodri; Bandettini, Peter

    2010-02-01

    Recent studies suggested that fMRI voxel patterns can convey information represented in columnar-scale neuronal population codes, even when spatial resolution is insufficient to directly image the patterns of columnar selectivity (Kamitani and Tong, 2005; Haynes and Rees, 2005). Sensitivity to subvoxel-scale pattern information, or "fMRI hyperacuity," would greatly enhance the power of fMRI when combined with pattern information analysis techniques (Kriegeskorte and Bandettini, 2007). An individual voxel might weakly reflect columnar-level information if the columns within its boundaries constituted a slightly unbalanced sample of columnar selectivities (Kamitani and Tong, 2005), providing a possible mechanism for fMRI hyperacuity. However, Op de Beeck (2009) suggests that a coarse-scale neuronal organization rather than fMRI hyperacuity may explain the presence of the information in the fMRI patterns. Here we argue (a) that the present evidence does not rule out fMRI hyperacuity, (b) that the mechanism originally suggested for fMRI hyperacuity (biased sampling by averaging within each voxel's boundaries; Kamitani and Tong, 2005) will only produce very weak sensitivity to fine-grained pattern information, and (c) that an alternative mechanism (voxel as complex spatiotemporal filter) is physiologically more accurate and promises stronger sensitivity to fine-grained pattern information: We know that each voxel samples the neuronal activity pattern through a unique fine-grained structure of venous vessels that supply its blood oxygen level-dependent signal. At the simplest level, the drainage domain of a venous vessel may sample the neuronal pattern with a selectivity bias (Gardner, 2009; Shmuel et al., 2009). Beyond biased drainage domains, we illustrate with a simple simulation how temporal properties of the hemodynamics (e.g., the speed of the blood in the capillary bed) can shape spatial properties of a voxel's filter (e.g., how finely structured it is). This

  11. Multivariate analysis of fMRI time series: classification and regression of brain responses using machine learning.

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    Formisano, Elia; De Martino, Federico; Valente, Giancarlo

    2008-09-01

    Machine learning and pattern recognition techniques are being increasingly employed in functional magnetic resonance imaging (fMRI) data analysis. By taking into account the full spatial pattern of brain activity measured simultaneously at many locations, these methods allow detecting subtle, non-strictly localized effects that may remain invisible to the conventional analysis with univariate statistical methods. In typical fMRI applications, pattern recognition algorithms "learn" a functional relationship between brain response patterns and a perceptual, cognitive or behavioral state of a subject expressed in terms of a label, which may assume discrete (classification) or continuous (regression) values. This learned functional relationship is then used to predict the unseen labels from a new data set ("brain reading"). In this article, we describe the mathematical foundations of machine learning applications in fMRI. We focus on two methods, support vector machines and relevance vector machines, which are respectively suited for the classification and regression of fMRI patterns. Furthermore, by means of several examples and applications, we illustrate and discuss the methodological challenges of using machine learning algorithms in the context of fMRI data analysis.

  12. Clinical fMRI of language function in aphasic patients: Reading paradigm successful, while word generation paradigm fails

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    Engstroem, Maria; Landtblom, Anne-Marie; Ragnehed, Mattias; Lundberg, Peter (Center for Medical Image Science and Visualization (CMIV), Linkoeping Univ., Linkoeping (Sweden)), e-mail: maria.engstrom@liu.se; Karlsson, Marie; Crone, Marie (Dept. of Clinical and Experimental Medicine/Logopedics, Linkoeping Univ., Linkoeping (Sweden)); Antepohl, Wolfram (Dept. of Clinical and Experimental Medicine/Rehabilitation, Linkoeping Univ., Linkoeping (Sweden))

    2010-07-15

    Background: In fMRI examinations, it is very important to select appropriate paradigms assessing the brain function of interest. In addition, the patients' ability to perform the required cognitive tasks during fMRI must be taken into account. Purpose: To evaluate two language paradigms, word generation and sentence reading for their usefulness in examinations of aphasic patients and to make suggestions for improvements of clinical fMRI. Material and Methods: Five patients with aphasia after stroke or trauma sequelae were examined by fMRI. The patients' language ability was screened by neurolinguistic tests and elementary pre-fMRI language tests. Results: The sentence-reading paradigm succeeded to elicit adequate language-related activation in perilesional areas whereas the word generation paradigm failed. These findings were consistent with results on the behavioral tests in that all patients showed very poor performance in phonemic fluency, but scored well above mean at a reading comprehension task. Conclusion: The sentence-reading paradigm is appropriate to assess language function in this patient group, while the word-generation paradigm seems to be inadequate. In addition, it is crucial to use elementary pre-fMRI language tests to guide the fMRI paradigm decision.

  13. Determination of hemisphere dominance for language: comparison of frontal and temporal fMRI activation with intracarotid amytal testing

    Energy Technology Data Exchange (ETDEWEB)

    Spreer, J.; Arnold, S.; Ziyeh, S.; Klisch, J.; Schumacher, M. [Section of Neuroradiology, Neurozentrum, University of Freiburg (Germany); Quiske, A.; Altenmueller, D.; Schulze-Bonhage, A. [Section for Presurgical Epilepsy Diagnosis, Neurozentrum, University of Freiburg (Germany); Wohlfarth, R.; Steinhoff, B.J. [Epilepsiezentrum, Kehl-Kork (Germany); Herpers, M.; Kassubek, J. [Department of Neurology, Neurozentrum, University of Freiburg (Germany); Honegger, J. [Department of Neurosurgery, Neurozentrum, University of Freiburg (Germany)

    2002-06-01

    The reliability of frontal and temporal fMRI activations for the determination of hemisphere language dominance was evaluated in comparison with intracarotid amytal testing (IAT). Twenty-two patients were studied by IAT (bilateral in 13, unilateral in 9 patients) and fMRI using a paradigm requiring semantic decisions. Global and regional (frontal and temporoparietal) lateralisation indices (LI) were calculated from the number of activated (r>0.4) voxels in both hemispheres. Frontolateral activations associated with the language task were seen in all patients, temporoparietal activations in 20 of 22. Regional LI corresponded better with IAT results than global LI. Frontolateral LI were consistent with IAT in all patients with bilateral IAT (including three patients with right dominant and one patient with bilateral language representation) and were not conflicting in any of the patients with unilateral IAT. Temporoparietal LI were discordant with IAT in two patients with atypical language representation. In the determination of hemisphere dominance for language, regional analysis of fMRI activation is superior to global analysis. In cases with clear-cut fMRI lateralisation, i.e. consistent lateralised activation of frontal and temporoparietal language zones, IAT may be unnecessary. FMRI should be performed prior to IAT in all patients going to be operated in brain regions potentially involved in language. (orig.)

  14. The application of independent component analysis with projection method to two-task fMRI data over multiple subjects

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    Li, Rui; Hui, Mingqi; Yao, Li; Chen, Kewei; Long, Zhiying

    2011-03-01

    Spatial Independent component analysis (sICA) has been successfully used to analyze functional magnetic resonance (fMRI) data. However, the application of ICA was limited in multi-task fMRI data due to the potential spatial dependence between task-related components. Long et al. (2009) proposed ICA with linear projection (ICAp) method and demonstrated its capacity to solve the interaction among task-related components in multi-task fMRI data of single subject. However, it's unclear that how to perform ICAp over a group of subjects. In this study, we proposed a group analysis framework on multi-task fMRI data by combining ICAp with the temporal concatenation method reported by Calhoun (2001). The results of real fMRI experiment containing multiple visual processing tasks demonstrated the feasibility and effectiveness of the group ICAp method. Moreover, compared to the GLM method, the group ICAp method is more sensitive to detect the regions specific to each task.

  15. Time course based artifact identification for independent components of resting state fMRI

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    Christian eRummel

    2013-05-01

    Full Text Available In functional magnetic resonance imaging (fMRI coherent oscillations of the blood oxygen level dependent (BOLD signal can be detected. These arise when brain regions respond to external stimuli or are activated by tasks. The same networks have been characterized during wakeful rest when functional connectivity of the human brain is organized in generic resting state networks (RSN. Alterations of RSN emerge as neurobiological markers of pathological conditions such as altered mental state. In single-subject fMRI data the coherent components can be identified by blind source separation of the pre-processed BOLD data using spatial independent component analysis (ICA and related approaches. The resulting maps may represent physiological RSNs or may be due to various artifacts. In this methodological study, we propose a conceptually simple and fully automatic time course based filtering procedure to detect obvious artifacts in the ICA output for resting state fMRI. The filter is trained on six and tested on 29 healthy subjects, yielding mean filter accuracy, sensitivity and specificity of 0.80, 0.82 and 0.75 in out-of-sample tests. To estimate the impact of clearly artifactual single-subject components on group resting state studies we analyze unfiltered and filtered output with a second level ICA procedure. Although the automated filter does not reach performance values of visual analysis by human raters, we propose that resting state compatible analysis of ICA time courses could be very useful to complement the existing map or task/event oriented artifact classification algorithms.

  16. Multilingualism and fMRI: Longitudinal Study of Second Language Acquisition

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    John Wright

    2013-05-01

    Full Text Available BOLD fMRI is often used for the study of human language. However, there are still very few attempts to conduct longitudinal fMRI studies in the study of language acquisition by measuring auditory comprehension and reading. The following paper is the first in a series concerning a unique longitudinal study devoted to the analysis of bi- and multilingual subjects who are: (1 already proficient in at least two languages; or (2 are acquiring Russian as a second/third language. The focus of the current analysis is to present data from the auditory sections of a set of three scans acquired from April, 2011 through April, 2012 on a five-person subject pool who are learning Russian during the study. All subjects were scanned using the same protocol for auditory comprehension on the same General Electric LX 3T Signa scanner in Duke University Hospital. Using a multivariate analysis of covariance (MANCOVA for statistical analysis, proficiency measurements are shown to correlate significantly with scan results in the Russian conditions over time. The importance of both the left and right hemispheres in language processing is discussed. Special attention is devoted to the importance of contextualizing imaging data with corresponding behavioral and empirical testing data using a multivariate analysis of variance. This is the only study to date that includes: (1 longitudinal fMRI data with subject-based proficiency and behavioral data acquired in the same time frame; and (2 statistical modeling that demonstrates the importance of covariate language proficiency data for understanding imaging results of language acquisition.

  17. Working memory dysfunction in delusional disorders: an fMRI investigation.

    Science.gov (United States)

    Oflaz, Serap; Akyuz, Fatma; Hamamci, Andac; Firat, Zeynep; Keskinkılıç, Cahit; Kilickesmez, Ozgur; Cihangiroglu, Mutlu

    2014-09-01

    Delusional disorder (DD) is a rare and understudied psychiatric disorder. There is limited number of studies concerning cognitive characteristics in DD. Using an established working memory paradigm with variable levels of memory load, we investigated alterations in functional magnetic resonance imaging (fMRI) of brain regions in patients with DD. This case control study included 9 patients with DD and 9 healthy control subjects matched for age, sex, and education level. Diagnosis of DD was confirmed using the Structured Clinical Interview for DSM-IV Axis I. The severity of the symptoms was evaluated using the Positive and Negative Syndrome Scale. All patients were asked to perform 0-back and 2-back tasks during fMRI experiments. Functional imaging was performed using the 3.0 T Philips whole-body scanner using an 8-channel head coil. Participants with DD had less neural activation of the left dorsolateral prefrontal cortex in fMRI scans obtained during performance tasks. On the other hand, neural activation of the left and right superior temporal gyrus, left middle and inferior temporal gyrus, right and left posterior cingulate gyrus, right amygdala, left and right fusiform gyrus was more prominent in patients with DD in comparison with the control group. Patients with DD had dysfunction in the prefrontal, temporal and limbic regions of the brain in particular, during performance tasks of working memory. Our findings were in line with the findings of the early reports on deficient functioning in temporal or limbic regions of the brain. Further, patients with DD displayed prefrontal dysfunction as seen in patients with schizophrenia. Copyright © 2014 Elsevier Ltd. All rights reserved.

  18. Mapping brain response to pain in fibromyalgia patients using temporal analysis of FMRI.

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    Jesus Pujol

    Full Text Available BACKGROUND: Nociceptive stimuli may evoke brain responses longer than the stimulus duration often partially detected by conventional neuroimaging. Fibromyalgia patients typically complain of severe pain from gentle stimuli. We aimed to characterize brain response to painful pressure in fibromyalgia patients by generating activation maps adjusted for the duration of brain responses. METHODOLOGY/PRINCIPAL FINDINGS: Twenty-seven women (mean age: 47.8 years were assessed with fMRI. The sample included nine fibromyalgia patients and nine healthy subjects who received 4 kg/cm(2 of pressure on the thumb. Nine additional control subjects received 6.8 kg/cm(2 to match the patients for the severity of perceived pain. Independent Component Analysis characterized the temporal dynamics of the actual brain response to pressure. Statistical parametric maps were estimated using the obtained time courses. Brain response to pressure (18 seconds consistently exceeded the stimulus application (9 seconds in somatosensory regions in all groups. fMRI maps following such temporal dynamics showed a complete pain network response (sensory-motor cortices, operculo-insula, cingulate cortex, and basal ganglia to 4 kg/cm(2 of pressure in fibromyalgia patients. In healthy subjects, response to this low intensity pressure involved mainly somatosensory cortices. When matched for perceived pain (6.8 kg/cm(2, control subjects showed also comprehensive activation of pain-related regions, but fibromyalgia patients showed significantly larger activation in the anterior insula-basal ganglia complex and the cingulate cortex. CONCLUSIONS/SIGNIFICANCE: The results suggest that data-driven fMRI assessments may complement conventional neuroimaging for characterizing pain responses and that enhancement of brain activation in fibromyalgia patients may be particularly relevant in emotion-related regions.

  19. Alternative-based thresholding with application to presurgical fMRI.

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    Durnez, Joke; Moerkerke, Beatrijs; Bartsch, Andreas; Nichols, Thomas E

    2013-12-01

    Functional magnetic reasonance imaging (fMRI) plays an important role in pre-surgical planning for patients with resectable brain lesions such as tumors. With appropriately designed tasks, the results of fMRI studies can guide resection, thereby preserving vital brain tissue. The mass univariate approach to fMRI data analysis consists of performing a statistical test in each voxel, which is used to classify voxels as either active or inactive-that is, related, or not, to the task of interest. In cognitive neuroscience, the focus is on controlling the rate of false positives while accounting for the severe multiple testing problem of searching the brain for activations. However, stringent control of false positives is accompanied by a risk of false negatives, which can be detrimental, particularly in clinical settings where false negatives may lead to surgical resection of vital brain tissue. Consequently, for clinical applications, we argue for a testing procedure with a stronger focus on preventing false negatives. We present a thresholding procedure that incorporates information on false positives and false negatives. We combine two measures of significance for each voxel: a classical p-value, which reflects evidence against the null hypothesis of no activation, and an alternative p-value, which reflects evidence against activation of a prespecified size. This results in a layered statistical map for the brain. One layer marks voxels exhibiting strong evidence against the traditional null hypothesis, while a second layer marks voxels where activation cannot be confidently excluded. The third layer marks voxels where the presence of activation can be rejected.

  20. A model selection method for nonlinear system identification based FMRI effective connectivity analysis.

    Science.gov (United States)

    Li, Xingfeng; Coyle, Damien; Maguire, Liam; McGinnity, Thomas M; Benali, Habib

    2011-07-01

    In this paper a model selection algorithm for a nonlinear system identification method is proposed to study functional magnetic resonance imaging (fMRI) effective connectivity. Unlike most other methods, this method does not need a pre-defined structure/model for effective connectivity analysis. Instead, it relies on selecting significant nonlinear or linear covariates for the differential equations to describe the mapping relationship between brain output (fMRI response) and input (experiment design). These covariates, as well as their coefficients, are estimated based on a least angle regression (LARS) method. In the implementation of the LARS method, Akaike's information criterion corrected (AICc) algorithm and the leave-one-out (LOO) cross-validation method were employed and compared for model selection. Simulation comparison between the dynamic causal model (DCM), nonlinear identification method, and model selection method for modelling the single-input-single-output (SISO) and multiple-input multiple-output (MIMO) systems were conducted. Results show that the LARS model selection method is faster than DCM and achieves a compact and economic nonlinear model simultaneously. To verify the efficacy of the proposed approach, an analysis of the dorsal and ventral visual pathway networks was carried out based on three real datasets. The results show that LARS can be used for model selection in an fMRI effective connectivity study with phase-encoded, standard block, and random block designs. It is also shown that the LOO cross-validation method for nonlinear model selection has less residual sum squares than the AICc algorithm for the study.

  1. Inter-subject correlation in fMRI: method validation against stimulus-model based analysis.

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    Juha Pajula

    Full Text Available Within functional magnetic resonance imaging (fMRI, the use of the traditional general linear model (GLM based analysis methods is often restricted to strictly controlled research setups requiring a parametric activation model. Instead, Inter-Subject Correlation (ISC method is based on voxel-wise correlation between the time series of the subjects, which makes it completely non-parametric and thus suitable for naturalistic stimulus paradigms such as movie watching. In this study, we compared an ISC based analysis results with those of a GLM based in five distinct controlled research setups. We used International Consortium for Brain Mapping functional reference battery (FRB fMRI data available from the Laboratory of Neuro Imaging image data archive. The selected data included measurements from 37 right-handed subjects, who all had performed the same five tasks from FRB. The GLM was expected to locate activations accurately in FRB data and thus provide good grounds for investigating relationship between ISC and stimulus induced fMRI activation. The statistical maps of ISC and GLM were compared with two measures. The first measure was the Pearson's correlation between the non-thresholded ISC test-statistics and absolute values of the GLM Z-statistics. The average correlation value over five tasks was 0.74. The second was the Dice index between the activation regions of the methods. The average Dice value over the tasks and three threshold levels was 0.73. The results of this study indicated how the data driven ISC analysis found the same foci as the model-based GLM analysis. The agreement of the results is highly interesting, because ISC is applicable in situations where GLM is not suitable, for example, when analyzing data from a naturalistic stimuli experiment.

