Dynamic Multi-Coil Shimming of the Human Brain at 7 Tesla

Science.gov (United States)

Juchem, Christoph; Nixon, Terence W.; McIntyre, Scott; Boer, Vincent O.; Rothman, Douglas L.; de Graaf, Robin A.

2011-01-01

High quality magnetic field homogenization of the human brain (i.e. shimming) for MR imaging and spectroscopy is a demanding task. The susceptibility differences between air and tissue are a longstanding problem as they induce complex field distortions in the prefrontal cortex and the temporal lobes. To date, the theoretical gains of high field MR have only been realized partially in the human brain due to limited magnetic field homogeneity. A novel shimming technique for the human brain is presented that is based on the combination of non-orthogonal basis fields from 48 individual, circular coils. Custom-built amplifier electronics enabled the dynamic application of the multi-coil shim fields in a slice-specific fashion. Dynamic multi-coil (DMC) shimming is shown to eliminate most of the magnetic field inhomogeneity apparent in the human brain at 7 Tesla and provided improved performance compared to state-of-the-art dynamic shim updating with zero through third order spherical harmonic functions. The novel technique paves the way for high field MR applications of the human brain for which excellent magnetic field homogeneity is a prerequisite. PMID:21824794

Progress in clinical research and application of resting state functional brain imaging

International Nuclear Information System (INIS)

Long Miaomiao; Ni Hongyan

2013-01-01

Resting state functional brain imaging experimental design is free of stimulus task and offers various parametric maps through different data-driven post processing methods with endogenous BOLD signal changes as the source of imaging. Mechanism of resting state brain activities could be extensively studied with improved patient compliance and clinical application compared with task related functional brain imaging. Also resting state functional brain imaging can be used as a method of data acquisition, with implicit neuronal activity as a kind of experimental design, to reveal characteristic brain activities of epileptic patient. Even resting state functional brain imaging data processing method can be used to analyze task related functional MRI data, opening new horizons of task related functional MRI study. (authors)

Multi circular-cavity surface coil for magnetic resonance imaging of monkey's brain at 4 Tesla

Science.gov (United States)

Osorio, A. I.; Solis-Najera, S. E.; Vázquez, F.; Wang, R. L.; Tomasi, D.; Rodriguez, A. O.

2014-11-01

Animal models in medical research has been used to study humans diseases for several decades. The use of different imaging techniques together with different animal models offers a great advantage due to the possibility to study some human pathologies without the necessity of chirurgical intervention. The employ of magnetic resonance imaging for the acquisition of anatomical and functional images is an excellent tool because its noninvasive nature. Dedicated coils to perform magnetic resonance imaging experiments are obligatory due to the improvement on the signal-to-noise ratio and reduced specific absorption ratio. A specifically designed surface coil for magnetic resonance imaging of monkey's brain is proposed based on the multi circular-slot coil. Numerical simulations of the magnetic and electric fields were also performed using the Finite Integration Method to solve Maxwell's equations for this particular coil design and, to study the behavior of various vector magnetic field configurations and specific absorption ratio. Monkey's brain images were then acquired with a research-dedicated magnetic resonance imaging system at 4T, to evaluate the anatomical images with conventional imaging sequences. This coil showed good quality images of a monkey's brain and full compatibility with standard pulse sequences implemented in research-dedicated imager.

Functional magnetic resonance imaging by visual stimulation

International Nuclear Information System (INIS)

Nishimura, Yukiko; Negoro, Kiyoshi; Morimatsu, Mitsunori; Hashida, Masahiro

1996-01-01

We evaluated functional magnetic resonance images obtained in 8 healthy subjects in response to visual stimulation using a conventional clinical magnetic resonance imaging system with multi-slice spin-echo echo planar imaging. Activation in the visual cortex was clearly demonstrated by the multi-slice experiment with a task-related change in signal intensity. In addition to the primary visual cortex, other areas were also activated by a complicated visual task. Multi-slice spin-echo echo planar imaging offers high temporal resolution and allows the three-dimensional analysis of brain function. Functional magnetic resonance imaging provides a useful noninvasive method of mapping brain function. (author)

Heuristically improved Bayesian segmentation of brain MR images ...

African Journals Online (AJOL)

Heuristically improved Bayesian segmentation of brain MR images. ... or even the most prevalent task in medical image processing is image segmentation. Among them, brain MR images suffer ... show that our algorithm performs well in comparison with the one implemented in SPM. It can be concluded that incorporating ...

Nonlocal atlas-guided multi-channel forest learning for human brain labeling.

Science.gov (United States)

Ma, Guangkai; Gao, Yaozong; Wu, Guorong; Wu, Ligang; Shen, Dinggang

2016-02-01

It is important for many quantitative brain studies to label meaningful anatomical regions in MR brain images. However, due to high complexity of brain structures and ambiguous boundaries between different anatomical regions, the anatomical labeling of MR brain images is still quite a challenging task. In many existing label fusion methods, appearance information is widely used. However, since local anatomy in the human brain is often complex, the appearance information alone is limited in characterizing each image point, especially for identifying the same anatomical structure across different subjects. Recent progress in computer vision suggests that the context features can be very useful in identifying an object from a complex scene. In light of this, the authors propose a novel learning-based label fusion method by using both low-level appearance features (computed from the target image) and high-level context features (computed from warped atlases or tentative labeling maps of the target image). In particular, the authors employ a multi-channel random forest to learn the nonlinear relationship between these hybrid features and target labels (i.e., corresponding to certain anatomical structures). Specifically, at each of the iterations, the random forest will output tentative labeling maps of the target image, from which the authors compute spatial label context features and then use in combination with original appearance features of the target image to refine the labeling. Moreover, to accommodate the high inter-subject variations, the authors further extend their learning-based label fusion to a multi-atlas scenario, i.e., they train a random forest for each atlas and then obtain the final labeling result according to the consensus of results from all atlases. The authors have comprehensively evaluated their method on both public LONI_LBPA40 and IXI datasets. To quantitatively evaluate the labeling accuracy, the authors use the dice similarity coefficient

Brain deactivation in the outperformance in bimodal tasks: an FMRI study.

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Tzu-Ching Chiang

Full Text Available While it is known that some individuals can effectively perform two tasks simultaneously, other individuals cannot. How the brain deals with performing simultaneous tasks remains unclear. In the present study, we aimed to assess which brain areas corresponded to various phenomena in task performance. Nineteen subjects were requested to sequentially perform three blocks of tasks, including two unimodal tasks and one bimodal task. The unimodal tasks measured either visual feature binding or auditory pitch comparison, while the bimodal task required performance of the two tasks simultaneously. The functional magnetic resonance imaging (fMRI results are compatible with previous studies showing that distinct brain areas, such as the visual cortices, frontal eye field (FEF, lateral parietal lobe (BA7, and medial and inferior frontal lobe, are involved in processing of visual unimodal tasks. In addition, the temporal lobes and Brodmann area 43 (BA43 were involved in processing of auditory unimodal tasks. These results lend support to concepts of modality-specific attention. Compared to the unimodal tasks, bimodal tasks required activation of additional brain areas. Furthermore, while deactivated brain areas were related to good performance in the bimodal task, these areas were not deactivated where the subject performed well in only one of the two simultaneous tasks. These results indicate that efficient information processing does not require some brain areas to be overly active; rather, the specific brain areas need to be relatively deactivated to remain alert and perform well on two tasks simultaneously. Meanwhile, it can also offer a neural basis for biofeedback in training courses, such as courses in how to perform multiple tasks simultaneously.

Nonlocal atlas-guided multi-channel forest learning for human brain labeling

Energy Technology Data Exchange (ETDEWEB)

Ma, Guangkai [Space Control and Inertial Technology Research Center, Harbin Institute of Technology, Harbin 150001, China and Department of Radiology and BRIC, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina 27599 (United States); Gao, Yaozong; Wu, Guorong [Department of Computer Science, Department of Radiology, and BRIC, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina 27599 (United States); Wu, Ligang [Space Control and Inertial Technology Research Center, Harbin Institute of Technology, Harbin 150001 (China); Shen, Dinggang, E-mail: dgshen@med.unc.edu [Department of Radiology and BRIC, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina 27599 and Department of Brain and Cognitive Engineering, Korea University, Seoul 02841 (Korea, Republic of)

2016-02-15

Purpose: It is important for many quantitative brain studies to label meaningful anatomical regions in MR brain images. However, due to high complexity of brain structures and ambiguous boundaries between different anatomical regions, the anatomical labeling of MR brain images is still quite a challenging task. In many existing label fusion methods, appearance information is widely used. However, since local anatomy in the human brain is often complex, the appearance information alone is limited in characterizing each image point, especially for identifying the same anatomical structure across different subjects. Recent progress in computer vision suggests that the context features can be very useful in identifying an object from a complex scene. In light of this, the authors propose a novel learning-based label fusion method by using both low-level appearance features (computed from the target image) and high-level context features (computed from warped atlases or tentative labeling maps of the target image). Methods: In particular, the authors employ a multi-channel random forest to learn the nonlinear relationship between these hybrid features and target labels (i.e., corresponding to certain anatomical structures). Specifically, at each of the iterations, the random forest will output tentative labeling maps of the target image, from which the authors compute spatial label context features and then use in combination with original appearance features of the target image to refine the labeling. Moreover, to accommodate the high inter-subject variations, the authors further extend their learning-based label fusion to a multi-atlas scenario, i.e., they train a random forest for each atlas and then obtain the final labeling result according to the consensus of results from all atlases. Results: The authors have comprehensively evaluated their method on both public LONI-LBPA40 and IXI datasets. To quantitatively evaluate the labeling accuracy, the authors use the

Nonlocal atlas-guided multi-channel forest learning for human brain labeling

International Nuclear Information System (INIS)

Ma, Guangkai; Gao, Yaozong; Wu, Guorong; Wu, Ligang; Shen, Dinggang

2016-01-01

Purpose: It is important for many quantitative brain studies to label meaningful anatomical regions in MR brain images. However, due to high complexity of brain structures and ambiguous boundaries between different anatomical regions, the anatomical labeling of MR brain images is still quite a challenging task. In many existing label fusion methods, appearance information is widely used. However, since local anatomy in the human brain is often complex, the appearance information alone is limited in characterizing each image point, especially for identifying the same anatomical structure across different subjects. Recent progress in computer vision suggests that the context features can be very useful in identifying an object from a complex scene. In light of this, the authors propose a novel learning-based label fusion method by using both low-level appearance features (computed from the target image) and high-level context features (computed from warped atlases or tentative labeling maps of the target image). Methods: In particular, the authors employ a multi-channel random forest to learn the nonlinear relationship between these hybrid features and target labels (i.e., corresponding to certain anatomical structures). Specifically, at each of the iterations, the random forest will output tentative labeling maps of the target image, from which the authors compute spatial label context features and then use in combination with original appearance features of the target image to refine the labeling. Moreover, to accommodate the high inter-subject variations, the authors further extend their learning-based label fusion to a multi-atlas scenario, i.e., they train a random forest for each atlas and then obtain the final labeling result according to the consensus of results from all atlases. Results: The authors have comprehensively evaluated their method on both public LONI-LBPA40 and IXI datasets. To quantitatively evaluate the labeling accuracy, the authors use the

PET imaging for brain function

International Nuclear Information System (INIS)

Fukuda, Hiroshi

2003-01-01

Described are the principle of PET and its characteristics, imaging of human brain function, mapping of detailed human cerebral functions and PET imaging of nerve transmission. Following compounds labeled by positron emitters are used for PET imaging of brain functions: for blood flow and oxygen metabolism, 15 O-O 2 gas, water and carbon dioxide; for energy metabolism, 18 F-fluorodeoxyglucose; and for nerve transmission functions in receptor binding, transporter, transmitter synthesis and enzyme, 11 C- or 18 F-dopamine, serotonin and their analogues, and acetylcholine analogues. For brain mapping, examples of cognition tasks, results and their statistics are presented with images for blood flow. Nerve transmissions in schizophrenia and Alzheimer disease are imaged with labeled analogues of dopamine and acetylcholine, respectively. PET is becoming more and more important in the field of psychiatric science particularly in the coming society of increasing aged people. (N.I.)

Multi-task linear programming discriminant analysis for the identification of progressive MCI individuals.

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Yu, Guan; Liu, Yufeng; Thung, Kim-Han; Shen, Dinggang

2014-01-01

Accurately identifying mild cognitive impairment (MCI) individuals who will progress to Alzheimer's disease (AD) is very important for making early interventions. Many classification methods focus on integrating multiple imaging modalities such as magnetic resonance imaging (MRI) and fluorodeoxyglucose positron emission tomography (FDG-PET). However, the main challenge for MCI classification using multiple imaging modalities is the existence of a lot of missing data in many subjects. For example, in the Alzheimer's Disease Neuroimaging Initiative (ADNI) study, almost half of the subjects do not have PET images. In this paper, we propose a new and flexible binary classification method, namely Multi-task Linear Programming Discriminant (MLPD) analysis, for the incomplete multi-source feature learning. Specifically, we decompose the classification problem into different classification tasks, i.e., one for each combination of available data sources. To solve all different classification tasks jointly, our proposed MLPD method links them together by constraining them to achieve the similar estimated mean difference between the two classes (under classification) for those shared features. Compared with the state-of-the-art incomplete Multi-Source Feature (iMSF) learning method, instead of constraining different classification tasks to choose a common feature subset for those shared features, MLPD can flexibly and adaptively choose different feature subsets for different classification tasks. Furthermore, our proposed MLPD method can be efficiently implemented by linear programming. To validate our MLPD method, we perform experiments on the ADNI baseline dataset with the incomplete MRI and PET images from 167 progressive MCI (pMCI) subjects and 226 stable MCI (sMCI) subjects. We further compared our method with the iMSF method (using incomplete MRI and PET images) and also the single-task classification method (using only MRI or only subjects with both MRI and PET images

Multi-task linear programming discriminant analysis for the identification of progressive MCI individuals.