  2. Different cerebral connectivity of obese and lean children studied with fMRI

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    Anaya Moreno, Maryan A.; Hernández López, Javier M.; Hidalgo Tobón, Silvia; Dies Suarez, Pilar; Barragán Pérez, Eduardo; De Celis Alonso, Benito

    2014-11-01

    In this work we studied the different fMRI brain activations and connections between normal weighted (NW) and obese (OB) infants for different types of food odours. A total of 30 right handed volunteers (infants 8.4±2 years) of both sexes were studied. Infants were divided in two group, one with BMI between 19 and 24 kg/m2 and the other with BMI over 30 kg/m2. The first part of this project consisted of a study in which fMRI BOLD activations to pleasant, neutral and healthy food was performed on both groups. Cerebellum regions were found to be more active in the NW group over the OB when presented with odour cues. OB volunteers in contrast showed larger activations in cingulate cortex structures than their NW counterparts when presented with food odours. The second part of this study performed connectivity studies (ROI to ROI) comparing both groups for each smell. The NW group presented for the onion smell a strong reward anticipation connection between the gustatory cortex and the cingulate cortex which the OB group did not have. In contrast the OB group presented strong orbitofrontal connections (decision making) with gustatory and somatosensory cortex when stimulated with the chocolate odour which the NW did not present. We can conclude that clear differences in fMRI BOLD activation as well as connectivity between the OB and NW groups were found. This points at a very different processing mechanisms of odour cues in infants. To our knowledge this study has never been performed before on infants.

  3. Olfactory fMRI in patients with Parkinson’s disease

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    Thomas Hummel

    2010-10-01

    Full Text Available Hyposmia is one of the early signs in Idiopathic Parkinson´s Disease (PD. Olfactory stimuli were applied during fMRI scanning to show disease-related modulation of central nervous system structures and to advance our understanding of olfactory dysfunction in PD patients. All participants received either unpleasant stimuli smelling like rotten eggs or pleasant ones smelling like roses. Using a block design at a 1.5 T scanner we investigated a total of 8 PD patients (mean age 60 ± 10.9 years and 13 age matched controls (mean age 58 ± 9.6 years. PD duration ranged from 1 to 9 years (mean 6.63 years; patients had an average “Unified Parkinson’s Disease Rating Scale III” score of 23.25 (range, 6-46. Olfactory function was established using the “Sniffin’ Sticks” test battery. Patients tended to rate the stimuli presented during fMRI scans as less intense, but also as more pleasant than controls. FMRI results revealed differences between PD patients and controls which depended on the type of stimulation. While both pleasant and unpleasant stimulation was associated with lower activation in the amygdalo-hippocampal complex in patients compared to controls, increased activity in response to pleasant stimuli was observed in the striatum and the left inferior frontal gyrus. In contrast, unpleasant stimulation led to hypoactivation of the ventral striatum in patients (but not in controls and did not enhance left inferior frontal activity. These results may partly reflect differences between PD patients and healthy controls in the processing of primary dimensions of odors, intensity and valence.

  4. Perception of biological motion in schizophrenia and healthy individuals: a behavioral and FMRI study.

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    Jejoong Kim

    Full Text Available BACKGROUND: Anomalous visual perception is a common feature of schizophrenia plausibly associated with impaired social cognition that, in turn, could affect social behavior. Past research suggests impairment in biological motion perception in schizophrenia. Behavioral and functional magnetic resonance imaging (fMRI experiments were conducted to verify the existence of this impairment, to clarify its perceptual basis, and to identify accompanying neural concomitants of those deficits. METHODOLOGY/FINDINGS: In Experiment 1, we measured ability to detect biological motion portrayed by point-light animations embedded within masking noise. Experiment 2 measured discrimination accuracy for pairs of point-light biological motion sequences differing in the degree of perturbation of the kinematics portrayed in those sequences. Experiment 3 measured BOLD signals using event-related fMRI during a biological motion categorization task. Compared to healthy individuals, schizophrenia patients performed significantly worse on both the detection (Experiment 1 and discrimination (Experiment 2 tasks. Consistent with the behavioral results, the fMRI study revealed that healthy individuals exhibited strong activation to biological motion, but not to scrambled motion in the posterior portion of the superior temporal sulcus (STSp. Interestingly, strong STSp activation was also observed for scrambled or partially scrambled motion when the healthy participants perceived it as normal biological motion. On the other hand, STSp activation in schizophrenia patients was not selective to biological or scrambled motion. CONCLUSION: Schizophrenia is accompanied by difficulties discriminating biological from non-biological motion, and associated with those difficulties are altered patterns of neural responses within brain area STSp. The perceptual deficits exhibited by schizophrenia patients may be an exaggerated manifestation of neural events within STSp associated with

  5. fMRI in patients with lumbar disc disease: a paradigm to study patients over time

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    Sharma HA

    2011-12-01

    Full Text Available Harish A Sharma1, Rajarsi Gupta2, William Olivero31Robarts Imaging, University of Western Ontario, London, ON, Canada; 2University of Illinois, Urbana, IL, USA; 3Neurological Surgery, University of Illinois UC/Carle Foundation Hospital, Urbana, IL, USAAbstract: Low back pain is a common human ailment. It is estimated that over 70% of the population will experience low back pain that will require medication and/or medical attention. There are many causes for low back pain, one being herniation of the discs of the lumbar spine. Treatment options are very limited. Why patients develop chronic pain especially when there is no known organic cause or when the offending painful stimulus has been removed remains poorly understood. Functional magnetic resonance imaging (fMRI is a technique that allows researchers to image which regions of the brain that are activated during motor, cognitive, and sensory experiences. Using fMRI to study pain has revealed new information about how the brain responds to painful stimuli and what regions of the brain are activated during pain. However, many of the paradigms used do not replicate the subject's pain or use painful stimuli in volunteers without pain. Also, following patients from their acute phase of pain to the chronic phase with serial fMRI has not been performed. In this study we developed a paradigm that would allow studying patients with low back pain and leg pain including lumbar radiculopathy to better mimic a clinical pain syndrome and to have a method of following patients with this type of pain over time.Keywords: functional magnetic resonance imaging, low back pain, pain syndrome, chronic pain

  6. Multivariate linear regression of high-dimensional fMRI data with multiple target variables.

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    Valente, Giancarlo; Castellanos, Agustin Lage; Vanacore, Gianluca; Formisano, Elia

    2014-05-01

    Multivariate regression is increasingly used to study the relation between fMRI spatial activation patterns and experimental stimuli or behavioral ratings. With linear models, informative brain locations are identified by mapping the model coefficients. This is a central aspect in neuroimaging, as it provides the sought-after link between the activity of neuronal populations and subject's perception, cognition or behavior. Here, we show that mapping of informative brain locations using multivariate linear regression (MLR) may lead to incorrect conclusions and interpretations. MLR algorithms for high dimensional data are designed to deal with targets (stimuli or behavioral ratings, in fMRI) separately, and the predictive map of a model integrates information deriving from both neural activity patterns and experimental design. Not accounting explicitly for the presence of other targets whose associated activity spatially overlaps with the one of interest may lead to predictive maps of troublesome interpretation. We propose a new model that can correctly identify the spatial patterns associated with a target while achieving good generalization. For each target, the training is based on an augmented dataset, which includes all remaining targets. The estimation on such datasets produces both maps and interaction coefficients, which are then used to generalize. The proposed formulation is independent of the regression algorithm employed. We validate this model on simulated fMRI data and on a publicly available dataset. Results indicate that our method achieves high spatial sensitivity and good generalization and that it helps disentangle specific neural effects from interaction with predictive maps associated with other targets.

  7. Predicting decisions in human social interactions using real-time fMRI and pattern classification.

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    Maurice Hollmann

    Full Text Available Negotiation and trade typically require a mutual interaction while simultaneously resting in uncertainty which decision the partner ultimately will make at the end of the process. Assessing already during the negotiation in which direction one's counterpart tends would provide a tremendous advantage. Recently, neuroimaging techniques combined with multivariate pattern classification of the acquired data have made it possible to discriminate subjective states of mind on the basis of their neuronal activation signature. However, to enable an online-assessment of the participant's mind state both approaches need to be extended to a real-time technique. By combining real-time functional magnetic resonance imaging (fMRI and online pattern classification techniques, we show that it is possible to predict human behavior during social interaction before the interacting partner communicates a specific decision. Average accuracy reached approximately 70% when we predicted online the decisions of volunteers playing the ultimatum game, a well-known paradigm in economic game theory. Our results demonstrate the successful online analysis of complex emotional and cognitive states using real-time fMRI, which will enable a major breakthrough for social fMRI by providing information about mental states of partners already during the mutual interaction. Interestingly, an additional whole brain classification across subjects confirmed the online results: anterior insula, ventral striatum, and lateral orbitofrontal cortex, known to act in emotional self-regulation and reward processing for adjustment of behavior, appeared to be strong determinants of later overt behavior in the ultimatum game. Using whole brain classification we were also able to discriminate between brain processes related to subjective emotional and motivational states and brain processes related to the evaluation of objective financial incentives.

  8. Pitfalls in fractal time series analysis: fMRI BOLD as an exemplary case

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    Andras eEke

    2012-11-01

    Full Text Available This article will be positioned on our previous work demonstrating the importance of adhering to a carefully selected set of criteria when choosing the suitable method from those available ensuring its adequate performance when applied to real temporal signals, such as fMRI BOLD, to evaluate one important facet of their behavior, fractality.Earlier, we have reviewed on a range of monofractal tools and evaluated their performance. Given the advance in the fractal field, in this article we will discuss the most widely used implementations of multifractal analyses, too.Our recommended flowchart for the fractal characterization of spontaneous, low frequency fluctuations in fMRI BOLD will be used as the framework for this article to make certain that it will provide a hands-on experience for the reader in handling the perplexed issues of fractal analysis. The reason why this particular signal modality and its fractal analysis has been chosen was due to its high impact on today's neuroscience given it had powerfully emerged as a new way of interpreting the complex functioning of the brain (see intrinsic activity.The reader will first be presented with the basic concepts of mono and multifractal time series analyses, followed by some of the most relevant implementations, characterization by numerical approaches. The notion of the dichotomy of fractional Gaussian noise (fGn and fractional Brownian motion (fBm signal classes and their impact on fractal time series analyses will be thoroughly discussed as the central theme of our application strategy. Sources of pitfalls and way how to avoid them will be identified followed by a demonstration on fractal studies of fMRI BOLD taken from the literature and that of our own in an attempt to consolidate the best practice in fractal analysis of empirical fMRI-BOLD signals mapped throughout the brain as an exemplary case of potentially wide interest.

  9. Multilingualism and fMRI: Longitudinal Study of Second Language Acquisition

    Science.gov (United States)

    Andrews, Edna; Frigau, Luca; Voyvodic-Casabo, Clara; Voyvodic, James; Wright, John

    2013-01-01

    BOLD fMRI is often used for the study of human language. However, there are still very few attempts to conduct longitudinal fMRI studies in the study of language acquisition by measuring auditory comprehension and reading. The following paper is the first in a series concerning a unique longitudinal study devoted to the analysis of bi- and multilingual subjects who are: (1) already proficient in at least two languages; or (2) are acquiring Russian as a second/third language. The focus of the current analysis is to present data from the auditory sections of a set of three scans acquired from April, 2011 through April, 2012 on a five-person subject pool who are learning Russian during the study. All subjects were scanned using the same protocol for auditory comprehension on the same General Electric LX 3T Signa scanner in Duke University Hospital. Using a multivariate analysis of covariance (MANCOVA) for statistical analysis, proficiency measurements are shown to correlate significantly with scan results in the Russian conditions over time. The importance of both the left and right hemispheres in language processing is discussed. Special attention is devoted to the importance of contextualizing imaging data with corresponding behavioral and empirical testing data using a multivariate analysis of variance. This is the only study to date that includes: (1) longitudinal fMRI data with subject-based proficiency and behavioral data acquired in the same time frame; and (2) statistical modeling that demonstrates the importance of covariate language proficiency data for understanding imaging results of language acquisition. PMID:24961428

  10. Multilingualism and fMRI: Longitudinal Study of Second Language Acquisition.

    Science.gov (United States)

    Andrews, Edna; Frigau, Luca; Voyvodic-Casabo, Clara; Voyvodic, James; Wright, John

    2013-05-28

    BOLD fMRI is often used for the study of human language. However, there are still very few attempts to conduct longitudinal fMRI studies in the study of language acquisition by measuring auditory comprehension and reading. The following paper is the first in a series concerning a unique longitudinal study devoted to the analysis of bi- and multilingual subjects who are: (1) already proficient in at least two languages; or (2) are acquiring Russian as a second/third language. The focus of the current analysis is to present data from the auditory sections of a set of three scans acquired from April, 2011 through April, 2012 on a five-person subject pool who are learning Russian during the study. All subjects were scanned using the same protocol for auditory comprehension on the same General Electric LX 3T Signa scanner in Duke University Hospital. Using a multivariate analysis of covariance (MANCOVA) for statistical analysis, proficiency measurements are shown to correlate significantly with scan results in the Russian conditions over time. The importance of both the left and right hemispheres in language processing is discussed. Special attention is devoted to the importance of contextualizing imaging data with corresponding behavioral and empirical testing data using a multivariate analysis of variance. This is the only study to date that includes: (1) longitudinal fMRI data with subject-based proficiency and behavioral data acquired in the same time frame; and (2) statistical modeling that demonstrates the importance of covariate language proficiency data for understanding imaging results of language acquisition.

  11. Bayesian Switching Factor Analysis for Estimating Time-varying Functional Connectivity in fMRI.

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    Taghia, Jalil; Ryali, Srikanth; Chen, Tianwen; Supekar, Kaustubh; Cai, Weidong; Menon, Vinod

    2017-03-03

    There is growing interest in understanding the dynamical properties of functional interactions between distributed brain regions. However, robust estimation of temporal dynamics from functional magnetic resonance imaging (fMRI) data remains challenging due to limitations in extant multivariate methods for modeling time-varying functional interactions between multiple brain areas. Here, we develop a Bayesian generative model for fMRI time-series within the framework of hidden Markov models (HMMs). The model is a dynamic variant of the static factor analysis model (Ghahramani and Beal, 2000). We refer to this model as Bayesian switching factor analysis (BSFA) as it integrates factor analysis into a generative HMM in a unified Bayesian framework. In BSFA, brain dynamic functional networks are represented by latent states which are learnt from the data. Crucially, BSFA is a generative model which estimates the temporal evolution of brain states and transition probabilities between states as a function of time. An attractive feature of BSFA is the automatic determination of the number of latent states via Bayesian model selection arising from penalization of excessively complex models. Key features of BSFA are validated using extensive simulations on carefully designed synthetic data. We further validate BSFA using fingerprint analysis of multisession resting-state fMRI data from the Human Connectome Project (HCP). Our results show that modeling temporal dependencies in the generative model of BSFA results in improved fingerprinting of individual participants. Finally, we apply BSFA to elucidate the dynamic functional organization of the salience, central-executive, and default mode networks-three core neurocognitive systems with central role in cognitive and affective information processing (Menon, 2011). Across two HCP sessions, we demonstrate a high level of dynamic interactions between these networks and determine that the salience network has the highest temporal

  12. FMRI and brain activation after sport concussion: a tale of two cases.

    Science.gov (United States)

    Hutchison, Michael G; Schweizer, Tom A; Tam, Fred; Graham, Simon J; Comper, Paul

    2014-01-01

    Sport-related concussions are now recognized as a major public health concern: the number of participants in sport and recreation is growing, possibly playing their games faster, and there is heightened public awareness of injuries to some high-profile athletes. However, many clinicians still rely on subjective symptom reports for the clinical determination of recovery. Relying on subjective symptom reports can be problematic, as it has been shown that some concussed athletes may downplay their symptoms. The use of neuropsychological (NP) testing has enabled clinicians to measure the effects and extent of impairment following concussion more precisely, providing more objective metrics for determining recovery. Nevertheless, there is a remaining concern that brain abnormalities may exist beyond the point at which individuals achieve recovery in self-reported symptoms and cognition measured by NP testing. Our understanding of brain recovery after concussion is important, not only from a neuroscience perspective, but also from the perspective of clinical decision-making for safe return-to-play. A number of advanced neuroimaging tools, including blood oxygen level dependent functional magnetic resonance imaging (fMRI), have independently yielded early information on abnormal brain functioning. In the two cases presented in this article, we report contrasting brain activation patterns and recovery profiles using fMRI. Importantly, fMRI was conducted using adapted versions of the most sensitive computerized NP tests administered in our current clinical practice to determine impairments and recovery after sport-related concussion. One of the cases is consistent with the concept of lagging brain recovery.

  13. Controlling an avatar by thought using real-time fMRI

    Science.gov (United States)

    Cohen, Ori; Koppel, Moshe; Malach, Rafael; Friedman, Doron

    2014-06-01

    Objective. We have developed a brain-computer interface (BCI) system based on real-time functional magnetic resonance imaging (fMRI) with virtual reality feedback. The advantage of fMRI is the relatively high spatial resolution and the coverage of the whole brain; thus we expect that it may be used to explore novel BCI strategies, based on new types of mental activities. However, fMRI suffers from a low temporal resolution and an inherent delay, since it is based on a hemodynamic response rather than electrical signals. Thus, our objective in this paper was to explore whether subjects could perform a BCI task in a virtual environment using our system, and how their performance was affected by the delay. Approach. The subjects controlled an avatar by left-hand, right-hand and leg motion or imagery. The BCI classification is based on locating the regions of interest (ROIs) related with each of the motor classes, and selecting the ROI with maximum average values online. The subjects performed a cue-based task and a free-choice task, and the analysis includes evaluation of the performance as well as subjective reports. Main results. Six subjects performed the task with high accuracy when allowed to move their fingers and toes, and three subjects achieved high accuracy using imagery alone. In the cue-based task the accuracy was highest 8-12 s after the trigger, whereas in the free-choice task the subjects performed best when the feedback was provided 6 s after the trigger. Significance. We show that subjects are able to perform a navigation task in a virtual environment using an fMRI-based BCI, despite the hemodynamic delay. The same approach can be extended to other mental tasks and other brain areas.

  14. Using nonlinear models in fMRI data analysis: model selection and activation detection.

    Science.gov (United States)

    Deneux, Thomas; Faugeras, Olivier

    2006-10-01

    There is an increasing interest in using physiologically plausible models in fMRI analysis. These models do raise new mathematical problems in terms of parameter estimation and interpretation of the measured data. In this paper, we show how to use physiological models to map and analyze brain activity from fMRI data. We describe a maximum likelihood parameter estimation algorithm and a statistical test that allow the following two actions: selecting the most statistically significant hemodynamic model for the measured data and deriving activation maps based on such model. Furthermore, as parameter estimation may leave much incertitude on the exact values of parameters, model identifiability characterization is a particular focus of our work. We applied these methods to different variations of the Balloon Model (Buxton, R.B., Wang, E.C., and Frank, L.R. 1998. Dynamics of blood flow and oxygenation changes during brain activation: the balloon model. Magn. Reson. Med. 39: 855-864; Buxton, R.B., Uludağ, K., Dubowitz, D.J., and Liu, T.T. 2004. Modelling the hemodynamic response to brain activation. NeuroImage 23: 220-233; Friston, K. J., Mechelli, A., Turner, R., and Price, C. J. 2000. Nonlinear responses in fMRI: the balloon model, volterra kernels, and other hemodynamics. NeuroImage 12: 466-477) in a visual perception checkerboard experiment. Our model selection proved that hemodynamic models better explain the BOLD response than linear convolution, in particular because they are able to capture some features like poststimulus undershoot or nonlinear effects. On the other hand, nonlinear and linear models are comparable when signals get noisier, which explains that activation maps obtained in both frameworks are comparable. The tools we have developed prove that statistical inference methods used in the framework of the General Linear Model might be generalized to nonlinear models.

  15. fMRI and brain activation after sport concussion: a tale of two cases

    Directory of Open Access Journals (Sweden)

    Michael G Hutchison

    2014-04-01

    Full Text Available Sport-related concussions are now recognized as a major public health concern: The number of participants in sport and recreation is growing, possibly playing their games faster, and there is heightened public awareness of injuries to some high-profile athletes. However, many clinicians still rely on subjective symptom reports for the clinical determination of recovery. Relying on subjective symptom reports can be dangerous, as it has been shown that some concussed athletes may downplay their symptoms. The use of neuropsychological (NP testing tools has enabled clinicians to measure the effects and extent of impairment following concussion more precisely, providing more objective metrics for determining recovery after concussion. Nevertheless, there is a remaining concern that brain abnormalities may exist beyond the point at which individuals achieve recovery in self-reported symptoms and cognition measured by NP testing. Our understanding of brain recovery after concussion is important not only from a neuroscience perspective, but also from the perspective of clinical decision making for safe return-to-play (RTP. A number of advanced neuroimaging tools, including blood oxygen level dependent (BOLD functional magnetic resonance imaging (fMRI, have independently yielded early information on these abnormal brain functions. In the two cases presented in this article, we report contrasting brain activation patterns and recovery profiles using fMRI. Importantly, fMRI was conducted using adapted versions of the most sensitive computerized NP tests administered in current clinical practice to determine impairments and recovery after sport-related concussion. One of the cases is consistent with the concept of lagging brain recovery.

  16. Simultaneous functional imaging using fPET and fMRI

    Energy Technology Data Exchange (ETDEWEB)

    Villien, Marjorie [CERMEP (France)

    2015-05-18

    Brain mapping of task-associated changes in metabolism with PET has been accomplished by subtracting scans acquired during two distinct static states. We have demonstrated that PET can provide truly dynamic information on cerebral energy metabolism using constant infusion of FDG and multiple stimuli in a single experiment. We demonstrate here that the functional PET (fPET-FDG) method accomplished simultaneously with fMRI, can enable the first direct comparisons in time, space and magnitude of hemodynamics and oxygen and glucose consumption. The imaging studies were performed on a 3T Tim-Trio MR scanner modified to support an MR-compatible BrainPET insert. Ten healthy subjects were included. The total PET acquisition and infusion time was 90 minutes. We did 3 blocks of right hand fingers tapping for 10 minutes at 30, 50 and 70 minutes after the beginning of the PET acquisition. ASL and BOLD imaging were acquired simultaneously during the motor paradigm. Changes in glucose utilization are easily observed as changes in the TAC slope of the PET data (FDG utilization rate) and in the derivative signal during motor stimuli in the activated voxels. PET and MRI (ASL, and BOLD) activations are largely colocalized but with very different statistical significance and temporal dynamic, especially in the ipsilateral side of the stimuli. This study demonstrated that motor activation can be measured dynamically during a single FDG PET scan. The complementary nature of fPET-FDG to fMRI capitalizes on the emerging technology of hybrid MR-PET scanners. fPET-FDG, combined with quantitative fMRI methods, allow us to simultaneously measure dynamic changes in glucose utilization and hemodynamic, addressing vital questions about neurovascular coupling.

  17. A neural network approach to fMRI binocular visual rivalry task analysis.

    Directory of Open Access Journals (Sweden)

    Nicola Bertolino

    Full Text Available The purpose of this study was to investigate whether artificial neural networks (ANN are able to decode participants' conscious experience perception from brain activity alone, using complex and ecological stimuli. To reach the aim we conducted pattern recognition data analysis on fMRI data acquired during the execution of a binocular visual rivalry paradigm (BR. Twelve healthy participants were submitted to fMRI during the execution of a binocular non-rivalry (BNR and a BR paradigm in which two classes of stimuli (faces and houses were presented. During the binocular rivalry paradigm, behavioral responses related to the switching between consciously perceived stimuli were also collected. First, we used the BNR paradigm as a functional localizer to identify the brain areas involved the processing of the stimuli. Second, we trained the ANN on the BNR fMRI data restricted to these regions of interest. Third, we applied the trained ANN to the BR data as a 'brain reading' tool to discriminate the pattern of neural activity between the two stimuli. Fourth, we verified the consistency of the ANN outputs with the collected behavioral indicators of which stimulus was consciously perceived by the participants. Our main results showed that the trained ANN was able to generalize across the two different tasks (i.e. BNR and BR and to identify with high accuracy the cognitive state of the participants (i.e. which stimulus was consciously perceived during the BR condition. The behavioral response, employed as control parameter, was compared with the network output and a statistically significant percentage of correspondences (p-value <0.05 were obtained for all subjects. In conclusion the present study provides a method based on multivariate pattern analysis to investigate the neural basis of visual consciousness during the BR phenomenon when behavioral indicators lack or are inconsistent, like in disorders of consciousness or sedated patients.

  18. Investigating Inhibitory Control in Children with Epilepsy: An fMRI Study

    Science.gov (United States)

    Triplett, Regina L.; Velanova, Katerina; Luna, Beatriz; Padmanabhan, Aarthi; Gaillard, William D.; Asato, Miya R.

    2014-01-01

    SUMMARY Objective Deficits in executive function are increasingly noted in children with epilepsy and have been associated with poor academic and psychosocial outcomes. Impaired inhibitory control contributes to executive dysfunction in children with epilepsy; however, its neuroanatomic basis has not yet been investigated. We used functional Magnetic Resonance Imaging (fMRI) to probe the integrity of activation in brain regions underlying inhibitory control in children with epilepsy. Methods This cross-sectional study consisted of 34 children aged 8 to 17 years: 17 with well-controlled epilepsy and 17 age-and sex-matched controls. Participants performed the antisaccade (AS) task, representative of inhibitory control, during fMRI scanning. We compared AS performance during neutral and reward task conditions and evaluated task-related blood-oxygen level dependent (BOLD) activation. Results Children with epilepsy demonstrated impaired AS performance compared to controls during both neutral (non-reward) and reward trials, but exhibited significant task improvement during reward trials. Post-hoc analysis revealed that younger patients made more errors than older patients and all controls. fMRI results showed preserved activation in task-relevant regions in patients and controls, with the exception of increased activation in the left posterior cingulate gyrus in patients specifically with generalized epilepsy across neutral and reward trials. Significance Despite impaired inhibitory control, children with epilepsy accessed typical neural pathways as did their peers without epilepsy. Children with epilepsy showed improved behavioral performance in response to the reward condition, suggesting potential benefits of the use of incentives in cognitive remediation. PMID:25223606

  19. Eye dominance predicts fMRI signals in human retinotopic cortex.

    Science.gov (United States)

    Mendola, Janine D; Conner, Ian P

    2007-02-27

    There have been many attempts to define eye dominance in normal subjects, but limited consensus exists, and relevant physiological data is scarce. In this study, we consider two different behavioral methods for assignment of eye dominance, and how well they predict fMRI signals evoked by monocular stimulation. Sighting eye dominance was assessed with two standard tests, the Porta Test, and a 'hole in hand' variation of the Miles Test. Acuity dominance was tested with a standard eye chart and with a computerized test of grating acuity. We found limited agreement between the sighting and acuity methods for assigning dominance in our individual subjects. We then compared the fMRI response generated by dominant eye stimulation to that generated by non-dominant eye, according to both methods, in 7 normal subjects. The stimulus consisted of a high contrast hemifield stimulus alternating with no stimulus in a blocked paradigm. In separate scans, we used standard techniques to label the borders of visual areas V1, V2, V3, VP, V4v, V3A, and MT. These regions of interest (ROIs) were used to analyze each visual area separately. We found that percent change in fMRI BOLD signal was stronger for the dominant eye as defined by the acuity method, and this effect was significant for areas located in the ventral occipital territory (V1v, V2v, VP, V4v). In contrast, assigning dominance based on sighting produced no significant interocular BOLD differences. We conclude that interocular BOLD differences in normal subjects exist, and may be predicted by acuity measures.

  20. Neural underpinnings of nocebo hyperalgesia in visceral pain: A fMRI study in healthy volunteers.

    Science.gov (United States)

    Schmid, Julia; Bingel, Ulrike; Ritter, Christoph; Benson, Sven; Schedlowski, Manfred; Gramsch, Carolin; Forsting, Michael; Elsenbruch, Sigrid

    2015-10-15

    Despite the clinical relevance of nocebo effects, few studies have addressed their underlying neural mechanisms in clinically-relevant pain models. We aimed to address the contribution of nocebo effects and their underlying neural circuitry to central pain amplification in visceral pain, as it may develop over repeated painful experiences due to negative pain-related expectations. Healthy volunteers received verbal suggestions of pain sensitization (nocebo group, N=28) or neutral instructions (control group, N=16). fMRI was used to investigate changes in neural responses during cued pain anticipation and painful rectal distensions delivered in successive fMRI sessions. Pain intensity was rated trial-by-trial, and expected pain intensity, state anxiety and tension were assessed prior to each session. Behavioral analyses demonstrated significantly greater increases in both expected and perceived pain in the nocebo group. The fMRI analysis performed on nocebo-responders only (N=14) revealed that these behavioral changes were associated with increased activation within the secondary somatosensory cortex and amygdala during pain anticipation and within the thalamus, insula and amygdala during painful stimulation when compared to controls. A subsequent psycho-physiological interaction analysis of the pain phase showed increased functional connectivity between the anterior insula, which was set-up as seed region based on group results, and midcingulate cortex as a function of negative expectations. These findings support that negative pain-related expectations can play a crucial role in pain amplification of visceral pain, which is mediated, at least in part, by a neural up-regulation of pain-associated areas and their connectivity. These findings may have implications for the pathophysiology and treatment of chronic abdominal pain. Copyright © 2015 Elsevier Inc. All rights reserved.

  1. Fusing Simultaneous EEG and fMRI Using Functional and Anatomical Information

    DEFF Research Database (Denmark)

    Hansen, Sofie Therese; Winkler, Irene; Hansen, Lars Kai;

    2015-01-01

    SPoC), to not only use functional but also anatomical information. The goal is to extract correlated source components from electroencephalography (EEG) and functional magnetic resonance imaging (fMRI). Anatomical information enters our proposed extension to mSPoC via the forward model, which relates the activity...... on cortex level to the EEG sensors. The augmented mSPoC is shown to outperform the original version in realistic simulations where the signal to noise ratio is low or where training epochs are scarce....

  2. "Implicit contamination" extends across multiple methodologies: Implications for fMRI.

    Science.gov (United States)

    Dew, Ilana T Z; Cabeza, Roberto

    2012-01-01

    Abstract The article "More than a feeling: Pervasive influences of memory without awareness of retrieval" reviews evidence from ERP studies of recognition memory that the FN400 effect typically ascribed to familiarity may index implicit memory that occurs during recognition testing. We find their argument compelling, and contend that this potential "implicit contamination" is not unique to ERP studies. We suggest an analogous problem affecting fMRI studies, focusing particularly on the perirhinal cortex. Resolving this issue is critical for understanding the relationship between memory and the medial temporal lobes.

  3. Processing of intentional and automatic number magnitudes in children born prematurely: evidence from fMRI.

    Science.gov (United States)

    Klein, Elise; Moeller, Korbinian; Kiechl-Kohlendorfer, Ursula; Kremser, Christian; Starke, Marc; Cohen Kadosh, Roi; Pupp-Peglow, Ulrike; Schocke, Michael; Kaufmann, Liane

    2014-01-01

    This study examined the neural correlates of intentional and automatic number processing (indexed by number comparison and physical Stroop task, respectively) in 6- and 7-year-old children born prematurely. Behavioral results revealed significant numerical distance and size congruity effects. Imaging results disclosed (1) largely overlapping fronto-parietal activation for intentional and automatic number processing, (2) a frontal to parietal shift of activation upon considering the risk factors gestational age and birth weight, and (3) a task-specific link between math proficiency and functional magnetic resonance imaging (fMRI) signal within distinct regions of the parietal lobes-indicating commonalities but also specificities of intentional and automatic number processing.

  4. fMRI assessment of somatotopy in human Brodmann area 3b by electrical finger stimulation.

    Science.gov (United States)

    Kurth, R; Villringer, K; Mackert, B M; Schwiemann, J; Braun, J; Curio, G; Villringer, A; Wolf, K J

    1998-01-26

    Functional magnetic resonance imaging (fMRI) is capable of detecting focal brain activation induced by electrical stimulation of single fingers in human subjects. In eight subjects somatotopic arrangement of the second and fifth finger was found in Brodmann area 3b of the primary somatosensory cortex. In four subjects the representation area of the second finger was located lateral and inferior to the fifth finger; in one subject the somatotopy was reversed. In three subjects representation areas of the two fingers in Brodmann area 3b were found overlapping. Additional activated areas were found on the crown of ipsilateral and contralateral postcentral gyrus (Brodmann areas 1 and 2) and posterior parietal cortex.

  5. Effect of "SOHAM" meditation on the human brain: an fMRI study.

    Science.gov (United States)

    Guleria, Anupam; Kumar, Uttam; Kishan, Sadguru Sri Kunal; Khetrapal, Chunni Lal

    2013-12-30

    The effect of "SOHAM" meditation has been investigated using functional magnetic resonance imaging (fMRI) in long-term meditators while they were meditating and not meditating. The results have revealed activation in left middle prefrontal cortex (MPFC) (Brodmann's area, BA 46), left inferior frontal gyrus (LIFG) (BA 44), left supplementary motor area (SMA) (BA 6) and left precuneus (BA 5) during the meditation period compared to the control period (no-meditation period). The results have been interpreted in terms of regulation of the emotional state, attention and working memory of the meditators.

  6. Relationship between saccadic eye movements and cortical activity as measured by fMRI

    DEFF Research Database (Denmark)

    Kimmig, H.; Greenlee, M.W.; Gondan, Matthias;

    2001-01-01

    quantitative changes in cortical activity associated with qualitative changes in the saccade task for comparable levels of saccadic activity. All experiments required the simultaneous acquisition of eye movement and fMRI data. For this purpose we used a new high-resolution limbus-tracking technique...... that repeated processing of saccades is integrated over time in the BOLD response. In contrast, there was no comparable BOLD change with variation of saccade amplitude. This finding speaks for a topological rather than activity-dependent coding of saccade amplitudes in most cortical regions. In the experiments...

  7. Neural correlate of vernier acuity tasks assessed by functional MRI (FMRI).

    Science.gov (United States)

    Sheth, Kevin N; Walker, B Michael; Modestino, Edward J; Miki, Atsushi; Terhune, Kyla P; Francis, Ellie L; Haselgrove, John C; Liu, Grant T

    2007-01-01

    Vernier acuity refers to the ability to discern a small offset within a line. However, while Vernier acuity has been extensively studied psychophysically, its neural correlates are uncertain. Based upon previous psychophysical and electrophysiologic data, we hypothesized that extrastriate areas of the brain would be involved in Vernier acuity tasks, so we designed event-related functional MRI (fMRI) paradigms to identify cortical regions of the brain involved in this behavior. Normal subjects identified suprathreshold and subthreshold Vernier offsets. The results suggest a cortical network including frontal, parietal, occipital, and cerebellar regions subserves the observation, processing, interpretation, and acknowledgment of briefly presented Vernier offsets.

  8. Volume Regulated Channels

    DEFF Research Database (Denmark)

    Klausen, Thomas Kjær

    - serves a multitude of functions in the mammalian cell, regulating the membrane potential (Em), cell volume, protein activity and the driving force for facilitated transporters giving Cl- and Cl- channels a major potential of regulating cellular function. These functions include control of the cell cycle...... of volume perturbations evolution have developed system of channels and transporters to tightly control volume homeostasis. In the past decades evidence has been mounting, that the importance of these volume regulated channels and transporters are not restricted to the defense of cellular volume......, controlled cell death and cellular migration. Volume regulatory mechanisms has long been in focus for regulating cellular proliferation and my thesis work have been focusing on the role of Cl- channels in proliferation with specific emphasis on ICl, swell. Pharmacological blockage of the ubiquitously...