Directory of Open Access Journals (Sweden)

Guan Yu

Full Text Available Accurately identifying mild cognitive impairment (MCI individuals who will progress to Alzheimer's disease (AD is very important for making early interventions. Many classification methods focus on integrating multiple imaging modalities such as magnetic resonance imaging (MRI and fluorodeoxyglucose positron emission tomography (FDG-PET. However, the main challenge for MCI classification using multiple imaging modalities is the existence of a lot of missing data in many subjects. For example, in the Alzheimer's Disease Neuroimaging Initiative (ADNI study, almost half of the subjects do not have PET images. In this paper, we propose a new and flexible binary classification method, namely Multi-task Linear Programming Discriminant (MLPD analysis, for the incomplete multi-source feature learning. Specifically, we decompose the classification problem into different classification tasks, i.e., one for each combination of available data sources. To solve all different classification tasks jointly, our proposed MLPD method links them together by constraining them to achieve the similar estimated mean difference between the two classes (under classification for those shared features. Compared with the state-of-the-art incomplete Multi-Source Feature (iMSF learning method, instead of constraining different classification tasks to choose a common feature subset for those shared features, MLPD can flexibly and adaptively choose different feature subsets for different classification tasks. Furthermore, our proposed MLPD method can be efficiently implemented by linear programming. To validate our MLPD method, we perform experiments on the ADNI baseline dataset with the incomplete MRI and PET images from 167 progressive MCI (pMCI subjects and 226 stable MCI (sMCI subjects. We further compared our method with the iMSF method (using incomplete MRI and PET images and also the single-task classification method (using only MRI or only subjects with both MRI and

Protocol to assess the neurophysiology associated with multi-segmental postural coordination

International Nuclear Information System (INIS)

Lomond, Karen V; Henry, Sharon M; Jacobs, Jesse V; Hitt, Juvena R; Horak, Fay B; Cohen, Rajal G; Schwartz, Daniel; Dumas, Julie A; Naylor, Magdalena R; Watts, Richard; DeSarno, Michael J

2013-01-01

Anticipatory postural adjustments (APAs) stabilize potential disturbances to posture caused by movement. Impaired APAs are common with disease and injury. Brain functions associated with generating APAs remain uncertain due to a lack of paired tasks that require similar limb motion from similar postural orientations, but differ in eliciting an APA while also being compatible with brain imaging techniques (e.g., functional magnetic resonance imaging; fMRI). This study developed fMRI-compatible tasks differentiated by the presence or absence of APAs during leg movement. Eighteen healthy subjects performed two leg movement tasks, supported leg raise (SLR) and unsupported leg raise (ULR), to elicit isolated limb motion (no APA) versus multi-segmental coordination patterns (including APA), respectively. Ground reaction forces under the feet and electromyographic activation amplitudes were assessed to determine the coordination strategy elicited for each task. Results demonstrated that the ULR task elicited a multi-segmental coordination that was either minimized or absent in the SLR task, indicating that it would serve as an adequate control task for fMRI protocols. A pilot study with a single subject performing each task in an MRI scanner demonstrated minimal head movement in both tasks and brain activation patterns consistent with an isolated limb movement for the SLR task versus multi-segmental postural coordination for the ULR task. (note)

A multi-channel magnetic induction tomography measurement system for human brain model imaging

International Nuclear Information System (INIS)

Xu, Zheng; Luo, Haijun; He, Wei; He, Chuanhong; Song, Xiaodong; Zahng, Zhanglong

2009-01-01

This paper proposes a multi-channel magnetic induction tomography measurement system for biological conductivity imaging in a human brain model. A hemispherical glass bowl filled with a salt solution is used as the human brain model; meanwhile, agar blocks of different conductivity are placed in the solution to simulate the intracerebral hemorrhage. The excitation and detection coils are fixed co-axially, and the axial gradiometer is used as the detection coil in order to cancel the primary field. On the outer surface of the glass bowl, 15 sensor units are arrayed in two circles as measurement parts, and a single sensor unit for canceling the phase drift is placed beside the glass bowl. The phase sensitivity of our system is 0.204°/S m −1 with the excitation frequency of 120 kHz and the phase noise is in the range of −0.03° to +0.05°. Only the coaxial detection coil is available for each excitation coil; therefore, 15 phase data are collected in each measurement turn. Finally, the two-dimensional images of conductivity distribution are obtained using an interpolation algorithm. The frequency-varying experiment indicates that the imaging quality becomes better as the excitation frequency is increased

Presurgical brain mapping of the language network in patients with brain tumors using resting-state fMRI: Comparison with task fMRI.

Science.gov (United States)

Sair, Haris I; Yahyavi-Firouz-Abadi, Noushin; Calhoun, Vince D; Airan, Raag D; Agarwal, Shruti; Intrapiromkul, Jarunee; Choe, Ann S; Gujar, Sachin K; Caffo, Brian; Lindquist, Martin A; Pillai, Jay J

2016-03-01

To compare language networks derived from resting-state fMRI (rs-fMRI) with task-fMRI in patients with brain tumors and investigate variables that affect rs-fMRI vs task-fMRI concordance. Independent component analysis (ICA) of rs-fMRI was performed with 20, 30, 40, and 50 target components (ICA20 to ICA50) and language networks identified for patients presenting for presurgical fMRI mapping between 1/1/2009 and 7/1/2015. 49 patients were analyzed fulfilling criteria for presence of brain tumors, no prior brain surgery, and adequate task-fMRI performance. Rs-vs-task-fMRI concordance was measured using Dice coefficients across varying fMRI thresholds before and after noise removal. Multi-thresholded Dice coefficient volume under the surface (DiceVUS) and maximum Dice coefficient (MaxDice) were calculated. One-way Analysis of Variance (ANOVA) was performed to determine significance of DiceVUS and MaxDice between the four ICA order groups. Age, Sex, Handedness, Tumor Side, Tumor Size, WHO Grade, number of scrubbed volumes, image intensity root mean square (iRMS), and mean framewise displacement (FD) were used as predictors for VUS in a linear regression. Artificial elevation of rs-fMRI vs task-fMRI concordance is seen at low thresholds due to noise. Noise-removed group-mean DiceVUS and MaxDice improved as ICA order increased, however ANOVA demonstrated no statistically significant difference between the four groups. Linear regression demonstrated an association between iRMS and DiceVUS for ICA30-50, and iRMS and MaxDice for ICA50. Overall there is moderate group level rs-vs-task fMRI language network concordance, however substantial subject-level variability exists; iRMS may be used to determine reliability of rs-fMRI derived language networks. © 2015 Wiley Periodicals, Inc.

Hemorrhage detection in MRI brain images using images features

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Moraru, Luminita; Moldovanu, Simona; Bibicu, Dorin; Stratulat (Visan), Mirela

2013-11-01

The abnormalities appear frequently on Magnetic Resonance Images (MRI) of brain in elderly patients presenting either stroke or cognitive impairment. Detection of brain hemorrhage lesions in MRI is an important but very time-consuming task. This research aims to develop a method to extract brain tissue features from T2-weighted MR images of the brain using a selection of the most valuable texture features in order to discriminate between normal and affected areas of the brain. Due to textural similarity between normal and affected areas in brain MR images these operation are very challenging. A trauma may cause microstructural changes, which are not necessarily perceptible by visual inspection, but they could be detected by using a texture analysis. The proposed analysis is developed in five steps: i) in the pre-processing step: the de-noising operation is performed using the Daubechies wavelets; ii) the original images were transformed in image features using the first order descriptors; iii) the regions of interest (ROIs) were cropped from images feature following up the axial symmetry properties with respect to the mid - sagittal plan; iv) the variation in the measurement of features was quantified using the two descriptors of the co-occurrence matrix, namely energy and homogeneity; v) finally, the meaningful of the image features is analyzed by using the t-test method. P-value has been applied to the pair of features in order to measure they efficacy.

Monitoring of human brain functions in risk decision-making task by diffuse optical tomography using voxel-wise general linear model

Science.gov (United States)

Lin, Zi-Jing; Li, Lin; Cazzell, Marry; Liu, Hanli

2013-03-01

Functional near-infrared spectroscopy (fNIRS) is a non-invasive imaging technique which measures the hemodynamic changes that reflect the brain activity. Diffuse optical tomography (DOT), a variant of fNIRS with multi-channel NIRS measurements, has demonstrated capability of three dimensional (3D) reconstructions of hemodynamic changes due to the brain activity. Conventional method of DOT image analysis to define the brain activation is based upon the paired t-test between two different states, such as resting-state versus task-state. However, it has limitation because the selection of activation and post-activation period is relatively subjective. General linear model (GLM) based analysis can overcome this limitation. In this study, we combine the 3D DOT image reconstruction with GLM-based analysis (i.e., voxel-wise GLM analysis) to investigate the brain activity that is associated with the risk-decision making process. Risk decision-making is an important cognitive process and thus is an essential topic in the field of neuroscience. The balloon analogue risk task (BART) is a valid experimental model and has been commonly used in behavioral measures to assess human risk taking action and tendency while facing risks. We have utilized the BART paradigm with a blocked design to investigate brain activations in the prefrontal and frontal cortical areas during decision-making. Voxel-wise GLM analysis was performed on 18human participants (10 males and 8females).In this work, we wish to demonstrate the feasibility of using voxel-wise GLM analysis to image and study cognitive functions in response to risk decision making by DOT. Results have shown significant changes in the dorsal lateral prefrontal cortex (DLPFC) during the active choice mode and a different hemodynamic pattern between genders, which are in good agreements with published literatures in functional magnetic resonance imaging (fMRI) and fNIRS studies.

Brain activations during bimodal dual tasks depend on the nature and combination of component tasks

Directory of Open Access Journals (Sweden)

Emma eSalo

2015-02-01

Full Text Available We used functional magnetic resonance imaging to investigate brain activations during nine different dual tasks in which the participants were required to simultaneously attend to concurrent streams of spoken syllables and written letters. They performed a phonological, spatial or simple (speaker-gender or font-shade discrimination task within each modality. We expected to find activations associated specifically with dual tasking especially in the frontal and parietal cortices. However, no brain areas showed systematic dual task enhancements common for all dual tasks. Further analysis revealed that dual tasks including component tasks that were according to Baddeley’s model modality atypical, that is, the auditory spatial task or the visual phonological task, were not associated with enhanced frontal activity. In contrast, for other dual tasks, activity specifically associated with dual tasking was found in the left or bilateral frontal cortices. Enhanced activation in parietal areas, however, appeared not to be specifically associated with dual tasking per se, but rather with intermodal attention switching. We also expected effects of dual tasking in left frontal supramodal phonological processing areas when both component tasks required phonological processing and in right parietal supramodal spatial processing areas when both tasks required spatial processing. However, no such effects were found during these dual tasks compared with their component tasks performed separately. Taken together, the current results indicate that activations during dual tasks depend in a complex manner on specific demands of component tasks.

Multi-layer imager design for mega-voltage spectral imaging

Science.gov (United States)

Myronakis, Marios; Hu, Yue-Houng; Fueglistaller, Rony; Wang, Adam; Baturin, Paul; Huber, Pascal; Morf, Daniel; Star-Lack, Josh; Berbeco, Ross

2018-05-01

The architecture of multi-layer imagers (MLIs) can be exploited to provide megavoltage spectral imaging (MVSPI) for specific imaging tasks. In the current work, we investigated bone suppression and gold fiducial contrast enhancement as two clinical tasks which could be improved with spectral imaging. A method based on analytical calculations that enables rapid investigation of MLI component materials and thicknesses was developed and validated against Monte Carlo computations. The figure of merit for task-specific imaging performance was the contrast-to-noise ratio (CNR) of the gold fiducial when the CNR of bone was equal to zero after a weighted subtraction of the signals obtained from each MLI layer. Results demonstrated a sharp increase in the CNR of gold when the build-up component or scintillation materials and thicknesses were modified. The potential for low-cost, prompt implementation of specific modifications (e.g. composition of the build-up component) could accelerate clinical translation of MVSPI.

HD-MTL: Hierarchical Deep Multi-Task Learning for Large-Scale Visual Recognition.

Science.gov (United States)

Fan, Jianping; Zhao, Tianyi; Kuang, Zhenzhong; Zheng, Yu; Zhang, Ji; Yu, Jun; Peng, Jinye

2017-02-09

In this paper, a hierarchical deep multi-task learning (HD-MTL) algorithm is developed to support large-scale visual recognition (e.g., recognizing thousands or even tens of thousands of atomic object classes automatically). First, multiple sets of multi-level deep features are extracted from different layers of deep convolutional neural networks (deep CNNs), and they are used to achieve more effective accomplishment of the coarseto- fine tasks for hierarchical visual recognition. A visual tree is then learned by assigning the visually-similar atomic object classes with similar learning complexities into the same group, which can provide a good environment for determining the interrelated learning tasks automatically. By leveraging the inter-task relatedness (inter-class similarities) to learn more discriminative group-specific deep representations, our deep multi-task learning algorithm can train more discriminative node classifiers for distinguishing the visually-similar atomic object classes effectively. Our hierarchical deep multi-task learning (HD-MTL) algorithm can integrate two discriminative regularization terms to control the inter-level error propagation effectively, and it can provide an end-to-end approach for jointly learning more representative deep CNNs (for image representation) and more discriminative tree classifier (for large-scale visual recognition) and updating them simultaneously. Our incremental deep learning algorithms can effectively adapt both the deep CNNs and the tree classifier to the new training images and the new object classes. Our experimental results have demonstrated that our HD-MTL algorithm can achieve very competitive results on improving the accuracy rates for large-scale visual recognition.

Multi-task pose-invariant face recognition.

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Ding, Changxing; Xu, Chang; Tao, Dacheng

2015-03-01

Face images captured in unconstrained environments usually contain significant pose variation, which dramatically degrades the performance of algorithms designed to recognize frontal faces. This paper proposes a novel face identification framework capable of handling the full range of pose variations within ±90° of yaw. The proposed framework first transforms the original pose-invariant face recognition problem into a partial frontal face recognition problem. A robust patch-based face representation scheme is then developed to represent the synthesized partial frontal faces. For each patch, a transformation dictionary is learnt under the proposed multi-task learning scheme. The transformation dictionary transforms the features of different poses into a discriminative subspace. Finally, face matching is performed at patch level rather than at the holistic level. Extensive and systematic experimentation on FERET, CMU-PIE, and Multi-PIE databases shows that the proposed method consistently outperforms single-task-based baselines as well as state-of-the-art methods for the pose problem. We further extend the proposed algorithm for the unconstrained face verification problem and achieve top-level performance on the challenging LFW data set.

The clinical use of brain SPECT imaging in neuropsychiatry

International Nuclear Information System (INIS)

Amen, Daniel G; Wu, Joseph C; Carmichael, Blake

2003-01-01

This article reviews the literature on brain SPECT imaging in brain trauma, dementia, and temporal lobe epilepsy. Brain SPECT allows clinicians the ability to view cerebral areas of healthy, low, and excessive perfusion. This information can be correlated with what is known about the function or dysfunction of each area. SPECT has a number of advantages over other imaging techniques, including wider availability, lower cost, and high quality resolution with multi-headed cameras. There are a number of issues that compromise the effective use of SPECT, including low quality of some imaging cameras, and variability of image rendering and readings (Au)

Multimodal Imaging of Human Brain Activity: Rational, Biophysical Aspects and Modes of Integration

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Blinowska, Katarzyna; Müller-Putz, Gernot; Kaiser, Vera; Astolfi, Laura; Vanderperren, Katrien; Van Huffel, Sabine; Lemieux, Louis

2009-01-01

Until relatively recently the vast majority of imaging and electrophysiological studies of human brain activity have relied on single-modality measurements usually correlated with readily observable or experimentally modified behavioural or brain state patterns. Multi-modal imaging is the concept of bringing together observations or measurements from different instruments. We discuss the aims of multi-modal imaging and the ways in which it can be accomplished using representative applications. Given the importance of haemodynamic and electrophysiological signals in current multi-modal imaging applications, we also review some of the basic physiology relevant to understanding their relationship. PMID:19547657

Clinical feasibility of simultaneous multi-slice imaging with blipped-CAIPI for diffusion-weighted imaging and diffusion-tensor imaging of the brain.

Science.gov (United States)

Yokota, Hajime; Sakai, Koji; Tazoe, Jun; Goto, Mariko; Imai, Hiroshi; Teramukai, Satoshi; Yamada, Kei

2017-12-01

Background Simultaneous multi-slice (SMS) imaging is starting to be used in clinical situation, although evidence of clinical feasibility is scanty. Purpose To prospectively assess the clinical feasibility of SMS diffusion-weighted imaging (DWI) and diffusion-tensor imaging (DTI) with blipped-controlled aliasing in parallel imaging for brain lesions. Material and Methods The institutional review board approved this study. This study included 156 hyperintense lesions on DWI from 32 patients. A slice acceleration factor of 2 was applied for SMS scans, which allowed shortening of the scan time by 41.3%. The signal-to-noise ratio (SNR) was calculated for brain tissue of a selected slice. The contrast-to-noise ratio (CNR), apparent diffusion coefficient (ADC), and fractional anisotropy (FA) were calculated in 36 hyperintense lesions with a diameter of three pixels or more. Visual assessment was performed for all 156 lesions. Tractography of the corticospinal tract of 29 patients was evaluated. The number of tracts and averaged tract length were used for quantitative analysis, and visual assessment was evaluated by grading. Results The SMS scan showed no bias and acceptable 95% limits of agreement compared to conventional scans in SNR, CNR, and ADC on Bland-Altman analyses. Only FA of the lesions was higher in the SMS scan by 9% ( P = 0.016), whereas FA of the surrounding tissues was similar. Quantitative analysis of tractography showed similar values. Visual assessment of DWI hyperintense lesions and tractography also resulted in comparable evaluation. Conclusion SMS imaging was clinically feasible for imaging quality and quantitative values compared with conventional DWI and DTI.

Connectome imaging for mapping human brain pathways.

Science.gov (United States)

Shi, Y; Toga, A W

2017-09-01

With the fast advance of connectome imaging techniques, we have the opportunity of mapping the human brain pathways in vivo at unprecedented resolution. In this article we review the current developments of diffusion magnetic resonance imaging (MRI) for the reconstruction of anatomical pathways in connectome studies. We first introduce the background of diffusion MRI with an emphasis on the technical advances and challenges in state-of-the-art multi-shell acquisition schemes used in the Human Connectome Project. Characterization of the microstructural environment in the human brain is discussed from the tensor model to the general fiber orientation distribution (FOD) models that can resolve crossing fibers in each voxel of the image. Using FOD-based tractography, we describe novel methods for fiber bundle reconstruction and graph-based connectivity analysis. Building upon these novel developments, there have already been successful applications of connectome imaging techniques in reconstructing challenging brain pathways. Examples including retinofugal and brainstem pathways will be reviewed. Finally, we discuss future directions in connectome imaging and its interaction with other aspects of brain imaging research.

Brain imaging tests for chronic pain: medical, legal and ethical issues and recommendations.

Science.gov (United States)

Davis, Karen D; Flor, Herta; Greely, Henry T; Iannetti, Gian Domenico; Mackey, Sean; Ploner, Markus; Pustilnik, Amanda; Tracey, Irene; Treede, Rolf-Detlef; Wager, Tor D

2017-10-01

Chronic pain is the greatest source of disability globally and claims related to chronic pain feature in many insurance and medico-legal cases. Brain imaging (for example, functional MRI, PET, EEG and magnetoencephalography) is widely considered to have potential for diagnosis, prognostication, and prediction of treatment outcome in patients with chronic pain. In this Consensus Statement, a presidential task force of the International Association for the Study of Pain examines the capabilities of brain imaging in the diagnosis of chronic pain, and the ethical and legal implications of its use in this way. The task force emphasizes that the use of brain imaging in this context is in a discovery phase, but has the potential to increase our understanding of the neural underpinnings of chronic pain, inform the development of therapeutic agents, and predict treatment outcomes for use in personalized pain management. The task force proposes standards of evidence that must be satisfied before any brain imaging measure can be considered suitable for clinical or legal purposes. The admissibility of such evidence in legal cases also strongly depends on laws that vary between jurisdictions. For these reasons, the task force concludes that the use of brain imaging findings to support or dispute a claim of chronic pain - effectively as a pain lie detector - is not warranted, but that imaging should be used to further our understanding of the mechanisms underlying pain.

Multi-population genomic prediction using a multi-task Bayesian learning model.

Science.gov (United States)

Chen, Liuhong; Li, Changxi; Miller, Stephen; Schenkel, Flavio

2014-05-03

Genomic prediction in multiple populations can be viewed as a multi-task learning problem where tasks are to derive prediction equations for each population and multi-task learning property can be improved by sharing information across populations. The goal of this study was to develop a multi-task Bayesian learning model for multi-population genomic prediction with a strategy to effectively share information across populations. Simulation studies and real data from Holstein and Ayrshire dairy breeds with phenotypes on five milk production traits were used to evaluate the proposed multi-task Bayesian learning model and compare with a single-task model and a simple data pooling method. A multi-task Bayesian learning model was proposed for multi-population genomic prediction. Information was shared across populations through a common set of latent indicator variables while SNP effects were allowed to vary in different populations. Both simulation studies and real data analysis showed the effectiveness of the multi-task model in improving genomic prediction accuracy for the smaller Ayshire breed. Simulation studies suggested that the multi-task model was most effective when the number of QTL was small (n = 20), with an increase of accuracy by up to 0.09 when QTL effects were lowly correlated between two populations (ρ = 0.2), and up to 0.16 when QTL effects were highly correlated (ρ = 0.8). When QTL genotypes were included for training and validation, the improvements were 0.16 and 0.22, respectively, for scenarios of the low and high correlation of QTL effects between two populations. When the number of QTL was large (n = 200), improvement was small with a maximum of 0.02 when QTL genotypes were not included for genomic prediction. Reduction in accuracy was observed for the simple pooling method when the number of QTL was small and correlation of QTL effects between the two populations was low. For the real data, the multi-task model achieved an

Quantitative analysis of task selection for brain-computer interfaces

Science.gov (United States)

Llera, Alberto; Gómez, Vicenç; Kappen, Hilbert J.

2014-10-01

Objective. To assess quantitatively the impact of task selection in the performance of brain-computer interfaces (BCI). Approach. We consider the task-pairs derived from multi-class BCI imagery movement tasks in three different datasets. We analyze for the first time the benefits of task selection on a large-scale basis (109 users) and evaluate the possibility of transferring task-pair information across days for a given subject. Main results. Selecting the subject-dependent optimal task-pair among three different imagery movement tasks results in approximately 20% potential increase in the number of users that can be expected to control a binary BCI. The improvement is observed with respect to the best task-pair fixed across subjects. The best task-pair selected for each subject individually during a first day of recordings is generally a good task-pair in subsequent days. In general, task learning from the user side has a positive influence in the generalization of the optimal task-pair, but special attention should be given to inexperienced subjects. Significance. These results add significant evidence to existing literature that advocates task selection as a necessary step towards usable BCIs. This contribution motivates further research focused on deriving adaptive methods for task selection on larger sets of mental tasks in practical online scenarios.

Multi-task transfer learning deep convolutional neural network: application to computer-aided diagnosis of breast cancer on mammograms

Science.gov (United States)

Samala, Ravi K.; Chan, Heang-Ping; Hadjiiski, Lubomir M.; Helvie, Mark A.; Cha, Kenny H.; Richter, Caleb D.

2017-12-01

Transfer learning in deep convolutional neural networks (DCNNs) is an important step in its application to medical imaging tasks. We propose a multi-task transfer learning DCNN with the aim of translating the ‘knowledge’ learned from non-medical images to medical diagnostic tasks through supervised training and increasing the generalization capabilities of DCNNs by simultaneously learning auxiliary tasks. We studied this approach in an important application: classification of malignant and benign breast masses. With Institutional Review Board (IRB) approval, digitized screen-film mammograms (SFMs) and digital mammograms (DMs) were collected from our patient files and additional SFMs were obtained from the Digital Database for Screening Mammography. The data set consisted of 2242 views with 2454 masses (1057 malignant, 1397 benign). In single-task transfer learning, the DCNN was trained and tested on SFMs. In multi-task transfer learning, SFMs and DMs were used to train the DCNN, which was then tested on SFMs. N-fold cross-validation with the training set was used for training and parameter optimization. On the independent test set, the multi-task transfer learning DCNN was found to have significantly (p  =  0.007) higher performance compared to the single-task transfer learning DCNN. This study demonstrates that multi-task transfer learning may be an effective approach for training DCNN in medical imaging applications when training samples from a single modality are limited.

Multi-task Vector Field Learning.

Science.gov (United States)

Lin, Binbin; Yang, Sen; Zhang, Chiyuan; Ye, Jieping; He, Xiaofei

2012-01-01

Multi-task learning (MTL) aims to improve generalization performance by learning multiple related tasks simultaneously and identifying the shared information among tasks. Most of existing MTL methods focus on learning linear models under the supervised setting. We propose a novel semi-supervised and nonlinear approach for MTL using vector fields. A vector field is a smooth mapping from the manifold to the tangent spaces which can be viewed as a directional derivative of functions on the manifold. We argue that vector fields provide a natural way to exploit the geometric structure of data as well as the shared differential structure of tasks, both of which are crucial for semi-supervised multi-task learning. In this paper, we develop multi-task vector field learning (MTVFL) which learns the predictor functions and the vector fields simultaneously. MTVFL has the following key properties. (1) The vector fields MTVFL learns are close to the gradient fields of the predictor functions. (2) Within each task, the vector field is required to be as parallel as possible which is expected to span a low dimensional subspace. (3) The vector fields from all tasks share a low dimensional subspace. We formalize our idea in a regularization framework and also provide a convex relaxation method to solve the original non-convex problem. The experimental results on synthetic and real data demonstrate the effectiveness of our proposed approach.

Emerging Techniques in Brain Tumor Imaging: What Radiologists Need to Know

Energy Technology Data Exchange (ETDEWEB)

Kim, Minjae; Kim, Ho Sung [Department of Radiology and Research Institute of Radiology, University of Ulsan College of Medicine, Asan Medical Center, Seoul 05505 (Korea, Republic of)

2016-11-01

Among the currently available brain tumor imaging, advanced MR imaging techniques, such as diffusion-weighted MR imaging and perfusion MR imaging, have been used for solving diagnostic challenges associated with conventional imaging and for monitoring the brain tumor treatment response. Further development of advanced MR imaging techniques and postprocessing methods may contribute to predicting the treatment response to a specific therapeutic regimen, particularly using multi-modality and multiparametric imaging. Over the next few years, new imaging techniques, such as amide proton transfer imaging, will be studied regarding their potential use in quantitative brain tumor imaging. In this review, the pathophysiologic considerations and clinical validations of these promising techniques are discussed in the context of brain tumor characterization and treatment response.

A digital 3D atlas of the marmoset brain based on multi-modal MRI.

Science.gov (United States)

Liu, Cirong; Ye, Frank Q; Yen, Cecil Chern-Chyi; Newman, John D; Glen, Daniel; Leopold, David A; Silva, Afonso C

2018-04-01

The common marmoset (Callithrix jacchus) is a New-World monkey of growing interest in neuroscience. Magnetic resonance imaging (MRI) is an essential tool to unveil the anatomical and functional organization of the marmoset brain. To facilitate identification of regions of interest, it is desirable to register MR images to an atlas of the brain. However, currently available atlases of the marmoset brain are mainly based on 2D histological data, which are difficult to apply to 3D imaging techniques. Here, we constructed a 3D digital atlas based on high-resolution ex-vivo MRI images, including magnetization transfer ratio (a T1-like contrast), T2w images, and multi-shell diffusion MRI. Based on the multi-modal MRI images, we manually delineated 54 cortical areas and 16 subcortical regions on one hemisphere of the brain (the core version). The 54 cortical areas were merged into 13 larger cortical regions according to their locations to yield a coarse version of the atlas, and also parcellated into 106 sub-regions using a connectivity-based parcellation method to produce a refined atlas. Finally, we compared the new atlas set with existing histology atlases and demonstrated its applications in connectome studies, and in resting state and stimulus-based fMRI. The atlas set has been integrated into the widely-distributed neuroimaging data analysis software AFNI and SUMA, providing a readily usable multi-modal template space with multi-level anatomical labels (including labels from the Paxinos atlas) that can facilitate various neuroimaging studies of marmosets. Published by Elsevier Inc.

Automated Multi-Contrast Brain Pathological Area Extraction from 2D MR Images

Czech Academy of Sciences Publication Activity Database

Dvořák, Pavel; Bartušek, Karel; Kropatsch, W.G.; Smékal, Z.