  9. A Review of fMRI as a Tool for Enhancing Eeg-Based Brain-Machine Interfaces

    Directory of Open Access Journals (Sweden)

    Luis J. Barrios

    2012-01-01

    Full Text Available Human-robot interaction has been going stronger and stronger, up to find a notorious level on brain-machines interfaces. This assistive technology offers a great hope for patients suffering severe neuromuscular disorders. Starting from the current limitations hindering its extensive application outside the research laboratories, this paper reviews findings and prospects on functional magnetic resonance imaging showing how fMRI can help to overcome those limitations, while playing a key role on improving the development of brain-machine interfaces based on electroencephalography. The different types of derived benefits for this interfaces, as well as the different kinds of impact on their components, are presented under a field classification that reveals the distinctive roles that fMRI can play on the present context. The review concludes that fMRI provides complementary knowledge of immediate application, and that a greater profit could be obtained from the own EEG signal by integrating both neuroimaging modalities.

  10. Cerebral correlates of heart rate variations during a spontaneous panic attack in the fMRI scanner.

    Science.gov (United States)

    Spiegelhalder, Kai; Hornyak, Magdolna; Kyle, Simon David; Paul, Dominik; Blechert, Jens; Seifritz, Erich; Hennig, Jürgen; Tebartz van Elst, Ludger; Riemann, Dieter; Feige, Bernd

    2009-12-01

    We report the first published case study of a suddenly occurring panic attack in a patient with no prior history of panic disorder during combined functional magnetic resonance imaging (fMRI, 1.5 Tesla) and electrocardiogram (ECG) recording. The single case was a 46-year-old woman who developed a panic attack near the planned end of the fMRI acquisition session, which therefore had to be aborted. Correlational analysis of heart rate fluctuations and fMRI data revealed a significant negative association in the left middle temporal gyrus. Additionally, regions-of-interest (ROI) analyses indicated significant positive associations in the left amygdala, and trends towards significance in the right amygdala and left insula.

  11. Qualification of fMRI as a biomarker for pain in anesthetized rats by comparison with behavioral response in conscious rats.

    Science.gov (United States)

    Zhao, Fuqiang; Williams, Mangay; Bowlby, Mark; Houghton, Andrea; Hargreaves, Richard; Evelhoch, Jeffrey; Williams, Donald S

    2014-01-01

    fMRI can objectively measure pain-related neural activities in humans and animals, providing a valuable tool for studying the mechanisms of nociception and for developing new analgesics. However, due to its extreme sensitivity to subject motion, pain fMRI studies are performed in animals that are immobilized, typically with anesthesia. Since anesthesia could confound the nociceptive processes, it is unknown how well nociceptive-related neural activities measured by fMRI in anesthetized animals correlate with nociceptive behaviors in conscious animals. The threshold to vocalization (VT) in response to an increasing noxious electrical stimulus (NES) was implemented in conscious rats as a behavioral measure of nociception. The antinociceptive effect of systemic (intravenous infusion) lidocaine on NES-induced fMRI signals in anesthetized rats was compared with the corresponding VT in conscious rats. Lidocaine infusion increased VT and suppressed the NES-induced fMRI signals in most activated brain regions. The temporal characteristics of the nociception signal by fMRI and by VT in response to lidocaine infusion were highly correlated with each other, and with the pharmacokinetics (PK) of lidocaine. These results indicate that the fMRI activations in these regions may be used as biomarkers of acute nociception in anesthetized rats. Interestingly, systemic lidocaine had no effect on NES-induced fMRI activations in the primary somatosensory cortex (S1), a result that warrants further investigation.

  12. Precision volume measurement system.

    Energy Technology Data Exchange (ETDEWEB)

    Fischer, Erin E.; Shugard, Andrew D.

    2004-11-01

    A new precision volume measurement system based on a Kansas City Plant (KCP) design was built to support the volume measurement needs of the Gas Transfer Systems (GTS) department at Sandia National Labs (SNL) in California. An engineering study was undertaken to verify or refute KCP's claims of 0.5% accuracy. The study assesses the accuracy and precision of the system. The system uses the ideal gas law and precise pressure measurements (of low-pressure helium) in a temperature and computer controlled environment to ratio a known volume to an unknown volume.

  13. A least angle regression method for fMRI activation detection in phase-encoded experimental designs.

    Science.gov (United States)

    Li, Xingfeng; Coyle, Damien; Maguire, Liam; McGinnity, Thomas M; Watson, David R; Benali, Habib

    2010-10-01

    This paper presents a new regression method for functional magnetic resonance imaging (fMRI) activation detection. Unlike general linear models (GLM), this method is based on selecting models for activation detection adaptively which overcomes the limitation of requiring a predefined design matrix in GLM. This limitation is because GLM designs assume that the response of the neuron populations will be the same for the same stimuli, which is often not the case. In this work, the fMRI hemodynamic response model is selected from a series of models constructed online by the least angle regression (LARS) method. The slow drift terms in the design matrix for the activation detection are determined adaptively according to the fMRI response in order to achieve the best fit for each fMRI response. The LARS method is then applied along with the Moore-Penrose pseudoinverse (PINV) and fast orthogonal search (FOS) algorithm for implementation of the selected model to include the drift effects in the design matrix. Comparisons with GLM were made using 11 normal subjects to test method superiority. This paper found that GLM with fixed design matrix was inferior compared to the described LARS method for fMRI activation detection in a phased-encoded experimental design. In addition, the proposed method has the advantage of increasing the degrees of freedom in the regression analysis. We conclude that the method described provides a new and novel approach to the detection of fMRI activation which is better than GLM based analyses.

  14. ICA-fNORM: Spatial normalization of fMRI data using intrinsic group-ICA networks

    Directory of Open Access Journals (Sweden)

    Siddharth eKhullar

    2011-11-01

    Full Text Available A common pre-processing challenge associated with group-level fMRI analysis is spatial registration of multiple subjects to a standard space. Spatial normalization, using a reference image such as the Montreal Neurological Institute (MNI brain template, is the most common technique currently in use to achieve spatial congruence across multiple subjects. This method corrects for global shape differences preserving regional asymmetries, but does not account for functional differences. We propose a novel approach to co-register task-based fMRI data using resting-state group ICA networks. We posit that these intrinsic networks can provide to the spatial normalization process with important information about how each individual’s brain is organized functionally. The algorithm is initiated by the extraction of single-subject representations of intrinsic networks using group level independent component analysis (ICA on resting state fMRI data. In this proof of concept work two of the robust, commonly identified, networks are chosen as functional templates. As an estimation step, the relevant intrinsic networks are utilized to derive a set of normalization parameters for each subject. Finally, the normalization parameters are applied individually to a different set of fMRI data acquired while the subjects performed an auditory oddball task. These normalization parameters, although derived using rest data, generalize successfully to data obtained with a cognitive paradigm for each subject. The improvement in results is verified using two widely applied fMRI analysis methods¬: the general linear model (GLM and ICA. Resulting activation patterns from each analysis method show significant improvements in terms of detection sensitivity and statistical significance at the group level. The results presented in this article provide initial evidence to show that common functional domains from the resting state brain may be used to improve the group statistics

  15. Cortical response variation with different sound pressure levels: a combined event-related potentials and FMRI study.

    Directory of Open Access Journals (Sweden)

    Irene Neuner

    Full Text Available Simultaneous recording of electroencephalography (EEG and functional magnetic resonance imaging (fMRI provides high spatial and temporal resolution. In this study we combined EEG and fMRI to investigate the structures involved in the processing of different sound pressure levels (SPLs.EEG data were recorded simultaneously with fMRI from 16 healthy volunteers using MR compatible devices at 3 T. Tones with different SPLs were delivered to the volunteers and the N1/P2 amplitudes were included as covariates in the fMRI data analysis in order to compare the structures activated with high and low SPLs. Analysis of variance (ANOVA and ROI analysis were also performed. Additionally, source localisation analysis was performed on the EEG data.The integration of averaged ERP parameters into the fMRI analysis showed an extended map of areas exhibiting covariation with the BOLD signal related to the auditory stimuli. The ANOVA and ROI analyses also revealed additional brain areas other than the primary auditory cortex (PAC which were active with the auditory stimulation at different SPLs. The source localisation analyses showed additional sources apart from the PAC which were active with the high SPLs.The PAC and the insula play an important role in the processing of different SPLs. In the fMRI analysis, additional activation was found in the anterior cingulate cortex, opercular and orbito-frontal cortices with high SPLs. A strong response of the visual cortex was also found with the high SPLs, suggesting the presence of cross-modal effects.

  16. An analysis approach for high-field fMRI data from awake non-human primates.

    Directory of Open Access Journals (Sweden)

    Steffen Stoewer

    Full Text Available fMRI experiments with awake non-human primates (NHP have seen a surge of applications in recent years. However, the standard fMRI analysis tools designed for human experiments are not optimal for analysis of NHP fMRI data collected at high fields. There are several reasons for this, including the trial-based nature of NHP experiments, with inter-trial periods being of no interest, and segmentation artefacts and distortions that may result from field changes due to movement. We demonstrate an approach that allows us to address some of these issues consisting of the following steps: 1 Trial-based experimental design. 2 Careful control of subject movement. 3 Computer-assisted selection of trials devoid of artefacts and animal motion. 4 Nonrigid between-trial and rigid within-trial realignment of concatenated data from temporally separated trials and sessions. 5 Linear interpolation of inter-trial intervals and high-pass filtering of temporally continuous data 6 Removal of interpolated data and reconcatenation of datasets before statistical analysis with SPM. We have implemented a software toolbox, fMRI Sandbox (http://code.google.com/p/fmri-sandbox/, for semi-automated application of these processing steps that interfaces with SPM software. Here, we demonstrate that our methodology provides significant improvements for the analysis of awake monkey fMRI data acquired at high-field. The method may also be useful for clinical applications with subjects that are unwilling or unable to remain motionless for the whole duration of a functional scan.

  17. Regional differences in the CBF and BOLD responses to hypercapnia: a combined PET and fMRI study

    DEFF Research Database (Denmark)

    Rostrup, Egill; Law, I; Blinkenberg, M

    2000-01-01

    Previous fMRI studies of the cerebrovascular response to hypercapnia have shown signal change in cerebral gray matter, but not in white matter. Therefore, the objective of the present study was to compare (15)O PET and T *(2)-weighted MRI during a hypercapnic challenge. The measurements were perf...... that the differences in the magnitude of the fMRI response can largely be attributed to differences in flow and that there is a considerable difference in the time course of the response between gray and white matter....

  18. Sensitivity and specificity considerations for fMRI encoding, decoding, and mapping of auditory cortex at ultra-high field.

    Science.gov (United States)

    Moerel, Michelle; De Martino, Federico; Kemper, Valentin G; Schmitter, Sebastian; Vu, An T; Uğurbil, Kâmil; Formisano, Elia; Yacoub, Essa

    2017-03-31

    Following rapid technological advances, ultra-high field functional MRI (fMRI) enables exploring correlates of neuronal population activity at an increasing spatial resolution. However, as the fMRI blood-oxygenation-level-dependent (BOLD) contrast is a vascular signal, the spatial specificity of fMRI data is ultimately determined by the characteristics of the underlying vasculature. At 7T, fMRI measurement parameters determine the relative contribution of the macro- and microvasculature to the acquired signal. Here we investigate how these parameters affect relevant high-end fMRI analyses such as encoding, decoding, and submillimeter mapping of voxel preferences in the human auditory cortex. Specifically, we compare a T2* weighted fMRI dataset, obtained with 2D gradient echo (GE) EPI, to a predominantly T2 weighted dataset obtained with 3D GRASE. We first investigated the decoding accuracy based on two encoding models that represented different hypotheses about auditory cortical processing. This encoding/decoding analysis profited from the large spatial coverage and sensitivity of the T2* weighted acquisitions, as evidenced by a significantly higher prediction accuracy in the GE-EPI dataset compared to the 3D GRASE dataset for both encoding models. The main disadvantage of the T2* weighted GE-EPI dataset for encoding/decoding analyses was that the prediction accuracy exhibited cortical depth dependent vascular biases. However, we propose that the comparison of prediction accuracy across the different encoding models may be used as a post processing technique to salvage the spatial interpretability of the GE-EPI cortical depth-dependent prediction accuracy. Second, we explored the mapping of voxel preferences. Large-scale maps of frequency preference (i.e., tonotopy) were similar across datasets, yet the GE-EPI dataset was preferable due to its larger spatial coverage and sensitivity. However, submillimeter tonotopy maps revealed biases in assigned frequency

  19. How skill expertise shapes the brain functional architecture: an fMRI study of visuo-spatial and motor processing in professional racing-car and naive drivers.

    Directory of Open Access Journals (Sweden)

    Giulio Bernardi

    Full Text Available The present study was designed to investigate the brain functional architecture that subserves visuo-spatial and motor processing in highly skilled individuals. By using functional magnetic resonance imaging (fMRI, we measured brain activity while eleven Formula racing-car drivers and eleven 'naïve' volunteers performed a motor reaction and a visuo-spatial task. Tasks were set at a relatively low level of difficulty such to ensure a similar performance in the two groups and thus avoid any potential confounding effects on brain activity due to discrepancies in task execution. The brain functional organization was analyzed in terms of regional brain response, inter-regional interactions and blood oxygen level dependent (BOLD signal variability. While performance levels were equal in the two groups, as compared to naïve drivers, professional drivers showed a smaller volume recruitment of task-related regions, stronger connections among task-related areas, and an increased information integration as reflected by a higher signal temporal variability. In conclusion, our results demonstrate that, as compared to naïve subjects, the brain functional architecture sustaining visuo-motor processing in professional racing-car drivers, trained to perform at the highest levels under extremely demanding conditions, undergoes both 'quantitative' and 'qualitative' modifications that are evident even when the brain is engaged in relatively simple, non-demanding tasks. These results provide novel evidence in favor of an increased 'neural efficiency' in the brain of highly skilled individuals.

  20. Surface-Based Regional Homogeneity in First-Episode, Drug-Naïve Major Depression: A Resting-State fMRI Study

    Directory of Open Access Journals (Sweden)

    Hui-Jie Li

    2014-01-01

    Full Text Available Background. Previous volume-based regional homogeneity (ReHo studies neglected the intersubject variability in cortical folding patterns. Recently, surface-based ReHo was developed to reduce the intersubject variability and to increase statistical power. The present study used this novel surface-based ReHo approach to explore the brain functional activity differences between first-episode, drug-naïve MDD patients and healthy controls. Methods. Thirty-three first-episode, drug-naïve MDD patients and 32 healthy controls participated in structural and resting-state fMRI scans. MDD patients were rated with a 17-item Hamilton Rating Scale for Depression prior to the scan. Results. In comparison with the healthy controls, MDD patients showed reduced surface-based ReHo in the left insula. There was no increase in surface-based ReHo in MDD patients. The surface-based ReHo value in the left insula was not significantly correlated with the clinical information or the depressive scores in the MDD group. Conclusions. The decreased surface-based ReHo in the left insula in MDD may lead to the abnormal top-down cortical-limbic regulation of emotional and cognitive information. The surface-based ReHo may be a useful index to explore the pathophysiological mechanism of MDD.

  1. Brain correlates of hypnotic paralysis-a resting-state fMRI study.

    Science.gov (United States)

    Pyka, M; Burgmer, M; Lenzen, T; Pioch, R; Dannlowski, U; Pfleiderer, B; Ewert, A W; Heuft, G; Arolt, V; Konrad, C

    2011-06-15

    Hypnotic paralysis has been used since the times of Charcot to study altered states of consciousness; however, the underlying neurobiological correlates are poorly understood. We investigated human brain function during hypnotic paralysis using resting-state functional magnetic resonance imaging (fMRI), focussing on two core regions of the default mode network and the representation of the paralysed hand in the primary motor cortex. Hypnotic suggestion induced an observable left-hand paralysis in 19 participants. Resting-state fMRI at 3T was performed in pseudo-randomised order awake and in the hypnotic condition. Functional connectivity analyses revealed increased connectivity of the precuneus with the right dorsolateral prefrontal cortex, angular gyrus, and a dorsal part of the precuneus. Functional connectivity of the medial frontal cortex and the primary motor cortex remained unchanged. Our results reveal that the precuneus plays a pivotal role during maintenance of an altered state of consciousness. The increased coupling of selective cortical areas with the precuneus supports the concept that hypnotic paralysis may be mediated by a modified representation of the self which impacts motor abilities. Copyright © 2011 Elsevier Inc. All rights reserved.

  2. Multi-voxel pattern analysis of fMRI data predicts clinical symptom severity.

    Science.gov (United States)

    Coutanche, Marc N; Thompson-Schill, Sharon L; Schultz, Robert T

    2011-07-01

    Multi-voxel pattern analysis (MVPA) has been applied successfully to a variety of fMRI research questions in healthy participants. The full potential of applying MVPA to functional data from patient groups has yet to be fully explored. Our goal in this study was to investigate whether MVPA might yield a sensitive predictor of patient symptoms. We also sought to demonstrate that this benefit can be realized from existing datasets, even when they were not designed with MVPA in mind. We analyzed data from an fMRI study of the neural basis for face processing in individuals with an Autism Spectrum Disorder (ASD), who often show fusiform gyrus hypoactivation when presented with unfamiliar faces, compared to controls. We found reliable correlations between MVPA classification performance and standardized measures of symptom severity that exceeded those observed using a univariate measure; a relation that was robust across variations in ROI definition. A searchlight analysis across the ventral temporal lobes identified regions with relationships between classification performance and symptom severity that were not detected using mean activation. These analyses illustrate that MVPA has the potential to act as a sensitive functional biomarker of patient severity.

  3. The quest for EEG power band correlation with ICA derived fMRI resting state networks

    Directory of Open Access Journals (Sweden)

    Matthias Christoph Meyer

    2013-06-01

    Full Text Available The neuronal underpinnings of blood oxygen level dependent (BOLD functional magnetic resonance imaging (fMRI resting state networks (RSNs are still unclear. To investigate the underlying mechanisms, specifically the relation to the electrophysiological signal, we used simultaneous recordings of electroencephalography (EEG and fMRI during eyes open resting state (RS. Earlier studies using the EEG signal as independent variable show inconclusive results possibly due to variability in the temporal correlations between RSNs and power in the low EEG frequency band, as recently reported (Goncalves et al. 2006 and 2008, Meyer et al. (2013. In this study we use three different methods, including one that uses RSN timelines as independent variable, to explore the temporal relationship of RSNs and EEG frequency power in eyes open RS in detail. The results of these three distinct analysis approaches support the hypothesis that the correlation between low EEG frequency power and BOLD RSNs is instable over time, at least in eyes open RS.