2015-01-01

Roč. 13, č. 1 (2015), s. 58-69 ISSN 1665-6423 R&D Projects: GA ČR GAP102/12/1104 Institutional support: RVO:68081731 Keywords : Brain Pathology * Brain Tumor * MRI * Multi-contrast MRI * Symmetry Analysis Subject RIV: BH - Optics, Masers, Lasers Impact factor: 0.447, year: 2013

Microprocessor multi-task monitor

International Nuclear Information System (INIS)

Ludemann, C.A.

1983-01-01

This paper describes a multi-task monitor program for microprocessors. Although written for the Intel 8085, it incorporates features that would be beneficial for implementation in other microprocessors used in controlling and monitoring experiments and accelerators. The monitor places permanent programs (tasks) arbitrarily located throughout ROM in a priority ordered queue. The programmer is provided with the flexibility to add new tasks or modified versions of existing tasks, without having to comply with previously defined task boundaries or having to reprogram all of ROM. Scheduling of tasks is triggered by timers, outside stimuli (interrupts), or inter-task communications. Context switching time is of the order of tenths of a milllisecond

Reproducibility of proton MR spectroscopic imaging (PEPSI): comparison of dyslexic and normal-reading children and effects of treatment on brain lactate levels during language tasks.

Science.gov (United States)

Richards, Todd L; Berninger, Virginia W; Aylward, Elizabeth H; Richards, Anne L; Thomson, Jennifer B; Nagy, William E; Carlisle, Joanne F; Dager, Stephen R; Abbott, Robert D

2002-01-01

We repeated a proton echo-planar spectroscopic imaging (PEPSI) study to test the hypothesis that children with dyslexia and good readers differ in brain lactate activation during a phonologic judgment task before but not after instructional treatment. We measured PEPSI brain lactate activation (TR/TE, 4000/144; 1.5 T) at two points 1-2 months apart during two language tasks (phonologic and lexical) and a control task (passive listening). Dyslexic participants (n = 10) and control participants (n = 8) (boys and girls aged 9-12 years) were matched in age, verbal intelligence quotients, and valid PEPSI voxels. In contrast to patients in past studies who received combined treatment, our patients were randomly assigned to either phonologic or morphologic (meaning-based) intervention between the scanning sessions. Before treatment, the patients showed significantly greater lactate elevation in the left frontal regions (including the inferior frontal gyrus) during the phonologic task. Both patients and control subjects differed significantly in the right parietal and occipital regions during both tasks. After treatment, the two groups did not significantly differ in any brain region during either task, but individuals given morphologic treatment were significantly more likely to have reduced left frontal lactate activation during the phonologic task. The previous finding of greater left frontal lactate elevation in children with dyslexia during a phonologic judgment task was replicated, and brain activation changed as a result of treatment. However, the treatment effect was due to the morphologic component rather than the phonologic component.

[Research on K-means clustering segmentation method for MRI brain image based on selecting multi-peaks in gray histogram].

Science.gov (United States)

Chen, Zhaoxue; Yu, Haizhong; Chen, Hao

2013-12-01

To solve the problem of traditional K-means clustering in which initial clustering centers are selected randomly, we proposed a new K-means segmentation algorithm based on robustly selecting 'peaks' standing for White Matter, Gray Matter and Cerebrospinal Fluid in multi-peaks gray histogram of MRI brain image. The new algorithm takes gray value of selected histogram 'peaks' as the initial K-means clustering center and can segment the MRI brain image into three parts of tissue more effectively, accurately, steadily and successfully. Massive experiments have proved that the proposed algorithm can overcome many shortcomings caused by traditional K-means clustering method such as low efficiency, veracity, robustness and time consuming. The histogram 'peak' selecting idea of the proposed segmentootion method is of more universal availability.

How task demands shape brain responses to visual food cues.

Science.gov (United States)

Pohl, Tanja Maria; Tempelmann, Claus; Noesselt, Toemme

2017-06-01

Several previous imaging studies have aimed at identifying the neural basis of visual food cue processing in humans. However, there is little consistency of the functional magnetic resonance imaging (fMRI) results across studies. Here, we tested the hypothesis that this variability across studies might - at least in part - be caused by the different tasks employed. In particular, we assessed directly the influence of task set on brain responses to food stimuli with fMRI using two tasks (colour vs. edibility judgement, between-subjects design). When participants judged colour, the left insula, the left inferior parietal lobule, occipital areas, the left orbitofrontal cortex and other frontal areas expressed enhanced fMRI responses to food relative to non-food pictures. However, when judging edibility, enhanced fMRI responses to food pictures were observed in the superior and middle frontal gyrus and in medial frontal areas including the pregenual anterior cingulate cortex and ventromedial prefrontal cortex. This pattern of results indicates that task sets can significantly alter the neural underpinnings of food cue processing. We propose that judging low-level visual stimulus characteristics - such as colour - triggers stimulus-related representations in the visual and even in gustatory cortex (insula), whereas discriminating abstract stimulus categories activates higher order representations in both the anterior cingulate and prefrontal cortex. Hum Brain Mapp 38:2897-2912, 2017. © 2017 Wiley Periodicals, Inc. © 2017 Wiley Periodicals, Inc.

Visual image reconstruction from human brain activity: A modular decoding approach

International Nuclear Information System (INIS)

Miyawaki, Yoichi; Uchida, Hajime; Yamashita, Okito; Sato, Masa-aki; Kamitani, Yukiyasu; Morito, Yusuke; Tanabe, Hiroki C; Sadato, Norihiro

2009-01-01

Brain activity represents our perceptual experience. But the potential for reading out perceptual contents from human brain activity has not been fully explored. In this study, we demonstrate constraint-free reconstruction of visual images perceived by a subject, from the brain activity pattern. We reconstructed visual images by combining local image bases with multiple scales, whose contrasts were independently decoded from fMRI activity by automatically selecting relevant voxels and exploiting their correlated patterns. Binary-contrast, 10 x 10-patch images (2 100 possible states), were accurately reconstructed without any image prior by measuring brain activity only for several hundred random images. The results suggest that our approach provides an effective means to read out complex perceptual states from brain activity while discovering information representation in multi-voxel patterns.

High-throughput isotropic mapping of whole mouse brain using multi-view light-sheet microscopy

Science.gov (United States)

Nie, Jun; Li, Yusha; Zhao, Fang; Ping, Junyu; Liu, Sa; Yu, Tingting; Zhu, Dan; Fei, Peng

2018-02-01

Light-sheet fluorescence microscopy (LSFM) uses an additional laser-sheet to illuminate selective planes of the sample, thereby enabling three-dimensional imaging at high spatial-temporal resolution. These advantages make LSFM a promising tool for high-quality brain visualization. However, even by the use of LSFM, the spatial resolution remains insufficient to resolve the neural structures across a mesoscale whole mouse brain in three dimensions. At the same time, the thick-tissue scattering prevents a clear observation from the deep of brain. Here we use multi-view LSFM strategy to solve this challenge, surpassing the resolution limit of standard light-sheet microscope under a large field-of-view (FOV). As demonstrated by the imaging of optically-cleared mouse brain labelled with thy1-GFP, we achieve a brain-wide, isotropic cellular resolution of 3μm. Besides the resolution enhancement, multi-view braining imaging can also recover complete signals from deep tissue scattering and attenuation. The identification of long distance neural projections across encephalic regions can be identified and annotated as a result.

Task-related signal decrease on functional magnetic resonance imaging

International Nuclear Information System (INIS)

Hara, Yoshie; Nakamura, Mitsugu; Tamaki, Norihiko; Tamura, Shogo; Kitamura, Junji

2001-01-01

An atypical pattern of signal change was identified on functional magnetic resonance (fMR) imaging in pathologic patients. Three normal volunteers and 34 patients with pathologic lesions near the primary motor cortex underwent fMR imaging with echo-planar imaging while performing a hand motor task. Signal intensities were evaluated with the z-score method, and the time course and changes of the signal intensity were calculated. Nine of the 34 patients with pathologic lesions displayed a significant task-related signal reduction in motor-related areas. They also presented a conventional task-related signal increase in other motor-related areas. The time courses of the increase and decrease were the inverse of each other. There was no significant difference between rates of signal increase and decrease. Our findings suggest that this atypical signal decrease is clinically significant, and that impaired vascular reactivity and altered oxygen metabolism could contribute to the task-related signal reduction. Brain areas showing such task-related signal decrease should be preserved at surgery. (author)

Brain activity during auditory and visual phonological, spatial and simple discrimination tasks.

Science.gov (United States)

Salo, Emma; Rinne, Teemu; Salonen, Oili; Alho, Kimmo

2013-02-16

We used functional magnetic resonance imaging to measure human brain activity during tasks demanding selective attention to auditory or visual stimuli delivered in concurrent streams. Auditory stimuli were syllables spoken by different voices and occurring in central or peripheral space. Visual stimuli were centrally or more peripherally presented letters in darker or lighter fonts. The participants performed a phonological, spatial or "simple" (speaker-gender or font-shade) discrimination task in either modality. Within each modality, we expected a clear distinction between brain activations related to nonspatial and spatial processing, as reported in previous studies. However, within each modality, different tasks activated largely overlapping areas in modality-specific (auditory and visual) cortices, as well as in the parietal and frontal brain regions. These overlaps may be due to effects of attention common for all three tasks within each modality or interaction of processing task-relevant features and varying task-irrelevant features in the attended-modality stimuli. Nevertheless, brain activations caused by auditory and visual phonological tasks overlapped in the left mid-lateral prefrontal cortex, while those caused by the auditory and visual spatial tasks overlapped in the inferior parietal cortex. These overlapping activations reveal areas of multimodal phonological and spatial processing. There was also some evidence for intermodal attention-related interaction. Most importantly, activity in the superior temporal sulcus elicited by unattended speech sounds was attenuated during the visual phonological task in comparison with the other visual tasks. This effect might be related to suppression of processing irrelevant speech presumably distracting the phonological task involving the letters. Copyright © 2012 Elsevier B.V. All rights reserved.

Task-evoked brain functional magnetic susceptibility mapping by independent component analysis (χICA).

Science.gov (United States)

Chen, Zikuan; Calhoun, Vince D

2016-03-01

Conventionally, independent component analysis (ICA) is performed on an fMRI magnitude dataset to analyze brain functional mapping (AICA). By solving the inverse problem of fMRI, we can reconstruct the brain magnetic susceptibility (χ) functional states. Upon the reconstructed χ dataspace, we propose an ICA-based brain functional χ mapping method (χICA) to extract task-evoked brain functional map. A complex division algorithm is applied to a timeseries of fMRI phase images to extract temporal phase changes (relative to an OFF-state snapshot). A computed inverse MRI (CIMRI) model is used to reconstruct a 4D brain χ response dataset. χICA is implemented by applying a spatial InfoMax ICA algorithm to the reconstructed 4D χ dataspace. With finger-tapping experiments on a 7T system, the χICA-extracted χ-depicted functional map is similar to the SPM-inferred functional χ map by a spatial correlation of 0.67 ± 0.05. In comparison, the AICA-extracted magnitude-depicted map is correlated with the SPM magnitude map by 0.81 ± 0.05. The understanding of the inferiority of χICA to AICA for task-evoked functional map is an ongoing research topic. For task-evoked brain functional mapping, we compare the data-driven ICA method with the task-correlated SPM method. In particular, we compare χICA with AICA for extracting task-correlated timecourses and functional maps. χICA can extract a χ-depicted task-evoked brain functional map from a reconstructed χ dataspace without the knowledge about brain hemodynamic responses. The χICA-extracted brain functional χ map reveals a bidirectional BOLD response pattern that is unavailable (or different) from AICA. Copyright © 2016 Elsevier B.V. All rights reserved.

Sensation Seeking Predicts Brain Responses in the Old-New Task: Converging Multimodal Neuroimaging Evidence

OpenAIRE

Lawson, Adam L.; Liu, Xun; Joseph, Jane; Vagnini, Victoria L.; Kelly, Thomas H.; Jiang, Yang

2012-01-01

Novel images and message content enhance visual attention and memory for high sensation seekers, but the neural mechanisms associated with this effect are unclear. To investigate the individual differences in brain responses to new and old (studied) visual stimuli, we utilized Event-related Potentials (ERP) and functional Magnetic Resonance Imaging (fMRI) measures to examine brain reactivity among high and low sensation seekers during a classic old-new memory recognition task. Twenty low and ...

Extendable supervised dictionary learning for exploring diverse and concurrent brain activities in task-based fMRI.

Science.gov (United States)

Zhao, Shijie; Han, Junwei; Hu, Xintao; Jiang, Xi; Lv, Jinglei; Zhang, Tuo; Zhang, Shu; Guo, Lei; Liu, Tianming

2018-06-01

Recently, a growing body of studies have demonstrated the simultaneous existence of diverse brain activities, e.g., task-evoked dominant response activities, delayed response activities and intrinsic brain activities, under specific task conditions. However, current dominant task-based functional magnetic resonance imaging (tfMRI) analysis approach, i.e., the general linear model (GLM), might have difficulty in discovering those diverse and concurrent brain responses sufficiently. This subtraction-based model-driven approach focuses on the brain activities evoked directly from the task paradigm, thus likely overlooks other possible concurrent brain activities evoked during the information processing. To deal with this problem, in this paper, we propose a novel hybrid framework, called extendable supervised dictionary learning (E-SDL), to explore diverse and concurrent brain activities under task conditions. A critical difference between E-SDL framework and previous methods is that we systematically extend the basic task paradigm regressor into meaningful regressor groups to account for possible regressor variation during the information processing procedure in the brain. Applications of the proposed framework on five independent and publicly available tfMRI datasets from human connectome project (HCP) simultaneously revealed more meaningful group-wise consistent task-evoked networks and common intrinsic connectivity networks (ICNs). These results demonstrate the advantage of the proposed framework in identifying the diversity of concurrent brain activities in tfMRI datasets.

An automatic fuzzy-based multi-temporal brain digital subtraction angiography image fusion algorithm using curvelet transform and content selection strategy.

Science.gov (United States)

Momeni, Saba; Pourghassem, Hossein

2014-08-01

Recently image fusion has prominent role in medical image processing and is useful to diagnose and treat many diseases. Digital subtraction angiography is one of the most applicable imaging to diagnose brain vascular diseases and radiosurgery of brain. This paper proposes an automatic fuzzy-based multi-temporal fusion algorithm for 2-D digital subtraction angiography images. In this algorithm, for blood vessel map extraction, the valuable frames of brain angiography video are automatically determined to form the digital subtraction angiography images based on a novel definition of vessel dispersion generated by injected contrast material. Our proposed fusion scheme contains different fusion methods for high and low frequency contents based on the coefficient characteristic of wrapping second generation of curvelet transform and a novel content selection strategy. Our proposed content selection strategy is defined based on sample correlation of the curvelet transform coefficients. In our proposed fuzzy-based fusion scheme, the selection of curvelet coefficients are optimized by applying weighted averaging and maximum selection rules for the high frequency coefficients. For low frequency coefficients, the maximum selection rule based on local energy criterion is applied to better visual perception. Our proposed fusion algorithm is evaluated on a perfect brain angiography image dataset consisting of one hundred 2-D internal carotid rotational angiography videos. The obtained results demonstrate the effectiveness and efficiency of our proposed fusion algorithm in comparison with common and basic fusion algorithms.

Males and females differ in brain activation during cognitive tasks.

Science.gov (United States)

Bell, Emily C; Willson, Morgan C; Wilman, Alan H; Dave, Sanjay; Silverstone, Peter H

2006-04-01

To examine the effect of gender on regional brain activity, we utilized functional magnetic resonance imaging (fMRI) during a motor task and three cognitive tasks; a word generation task, a spatial attention task, and a working memory task in healthy male (n = 23) and female (n = 10) volunteers. Functional data were examined for group differences both in the number of pixels activated, and the blood-oxygen-level-dependent (BOLD) magnitude during each task. Males had a significantly greater mean activation than females in the working memory task with a greater number of pixels being activated in the right superior parietal gyrus and right inferior occipital gyrus, and a greater BOLD magnitude occurring in the left inferior parietal lobe. However, despite these fMRI changes, there were no significant differences between males and females on cognitive performance of the task. In contrast, in the spatial attention task, men performed better at this task than women, but there were no significant functional differences between the two groups. In the word generation task, there were no external measures of performance, but in the functional measurements, males had a significantly greater mean activation than females, where males had a significantly greater BOLD signal magnitude in the left and right dorsolateral prefrontal cortex, the right inferior parietal lobe, and the cingulate. In neither of the motor tasks (right or left hand) did males and females perform differently. Our fMRI findings during the motor tasks were a greater mean BOLD signal magnitude in males in the right hand motor task, compared to females where males had an increased BOLD signal magnitude in the right inferior parietal gyrus and in the left inferior frontal gyrus. In conclusion, these results demonstrate differential patterns of activation in males and females during a variety of cognitive tasks, even though performance in these tasks may not vary, and also that variability in performance may not

Large-scale automated image analysis for computational profiling of brain tissue surrounding implanted neuroprosthetic devices using Python.

Science.gov (United States)

Rey-Villamizar, Nicolas; Somasundar, Vinay; Megjhani, Murad; Xu, Yan; Lu, Yanbin; Padmanabhan, Raghav; Trett, Kristen; Shain, William; Roysam, Badri

2014-01-01

In this article, we describe the use of Python for large-scale automated server-based bio-image analysis in FARSIGHT, a free and open-source toolkit of image analysis methods for quantitative studies of complex and dynamic tissue microenvironments imaged by modern optical microscopes, including confocal, multi-spectral, multi-photon, and time-lapse systems. The core FARSIGHT modules for image segmentation, feature extraction, tracking, and machine learning are written in C++, leveraging widely used libraries including ITK, VTK, Boost, and Qt. For solving complex image analysis tasks, these modules must be combined into scripts using Python. As a concrete example, we consider the problem of analyzing 3-D multi-spectral images of brain tissue surrounding implanted neuroprosthetic devices, acquired using high-throughput multi-spectral spinning disk step-and-repeat confocal microscopy. The resulting images typically contain 5 fluorescent channels. Each channel consists of 6000 × 10,000 × 500 voxels with 16 bits/voxel, implying image sizes exceeding 250 GB. These images must be mosaicked, pre-processed to overcome imaging artifacts, and segmented to enable cellular-scale feature extraction. The features are used to identify cell types, and perform large-scale analysis for identifying spatial distributions of specific cell types relative to the device. Python was used to build a server-based script (Dell 910 PowerEdge servers with 4 sockets/server with 10 cores each, 2 threads per core and 1TB of RAM running on Red Hat Enterprise Linux linked to a RAID 5 SAN) capable of routinely handling image datasets at this scale and performing all these processing steps in a collaborative multi-user multi-platform environment. Our Python script enables efficient data storage and movement between computers and storage servers, logs all the processing steps, and performs full multi-threaded execution of all codes, including open and closed-source third party libraries.

Large-scale automated image analysis for computational profiling of brain tissue surrounding implanted neuroprosthetic devices using Python

Directory of Open Access Journals (Sweden)

Nicolas eRey-Villamizar

2014-04-01

Full Text Available In this article, we describe use of Python for large-scale automated server-based bio-image analysis in FARSIGHT, a free and open-source toolkit of image analysis methods for quantitative studies of complex and dynamic tissue microenvironments imaged by modern optical microscopes including confocal, multi-spectral, multi-photon, and time-lapse systems. The core FARSIGHT modules for image segmentation, feature extraction, tracking, and machine learning are written in C++, leveraging widely used libraries including ITK, VTK, Boost, and Qt. For solving complex image analysis task, these modules must be combined into scripts using Python. As a concrete example, we consider the problem of analyzing 3-D multi-spectral brain tissue images surrounding implanted neuroprosthetic devices, acquired using high-throughput multi-spectral spinning disk step-and-repeat confocal microscopy. The resulting images typically contain 5 fluorescent channels, 6,000$times$10,000$times$500 voxels with 16 bits/voxel, implying image sizes exceeding 250GB. These images must be mosaicked, pre-processed to overcome imaging artifacts, and segmented to enable cellular-scale feature extraction. The features are used to identify cell types, and perform large-scale analytics for identifying spatial distributions of specific cell types relative to the device. Python was used to build a server-based script (Dell 910 PowerEdge servers with 4 sockets/server with 10 cores each, 2 threads per core and 1TB of RAM running on Red Hat Enterprise Linux linked to a RAID 5 SAN capable of routinely handling image datasets at this scale and performing all these processing steps in a collaborative multi-user multi-platform environment consisting. Our Python script enables efficient data storage and movement between compute and storage servers, logging all processing steps, and performs full multi-threaded execution of all codes, including open and closed-source third party libraries.

Directionality analysis on functional magnetic resonance imaging during motor task using Granger causality.

Science.gov (United States)

Anwar, A R; Muthalib, M; Perrey, S; Galka, A; Granert, O; Wolff, S; Deuschl, G; Raethjen, J; Heute, U; Muthuraman, M

2012-01-01

Directionality analysis of signals originating from different parts of brain during motor tasks has gained a lot of interest. Since brain activity can be recorded over time, methods of time series analysis can be applied to medical time series as well. Granger Causality is a method to find a causal relationship between time series. Such causality can be referred to as a directional connection and is not necessarily bidirectional. The aim of this study is to differentiate between different motor tasks on the basis of activation maps and also to understand the nature of connections present between different parts of the brain. In this paper, three different motor tasks (finger tapping, simple finger sequencing, and complex finger sequencing) are analyzed. Time series for each task were extracted from functional magnetic resonance imaging (fMRI) data, which have a very good spatial resolution and can look into the sub-cortical regions of the brain. Activation maps based on fMRI images show that, in case of complex finger sequencing, most parts of the brain are active, unlike finger tapping during which only limited regions show activity. Directionality analysis on time series extracted from contralateral motor cortex (CMC), supplementary motor area (SMA), and cerebellum (CER) show bidirectional connections between these parts of the brain. In case of simple finger sequencing and complex finger sequencing, the strongest connections originate from SMA and CMC, while connections originating from CER in either direction are the weakest ones in magnitude during all paradigms.

A scalable multi-resolution spatio-temporal model for brain activation and connectivity in fMRI data

KAUST Repository

Castruccio, Stefano

2018-01-23

Functional Magnetic Resonance Imaging (fMRI) is a primary modality for studying brain activity. Modeling spatial dependence of imaging data at different spatial scales is one of the main challenges of contemporary neuroimaging, and it could allow for accurate testing for significance in neural activity. The high dimensionality of this type of data (on the order of hundreds of thousands of voxels) poses serious modeling challenges and considerable computational constraints. For the sake of feasibility, standard models typically reduce dimensionality by modeling covariance among regions of interest (ROIs)—coarser or larger spatial units—rather than among voxels. However, ignoring spatial dependence at different scales could drastically reduce our ability to detect activation patterns in the brain and hence produce misleading results. We introduce a multi-resolution spatio-temporal model and a computationally efficient methodology to estimate cognitive control related activation and whole-brain connectivity. The proposed model allows for testing voxel-specific activation while accounting for non-stationary local spatial dependence within anatomically defined ROIs, as well as regional dependence (between-ROIs). The model is used in a motor-task fMRI study to investigate brain activation and connectivity patterns aimed at identifying associations between these patterns and regaining motor functionality following a stroke.

Characteristics of brain functional alterations and task functional magnetic resonance imaging in patients with Cushing’s disease

Directory of Open Access Journals (Sweden)

Dan-dan LIU

2017-08-01

Full Text Available Objective To analyze the relationship between the brain functional alterations of patients with Cushing's disease (CD and patients' mental symptom by applying the Evaluating Emotional Scales and task functional magnetic resonance imaging (Task fMRI. Methods Task fMRI was performed on 8 patients with diagnosed CD admitted in the Department of Endocrinology of Chinese PLA General Hospital from Nov. 2015 to Nov. 2016 and 21 healthy people with matched age, gender and education level as control. Meanwhile, Self-Rating Depression Scale (SDS, Self-Rating Anxiety Scale (SAS, Positive and Negative Affective Scale (PANAS and Cushing Quality of Life Scale (Cushing QOL were obtained to assess the brain functions. Results Significant depression and anxiety were observed in patients with CD, and their positive affective score was substantially lower while the negative affective score was relatively higher compared with that in the controls. Task fMRI revealed that, when watching the positive pictures, the activation degree of left cerebellum and right postcentral gyrus weakened in CD patients than in the controls, and the positive correlations existed between the activation degree of left cerebellum and the 16 o'clock adrenocorticotrophic hormone (ACTH level, and between the activation degree of right postcentral gyrus and the urinary free cortisol (UFC level in CD patients. In contrast, when watching the negative pictures, the activation degree of left cerebellum, bilateral parahippocampal gyrus and left inferior frontal gyrus was weakened in CD patients than in the controls, and the activation degree of left cerebellum was negatively correlated to the 0 o'clock cortisol level and SAS score, but is positively correlated to the UFC level. When watching the neutral pictures, the activation degree of left cerebellum and left parahippocampal gyrus was weakened in CD patients than in the controls. Conclusions CD patients may have impaired brain function with

Apparent brain temperature imaging with multi-voxel proton magnetic resonance spectroscopy compared with cerebral blood flow and metabolism imaging on positron emission tomography in patients with unilateral chronic major cerebral artery steno-occlusive disease

Energy Technology Data Exchange (ETDEWEB)

Nanba, Takamasa; Nishimoto, Hideaki; Murakami, Toshiyuki; Fujiwara, Shunrou; Ogasawara, Kuniaki [Iwate Medical University, Department of Neurosurgery, Iwate (Japan); Yoshioka, Yoshichika [Osaka University, Open and Transdisciplinary Research Initiatives, Osaka (Japan); Sasaki, Makoto; Uwano, Ikuko [Iwate Medical University, Institute for Biomedical Science, Iwate (Japan); Terasaki, Kazunori [Iwate Medical University, Cyclotron Research Center, Iwate (Japan)

2017-09-15

The purpose of the present study was to determine whether apparent brain temperature imaging using multi-voxel proton magnetic resonance (MR) spectroscopy correlates with cerebral blood flow (CBF) and metabolism imaging in the deep white matter of patients with unilateral chronic major cerebral artery steno-occlusive disease. Apparent brain temperature and CBF and metabolism imaging were measured using proton MR spectroscopy and {sup 15}O-positron emission tomography (PET), respectively, in 35 patients. A set of regions of interest (ROIs) of 5 x 5 voxels was placed on an MR image so that the voxel row at each edge was located in the deep white matter of the centrum semiovale in each cerebral hemisphere. PET images were co-registered with MR images with these ROIs and were re-sliced automatically using image analysis software. In 175 voxel pairs located in the deep white matter, the brain temperature difference (affected hemisphere - contralateral hemisphere: ΔBT) was correlated with cerebral blood volume (CBV) (r = 0.570) and oxygen extraction fraction (OEF) ratios (affected hemisphere/contralateral hemisphere) (r = 0.641). We excluded voxels that contained ischemic lesions or cerebrospinal fluid and calculated the mean values of voxel pairs in each patient. The mean ΔBT was correlated with the mean CBF (r = - 0.376), mean CBV (r = 0.702), and mean OEF ratio (r = 0.774). Apparent brain temperature imaging using multi-voxel proton MR spectroscopy was correlated with CBF and metabolism imaging in the deep white matter of patients with unilateral major cerebral artery steno-occlusive disease. (orig.)

Are women better than men at multi-tasking?

OpenAIRE

Stoet, Gijsbert; O’Connor, Daryl B.; Conner, Mark; Laws, Keith R.

2013-01-01

Background: There seems to be a common belief that women are better in multi-tasking than men, but there is practically no scientific research on this topic. Here, we tested whether women have better multi-tasking skills than men.\\ensuremath\\ensuremath Methods: In Experiment 1, we compared performance of 120 women and 120 men in a computer-based task-switching paradigm. In Experiment 2, we compared a different group of 47 women and 47 men on "paper-and-pencil" multi-tasking tests.\\ensuremath\\...