  4. A high performance 3D cluster-based test of unsmoothed fMRI data.

    Science.gov (United States)

    Li, Huanjie; Nickerson, Lisa D; Xiong, Jinhu; Zou, Qihong; Fan, Yang; Ma, Yajun; Shi, Tingqi; Ge, Jianqiao; Gao, Jia-Hong

    2014-09-01

    Cluster-size tests (CST) based on random field theory have been widely adopted in fMRI data analysis to detect brain activation. However, most existing approaches can be used appropriately only when the image is highly smoothed in the spatial domain. Unfortunately, spatial smoothing degrades spatial specificity. Recently, a threshold-free cluster enhancement technique was proposed which does not require spatial smoothing, but this method can be used only for group level analysis. Advances in imaging technology now yield high quality high spatial resolution imaging data in single subjects and an inference approach that retains the benefits of greater spatial resolution is called for. In this work, we present a new CST with a correction for voxelation to address this problem. The theoretical formulation of the new approach based on Gaussian random fields is developed to estimate statistical significance using 3D statistical parametric maps without assuming spatial smoothness. Simulated phantom and resting-state fMRI experimental data are then used to compare the voxelation-corrected procedure to the widely used standard random field theory. Unlike standard random field theory approaches, which require heavy spatial smoothing, the new approach has a higher sensitivity for localizing activation regions without the requirement of spatial smoothness. Copyright © 2014 Elsevier Inc. All rights reserved.

  5. Brain Activities Associated with Graphic Emoticons: An fMRI Study

    Science.gov (United States)

    Yuasa, Masahide; Saito, Keiichi; Mukawa, Naoki

    In this paper, we describe the brain activities that are associated with graphic emoticons by using functional MRI (fMRI). We use various types of faces from abstract to photorealistic in computer network applications. A graphics emoticon is an abstract face in communication over computer network. In this research, we created various graphic emoticons for the fMRI study and the graphic emoticons were classified according to friendliness and level of arousal. We investigated the brain activities of participants who were required to evaluate the emotional valence of the graphic emoticons (happy or sad). The experimental results showed that not only the right inferior frontal gyrus and the cingulate gyrus, but also the inferior and middle temporal gyrus and the fusiform gyrus, were found to be activated during the experiment. Forthermore, it is possible that the activation of the right inferior frontal gyrus and the cingulate gyrus is related to the type of abstract face. Since the inferior and middle temporal gyrus were activated, even though the graphic emoticons are static, we may perceive graphic emoticons as dynamic and living agents. Moreover, it is believed that text and graphics emoticons play an important role in enriching communication among users.

  6. On the generalizability of resting-state fMRI machine learning classifiers.

    Science.gov (United States)

    Huf, Wolfgang; Kalcher, Klaudius; Boubela, Roland N; Rath, Georg; Vecsei, Andreas; Filzmoser, Peter; Moser, Ewald

    2014-01-01

    Machine learning classifiers have become increasingly popular tools to generate single-subject inferences from fMRI data. With this transition from the traditional group level difference investigations to single-subject inference, the application of machine learning methods can be seen as a considerable step forward. Existing studies, however, have given scarce or no information on the generalizability to other subject samples, limiting the use of such published classifiers in other research projects. We conducted a simulation study using publicly available resting-state fMRI data from the 1000 Functional Connectomes and COBRE projects to examine the generalizability of classifiers based on regional homogeneity of resting-state time series. While classification accuracies of up to 0.8 (using sex as the target variable) could be achieved on test datasets drawn from the same study as the training dataset, the generalizability of classifiers to different study samples proved to be limited albeit above chance. This shows that on the one hand a certain amount of generalizability can robustly be expected, but on the other hand this generalizability should not be overestimated. Indeed, this study substantiates the need to include data from several sites in a study investigating machine learning classifiers with the aim of generalizability.

  7. Scale-Free Brain-Wave Music from Simultaneously EEG and fMRI Recordings

    Science.gov (United States)

    Lu, Jing; Wu, Dan; Yang, Hua; Luo, Cheng; Li, Chaoyi; Yao, Dezhong

    2012-01-01

    In the past years, a few methods have been developed to translate human EEG to music. In 2009, PloS One 4 e5915, we developed a method to generate scale-free brainwave music where the amplitude of EEG was translated to music pitch according to the power law followed by both of them, the period of an EEG waveform is translated directly to the duration of a note, and the logarithm of the average power change of EEG is translated to music intensity according to the Fechner's law. In this work, we proposed to adopt simultaneously-recorded fMRI signal to control the intensity of the EEG music, thus an EEG-fMRI music is generated by combining two different and simultaneous brain signals. And most importantly, this approach further realized power law for music intensity as fMRI signal follows it. Thus the EEG-fMRI music makes a step ahead in reflecting the physiological process of the scale-free brain. PMID:23166768

  8. The "mirror-neuron system" in MS: A 3 tesla fMRI study.

    Science.gov (United States)

    Rocca, M A; Tortorella, P; Ceccarelli, A; Falini, A; Tango, D; Scotti, G; Comi, G; Filippi, M

    2008-01-22

    The mirror neuron system (MNS) is an observation-execution matching system activated, in humans, during action observation, motor learning, and imitation of action. We used functional MRI (fMRI) to investigate the properties of the MNS in patients with multiple sclerosis (MS). Using a 3 tesla scanner, we acquired fMRI in 16 right-handed patients with relapsing-remitting MS and 14 controls. Two motor tasks were studied. The first consisted of repetitive flexion-extension of the last four fingers of the right hand (simple task) alternated to epochs of rest; the second (MNS task) consisted of observation of a movie showing the hand of another subject while performing the same task. During the simple task, compared to controls, patients with MS had more significant activations of the contralateral primary sensorimotor cortex and supplementary motor area. During the MNS task, both groups showed the activation of several visual areas, the infraparietal sulcus, and the inferior frontal gyrus (IFG), bilaterally. The IFG and the visual areas were significantly more active in patients than controls. The between-group interaction analysis showed that in patients with MS, part of the regions of the MNS were more active also during the simple task. This study suggests increased activation of the mirror neuron system in patients with multiple sclerosis (MS) with a normal level of function and widespread CNS damage. The potentialities of this system in facilitating clinical recovery in patients with MS and other neurologic conditions should be investigated.

  9. FMRI study relevant to the Mozart effect: brain areas involved in spatial-temporal reasoning.

    Science.gov (United States)

    Bodner, M; Muftuler, L T; Nalcioglu, O; Shaw, G L

    2001-10-01

    Behavioral studies, motivated by columnar cortical model predictions, have given evidence for music causally enhancing spatial-temporal reasoning. A wide range of behavioral experiments showed that listening to a Mozart Sonata (K.448) gave subsequent enhancements. An EEG coherence study gave evidence for a carryover from that Mozart Sonata listening condition to the subsequent spatial-temporal task in specific cortical regions. Here we present fMRI studies comparing cortical blood flow activation by the Mozart Sonata vs. other music. In addition to expected temporal cortex activation, we report dramatic statistically significant differences in activation by the Mozart Sonata (in comparison to Beethoven's Fur Elise and 1930s piano music) in dorsolateral pre-frontal cortex, occipital cortex and cerebellum, all expected to be important for spatial-temporal reasoning. It would be of great interest to explicitly test this expectation. We propose an fMRI study comparing (subject by subject) brain areas activated in music listening conditions and in spatial-temporal tasks.

  10. Differentiating maturational and training influences on fMRI activation during music processing.

    Science.gov (United States)

    Ellis, Robert J; Norton, Andrea C; Overy, Katie; Winner, Ellen; Alsop, David C; Schlaug, Gottfried

    2012-04-15

    Two major influences on how the brain processes music are maturational development and active musical training. Previous functional neuroimaging studies investigating music processing have typically focused on either categorical differences between "musicians versus nonmusicians" or "children versus adults." In the present study, we explored a cross-sectional data set (n=84) using multiple linear regression to isolate the performance-independent effects of age (5 to 33 years) and cumulative duration of musical training (0 to 21,000 practice hours) on fMRI activation similarities and differences between melodic discrimination (MD) and rhythmic discrimination (RD). Age-related effects common to MD and RD were present in three left hemisphere regions: temporofrontal junction, ventral premotor cortex, and the inferior part of the intraparietal sulcus, regions involved in active attending to auditory rhythms, sensorimotor integration, and working memory transformations of pitch and rhythmic patterns. By contrast, training-related effects common to MD and RD were localized to the posterior portion of the left superior temporal gyrus/planum temporale, an area implicated in spectrotemporal pattern matching and auditory-motor coordinate transformations. A single cluster in right superior temporal gyrus showed significantly greater activation during MD than RD. This is the first fMRI which has distinguished maturational from training effects during music processing. Copyright © 2012 Elsevier Inc. All rights reserved.

  11. A critical review of ERP and fMRI evidence on L2 syntactic processing.

    Science.gov (United States)

    Kotz, Sonja A

    2009-01-01

    The current review focuses on recent event-related brain potential (ERPs) and functional magnetic resonance imaging (fMRI) in L2 syntactic processing data. To this end, critical factors influencing both the dynamics of neural mechanisms (ERPs) and critical functional brain correlates (fMRI) are discussed. These entail the critical period hypothesis, levels of proficiency, cross-linguistic syntactic similarities and dissimilarities as well as brain bases that may or may not be shared during syntactic processing in a first (L1) and a second (L2) language. The data to date reveal that (i) the critical period hypothesis plays less of a significant role than initially discussed, (ii) L2 proficiency is a driving factor influencing peak and extent of activation in brain correlates and in neurophysiological mechanisms as a function of learning, and (iii) language transfer effects (i.e., positive transfer effects when L1 and L2 are structurally similar or negative transfer effects when L1 and L2 are structurally dissimilar) primarily from the L1 to the L2 and potentially vice versa need to be critically considered in future research.

  12. Residual fMRI sensitivity for identity changes in acquired prosopagnosia

    Directory of Open Access Journals (Sweden)

    Christopher J Fox

    2013-10-01

    Full Text Available While a network of cortical regions contribute to face processing, the lesions in acquired prosopagnosia are highly variable, and likely result in different combinations of spared and affected regions of this network. To assess the residual functional sensitivities of spared regions in prosopagnosia, we designed a rapid event-related functional magnetic resonance imaging (fMRI experiment that included pairs of faces with same or different identities and same or different expressions. By measuring the release from adaptation to these facial changes we determined the residual sensitivity of face-selective regions-of-interest. We tested three patients with acquired prosopagnosia, and all three of these patients demonstrated residual sensitivity for facial identity changes in surviving fusiform and occipital face areas of either the right or left hemisphere, but not in the right posterior superior temporal sulcus. The patients also showed some residual capabilities for facial discrimination with normal performance on the Benton Facial Recognition Test, but impaired performance on more complex tasks of facial discrimination. We conclude that fMRI can demonstrate residual processing of facial identity in acquired prosopagnosia, that this adaptation can occur in the same structures that show similar processing in healthy subjects, and further, that this adaptation may be related to behavioral indices of face perception.

  13. Assessment of lexical semantic judgment abilities in alcohol-dependent subjects: An fMRI study

    Indian Academy of Sciences (India)

    D Bagga; N Singh; S Modi; P Kumar; D Bhattacharya; M L Garg; S Khushu

    2013-12-01

    Neuropsychological studies have shown that alcohol dependence is associated with neurocognitive deficits in tasks requiring memory, perceptual motor skills, abstraction and problem solving, whereas language skills are relatively spared in alcoholics despite structural abnormalities in the language-related brain regions. To investigate the preserved mechanisms of language processing in alcohol-dependents, functional brain imaging was undertaken in healthy controls (=18) and alcohol-dependents (=16) while completing a lexical semantic judgment task in a 3 T MR scanner. Behavioural data indicated that alcohol-dependents took more time than controls for performing the task but there was no significant difference in their response accuracy. fMRI data analysis revealed that while performing the task, the alcoholics showed enhanced activations in left supramarginal gyrus, precuneus bilaterally, left angular gyrus, and left middle temporal gyrus as compared to control subjects. The extensive activations observed in alcoholics as compared to controls suggest that alcoholics recruit additional brain areas to meet the behavioural demands for equivalent task performance. The results are consistent with previous fMRI studies suggesting compensatory mechanisms for the execution of task for showing an equivalent performance or decreased neural efficiency of relevant brain networks. However, on direct comparison of the two groups, the results did not survive correction for multiple comparisons; therefore, the present findings need further exploration.

  14. Navigation of a telepresence robot via covert visuospatial attention and real-time fMRI.

    Science.gov (United States)

    Andersson, Patrik; Pluim, Josien P W; Viergever, Max A; Ramsey, Nick F

    2013-01-01

    Brain-computer interfaces (BCIs) allow people with severe neurological impairment and without ability to control their muscles to regain some control over their environment. The BCI user performs a mental task to regulate brain activity, which is measured and translated into commands controlling some external device. We here show that healthy participants are capable of navigating a robot by covertly shifting their visuospatial attention. Covert Visuospatial Attention (COVISA) constitutes a very intuitive brain function for spatial navigation and does not depend on presented stimuli or on eye movements. Our robot is equipped with motors and a camera that sends visual feedback to the user who can navigate it from a remote location. We used an ultrahigh field MRI scanner (7 Tesla) to obtain fMRI signals that were decoded in real time using a support vector machine. Four healthy subjects with virtually no training succeeded in navigating the robot to at least three of four target locations. Our results thus show that with COVISA BCI, realtime robot navigation can be achieved. Since the magnitude of the fMRI signal has been shown to correlate well with the magnitude of spectral power changes in the gamma frequency band in signals measured by intracranial electrodes, the COVISA concept may in future translate to intracranial application in severely paralyzed people.

  15. Scale-free brain-wave music from simultaneously EEG and fMRI recordings.

    Science.gov (United States)

    Lu, Jing; Wu, Dan; Yang, Hua; Luo, Cheng; Li, Chaoyi; Yao, Dezhong

    2012-01-01

    In the past years, a few methods have been developed to translate human EEG to music. In 2009, PloS One 4 e5915, we developed a method to generate scale-free brainwave music where the amplitude of EEG was translated to music pitch according to the power law followed by both of them, the period of an EEG waveform is translated directly to the duration of a note, and the logarithm of the average power change of EEG is translated to music intensity according to the Fechner's law. In this work, we proposed to adopt simultaneously-recorded fMRI signal to control the intensity of the EEG music, thus an EEG-fMRI music is generated by combining two different and simultaneous brain signals. And most importantly, this approach further realized power law for music intensity as fMRI signal follows it. Thus the EEG-fMRI music makes a step ahead in reflecting the physiological process of the scale-free brain.

  16. Activation of dorsolateral prefrontal cortex in a dual neuropsychological screening test: an fMRI approach.

    Science.gov (United States)

    Tachibana, Atsumichi; Noah, J Adam; Bronner, Shaw; Ono, Yumie; Hirano, Yoshiyuki; Niwa, Masami; Watanabe, Kazuko; Onozuka, Minoru

    2012-05-28

    The Kana Pick-out Test (KPT), which uses Kana or Japanese symbols that represent syllables, requires parallel processing of discrete (pick-out) and continuous (reading) dual tasks. As a dual task, the KPT is thought to test working memory and executive function, particularly in the prefrontal cortex (PFC), and is widely used in Japan as a clinical screen for dementia. Nevertheless, there has been little neurological investigation into PFC activity during this test. We used functional magnetic resonance imaging (fMRI) to evaluate changes in the blood oxygenation level-dependent (BOLD) signal in young healthy adults during performance of a computerized KPT dual task (comprised of reading comprehension and picking out vowels) and compared it to its single task components (reading or vowel pick-out alone). Behavioral performance of the KPT degraded compared to its single task components. Performance of the KPT markedly increased BOLD signal intensity in the PFC, and also activated sensorimotor, parietal association, and visual cortex areas. In conjunction analyses, bilateral BOLD signal in the dorsolateral PFC (Brodmann's areas 45, 46) was present only in the KPT. Our results support the central bottleneck theory and suggest that the dorsolateral PFC is an important mediator of neural activity for both short-term storage and executive processes. Quantitative evaluation of the KPT with fMRI in healthy adults is the first step towards understanding the effects of aging or cognitive impairment on KPT performance.

  17. Activation of dorsolateral prefrontal cortex in a dual neuropsychological screening test: An fMRI approach

    Directory of Open Access Journals (Sweden)

    Tachibana Atsumichi

    2012-05-01

    Full Text Available Abstract Background The Kana Pick-out Test (KPT, which uses Kana or Japanese symbols that represent syllables, requires parallel processing of discrete (pick-out and continuous (reading dual tasks. As a dual task, the KPT is thought to test working memory and executive function, particularly in the prefrontal cortex (PFC, and is widely used in Japan as a clinical screen for dementia. Nevertheless, there has been little neurological investigation into PFC activity during this test. Methods We used functional magnetic resonance imaging (fMRI to evaluate changes in the blood oxygenation level-dependent (BOLD signal in young healthy adults during performance of a computerized KPT dual task (comprised of reading comprehension and picking out vowels and compared it to its single task components (reading or vowel pick-out alone. Results Behavioral performance of the KPT degraded compared to its single task components. Performance of the KPT markedly increased BOLD signal intensity in the PFC, and also activated sensorimotor, parietal association, and visual cortex areas. In conjunction analyses, bilateral BOLD signal in the dorsolateral PFC (Brodmann's areas 45, 46 was present only in the KPT. Conclusions Our results support the central bottleneck theory and suggest that the dorsolateral PFC is an important mediator of neural activity for both short-term storage and executive processes. Quantitative evaluation of the KPT with fMRI in healthy adults is the first step towards understanding the effects of aging or cognitive impairment on KPT performance.