BrainK for Structural Image Processing: Creating Electrical Models of the Human Head

Directory of Open Access Journals (Sweden)

Kai Li

2016-01-01

Full Text Available BrainK is a set of automated procedures for characterizing the tissues of the human head from MRI, CT, and photogrammetry images. The tissue segmentation and cortical surface extraction support the primary goal of modeling the propagation of electrical currents through head tissues with a finite difference model (FDM or finite element model (FEM created from the BrainK geometries. The electrical head model is necessary for accurate source localization of dense array electroencephalographic (dEEG measures from head surface electrodes. It is also necessary for accurate targeting of cerebral structures with transcranial current injection from those surface electrodes. BrainK must achieve five major tasks: image segmentation, registration of the MRI, CT, and sensor photogrammetry images, cortical surface reconstruction, dipole tessellation of the cortical surface, and Talairach transformation. We describe the approach to each task, and we compare the accuracies for the key tasks of tissue segmentation and cortical surface extraction in relation to existing research tools (FreeSurfer, FSL, SPM, and BrainVisa. BrainK achieves good accuracy with minimal or no user intervention, it deals well with poor quality MR images and tissue abnormalities, and it provides improved computational efficiency over existing research packages.

Dynamic multi-coil technique (DYNAMITE) shimming for echo-planar imaging of the human brain at 7 Tesla.

Science.gov (United States)

Juchem, Christoph; Umesh Rudrapatna, S; Nixon, Terence W; de Graaf, Robin A

2015-01-15

Gradient-echo echo-planar imaging (EPI) is the primary method of choice in functional MRI and other methods relying on fast MRI to image brain activation and connectivity. However, the high susceptibility of EPI towards B0 magnetic field inhomogeneity poses serious challenges. Conventional magnetic field shimming with low-order spherical harmonic (SH) functions is capable of compensating shallow field distortions, but performs poorly for global brain shimming or on specific areas with strong susceptibility-induced B0 distortions such as the prefrontal cortex (PFC). Excellent B0 homogeneity has been demonstrated recently in the human brain at 7 Tesla with the DYNAmic Multi-coIl TEchnique (DYNAMITE) for magnetic field shimming (J Magn Reson (2011) 212:280-288). Here, we report the benefits of DYNAMITE shimming for multi-slice EPI and T2* mapping. A standard deviation of 13Hz was achieved for the residual B0 distribution in the human brain at 7 Tesla with DYNAMITE shimming and was 60% lower compared to conventional shimming that employs static zero through third order SH shapes. The residual field inhomogeneity with SH shimming led to an average 8mm shift at acquisition parameters commonly used for fMRI and was reduced to 1.5-3mm with DYNAMITE shimming. T2* values obtained from the prefrontal and temporal cortices with DYNAMITE shimming were 10-50% longer than those measured with SH shimming. The reduction of the confounding macroscopic B0 field gradients with DYNAMITE shimming thereby promises improved access to the relevant microscopic T2* effects. The combination of high spatial resolution and DYNAMITE shimming allows largely artifact-free EPI and T2* mapping throughout the brain, including prefrontal and temporal lobe areas. DYNAMITE shimming is expected to critically benefit a wide range of MRI applications that rely on excellent B0 magnetic field conditions including EPI-based fMRI to study various cognitive processes and assessing large-scale brain connectivity

Dynamic Multi-Coil Technique (DYNAMITE) Shimming for Echo-Planar Imaging of the Human Brain at 7 Tesla

Science.gov (United States)

Juchem, Christoph; Rudrapatna, S. Umesh; Nixon, Terence W.; de Graaf, Robin A.

2014-01-01

Gradient-echo echo-planar imaging (EPI) is the primary method of choice in functional MRI and other methods relying on fast MRI to image brain activation and connectivity. However, the high susceptibility of EPI towards B0 magnetic field inhomogeneity poses serious challenges. Conventional magnetic field shimming with low-order spherical harmonic (SH) functions is capable of compensating shallow field distortions, but performs poorly for global brain shimming or on specific areas with strong susceptibility-induced B0 distortions such as the prefrontal cortex (PFC). Excellent B0 homogeneity has been demonstrated recently in the human brain at 7 Tesla with the DYNAmic Multi-coIl TEchnique (DYNAMITE) for magnetic field shimming (Juchem et al., J Magn Reson (2011) 212:280-288). Here, we report the benefits of DYNAMITE shimming for multi-slice EPI and T2* mapping. A standard deviation of 13 Hz was achieved for the residual B0 distribution in the human brain at 7 Tesla with DYNAMITE shimming and was 60% lower compared to conventional shimming that employs static zero through third order SH shapes. The residual field inhomogeneity with SH shimming led to an average 8 mm shift at acquisition parameters commonly used for fMRI and was reduced to 1.5-3 mm with DYNAMITE shimming. T2* values obtained from the prefrontal and temporal cortices with DYNAMITE shimming were 10-50% longer than those measured with SH shimming. The reduction of the confounding macroscopic B0 field gradients with DYNAMITE shimming thereby promises improved access to the relevant microscopic T2* effects. The combination of high spatial resolution and DYNAMITE shimming allows largely artifact-free EPI and T2* mapping throughout the brain, including prefrontal and temporal lobe areas. DYNAMITE shimming is expected to critically benefit a wide range of MRI applications that rely on excellent B0 magnetic field conditions including EPI-based fMRI to study various cognitive processes and assessing large

Functional brain imaging of a complex navigation task following one night of total sleep deprivation

Science.gov (United States)

Strangman, Gary; Thompson, John H.; Strauss, Monica M.; Marshburn, Thomas H.; Sutton, Jeffrey P.

2006-01-01

Study Objectives: To assess the cerebral effects associated with sleep deprivation in a simulation of a complex, real-world, high-risk task. Design and Interventions: A two-week, repeated measures, cross-over experimental protocol, with counterbalanced orders of normal sleep (NS) and total sleep deprivation (TSD). Setting: Each subject underwent functional magnetic resonance imaging (fMRI) while performing a dual-joystick, 3D sensorimotor navigation task (simulated orbital docking). Scanning was performed twice per subject, once following a night of normal sleep (NS), and once following a single night of total sleep deprivation (TSD). Five runs (eight 24s docking trials each) were performed during each scanning session. Participants: Six healthy, young, right-handed volunteers (2 women; mean age 20) participated. Measurements and Results: Behavioral performance on multiple measures was comparable in the two sleep conditions. Neuroimaging results within sleep conditions revealed similar locations of peak activity for NS and TSD, including left sensorimotor cortex, left precuneus (BA 7), and right visual areas (BA 18/19). However, cerebral activation following TSD was substantially larger and exhibited higher amplitude modulations from baseline. When directly comparing NS and TSD, most regions exhibited TSD>NS activity, including multiple prefrontal cortical areas (BA 8/9,44/45,47), lateral parieto-occipital areas (BA 19/39, 40), superior temporal cortex (BA 22), and bilateral thalamus and amygdala. Only left parietal cortex (BA 7) demonstrated NS>TSD activity. Conclusions: The large network of cerebral differences between the two conditions, even with comparable behavioral performance, suggests the possibility of detecting TSD-induced stress via functional brain imaging techniques on complex tasks before stress-induced failures.

Multi-Source Multi-Target Dictionary Learning for Prediction of Cognitive Decline.

Science.gov (United States)

Zhang, Jie; Li, Qingyang; Caselli, Richard J; Thompson, Paul M; Ye, Jieping; Wang, Yalin

2017-06-01

Alzheimer's Disease (AD) is the most common type of dementia. Identifying correct biomarkers may determine pre-symptomatic AD subjects and enable early intervention. Recently, Multi-task sparse feature learning has been successfully applied to many computer vision and biomedical informatics researches. It aims to improve the generalization performance by exploiting the shared features among different tasks. However, most of the existing algorithms are formulated as a supervised learning scheme. Its drawback is with either insufficient feature numbers or missing label information. To address these challenges, we formulate an unsupervised framework for multi-task sparse feature learning based on a novel dictionary learning algorithm. To solve the unsupervised learning problem, we propose a two-stage Multi-Source Multi-Target Dictionary Learning (MMDL) algorithm. In stage 1, we propose a multi-source dictionary learning method to utilize the common and individual sparse features in different time slots. In stage 2, supported by a rigorous theoretical analysis, we develop a multi-task learning method to solve the missing label problem. Empirical studies on an N = 3970 longitudinal brain image data set, which involves 2 sources and 5 targets, demonstrate the improved prediction accuracy and speed efficiency of MMDL in comparison with other state-of-the-art algorithms.

A Hybrid Task Graph Scheduler for High Performance Image Processing Workflows.

Science.gov (United States)

Blattner, Timothy; Keyrouz, Walid; Bhattacharyya, Shuvra S; Halem, Milton; Brady, Mary

2017-12-01

Designing applications for scalability is key to improving their performance in hybrid and cluster computing. Scheduling code to utilize parallelism is difficult, particularly when dealing with data dependencies, memory management, data motion, and processor occupancy. The Hybrid Task Graph Scheduler (HTGS) improves programmer productivity when implementing hybrid workflows for multi-core and multi-GPU systems. The Hybrid Task Graph Scheduler (HTGS) is an abstract execution model, framework, and API that increases programmer productivity when implementing hybrid workflows for such systems. HTGS manages dependencies between tasks, represents CPU and GPU memories independently, overlaps computations with disk I/O and memory transfers, keeps multiple GPUs occupied, and uses all available compute resources. Through these abstractions, data motion and memory are explicit; this makes data locality decisions more accessible. To demonstrate the HTGS application program interface (API), we present implementations of two example algorithms: (1) a matrix multiplication that shows how easily task graphs can be used; and (2) a hybrid implementation of microscopy image stitching that reduces code size by ≈ 43% compared to a manually coded hybrid workflow implementation and showcases the minimal overhead of task graphs in HTGS. Both of the HTGS-based implementations show good performance. In image stitching the HTGS implementation achieves similar performance to the hybrid workflow implementation. Matrix multiplication with HTGS achieves 1.3× and 1.8× speedup over the multi-threaded OpenBLAS library for 16k × 16k and 32k × 32k size matrices, respectively.

Dynamic whole-body PET parametric imaging: II. Task-oriented statistical estimation.

Science.gov (United States)

Karakatsanis, Nicolas A; Lodge, Martin A; Zhou, Y; Wahl, Richard L; Rahmim, Arman

2013-10-21

In the context of oncology, dynamic PET imaging coupled with standard graphical linear analysis has been previously employed to enable quantitative estimation of tracer kinetic parameters of physiological interest at the voxel level, thus, enabling quantitative PET parametric imaging. However, dynamic PET acquisition protocols have been confined to the limited axial field-of-view (~15-20 cm) of a single-bed position and have not been translated to the whole-body clinical imaging domain. On the contrary, standardized uptake value (SUV) PET imaging, considered as the routine approach in clinical oncology, commonly involves multi-bed acquisitions, but is performed statically, thus not allowing for dynamic tracking of the tracer distribution. Here, we pursue a transition to dynamic whole-body PET parametric imaging, by presenting, within a unified framework, clinically feasible multi-bed dynamic PET acquisition protocols and parametric imaging methods. In a companion study, we presented a novel clinically feasible dynamic (4D) multi-bed PET acquisition protocol as well as the concept of whole-body PET parametric imaging employing Patlak ordinary least squares (OLS) regression to estimate the quantitative parameters of tracer uptake rate Ki and total blood distribution volume V. In the present study, we propose an advanced hybrid linear regression framework, driven by Patlak kinetic voxel correlations, to achieve superior trade-off between contrast-to-noise ratio (CNR) and mean squared error (MSE) than provided by OLS for the final Ki parametric images, enabling task-based performance optimization. Overall, whether the observer's task is to detect a tumor or quantitatively assess treatment response, the proposed statistical estimation framework can be adapted to satisfy the specific task performance criteria, by adjusting the Patlak correlation-coefficient (WR) reference value. The multi-bed dynamic acquisition protocol, as optimized in the preceding companion study

Dynamic whole-body PET parametric imaging: II. Task-oriented statistical estimation

International Nuclear Information System (INIS)

Karakatsanis, Nicolas A; Lodge, Martin A; Zhou, Y; Wahl, Richard L; Rahmim, Arman

2013-01-01

In the context of oncology, dynamic PET imaging coupled with standard graphical linear analysis has been previously employed to enable quantitative estimation of tracer kinetic parameters of physiological interest at the voxel level, thus, enabling quantitative PET parametric imaging. However, dynamic PET acquisition protocols have been confined to the limited axial field-of-view (∼15–20 cm) of a single-bed position and have not been translated to the whole-body clinical imaging domain. On the contrary, standardized uptake value (SUV) PET imaging, considered as the routine approach in clinical oncology, commonly involves multi-bed acquisitions, but is performed statically, thus not allowing for dynamic tracking of the tracer distribution. Here, we pursue a transition to dynamic whole-body PET parametric imaging, by presenting, within a unified framework, clinically feasible multi-bed dynamic PET acquisition protocols and parametric imaging methods. In a companion study, we presented a novel clinically feasible dynamic (4D) multi-bed PET acquisition protocol as well as the concept of whole-body PET parametric imaging employing Patlak ordinary least squares (OLS) regression to estimate the quantitative parameters of tracer uptake rate K i and total blood distribution volume V. In the present study, we propose an advanced hybrid linear regression framework, driven by Patlak kinetic voxel correlations, to achieve superior trade-off between contrast-to-noise ratio (CNR) and mean squared error (MSE) than provided by OLS for the final K i parametric images, enabling task-based performance optimization. Overall, whether the observer's task is to detect a tumor or quantitatively assess treatment response, the proposed statistical estimation framework can be adapted to satisfy the specific task performance criteria, by adjusting the Patlak correlation-coefficient (WR) reference value. The multi-bed dynamic acquisition protocol, as optimized in the preceding companion

Dynamic Multi-Coil Technique (DYNAMITE) Shimming of the Rat Brain at 11.7 Tesla

Science.gov (United States)

Juchem, Christoph; Herman, Peter; Sanganahalli, Basavaraju G.; Brown, Peter B.; McIntyre, Scott; Nixon, Terence W.; Green, Dan; Hyder, Fahmeed; de Graaf, Robin A.

2014-01-01

The in vivo rat model is a workhorse in neuroscience research, preclinical studies and drug development. A repertoire of MR tools has been developed for its investigation, however, high levels of B0 magnetic field homogeneity are required for meaningful results. The homogenization of magnetic fields in the rat brain, i.e. shimming, is a difficult task due to a multitude of complex, susceptibility-induced field distortions. Conventional shimming with spherical harmonic (SH) functions is capable of compensating shallow field distortions in limited areas, e.g. in the cortex, but performs poorly in difficult-to-shim subcortical structures or for the entire brain. Based on the recently introduced multi-coil approach for magnetic field modeling, the DYNAmic Multi-coIl TEchnique (DYNAMITE) is introduced for magnetic field shimming of the in vivo rat brain and its benefits for gradient-echo echo-planar imaging (EPI) are demonstrated. An integrated multi-coil/radio-frequency (MC/RF) system comprising 48 individual localized DC coils for B0 shimming and a surface transceive RF coil has been developed that allows MR investigations of the anesthetized rat brain in vivo. DYNAMITE shimming with this MC/RF setup is shown to reduce the B0 standard deviation to a third of that achieved with current shim technology employing static first through third order SH shapes. The EPI signal over the rat brain increased by 31% and a 24% gain in usable EPI voxels could be realized. DYNAMITE shimming is expected to critically benefit a wide range of preclinical and neuroscientific MR research. Improved magnetic field homogeneity, along with the achievable large brain coverage of this method will be crucial when signal pathways, cortical circuitry or the brain’s default network are studied. Along with the efficiency gains of MC-based shimming compared to SH approaches demonstrated recently, DYNAMITE shimming has the potential to replace conventional SH shim systems in small bore animal

Functional MR imaging of working memory in the human brain

International Nuclear Information System (INIS)

Na, Dong Gyu; Ryu, Jae Wook; Byun, Hong Sik; Lee, Eun Jeong; Chung, Woo In; Cho, Jae Min; Han, Boo Kyung; Choi, Dae Seob

2000-01-01

In order to investigate the functional brain anatomy associated with verbal and visual working memory, functional magnetic resonance imaging was performed. In ten normal right handed subjects, functional MR images were obtained using a 1.5-T MR scanner and the EPI BOLD technique. An item recognition task was used for stimulation, and during the activation period of the verbal working memory task, consonant letters were used. During the activation period of the visual working memory task, symbols or diagrams were employed instead of letters. For the post-processing of images, the SPM program was used, with the threshold of significance set at p < .001. We assessed activated brain areas during the two stimulation tasks and compared the activated regions between the two tasks. The prefrontal cortex and secondary visual cortex were activated bilaterally by both verbal and visual working memory tasks, and the patterns of activated signals were similar in both tasks. The superior parietal cortex was also activated by both tasks, with lateralization to the left in the verbal task, and bilaterally without lateralization in the visual task. The inferior frontal cortex, inferior parietal cortex and temporal gyrus were activated exclusively by the verbal working memory task, predominantly in the left hemisphere. The prefrontal cortex is activated by two stimulation tasks, and this is related to the function of the central executive. The language areas activated by the verbal working memory task may be a function of the phonological loop. Bilateral prefrontal and superior parietal cortices activated by the visual working memory task may be related to the visual maintenance of objects, representing visual working memory

Functional MR imaging of working memory in the human brain

Energy Technology Data Exchange (ETDEWEB)

Na, Dong Gyu; Ryu, Jae Wook; Byun, Hong Sik; Lee, Eun Jeong; Chung, Woo In; Cho, Jae Min; Han, Boo Kyung [Sungkyunkwan University School of Medicine, Seoul (Korea, Republic of); Choi, Dae Seob [Dongguk University College of Medicine, Seoul (Korea, Republic of)

2000-03-01

In order to investigate the functional brain anatomy associated with verbal and visual working memory, functional magnetic resonance imaging was performed. In ten normal right handed subjects, functional MR images were obtained using a 1.5-T MR scanner and the EPI BOLD technique. An item recognition task was used for stimulation, and during the activation period of the verbal working memory task, consonant letters were used. During the activation period of the visual working memory task, symbols or diagrams were employed instead of letters. For the post-processing of images, the SPM program was used, with the threshold of significance set at p < .001. We assessed activated brain areas during the two stimulation tasks and compared the activated regions between the two tasks. The prefrontal cortex and secondary visual cortex were activated bilaterally by both verbal and visual working memory tasks, and the patterns of activated signals were similar in both tasks. The superior parietal cortex was also activated by both tasks, with lateralization to the left in the verbal task, and bilaterally without lateralization in the visual task. The inferior frontal cortex, inferior parietal cortex and temporal gyrus were activated exclusively by the verbal working memory task, predominantly in the left hemisphere. The prefrontal cortex is activated by two stimulation tasks, and this is related to the function of the central executive. The language areas activated by the verbal working memory task may be a function of the phonological loop. Bilateral prefrontal and superior parietal cortices activated by the visual working memory task may be related to the visual maintenance of objects, representing visual working memory.

Clinical evaluation of FMPSPGR sequence of the brain MR imaging

International Nuclear Information System (INIS)

Takahashi, Mitsuyuki; Hasegawa, Makoto; Mori, Naohiko; Yamanoguchi, Minoru; Matsubara, Tadashi

1998-01-01

In order to apply the FMPSPGR (fast multi planar spoiled GRASS) method to diagnose brain diseases, authors obtained the optimal condition for imaging by the phantom experiments and examined the clinical usefulness. Six kinds of the phantom, which were 4 of diluted Gd solution with different concentrations, olive oil and physiological saline solution were used. From the phantom experiments, TR/TE/FR=300/3.3/90 degrees was the optimal condition. The evaluation of the clinical images was performed on the same section by the ST method and the FMPSPGR method. Fifteen patients (9 men and 6 women, aged from 17 to 80 years) suspected of brain diseases were examined, including 8 of cerebral infarction, 1 of pontine infarction, 1 of brain contusion, 1 of intracerebral bleeding and 4 of brain tumors. Four cases of brain tumor were evaluated on the contrast imaging and the others were on the plain imaging. In the plain imaging, the FMPSPGR method was better than the SE method on the low signal region in the T1 weighted imaging. Furthermore, in the contrast imaging, it could give more clear images of the lesion in anterior cranial pit by suppressing artifacts of blood flow. The present results indicate that the FMPSPGR method is useful to diagnose brain diseases. (K.H.)

Multi-compartment microscopic diffusion imaging

OpenAIRE

Kaden, Enrico; Kelm, Nathaniel D.; Carson, Robert P.; Does, Mark D.; Alexander, Daniel C.

2016-01-01

This paper introduces a multi-compartment model for microscopic diffusion anisotropy imaging. The aim is to estimate microscopic features specific to the intra- and extra-neurite compartments in nervous tissue unconfounded by the effects of fibre crossings and orientation dispersion, which are ubiquitous in the brain. The proposed MRI method is based on the Spherical Mean Technique (SMT), which factors out the neurite orientation distribution and thus provides direct estimates of the microsco...

The functional neuroanatomy of multitasking: combining dual tasking with a short term memory task.

Science.gov (United States)

Deprez, Sabine; Vandenbulcke, Mathieu; Peeters, Ron; Emsell, Louise; Amant, Frederic; Sunaert, Stefan

2013-09-01

Insight into the neural architecture of multitasking is crucial when investigating the pathophysiology of multitasking deficits in clinical populations. Presently, little is known about how the brain combines dual-tasking with a concurrent short-term memory task, despite the relevance of this mental operation in daily life and the frequency of complaints related to this process, in disease. In this study we aimed to examine how the brain responds when a memory task is added to dual-tasking. Thirty-three right-handed healthy volunteers (20 females, mean age 39.9 ± 5.8) were examined with functional brain imaging (fMRI). The paradigm consisted of two cross-modal single tasks (a visual and auditory temporal same-different task with short delay), a dual-task combining both single tasks simultaneously and a multi-task condition, combining the dual-task with an additional short-term memory task (temporal same-different visual task with long delay). Dual-tasking compared to both individual visual and auditory single tasks activated a predominantly right-sided fronto-parietal network and the cerebellum. When adding the additional short-term memory task, a larger and more bilateral frontoparietal network was recruited. We found enhanced activity during multitasking in components of the network that were already involved in dual-tasking, suggesting increased working memory demands, as well as recruitment of multitask-specific components including areas that are likely to be involved in online holding of visual stimuli in short-term memory such as occipito-temporal cortex. These results confirm concurrent neural processing of a visual short-term memory task during dual-tasking and provide evidence for an effective fMRI multitasking paradigm. © 2013 Elsevier Ltd. All rights reserved.

EEG source imaging assists decoding in a face recognition task

DEFF Research Database (Denmark)

Andersen, Rasmus S.; Eliasen, Anders U.; Pedersen, Nicolai

2017-01-01

of face recognition. This task concerns the differentiation of brain responses to images of faces and scrambled faces and poses a rather difficult decoding problem at the single trial level. We implement the pipeline using spatially focused features and show that this approach is challenged and source...

Brain-state dependent robotic reaching movement with a multi-joint arm exoskeleton: combining brain-machine interfacing and robotic rehabilitation

Directory of Open Access Journals (Sweden)

Daniel eBrauchle

2015-10-01

Full Text Available While robot-assisted arm and hand training after stroke allows for intensive task-oriented practice, it has provided only limited additional benefit over dose-matched physiotherapy up to now. These rehabilitation devices are possibly too supportive during the exercises. Neurophysiological signals might be one way of avoiding slacking and providing robotic support only when the brain is particularly responsive to peripheral input.We tested the feasibility of three-dimensional robotic assistance for reach-to-grasp movements with a multi-joint exoskeleton during motor imagery-related desynchronization of sensorimotor oscillations in the β-band only. We also registered task-related network changes of cortical functional connectivity by electroencephalography via the imaginary part of the coherence function.Healthy subjects and stroke survivors showed similar patterns – but different aptitudes – of controlling the robotic movement. All participants in this pilot study with nine healthy subjects and two stroke patients achieved their maximum performance during the early stages of the task. Robotic control was significantly higher and less variable when proprioceptive feedback was provided in addition to visual feedback, i.e. when the orthosis was actually attached to the subject’s arm during the task. A distributed cortical network of task-related coherent activity in the θ-band showed significant differences between healthy subjects and stroke patients as well as between early and late periods of the task.Brain-robot interfaces may successfully link three-dimensional robotic training to the participants’ efforts and allow for task-oriented practice of activities of daily living with a physiologically controlled multi-joint exoskeleton. Changes of cortical physiology during the task might also help to make subject-specific adjustments of task difficulty and guide adjunct interventions to facilitate motor learning for functional restoration.

Task vs. rest-different network configurations between the coactivation and the resting-state brain networks.

Science.gov (United States)

Di, Xin; Gohel, Suril; Kim, Eun H; Biswal, Bharat B

2013-01-01

There is a growing interest in studies of human brain networks using resting-state functional magnetic resonance imaging (fMRI). However, it is unclear whether and how brain networks measured during the resting-state exhibit comparable properties to brain networks during task performance. In the present study, we investigated meta-analytic coactivation patterns among brain regions based upon published neuroimaging studies, and compared the coactivation network configurations with those in the resting-state network. The strength of resting-state functional connectivity between two regions were strongly correlated with the coactivation strength. However, the coactivation network showed greater global efficiency, smaller mean clustering coefficient, and lower modularity compared with the resting-state network, which suggest a more efficient global information transmission and between system integrations during task performing. Hub shifts were also observed within the thalamus and the left inferior temporal cortex. The thalamus and the left inferior temporal cortex exhibited higher and lower degrees, respectively in the coactivation network compared with the resting-state network. These results shed light regarding the reconfiguration of the brain networks between task and resting-state conditions, and highlight the role of the thalamus in change of network configurations in task vs. rest.

Improving multi-tasking ability through action videogames.

Science.gov (United States)

Chiappe, Dan; Conger, Mark; Liao, Janet; Caldwell, J Lynn; Vu, Kim-Phuong L

2013-03-01

The present study examined whether action videogames can improve multi-tasking in high workload environments. Two groups with no action videogame experience were pre-tested using the Multi-Attribute Task Battery (MATB). It consists of two primary tasks; tracking and fuel management, and two secondary tasks; systems monitoring and communication. One group served as a control group, while a second played action videogames a minimum of 5 h a week for 10 weeks. Both groups returned for a post-assessment on the MATB. We found the videogame treatment enhanced performance on secondary tasks, without interfering with the primary tasks. Our results demonstrate action videogames can increase people's ability to take on additional tasks by increasing attentional capacity. Copyright © 2012 Elsevier Ltd and The Ergonomics Society. All rights reserved.

Classification of MR brain images by combination of multi-CNNs for AD diagnosis

Science.gov (United States)

Cheng, Danni; Liu, Manhua; Fu, Jianliang; Wang, Yaping

2017-07-01

Alzheimer's disease (AD) is an irreversible neurodegenerative disorder with progressive impairment of memory and cognitive functions. Its early diagnosis is crucial for development of future treatment. Magnetic resonance images (MRI) play important role to help understand the brain anatomical changes related to AD. Conventional methods extract the hand-crafted features such as gray matter volumes and cortical thickness and train a classifier to distinguish AD from other groups. Different from these methods, this paper proposes to construct multiple deep 3D convolutional neural networks (3D-CNNs) to learn the various features from local brain images which are combined to make the final classification for AD diagnosis. First, a number of local image patches are extracted from the whole brain image and a 3D-CNN is built upon each local patch to transform the local image into more compact high-level features. Then, the upper convolution and fully connected layers are fine-tuned to combine the multiple 3D-CNNs for image classification. The proposed method can automatically learn the generic features from imaging data for classification. Our method is evaluated using T1-weighted structural MR brain images on 428 subjects including 199 AD patients and 229 normal controls (NC) from Alzheimer's Disease Neuroimaging Initiative (ADNI) database. Experimental results show that the proposed method achieves an accuracy of 87.15% and an AUC (area under the ROC curve) of 92.26% for AD classification, demonstrating the promising classification performances.

Functional brain imaging across development.