  18. Sparse representation of higher-order functional interaction patterns in task-based FMRI data.

    Science.gov (United States)

    Zhang, Shu; Li, Xiang; Lv, Jinglei; Jiang, Xi; Zhu, Dajiang; Chen, Hanbo; Zhang, Tuo; Guo, Lei; Liu, Tianming

    2013-01-01

    Traditional task-based fMRI activation detection methods, e.g., the widely used general linear model (GLM), assume that the brain's hemodynamic responses follow the block-based or event-related stimulus paradigm. Typically, these activation detections are performed voxel-wise independently, and then are usually followed by statistical corrections. Despite remarkable successes and wide adoption of these methods, it remains largely unknown how functional brain regions interact with each other within specific networks during task performance blocks and in the baseline. In this paper, we present a novel algorithmic pipeline to statistically infer and sparsely represent higher-order functional interaction patterns within the working memory network during task performance and in the baseline. Specifically, a collection of higher-order interactions are inferred via the greedy equivalence search (GES) algorithm for both task and baseline blocks. In the next stage, an effective online dictionary learning algorithm is utilized for sparse representation of the inferred higher-order interaction patterns. Application of this framework on a working memory task-based fMRI data reveals interesting and meaningful distributions of the learned sparse dictionary atoms in task and baseline blocks. In comparison with traditional voxel-wise activation detection and recent pair-wise functional connectivity analysis, our framework offers a new methodology for representation and exploration of higher-order functional activities in the brain.

  19. Cognitive Pragmatic Rehabilitation Program in Schizophrenia: A Single Case fMRI Study

    Science.gov (United States)

    Gabbatore, Ilaria; Geda, Elisabetta; Gastaldo, Luigi; Duca, Sergio; Costa, Tommaso; Bara, Bruno G.; Sacco, Katiuscia

    2017-01-01

    Introduction. The present study was intended to evaluate the effects of a rehabilitative training, the Cognitive Pragmatic Treatment (CPT), aimed at improving communicative-pragmatic abilities and the related cognitive components, on the cerebral modifications of a single case patient diagnosed with schizophrenia. Methods. The patient underwent two functional magnetic resonance imaging (fMRI) sessions, before and after the treatment. In order to assess brain changes, we calculated the Amplitude of Low Frequency Fluctuation (ALFF) index of the resting-state fMRI signal, which is interpreted as reflecting the intensity of the spontaneous regional activity of the brain. Behavioural measures of the patient's communicative performance were also gathered before and after training and at follow-up. Results. The patient improved his communicative performance in almost all tests. Posttraining stronger ALFF signal emerged in the superior, inferior, and medial frontal gyri, as well as the superior temporal gyri. Conclusions. Even if based on a single case study, these preliminary results show functional changes at the cerebral level that seem to support the patient's behavioural improvements.

  20. Criticality in large-scale brain FMRI dynamics unveiled by a novel point process analysis.

    Science.gov (United States)

    Tagliazucchi, Enzo; Balenzuela, Pablo; Fraiman, Daniel; Chialvo, Dante R

    2012-01-01

    Functional magnetic resonance imaging (fMRI) techniques have contributed significantly to our understanding of brain function. Current methods are based on the analysis of gradual and continuous changes in the brain blood oxygenated level dependent (BOLD) signal. Departing from that approach, recent work has shown that equivalent results can be obtained by inspecting only the relatively large amplitude BOLD signal peaks, suggesting that relevant information can be condensed in discrete events. This idea is further explored here to demonstrate how brain dynamics at resting state can be captured just by the timing and location of such events, i.e., in terms of a spatiotemporal point process. The method allows, for the first time, to define a theoretical framework in terms of an order and control parameter derived from fMRI data, where the dynamical regime can be interpreted as one corresponding to a system close to the critical point of a second order phase transition. The analysis demonstrates that the resting brain spends most of the time near the critical point of such transition and exhibits avalanches of activity ruled by the same dynamical and statistical properties described previously for neuronal events at smaller scales. Given the demonstrated functional relevance of the resting state brain dynamics, its representation as a discrete process might facilitate large-scale analysis of brain function both in health and disease.

  1. Functional subdivision of group-ICA results of fMRI data collected during cinema viewing.

    Directory of Open Access Journals (Sweden)

    Siina Pamilo

    Full Text Available Independent component analysis (ICA can unravel functional brain networks from functional magnetic resonance imaging (fMRI data. The number of the estimated components affects both the spatial pattern of the identified networks and their time-course estimates. Here group-ICA was applied at four dimensionalities (10, 20, 40, and 58 components to fMRI data collected from 15 subjects who viewed a 15-min silent film ("At land" by Maya Deren. We focused on the dorsal attention network, the default-mode network, and the sensorimotor network. The lowest dimensionalities demonstrated most prominent activity within the dorsal attention network, combined with the visual areas, and in the default-mode network; the sensorimotor network only appeared with ICA comprising at least 20 components. The results suggest that even very low-dimensional ICA can unravel the most prominent functionally-connected brain networks. However, increasing the number of components gives a more detailed picture and functionally feasible subdivision of the major networks. These results improve our understanding of the hierarchical subdivision of brain networks during viewing of a movie that provides continuous stimulation embedded in an attention-directing narrative.

  2. Acupuncture needling sensation: the neural correlates of deqi using fMRI.

    Science.gov (United States)

    Asghar, Aziz U R; Green, Gary; Lythgoe, Mark F; Lewith, George; MacPherson, Hugh

    2010-02-22

    The needling sensation of deqi is considered by most acupuncturists to be an important component of acupuncture, yet neuroimaging research that investigates this needle sensation has been limited. In this study we have investigated the effect of deqi and acute pain needling sensations upon brain fMRI blood oxygen level-dependent (BOLD) signals. Seventeen right-handed participants who received acupuncture at the right LI-4 (Hegu) acupoint were imaged in a 3T MRI scanner. fMRI datasets were classified, on the basis of psychophysical participants' reports of needling scores, into those that were associated with predominantly deqi sensations versus those with predominantly acute pain sensations. Brain areas showing changes in BOLD signal increases (activations) and decreases (deactivations) were identified. Differences were demonstrated in the pattern of activations and deactivations between groupings of scans associated with deqi versus pain sensations. For the deqi grouping, significant deactivations occurred, whereas significant activations did not. In contrast, the predominantly acute pain grouping was associated with a mixture of activations and deactivations. For the comparison between the predominately deqi sensation grouping and the acute pain sensation grouping (deqi>pain contrast), only negative Z value voxels resulted (mainly from deactivations in the deqi grouping and activations in the pain grouping) in the limbic/sub-cortical structures and the cerebellum regions of interest. Our results show the importance of collecting and accounting for needle sensation data in neuroimaging studies of acupuncture. 2009 Elsevier B.V. All rights reserved.

  3. Evaluation of group homogeneity during acupuncture stimulation in fMRI studies.

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    Sun, Jinbo; Qin, Wei; Dong, Minghao; Yuan, Kai; Liu, Jixin; Liu, Peng; Zhang, Yi; von Deneen, Karen M; Tian, Jie

    2010-08-01

    To evaluate the extent of individual differences of functional MRI (fMRI) signal changes induced by acupuncture stimulation. Acupuncture at ST36 and checkerboard stimulation was applied to 16 subjects. We calculated the mean distance using beta values in a generalized linear model (GLM) analysis and employed it to study the group homogeneity by detecting the outliers. A more significant individual difference was presented in acupuncture stimulation compared with visual stimulation through evaluation of the mean distance. From the group results, we found that the activations were more significant in the homogeneous group results. Combining the behavior and fMRI results, there was no direct correlation between deqi index and mean distance in acupuncture stimulation. The deqi index of the outlier was in the normal range and did not differ significantly from others. Traditional group results without removing outliers were not sensitive enough to detect the real acupuncture effect. We suggest that individual difference should be taken into consideration for future acupuncture studies. Also, group analysis paralleled with individual analysis is critical for a full understanding of acupuncture effects. 2010 Wiley-Liss, Inc.

  4. Joint ICA of ERP and fMRI during error-monitoring.

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    Edwards, Bethany G; Calhoun, Vince D; Kiehl, Kent A

    2012-01-16

    The anterior cingulate cortex (ACC) is commonly separated into two functional divisions: the cognitive division, which lies in the caudal region and the affective division, which lies in the rostral region of the ACC. Both regions of the ACC are engaged during error-monitoring tasks; however, little is known about the temporal sequencing associated with cognition and affective processes during error-monitoring. Here we use joint Independent Component Analysis (jICA) to couple event-related potential (ERP) time courses and functional magnetic resonance imaging (fMRI) spatial maps to examine the spatio-temporal stages of engagement in the two divisions of the ACC during error-monitoring. Consistent with hypotheses, two of the five significant spatio-temporal components identified by jICA revealed that the error-related negativity (ERN) ERP was associated with distinct spatial fMRI patterns in the ACC. The ERN(1) was associated with activity in the caudal ACC and lateral prefrontal cortex (lPFC) while the ERN(2) was associated with activity in the rostral ACC. These results suggest that during error-monitoring the caudal ACC and lPFC engage prior to the rostral ACC. These results suggest that cognition precedes affect during error-monitoring. Copyright © 2011 Elsevier Inc. All rights reserved.

  5. Aging affects the interaction between attentional control and source memory: an fMRI study.

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    Dulas, Michael R; Duarte, Audrey

    2014-12-01

    Age-related source memory impairments may be due, at least in part, to deficits in executive processes mediated by the PFC at both study and test. Behavioral work suggests that providing environmental support at encoding, such as directing attention toward item-source associations, may improve source memory and reduce age-related deficits in the recruitment of these executive processes. The present fMRI study investigated the effects of directed attention and aging on source memory encoding and retrieval. At study, participants were shown pictures of objects. They were either asked to attend to the objects and their color (source) or to their size. At test, participants determined if objects were seen before, and if so, whether they were the same color as previously. Behavioral results showed that direction of attention improved source memory for both groups; however, age-related deficits persisted. fMRI results revealed that, across groups, direction of attention facilitated medial temporal lobe-mediated contextual binding processes during study and attenuated right PFC postretrieval monitoring effects at test. However, persistent age-related source memory deficits may be related to increased recruitment of medial anterior PFC during encoding, indicative of self-referential processing, as well as underrecruitment of lateral anterior PFC-mediated relational processes. Taken together, this study suggests that, even when supported, older adults may fail to selectively encode goal-relevant contextual details supporting source memory performance.

  6. Children’s head motion during fMRI tasks is heritable and stable over time

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    Laura E. Engelhardt

    2017-06-01

    Full Text Available Head motion during fMRI scans negatively impacts data quality, and as post-acquisition techniques for addressing motion become increasingly stringent, data retention decreases. Studies conducted with adult participants suggest that movement acts as a relatively stable, heritable phenotype that serves as a marker for other genetically influenced phenotypes. Whether these patterns extend downward to childhood has critical implications for the interpretation and generalizability of fMRI data acquired from children. We examined factors affecting scanner motion in two samples: a population-based twin sample of 73 participants (ages 7–12 years and a case-control sample of 32 non-struggling and 78 struggling readers (ages 8–11 years, 30 of whom were scanned multiple times. Age, but not ADHD symptoms, was significantly related to scanner movement. Movement also varied as a function of task type, run length, and session length. Twin pair concordance for head motion was high for monozygotic twins and moderate for dizygotic twins. Cross-session test-retest reliability was high. Together, these findings suggest that children’s head motion is a genetically influenced trait that has the potential to systematically affect individual differences in BOLD changes within and across groups. We discuss recommendations for future work and best practices for pediatric neuroimaging.

  7. Counterfactual thinking: an fMRI study on changing the past for a better future

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    Ma, Ning; Ampe, Lisa; Baetens, Kris; Van Overwalle, Frank

    2013-01-01

    Recent studies suggest that a brain network mainly associated with episodic memory has a more general function in imagining oneself in another time, place or perspective (e.g. episodic future thought, theory of mind, default mode). If this is true, counterfactual thinking (e.g. ‘If I had left the office earlier, I wouldn’t have missed my train.’) should also activate this network. Present functional magnetic resonance imaging (fMRI) study explores the common and distinct neural activity of counterfactual and episodic thinking by directly comparing the imagining of upward counterfactuals (creating better outcomes for negative past events) with the re-experiencing of negative past events and the imagining of positive future events. Results confirm that episodic and counterfactual thinking share a common brain network, involving a core memory network (hippocampal area, temporal lobes, midline, and lateral parietal lobes) and prefrontal areas that might be related to mentalizing (medial prefrontal cortex) and performance monitoring (right prefrontal cortex). In contrast to episodic past and future thinking, counterfactual thinking recruits some of these areas more strongly and extensively, and additionally activates the bilateral inferior parietal lobe and posterior medial frontal cortex. We discuss these findings in view of recent fMRI evidence on the working of episodic memory and theory of mind. PMID:22403155

  8. An fMRI study of caring vs self-focus during induced compassion and pride.

    Science.gov (United States)

    Simon-Thomas, Emiliana R; Godzik, Jakub; Castle, Elizabeth; Antonenko, Olga; Ponz, Aurelie; Kogan, Aleksander; Keltner, Dacher J

    2012-08-01

    This study examined neural activation during the experience of compassion, an emotion that orients people toward vulnerable others and prompts caregiving, and pride, a self-focused emotion that signals individual strength and heightened status. Functional magnetic resonance images (fMRI) were acquired as participants viewed 55 s continuous sequences of slides to induce either compassion or pride, presented in alternation with sequences of neutral slides. Emotion self-report data were collected after each slide condition within the fMRI scanner. Compassion induction was associated with activation in the midbrain periaqueductal gray (PAG), a region that is activated during pain and the perception of others' pain, and that has been implicated in parental nurturance behaviors. Pride induction engaged the posterior medial cortex, a region that has been associated with self-referent processing. Self-reports of compassion experience were correlated with increased activation in a region near the PAG, and in the right inferior frontal gyrus (IFG). Self-reports of pride experience, in contrast, were correlated with reduced activation in the IFG and the anterior insula. These results provide preliminary evidence towards understanding the neural correlates of important interpersonal dimensions of compassion and pride. Caring (compassion) and self-focus (pride) may represent core appraisals that differentiate the response profiles of many emotions.

  9. An fMRI Study of Intra-Individual Functional Topography in the Human Cerebellum

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    Catherine J. Stoodley

    2010-01-01

    Full Text Available Neuroimaging studies report cerebellar activation during both motor and non-motor paradigms, and suggest a functional topography within the cerebellum. Sensorimotor tasks activate the anterior lobe, parts of lobule VI, and lobule VIII, whereas higher-level tasks activate lobules VI and VII in the posterior lobe. To determine whether these activation patterns are evident at a single-subject level, we conducted functional magnetic resonance imaging (fMRI during five tasks investigating sensorimotor (finger tapping, language (verb generation, spatial (mental rotation, working memory (N-back, and emotional processing (viewing images from the International Affective Picture System. Finger tapping activated the ipsilateral anterior lobe (lobules IV-V as well as lobules VI and VIII. Activation during verb generation was found in right lobules VII and VIIIA. Mental rotation activated left-lateralized clusters in lobules VII-VIIIA, VI-Crus I, and midline VIIAt. The N-back task showed bilateral activation in right lobules VI-Crus I and left lobules VIIB-VIIIA. Cerebellar activation was evident bilaterally in lobule VI while viewing arousing vs. neutral images. This fMRI study provides the first proof of principle demonstration that there is topographic organization of motor execution vs. cognitive/emotional domains within the cerebellum of a single individual, likely reflecting the anatomical specificity of cerebro-cerebellar circuits underlying different task domains. Inter-subject variability of motor and non-motor topography remains to be determined.

  10. Diagnostic classification of schizophrenia patients on the basis of regional reward-related FMRI signal patterns.

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    Stefan P Koch

    Full Text Available Functional neuroimaging has provided evidence for altered function of mesolimbic circuits implicated in reward processing, first and foremost the ventral striatum, in patients with schizophrenia. While such findings based on significant group differences in brain activations can provide important insights into the pathomechanisms of mental disorders, the use of neuroimaging results from standard univariate statistical analysis for individual diagnosis has proven difficult. In this proof of concept study, we tested whether the predictive accuracy for the diagnostic classification of schizophrenia patients vs. healthy controls could be improved using multivariate pattern analysis (MVPA of regional functional magnetic resonance imaging (fMRI activation patterns for the anticipation of monetary reward. With a searchlight MVPA approach using support vector machine classification, we found that the diagnostic category could be predicted from local activation patterns in frontal, temporal, occipital and midbrain regions, with a maximal cluster peak classification accuracy of 93% for the right pallidum. Region-of-interest based MVPA for the ventral striatum achieved a maximal cluster peak accuracy of 88%, whereas the classification accuracy on the basis of standard univariate analysis reached only 75%. Moreover, using support vector regression we could additionally predict the severity of negative symptoms from ventral striatal activation patterns. These results show that MVPA can be used to substantially increase the accuracy of diagnostic classification on the basis of task-related fMRI signal patterns in a regionally specific way.

  11. Diagnostic classification of schizophrenia patients on the basis of regional reward-related FMRI signal patterns.

    Science.gov (United States)

    Koch, Stefan P; Hägele, Claudia; Haynes, John-Dylan; Heinz, Andreas; Schlagenhauf, Florian; Sterzer, Philipp

    2015-01-01

    Functional neuroimaging has provided evidence for altered function of mesolimbic circuits implicated in reward processing, first and foremost the ventral striatum, in patients with schizophrenia. While such findings based on significant group differences in brain activations can provide important insights into the pathomechanisms of mental disorders, the use of neuroimaging results from standard univariate statistical analysis for individual diagnosis has proven difficult. In this proof of concept study, we tested whether the predictive accuracy for the diagnostic classification of schizophrenia patients vs. healthy controls could be improved using multivariate pattern analysis (MVPA) of regional functional magnetic resonance imaging (fMRI) activation patterns for the anticipation of monetary reward. With a searchlight MVPA approach using support vector machine classification, we found that the diagnostic category could be predicted from local activation patterns in frontal, temporal, occipital and midbrain regions, with a maximal cluster peak classification accuracy of 93% for the right pallidum. Region-of-interest based MVPA for the ventral striatum achieved a maximal cluster peak accuracy of 88%, whereas the classification accuracy on the basis of standard univariate analysis reached only 75%. Moreover, using support vector regression we could additionally predict the severity of negative symptoms from ventral striatal activation patterns. These results show that MVPA can be used to substantially increase the accuracy of diagnostic classification on the basis of task-related fMRI signal patterns in a regionally specific way.

  12. Decreased thalamocortical functional connectivity after 36 hours of total sleep deprivation: evidence from resting state FMRI.

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    Yongcong Shao

    Full Text Available OBJECTIVES: The thalamus and cerebral cortex are connected via topographically organized, reciprocal connections, which hold a key function in segregating internally and externally directed awareness information. Previous task-related studies have revealed altered activities of the thalamus after total sleep deprivation (TSD. However, it is still unclear how TSD impacts on the communication between the thalamus and cerebral cortex. In this study, we examined changes of thalamocortical functional connectivity after 36 hours of total sleep deprivation by using resting state function MRI (fMRI. MATERIALS AND METHODS: Fourteen healthy volunteers were recruited and performed fMRI scans before and after 36 hours of TSD. Seed-based functional connectivity analysis was employed and differences of thalamocortical functional connectivity were tested between the rested wakefulness (RW and TSD conditions. RESULTS: We found that the right thalamus showed decreased functional connectivity with the right parahippocampal gyrus, right middle temporal gyrus and right superior frontal gyrus in the resting brain after TSD when compared with that after normal sleep. As to the left thalamus, decreased connectivity was found with the right medial frontal gyrus, bilateral middle temporal gyri and left superior frontal gyrus. CONCLUSION: These findings suggest disruptive changes of the thalamocortical functional connectivity after TSD, which may lead to the decline of the arousal level and information integration, and subsequently, influence the human cognitive functions.

  13. Cerebral reorganization as a function of linguistic recovery in children: An fMRI study.

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    Elkana, Odelia; Frost, Ram; Kramer, Uri; Ben-Bashat, Dafna; Hendler, Talma; Schmidt, David; Schweiger, Avraham

    2011-02-01

    Characterizing and mapping the relationship between neuronal reorganization and functional recovery are essential to the understanding of cerebral plasticity and the dynamic processes which occur following brain damage. The neuronal mechanisms underlying linguistic recovery following left hemisphere (LH) lesions are still unknown. Using functional magnetic resonance imaging (fMRI), we investigated whether the extent of brain lateralization of linguistic functioning in specific regions of interest (ROIs) is correlated with the level of linguistic performance following recovery from acquired childhood aphasia. The study focused on a rare group of children in whom lesions occurred after normal language acquisition, but prior to complete maturation of the brain. During fMRI scanning, rhyming, comprehension and verb generation activation tasks were monitored. The imaging data were evaluated with reference to linguistic performance measured behaviorally during imaging, as well as outside the scanner. Compared with normal controls, we found greater right hemisphere (RH) lateralization in patients. However, correlations with linguistic performance showed that increased proficiency in linguistic tasks was associated with greater lateralization to the LH. These results were replicated in a longitudinal case study of a patient scanned twice, 3 years apart. Additional improvement in linguistic performance of the patient was accompanied by increasing lateralization to the LH in the anterior language region. This, however, was the result of a decreased involvement of the RH. These findings suggest that recovery is a dynamic, ongoing process, which may last for years after onset. The role of each hemisphere in the recovery process may continuously change within the chronic stage.

  14. Residual fMRI sensitivity for identity changes in acquired prosopagnosia.

    Science.gov (United States)

    Fox, Christopher J; Iaria, Giuseppe; Duchaine, Bradley C; Barton, Jason J S

    2013-01-01

    While a network of cortical regions contribute to face processing, the lesions in acquired prosopagnosia are highly variable, and likely result in different combinations of spared and affected regions of this network. To assess the residual functional sensitivities of spared regions in prosopagnosia, we designed a rapid event-related functional magnetic resonance imaging (fMRI) experiment that included pairs of faces with same or different identities and same or different expressions. By measuring the release from adaptation to these facial changes we determined the residual sensitivity of face-selective regions-of-interest. We tested three patients with acquired prosopagnosia, and all three of these patients demonstrated residual sensitivity for facial identity changes in surviving fusiform and occipital face areas of either the right or left hemisphere, but not in the right posterior superior temporal sulcus. The patients also showed some residual capabilities for facial discrimination with normal performance on the Benton Facial Recognition Test, but impaired performance on more complex tasks of facial discrimination. We conclude that fMRI can demonstrate residual processing of facial identity in acquired prosopagnosia, that this adaptation can occur in the same structures that show similar processing in healthy subjects, and further, that this adaptation may be related to behavioral indices of face perception.

  15. Detecting functional connectivity change points for single-subject fMRI data.

    Science.gov (United States)

    Cribben, Ivor; Wager, Tor D; Lindquist, Martin A

    2013-01-01

    Recently in functional magnetic resonance imaging (fMRI) studies there has been an increased interest in understanding the dynamic manner in which brain regions communicate with one another, as subjects perform a set of experimental tasks or as their psychological state changes. Dynamic Connectivity Regression (DCR) is a data-driven technique used for detecting temporal change points in functional connectivity between brain regions where the number and location of the change points are unknown a priori. After finding the change points, DCR estimates a graph or set of relationships between the brain regions for data that falls between pairs of change points. In previous work, the method was predominantly validated using multi-subject data. In this paper, we concentrate on single-subject data and introduce a new DCR algorithm. The new algorithm increases accuracy for individual subject data with a small number of observations and reduces the number of false positives in the estimated undirected graphs. We also introduce a new Likelihood Ratio test for comparing sparse graphs across (or within) subjects; thus allowing us to determine whether data should be combined across subjects. We perform an extensive simulation analysis on vector autoregression (VAR) data as well as to an fMRI data set from a study (n = 23) of a state anxiety induction using a socially evaluative threat challenge. The focus on single-subject data allows us to study the variation between individuals and may provide us with a deeper knowledge of the workings of the brain.

  16. The somatosensory representation of the human clitoris: an fMRI study.

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    Michels, Lars; Mehnert, Ulrich; Boy, Sönke; Schurch, Brigitte; Kollias, Spyros

    2010-01-01

    We studied the central representation of pudendal afferents arising from the clitoral nerves in 15 healthy adult female subjects using electrical dorsal clitoral nerve stimulation and fMRI. As a control body region, we electrically stimulated the right hallux in eight subjects. In a block design experiment, we applied bilateral clitoral stimulation and unilateral (right) hallux stimulation. Activation maps were calculated for the contrasts 'electrical dorsal clitoral nerve stimulation versus rest' and 'electrical hallux stimulation versus rest'. A random-effect group analysis for the clitoral stimulation showed significant activations bilateral in the superior and inferior frontal gyri, insulae and putamen and in the postcentral, precentral and inferior parietal gyri (including the primary and secondary somatosensory cortices). No activation was found on the mesial surface of the postcentral gyrus. For the hallux, activations occurred in a similar neuronal network but the activation in the primary somatosensory cortex was localized in the inter-hemispheric fissure. The results of this study demonstrate that the central representation of pudendal afferents arising from the clitoral nerves and sensory inputs from the hallux can be studied and distinguished from each other by fMRI. From the somatotopic order described in the somatosensory homunculus one would expect for electrical clitoral nerve stimulation activation of the mesial wall of the postcentral gyrus. In contrast, we found activations on the lateral surface of the postcentral gyrus.

  17. Enhanced subject-specific resting-state network detection and extraction with fast fMRI.

    Science.gov (United States)

    Akin, Burak; Lee, Hsu-Lei; Hennig, Jürgen; LeVan, Pierre

    2017-02-01

    Resting-state networks have become an important tool for the study of brain function. An ultra-fast imaging technique that allows to measure brain function, called Magnetic Resonance Encephalography (MREG), achieves an order of magnitude higher temporal resolution than standard echo-planar imaging (EPI). This new sequence helps to correct physiological artifacts and improves the sensitivity of the fMRI analysis. In this study, EPI is compared with MREG in terms of capability to extract resting-state networks. Healthy controls underwent two consecutive resting-state scans, one with EPI and the other with MREG. Subject-level independent component analyses (ICA) were performed separately for each of the two datasets. Using Stanford FIND atlas parcels as network templates, the presence of ICA maps corresponding to each network was quantified in each subject. The number of detected individual networks was significantly higher in the MREG data set than for EPI. Moreover, using short time segments of MREG data, such as 50 seconds, one can still detect and track consistent networks. Fast fMRI thus results in an increased capability to extract distinct functional regions at the individual subject level for the same scan times, and also allow the extraction of consistent networks within shorter time intervals than when using EPI, which is notably relevant for the analysis of dynamic functional connectivity fluctuations. Hum Brain Mapp 38:817-830, 2017. © 2016 Wiley Periodicals, Inc. © 2016 Wiley Periodicals, Inc.

  18. Measuring adult attachment representation in an fMRI environment: concepts and assessment.

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    Buchheim, Anna; George, Carol; Kachele, Horst; Erk, Susanne; Walter, Henrik

    2006-01-01

    Human attachment is defined as a biologically based behavioral system that influences motivational, cognitive, emotional, and memory processes with respect to intimate relationships (parents, life partner, own children). Recent neurobiological studies in this field have in common that they investigated social relationships by examining fMRI neuroimaging patterns while individuals viewed pictures of their beloved relationship partner versus friends, acquaintances, strangers, or mothers' responses to their young children. The researchers showed that the neural underpinnings of these unique intimate emotional states are linked to functionally specialized areas in the brain. Conceptualizing this work from a behavioral systems-attachment theory perspective, these studies did not directly address the subject's attachment representational system. Traditional attachment theory and research has been built on the analysis of attachment narratives, called 'attachment representation'. The Adult Attachment Projective developed by George and West in 2001 is a set of attachment-based schematic pictures. It is constructed to increasingly activate the participant's attachment system in the course of the task, that is, by the introduction of increasingly stressful attachment scenes concluding with pictures of individuals facing death and potential abuse alone. The attachment patterns are evaluated based on individuals' overall verbal response to the picture set. This paper proposes that the AAP is a fruitful measure to use in an fMRI environment to examine brain activation patterns in adults while they are speaking overtly about attachment stories in a standardized setting.

  19. Counterfactual thinking: an fMRI study on changing the past for a better future.

    Science.gov (United States)

    Van Hoeck, Nicole; Ma, Ning; Ampe, Lisa; Baetens, Kris; Vandekerckhove, Marie; Van Overwalle, Frank

    2013-06-01

    Recent studies suggest that a brain network mainly associated with episodic memory has a more general function in imagining oneself in another time, place or perspective (e.g. episodic future thought, theory of mind, default mode). If this is true, counterfactual thinking (e.g. 'If I had left the office earlier, I wouldn't have missed my train.') should also activate this network. Present functional magnetic resonance imaging (fMRI) study explores the common and distinct neural activity of counterfactual and episodic thinking by directly comparing the imagining of upward counterfactuals (creating better outcomes for negative past events) with the re-experiencing of negative past events and the imagining of positive future events. Results confirm that episodic and counterfactual thinking share a common brain network, involving a core memory network (hippocampal area, temporal lobes, midline, and lateral parietal lobes) and prefrontal areas that might be related to mentalizing (medial prefrontal cortex) and performance monitoring (right prefrontal cortex). In contrast to episodic past and future thinking, counterfactual thinking recruits some of these areas more strongly and extensively, and additionally activates the bilateral inferior parietal lobe and posterior medial frontal cortex. We discuss these findings in view of recent fMRI evidence on the working of episodic memory and theory of mind.

  20. The neural correlates of problem states: testing FMRI predictions of a computational model of multitasking.

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    Jelmer P Borst

    Full Text Available BACKGROUND: It has been shown that people can only maintain one problem state, or intermediate mental representation, at a time. When more than one problem state is required, for example in multitasking, performance decreases considerably. This effect has been explained in terms of a problem state bottleneck. METHODOLOGY: In the current study we use the complimentary methodologies of computational cognitive modeling and neuroimaging to investigate the neural correlates of this problem state bottleneck. In particular, an existing computational cognitive model was used to generate a priori fMRI predictions for a multitasking experiment in which the problem state bottleneck plays a major role. Hemodynamic responses were predicted for five brain regions, corresponding to five cognitive resources in the model. Most importantly, we predicted the intraparietal sulcus to show a strong effect of the problem state manipulations. CONCLUSIONS: Some of the predictions were confirmed by a subsequent fMRI experiment, while others were not matched by the data. The experiment supported the hypothesis that the problem state bottleneck is a plausible cause of the interference in the experiment and that it could be located in the intraparietal sulcus.

  1. A template of rat brain based on fMRI T2* imaging

    Institute of Scientific and Technical Information of China (English)

    HU Zhenghui; WU Yigen; WANG Xiaochuan; WANG Jianzhi; CHEN Feiyan; TANG Xiaowei

    2005-01-01

    The development of functional magnetic resonance imaging (fMRI) technology has made it possible to carry out functional brain imaging experiments in small animals. Usually, group data is required to form the assessment of population, which can not only increase the sensitivity of the overall experiment, but also allow the generalization of the conclusion to the whole population. In order to average the signals of functional brain images from different subjects, it is necessary to put all the mapping images into the same standard space (template image). However, up to now, most animal brain templates remain unavailable and it must be done by ourselves. In this study, a template image based on the brains of eight male Wistar rats is obtained, and it is successfully used in our present Alzheimer disease (AD)-like rat model studies as template for spatially normalizing images to the same stereotaxical space. The fMRI results processed with statistical parametric mapping (SPM99) software are in agreement with the results from immunohistochemical experiment, which proves that this method is universally applicable to the pathologic models of other small animals and to human brain lesion studies.

  2. fMRI analysis for motor paradigms using EMG-based designs: a validation study.

    Science.gov (United States)

    van Rootselaar, Anne-Fleur; Renken, Remco; de Jong, Bauke M; Hoogduin, Johannes M; Tijssen, Marina A J; Maurits, Natasha M

    2007-11-01

    The goal of the present validation study is to show that continuous surface EMG recorded simultaneously with 3T fMRI can be used to identify local brain activity related to (1) motor tasks, and to (2) muscle activity independently of a specific motor task, i.e. spontaneous (abnormal) movements. Five healthy participants performed a motor task, consisting of posture (low EMG power), and slow (medium EMG power) and fast (high EMG power) wrist flexion-extension movements. Brain activation maps derived from a conventional block design analysis (block-only design) were compared with brain activation maps derived using EMG-based regressors: (1) using the continuous EMG power as a single regressor of interest (EMG-only design) to relate motor performance and brain activity, and (2) using EMG power variability as an additional regressor in the fMRI block design analysis to relate movement variability and brain activity (mathematically) independent of the motor task. The agreement between the identified brain areas for the block-only design and the EMG-only design was excellent for all participants. Additionally, we showed that EMG power variability correlated well with activity in brain areas known to be involved in movement modulation. These innovative EMG-fMRI analysis techniques will allow the application of novel motor paradigms. This is an important step forward in the study of both the normally functioning motor system and the pathophysiological mechanisms in movement disorders.

  3. Differential Activation Patterns of fMRI in Sleep-Deprived Brain: Restoring Effects of Acupuncture

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    Lei Gao

    2014-01-01

    Full Text Available Previous studies suggested a remediation role of acupuncture in insomnia, and acupuncture also has been used in insomnia empirically and clinically. In this study, we employed fMRI to test the role of acupuncture in sleep deprivation (SD. Sixteen healthy volunteers (8 males were recruited and scheduled for three fMRI scanning procedures, one following the individual’s normal sleep and received acupuncture SP6 (NOR group and the other two after 24 h of total SD with acupuncture on SP6 (SD group or sham (Sham group. The sessions were counterbalanced approximately two weeks apart. Acupuncture stimuli elicited significantly different activation patterns of three groups. In NOR group, the right superior temporal lobe, left inferior parietal lobule, and left postcentral gyrus were activated; in SD group, the anterior cingulate cortex, bilateral insula, left basal ganglia, and thalamus were significantly activated while, in Sham group, the bilateral thalamus and left cerebellum were activated. Different activation patterns suggest a unique role of acupuncture on SP6 in remediation of SD. SP6 elicits greater and anatomically different activations than those of sham stimuli; that is, the salience network, a unique interoceptive autonomic circuit, may indicate the mechanism underlying acupuncture in restoring sleep deprivation.

  4. Neural coupling of cooperative hand movements: a reflex and fMRI study.

    Science.gov (United States)

    Dietz, Volker; Macauda, Gianluca; Schrafl-Altermatt, Miriam; Wirz, Markus; Kloter, Evelyne; Michels, Lars

    2015-04-01

    The neural control of "cooperative" hand movements reflecting "opening a bottle" was explored in human subjects by electromyographic (EMG) and functional magnetic resonance imaging (fMRI) recordings. EMG responses to unilateral nonnoxious ulnar nerve stimulation were analyzed in the forearm muscles of both sides during dynamic movements against a torque applied by the right hand to a device which was compensated for by the left hand. For control, stimuli were applied while task was performed in a static/isometric mode and during bilateral synchronous pro-/supination movements. During the dynamic cooperative task, EMG responses to stimulations appeared in the right extensor and left flexor muscles, regardless of which side was stimulated. Under the control conditions, responses appeared only on the stimulated side. fMRI recordings showed a bilateral extra-activation and functional coupling of the secondary somatosensory cortex (S2) during the dynamic cooperative, but not during the control, tasks. This activation might reflect processing of shared cutaneous input during the cooperative task. Correspondingly, it is assumed that stimulation-induced unilateral volleys are processed in S2, leading to a release of EMG responses to both forearms. This indicates a task-specific neural coupling during cooperative hand movements, which has consequences for the rehabilitation of hand function in poststroke patients.

  5. High resolution fMRI of subcortical regions during visual erotic stimulation at 7 T.

    Science.gov (United States)

    Walter, Martin; Stadler, Joerg; Tempelmann, Claus; Speck, Oliver; Northoff, Georg

    2008-03-01

    Involvement of distinct subcortical structures during sexual arousal was shown in animals and functional imaging studies gave coarse evidence for a similar organisation in humans. In contrast to previous imaging studies at lower field strengths, we tried to investigate activation in distinguishable subcortical structures at high spatial resolution during a short stimulating paradigm to further account for potential effects of attenuation or adaptation. Seven healthy subjects were investigated using functional magnetic resonance imaging (fMRI) on a 7 T scanner. High resolution EPI images of 1.4 x 1.4 mm2 inplane resolution were acquired in a single functional session of 13.6 minutes. During the session erotic and non-erotic pictures were presented in an event-related design. In the unsmoothed data with preserved high spatial resolution significant effects were detected in relevant structures, including anterior caudate and mediodorsal thalamus. These effects were restricted to subcortical target structures and their anatomical boundaries. This study demonstrates that fMRI at high fields provides an ideal tool to investigate functional anatomy of subcortical structures. Due to an increased signal-to-noise ratio, functional scans of short duration can be acquired at high resolution without the need for further spatial smoothing.

  6. Cerebral blood flow measurement using fMRI and PET: a cross-validation study.

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    Chen, Jean J; Wieckowska, Marguerite; Meyer, Ernst; Pike, G Bruce

    2008-01-01

    An important aspect of functional magnetic resonance imaging (fMRI) is the study of brain hemodynamics, and MR arterial spin labeling (ASL) perfusion imaging has gained wide acceptance as a robust and noninvasive technique. However, the cerebral blood flow (CBF) measurements obtained with ASL fMRI have not been fully validated, particularly during global CBF modulations. We present a comparison of cerebral blood flow changes (DeltaCBF) measured using a flow-sensitive alternating inversion recovery (FAIR) ASL perfusion method to those obtained using H(2) (15)O PET, which is the current gold standard for in vivo imaging of CBF. To study regional and global CBF changes, a group of 10 healthy volunteers were imaged under identical experimental conditions during presentation of 5 levels of visual stimulation and one level of hypercapnia. The CBF changes were compared using 3 types of region-of-interest (ROI) masks. FAIR measurements of CBF changes were found to be slightly lower than those measured with PET (average DeltaCBF of 21.5 +/- 8.2% for FAIR versus 28.2 +/- 12.8% for PET at maximum stimulation intensity). Nonetheless, there was a strong correlation between measurements of the two modalities. Finally, a t-test comparison of the slopes of the linear fits of PET versus ASL DeltaCBF for all 3 ROI types indicated no significant difference from unity (P > .05).

  7. Cerebral Blood Flow Measurement Using fMRI and PET: A Cross-Validation Study

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    Jean J. Chen

    2008-01-01

    Full Text Available An important aspect of functional magnetic resonance imaging (fMRI is the study of brain hemodynamics, and MR arterial spin labeling (ASL perfusion imaging has gained wide acceptance as a robust and noninvasive technique. However, the cerebral blood flow (CBF measurements obtained with ASL fMRI have not been fully validated, particularly during global CBF modulations. We present a comparison of cerebral blood flow changes (ΔCBF measured using a flow-sensitive alternating inversion recovery (FAIR ASL perfusion method to those obtained using H2O15 PET, which is the current gold standard for in vivo imaging of CBF. To study regional and global CBF changes, a group of 10 healthy volunteers were imaged under identical experimental conditions during presentation of 5 levels of visual stimulation and one level of hypercapnia. The CBF changes were compared using 3 types of region-of-interest (ROI masks. FAIR measurements of CBF changes were found to be slightly lower than those measured with PET (average ΔCBF of 21.5±8.2% for FAIR versus 28.2±12.8% for PET at maximum stimulation intensity. Nonetheless, there was a strong correlation between measurements of the two modalities. Finally, a t-test comparison of the slopes of the linear fits of PET versus ASL ΔCBF for all 3 ROI types indicated no significant difference from unity (P>.05.

  8. Effective Preprocessing Procedures Virtually Eliminate Distance-Dependent Motion Artifacts in Resting State FMRI.

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    Jo, Hang Joon; Gotts, Stephen J; Reynolds, Richard C; Bandettini, Peter A; Martin, Alex; Cox, Robert W; Saad, Ziad S

    2013-05-21

    Artifactual sources of resting-state (RS) FMRI can originate from head motion, physiology, and hardware. Of these sources, motion has received considerable attention and was found to induce corrupting effects by differentially biasing correlations between regions depending on their distance. Numerous corrective approaches have relied on the identification and censoring of high-motion time points and the use of the brain-wide average time series as a nuisance regressor to which the data are orthogonalized (Global Signal Regression, GSReg). We first replicate the previously reported head-motion bias on correlation coefficients using data generously contributed by Power et al. (2012). We then show that while motion can be the source of artifact in correlations, the distance-dependent bias-taken to be a manifestation of the motion effect on correlation-is exacerbated by the use of GSReg. Put differently, correlation estimates obtained after GSReg are more susceptible to the presence of motion and by extension to the levels of censoring. More generally, the effect of motion on correlation estimates depends on the preprocessing steps leading to the correlation estimate, with certain approaches performing markedly worse than others. For this purpose, we consider various models for RS FMRI preprocessing and show that WMeLOCAL, as subset of the ANATICOR discussed by Jo et al. (2010), denoising approach results in minimal sensitivity to motion and reduces by extension the dependence of correlation results on censoring.

  9. Are fMRI event-related response constant in time? A model selection answer.

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    Donnet, Sophie; Lavielle, Marc; Poline, Jean-Baptiste

    2006-07-01

    An accurate estimation of the hemodynamic response function (HRF) in functional magnetic resonance imaging (fMRI) is crucial for a precise spatial and temporal estimate of the underlying neuronal processes. Recent works have proposed non-parametric estimation of the HRF under the hypotheses of linearity and stationarity in time. Biological literature suggests, however, that response magnitude may vary with attention or ongoing activity. We therefore test a more flexible model that allows for the variation of the magnitude of the HRF with time in a maximum likelihood framework. Under this model, the magnitude of the HRF evoked by a single event may vary across occurrences of the same type of event. This model is tested against a simpler model with a fixed magnitude using information theory. We develop a standard EM algorithm to identify the event magnitudes and the HRF. We test this hypothesis on a series of 32 regions (4 ROIS on eight subjects) of interest and find that the more flexible model is better than the usual model in most cases. The important implications for the analysis of fMRI time series for event-related neuroimaging experiments are discussed.

  10. Adjudicating between face-coding models with individual-face fMRI responses.

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    Johan D Carlin

    2017-07-01

    Full Text Available The perceptual representation of individual faces is often explained with reference to a norm-based face space. In such spaces, individuals are encoded as vectors where identity is primarily conveyed by direction and distinctiveness by eccentricity. Here we measured human fMRI responses and psychophysical similarity judgments of individual face exemplars, which were generated as realistic 3D animations using a computer-graphics model. We developed and evaluated multiple neurobiologically plausible computational models, each of which predicts a representational distance matrix and a regional-mean activation profile for 24 face stimuli. In the fusiform face area, a face-space coding model with sigmoidal ramp tuning provided a better account of the data than one based on exemplar tuning. However, an image-processing model with weighted banks of Gabor filters performed similarly. Accounting for the data required the inclusion of a measurement-level population averaging mechanism that approximates how fMRI voxels locally average distinct neuronal tunings. Our study demonstrates the importance of comparing multiple models and of modeling the measurement process in computational neuroimaging.

  11. Intrinsic brain network abnormalities in migraines without aura revealed in resting-state fMRI.

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    Ting Xue

    Full Text Available BACKGROUND: Previous studies have defined low-frequency, spatially consistent intrinsic connectivity networks (ICN in resting functional magnetic resonance imaging (fMRI data which reflect functional interactions among distinct brain areas. We sought to explore whether and how repeated migraine attacks influence intrinsic brain connectivity, as well as how activity in these networks correlates with clinical indicators of migraine. METHODS/PRINCIPAL FINDINGS: Resting-state fMRI data in twenty-three patients with migraines without aura (MwoA and 23 age- and gender-matched healthy controls (HC were analyzed using independent component analysis (ICA, in combination with a "dual-regression" technique to identify the group differences of three important pain-related networks [default mode network (DMN, bilateral central executive network (CEN, salience network (SN] between the MwoA patients and HC. Compared with the HC, MwoA patients showed aberrant intrinsic connectivity within the bilateral CEN and SN, and greater connectivity between both the DMN and right CEN (rCEN and the insula cortex - a critical region involving in pain processing. Furthermore, greater connectivity between both the DMN and rCEN and the insula correlated with duration of migraine. CONCLUSIONS: Our findings may provide new insights into the characterization of migraine as a condition affecting brain activity in intrinsic connectivity networks. Moreover, the abnormalities may be the consequence of a persistent central neural system dysfunction, reflecting cumulative brain insults due to frequent ongoing migraine attacks.

  12. Time, space and emotion: fMRI reveals content-specific activation during text comprehension.

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    Ferstl, Evelyn C; von Cramon, D Yves

    2007-11-12

    Story comprehension involves building a situation model of the text, i.e., a representation containing information on the who, where, when and why of the story. Using fMRI at 3T, domain-specific activations for three different information aspects were sought. Twenty participants read two sentence stories half of which contained inconsistencies concerning emotional, temporal or spatial information. Partly replicating previous results [E.C. Ferstl, M. Rinck, D.Y. von Cramon, Emotional and temporal aspects of situation model processing during text comprehension: an event-related fMRI study, J. Cogn. Neurosci. 17 (2005) 724-739], the anterior lateral prefrontal cortex/orbito-frontal cortex proved important for processing temporal information. The left anterior temporal lobe was particularly important during emotional stories. Most importantly, spatial information elicited bilateral activation in the collateral sulci and the posterior cingulate cortex, areas important for visuo-spatial cognition. These findings provide further evidence for content-specific processes during text comprehension.

  13. The Potentiation of Associative Memory by Emotions: An Event-Related FMRI Study

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    David Luck

    2014-01-01

    Full Text Available Establishing associations between pieces of information is related to the medial temporal lobe (MTL. However, it remains unclear how emotions affect memory for associations and, consequently, MTL activity. Thus, this event-related fMRI study attempted to identify neural correlates of the influence of positive and negative emotions on associative memory. Twenty-five participants were instructed to memorize 90 pairs of standardized pictures during a scanned encoding phase. Each pair was composed of a scene and an unrelated object. Trials were neutral, positive, or negative as a function of the emotional valence of the scene. At the behavioral level, participants exhibited better memory retrieval for both emotional conditions relative to neutral trials. Within the right MTL, a functional dissociation was observed, with entorhinal activation elicited by emotional associations, posterior parahippocampal activation elicited by neutral associations, and hippocampal activation elicited by both emotional and neutral associations. In addition, emotional associations induced greater activation than neutral trials in the right amygdala. This fMRI study shows that emotions are associated with the performance improvement of associative memory, by enhancing activity in the right amygdala and the right entorhinal cortex. It also provides evidence for a rostrocaudal specialization within the MTL regarding the emotional valence of associations.

  14. Ageing differentially affects neural processing of different conflict types – an fMRI study

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    Margarethe eKorsch

    2014-04-01

    Full Text Available Interference control and conflict resolution is affected by ageing. There is increasing evidence that ageing does not compromise interference control in general but rather shows distinctive effects on different components of interference control. Different conflict types, (e.g. stimulus-stimulus (S-S or stimulus-response (S-R conflicts trigger different cognitive processes and thus activate different neural networks. In the present functional magnetic resonance imaging (fMRI study, we used a combined Flanker and Stimulus Response Conflict (SRC task to investigate the effect of ageing on S-S and S-R conflicts. Behavioral data analysis revealed larger SRC effects in elderly. fMRI Results show that both age groups recruited similar regions (caudate nucleus, cingulate gyrus and middle occipital gyrus during Flanker conflict processing. Furthermore, elderly show an additional activation pattern in parietal and frontal areas. In contrast, no common activation of both age groups was found in response to the SRC. These data suggest that ageing has distinctive effects on S-S and S-R conflicts.

  15. Verbal to visual code switching improves working memory in older adults: An fMRI study

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    Mariko eOsaka

    2012-02-01

    Full Text Available The effects of verbal to visual code switching training on working memory performance were investigated in the elderly. Twenty-five elderly people were introduced to a verbal to visual code switching strategy (training group while the other 25 were not (control group. During this strategy training period, participants in the training group practiced focusing their attention on a target word both by drawing the target’s figure and by forming mental images of the target. To explore the neural substrates underlying strategy effects, fMRI was used to measure brain activity of the elderly in both groups while they performed a working memory task (reading span test, RST, before and after the attention training period. RST recognition accuracy was enhanced only in the training group. fMRI data for this group showed increased activation in the anterior cingulate cortex (ACC, a region that typically shows activation in young adults performing the RST. Furthermore, activation was found both in the left and right inferior parietal lobule (IPL and right superior parietal lobule (SPL, while there was no activation in these areas for the control group. These findings suggest that using a strategy of verbal to visual code switching helped the elderly participants to maintain the words in working memory.

  16. Task-dependent semantic interference in language production: an fMRI study.

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    Spalek, Katharina; Thompson-Schill, Sharon L

    2008-12-01

    We used fMRI to investigate competition during language production in two word production tasks: object naming and color naming of achromatic line drawings. Generally, fMRI activation was higher for color naming. The line drawings were followed by a word (the distractor word) that referred to either the object, a related object, or an unrelated object. The effect of the distractor word on the BOLD response was qualitatively different for the two tasks. The activation pattern suggests two different kinds of competition during lexical retrieval: (1) Task-relevant responses (e.g., red in color naming) compete with task-irrelevant responses (i.e., the object's name). This competition effect was dominant in prefrontal cortex. (2) Multiple task-relevant responses (i.e., target word and distractor word) compete for selection. This competition effect was dominant in ventral temporal cortex. This study provides further evidence for the distinct roles of frontal and temporal cortex in language production, while highlighting the effects of competition, albeit from different sources, in both regions.

  17. Identifying Core Affect in Individuals from fMRI Responses to Dynamic Naturalistic Audiovisual Stimuli.

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    Kim, Jongwan; Wang, Jing; Wedell, Douglas H; Shinkareva, Svetlana V

    2016-01-01

    Recent research has demonstrated that affective states elicited by viewing pictures varying in valence and arousal are identifiable from whole brain activation patterns observed with functional magnetic resonance imaging (fMRI). Identification of affective states from more naturalistic stimuli has clinical relevance, but the feasibility of identifying these states on an individual trial basis from fMRI data elicited by dynamic multimodal stimuli is unclear. The goal of this study was to determine whether affective states can be similarly identified when participants view dynamic naturalistic audiovisual stimuli. Eleven participants viewed 5s audiovisual clips in a passive viewing task in the scanner. Valence and arousal for individual trials were identified both within and across participants based on distributed patterns of activity in areas selectively responsive to audiovisual naturalistic stimuli while controlling for lower level features of the stimuli. In addition, the brain regions identified by searchlight analyses to represent valence and arousal were consistent with previously identified regions associated with emotion processing. These findings extend previous results on the distributed representation of affect to multimodal dynamic stimuli.

  18. Evaluation of statistical inference on empirical resting state fMRI.

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    Yang, Xue; Kang, Hakmook; Newton, Allen T; Landman, Bennett A

    2014-04-01

    Modern statistical inference techniques may be able to improve the sensitivity and specificity of resting state functional magnetic resonance imaging (rs-fMRI) connectivity analysis through more realistic assumptions. In simulation, the advantages of such methods are readily demonstrable. However, quantitative empirical validation remains elusive in vivo as the true connectivity patterns are unknown and noise distributions are challenging to characterize, especially in ultra-high field (e.g., 7T fMRI). Though the physiological characteristics of the fMRI signal are difficult to replicate in controlled phantom studies, it is critical that the performance of statistical techniques be evaluated. The SIMulation EXtrapolation (SIMEX) method has enabled estimation of bias with asymptotically consistent estimators on empirical finite sample data by adding simulated noise . To avoid the requirement of accurate estimation of noise structure, the proposed quantitative evaluation approach leverages the theoretical core of SIMEX to study the properties of inference methods in the face of diminishing data (in contrast to increasing noise). The performance of ordinary and robust inference methods in simulation and empirical rs-fMRI are compared using the proposed quantitative evaluation approach. This study provides a simple, but powerful method for comparing a proxy for inference accuracy using empirical data.

  19. Effective Preprocessing Procedures Virtually Eliminate Distance-Dependent Motion Artifacts in Resting State FMRI

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    Hang Joon Jo

    2013-01-01

    Full Text Available Artifactual sources of resting-state (RS FMRI can originate from head motion, physiology, and hardware. Of these sources, motion has received considerable attention and was found to induce corrupting effects by differentially biasing correlations between regions depending on their distance. Numerous corrective approaches have relied on the identification and censoring of high-motion time points and the use of the brain-wide average time series as a nuisance regressor to which the data are orthogonalized (Global Signal Regression, GSReg. We replicate the previously reported head-motion bias on correlation coefficients and then show that while motion can be the source of artifact in correlations, the distance-dependent bias is exacerbated by GSReg. Put differently, correlation estimates obtained after GSReg are more susceptible to the presence of motion and by extension to the levels of censoring. More generally, the effect of motion on correlation estimates depends on the preprocessing steps leading to the correlation estimate, with certain approaches performing markedly worse than others. For this purpose, we consider various models for RS FMRI preprocessing and show that the local white matter regressor (WMeLOCAL, a subset of ANATICOR, results in minimal sensitivity to motion and reduces by extension the dependence of correlation results on censoring.

  20. Enhanced emotional reactivity after selective REM sleep deprivation in humans: an fMRI study

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    Alejandra eRosales-Lagarde

    2012-06-01

    Full Text Available Converging evidence from animal and human studies suggest that REM sleep modulates emotional processing. The aim of the present study was to explore the effects of selective REM sleep deprivation on emotional responses to threatening visual stimuli and their brain correlates using functional magnetic resonance imaging (fMRI. Twenty healthy s