Science.gov (United States)

Rubia, Katya

2013-12-01

The developmental cognitive neuroscience literature has grown exponentially over the last decade. This paper reviews the functional magnetic resonance imaging (fMRI) literature on brain function development of typically late developing functions of cognitive and motivation control, timing and attention as well as of resting state neural networks. Evidence shows that between childhood and adulthood, concomitant with cognitive maturation, there is progressively increased functional activation in task-relevant lateral and medial frontal, striatal and parieto-temporal brain regions that mediate these higher level control functions. This is accompanied by progressively stronger functional inter-regional connectivity within task-relevant fronto-striatal and fronto-parieto-temporal networks. Negative age associations are observed in earlier developing posterior and limbic regions, suggesting a shift with age from the recruitment of "bottom-up" processing regions towards "top-down" fronto-cortical and fronto-subcortical connections, leading to a more mature, supervised cognition. The resting state fMRI literature further complements this evidence by showing progressively stronger deactivation with age in anti-correlated task-negative resting state networks, which is associated with better task performance. Furthermore, connectivity analyses during the resting state show that with development increasingly stronger long-range connections are being formed, for example, between fronto-parietal and fronto-cerebellar connections, in both task-positive networks and in task-negative default mode networks, together with progressively lesser short-range connections, suggesting progressive functional integration and segregation with age. Overall, evidence suggests that throughout development between childhood and adulthood, there is progressive refinement and integration of both task-positive fronto-cortical and fronto-subcortical activation and task-negative deactivation, leading to

Intelligence is differentially related to neural effort in the task-positive and the task-negative brain network

NARCIS (Netherlands)

Basten, U.; Stelzel, C.; Fiebach, C.J.

2013-01-01

Previous studies on individual differences in intelligence and brain activation during cognitive processing focused on brain regions where activation increases with task demands (task-positive network, TPN). Our study additionally considers brain regions where activation decreases with task demands

Robust visual tracking via multi-task sparse learning

KAUST Repository

Zhang, Tianzhu; Ghanem, Bernard; Liu, Si; Ahuja, Narendra

2012-01-01

In this paper, we formulate object tracking in a particle filter framework as a multi-task sparse learning problem, which we denote as Multi-Task Tracking (MTT). Since we model particles as linear combinations of dictionary templates

Imaging brain activity during seizures in freely behaving rats using a miniature multi-modal imaging system.

Science.gov (United States)

Sigal, Iliya; Koletar, Margaret M; Ringuette, Dene; Gad, Raanan; Jeffrey, Melanie; Carlen, Peter L; Stefanovic, Bojana; Levi, Ofer

2016-09-01

We report on a miniature label-free imaging system for monitoring brain blood flow and blood oxygenation changes in awake, freely behaving rats. The device, weighing 15 grams, enables imaging in a ∼ 2 × 2 mm field of view with 4.4 μm lateral resolution and 1 - 8 Hz temporal sampling rate. The imaging is performed through a chronically-implanted cranial window that remains optically clear between 2 to > 6 weeks after the craniotomy. This imaging method is well suited for longitudinal studies of chronic models of brain diseases and disorders. In this work, it is applied to monitoring neurovascular coupling during drug-induced absence-like seizures 6 weeks following the craniotomy.

MultiDrizzle: An Integrated Pyraf Script for Registering, Cleaning and Combining Images

Science.gov (United States)

Koekemoer, A. M.; Fruchter, A. S.; Hook, R. N.; Hack, W.

We present the new PyRAF-based `MultiDrizzle' script, which is aimed at providing a one-step approach to combining dithered HST images. The purpose of this script is to allow easy interaction with the complex suite of tasks in the IRAF/STSDAS `dither' package, as well as the new `PyDrizzle' task, while at the same time retaining the flexibility of these tasks through a number of parameters. These parameters control the various individual steps, such as sky subtraction, image registration, `drizzling' onto separate output images, creation of a clean median image, transformation of the median with `blot' and creation of cosmic ray masks, as well as the final image combination step using `drizzle'. The default parameters of all the steps are set so that the task will work automatically for a wide variety of different types of images, while at the same time allowing adjustment of individual parameters for special cases. The script currently works for both ACS and WFPC2 data, and is now being tested on STIS and NICMOS images. We describe the operation of the script and the effect of various parameters, particularly in the context of combining images from dithered observations using ACS and WFPC2. Additional information is also available at the `MultiDrizzle' home page: http://www.stsci.edu/~koekemoe/multidrizzle/

Brain imaging and brain function

International Nuclear Information System (INIS)

Sokoloff, L.

1985-01-01

This book is a survey of the applications of imaging studies of regional cerebral blood flow and metabolism to the investigation of neurological and psychiatric disorders. Contributors review imaging techniques and strategies for measuring regional cerebral blood flow and metabolism, for mapping functional neural systems, and for imaging normal brain functions. They then examine the applications of brain imaging techniques to the study of such neurological and psychiatric disorders as: cerebral ischemia; convulsive disorders; cerebral tumors; Huntington's disease; Alzheimer's disease; depression and other mood disorders. A state-of-the-art report on magnetic resonance imaging of the brain and central nervous system rounds out the book's coverage

Tensor-based Multi-view Feature Selection with Applications to Brain Diseases

Science.gov (United States)

Cao, Bokai; He, Lifang; Kong, Xiangnan; Yu, Philip S.; Hao, Zhifeng; Ragin, Ann B.

2015-01-01

In the era of big data, we can easily access information from multiple views which may be obtained from different sources or feature subsets. Generally, different views provide complementary information for learning tasks. Thus, multi-view learning can facilitate the learning process and is prevalent in a wide range of application domains. For example, in medical science, measurements from a series of medical examinations are documented for each subject, including clinical, imaging, immunologic, serologic and cognitive measures which are obtained from multiple sources. Specifically, for brain diagnosis, we can have different quantitative analysis which can be seen as different feature subsets of a subject. It is desirable to combine all these features in an effective way for disease diagnosis. However, some measurements from less relevant medical examinations can introduce irrelevant information which can even be exaggerated after view combinations. Feature selection should therefore be incorporated in the process of multi-view learning. In this paper, we explore tensor product to bring different views together in a joint space, and present a dual method of tensor-based multi-view feature selection (dual-Tmfs) based on the idea of support vector machine recursive feature elimination. Experiments conducted on datasets derived from neurological disorder demonstrate the features selected by our proposed method yield better classification performance and are relevant to disease diagnosis. PMID:25937823

Same task, different strategies: how brain networks can be influenced by memory strategy.

Science.gov (United States)

Sanfratello, Lori; Caprihan, Arvind; Stephen, Julia M; Knoefel, Janice E; Adair, John C; Qualls, Clifford; Lundy, S Laura; Aine, Cheryl J

2014-10-01

Previous functional neuroimaging studies demonstrated that different neural networks underlie different types of cognitive processing by engaging participants in particular tasks, such as verbal or spatial working memory (WM) tasks. However, we report here that even when a WM task is defined as verbal or spatial, different types of memory strategies may be used to complete it, with concomitant variations in brain activity. We developed a questionnaire to characterize the type of strategy used by individual members in a group of 28 young healthy participants (18-25 years) during a spatial WM task. A cluster analysis was performed to differentiate groups. We acquired functional magnetoencephalography and structural diffusion tensor imaging measures to characterize the brain networks associated with the use of different strategies. We found two types of strategies were used during the spatial WM task, a visuospatial and a verbal strategy, and brain regions and time courses of activation differed between participants who used each. Task performance also varied by type of strategy used with verbal strategies showing an advantage. In addition, performance on neuropsychological tests (indices from Wechsler Adult Intelligence Scale-IV, Rey Complex Figure Test) correlated significantly with fractional anisotropy measures for the visuospatial strategy group in white matter tracts implicated in other WM and attention studies. We conclude that differences in memory strategy can have a pronounced effect on the locations and timing of brain activation and that these differences need further investigation as a possible confounding factor for studies using group averaging as a means for summarizing results. Copyright © 2014 Wiley Periodicals, Inc.

Bedside functional brain imaging in critically-ill children using high-density EEG source modeling and multi-modal sensory stimulation

Directory of Open Access Journals (Sweden)

Danny Eytan

2016-01-01

Full Text Available Acute brain injury is a common cause of death and critical illness in children and young adults. Fundamental management focuses on early characterization of the extent of injury and optimizing recovery by preventing secondary damage during the days following the primary injury. Currently, bedside technology for measuring neurological function is mainly limited to using electroencephalography (EEG for detection of seizures and encephalopathic features, and evoked potentials. We present a proof of concept study in patients with acute brain injury in the intensive care setting, featuring a bedside functional imaging set-up designed to map cortical brain activation patterns by combining high density EEG recordings, multi-modal sensory stimulation (auditory, visual, and somatosensory, and EEG source modeling. Use of source-modeling allows for examination of spatiotemporal activation patterns at the cortical region level as opposed to the traditional scalp potential maps. The application of this system in both healthy and brain-injured participants is demonstrated with modality-specific source-reconstructed cortical activation patterns. By combining stimulation obtained with different modalities, most of the cortical surface can be monitored for changes in functional activation without having to physically transport the subject to an imaging suite. The results in patients in an intensive care setting with anatomically well-defined brain lesions suggest a topographic association between their injuries and activation patterns. Moreover, we report the reproducible application of a protocol examining a higher-level cortical processing with an auditory oddball paradigm involving presentation of the patient's own name. This study reports the first successful application of a bedside functional brain mapping tool in the intensive care setting. This application has the potential to provide clinicians with an additional dimension of information to manage

Multi-Label Classification Based on Low Rank Representation for Image Annotation

Directory of Open Access Journals (Sweden)

Qiaoyu Tan

2017-01-01

Full Text Available Annotating remote sensing images is a challenging task for its labor demanding annotation process and requirement of expert knowledge, especially when images can be annotated with multiple semantic concepts (or labels. To automatically annotate these multi-label images, we introduce an approach called Multi-Label Classification based on Low Rank Representation (MLC-LRR. MLC-LRR firstly utilizes low rank representation in the feature space of images to compute the low rank constrained coefficient matrix, then it adapts the coefficient matrix to define a feature-based graph and to capture the global relationships between images. Next, it utilizes low rank representation in the label space of labeled images to construct a semantic graph. Finally, these two graphs are exploited to train a graph-based multi-label classifier. To validate the performance of MLC-LRR against other related graph-based multi-label methods in annotating images, we conduct experiments on a public available multi-label remote sensing images (Land Cover. We perform additional experiments on five real-world multi-label image datasets to further investigate the performance of MLC-LRR. Empirical study demonstrates that MLC-LRR achieves better performance on annotating images than these comparing methods across various evaluation criteria; it also can effectively exploit global structure and label correlations of multi-label images.

Task vs. rest—different network configurations between the coactivation and the resting-state brain networks

Science.gov (United States)

Di, Xin; Gohel, Suril; Kim, Eun H.; Biswal, Bharat B.

2013-01-01

There is a growing interest in studies of human brain networks using resting-state functional magnetic resonance imaging (fMRI). However, it is unclear whether and how brain networks measured during the resting-state exhibit comparable properties to brain networks during task performance. In the present study, we investigated meta-analytic coactivation patterns among brain regions based upon published neuroimaging studies, and compared the coactivation network configurations with those in the resting-state network. The strength of resting-state functional connectivity between two regions were strongly correlated with the coactivation strength. However, the coactivation network showed greater global efficiency, smaller mean clustering coefficient, and lower modularity compared with the resting-state network, which suggest a more efficient global information transmission and between system integrations during task performing. Hub shifts were also observed within the thalamus and the left inferior temporal cortex. The thalamus and the left inferior temporal cortex exhibited higher and lower degrees, respectively in the coactivation network compared with the resting-state network. These results shed light regarding the reconfiguration of the brain networks between task and resting-state conditions, and highlight the role of the thalamus in change of network configurations in task vs. rest. PMID:24062654

Image-guided recording system for spatial and temporal mapping of neuronal activities in brain slice.

Science.gov (United States)

Choi, Geonho; Lee, Jeonghyeon; Kim, Hyeongeun; Jang, Jaemyung; Im, Changkyun; Jeon, Nooli; Jung, Woonggyu

2018-03-01

In this study, we introduce the novel image-guided recording system (IGRS) for efficient interpretation of neuronal activities in the brain slice. IGRS is designed to combine microelectrode array (MEA) and optical coherence tomography at the customized upright microscope. It allows to record multi-site neuronal signals and image of the volumetric brain anatomy in a single body configuration. For convenient interconnection between a brain image and neuronal signals, we developed the automatic mapping protocol that enables us to project acquired neuronal signals on a brain image. To evaluate the performance of IGRS, hippocampal signals of the brain slice were monitored, and corresponding with two-dimensional neuronal maps were successfully reconstructed. Our results indicated that IGRS and mapping protocol can provide the intuitive information regarding long-term and multi-sites neuronal signals. In particular, the temporal and spatial mapping capability of neuronal signals would be a very promising tool to observe and analyze the massive neuronal activity and connectivity in MEA-based electrophysiological studies. © 2017 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Landmark-based deep multi-instance learning for brain disease diagnosis.

Science.gov (United States)

Liu, Mingxia; Zhang, Jun; Adeli, Ehsan; Shen, Dinggang

2018-01-01

In conventional Magnetic Resonance (MR) image based methods, two stages are often involved to capture brain structural information for disease diagnosis, i.e., 1) manually partitioning each MR image into a number of regions-of-interest (ROIs), and 2) extracting pre-defined features from each ROI for diagnosis with a certain classifier. However, these pre-defined features often limit the performance of the diagnosis, due to challenges in 1) defining the ROIs and 2) extracting effective disease-related features. In this paper, we propose a landmark-based deep multi-instance learning (LDMIL) framework for brain disease diagnosis. Specifically, we first adopt a data-driven learning approach to discover disease-related anatomical landmarks in the brain MR images, along with their nearby image patches. Then, our LDMIL framework learns an end-to-end MR image classifier for capturing both the local structural information conveyed by image patches located by landmarks and the global structural information derived from all detected landmarks. We have evaluated our proposed framework on 1526 subjects from three public datasets (i.e., ADNI-1, ADNI-2, and MIRIAD), and the experimental results show that our framework can achieve superior performance over state-of-the-art approaches. Copyright © 2017 Elsevier B.V. All rights reserved.

Task-based statistical image reconstruction for high-quality cone-beam CT

Science.gov (United States)

Dang, Hao; Webster Stayman, J.; Xu, Jennifer; Zbijewski, Wojciech; Sisniega, Alejandro; Mow, Michael; Wang, Xiaohui; Foos, David H.; Aygun, Nafi; Koliatsos, Vassilis E.; Siewerdsen, Jeffrey H.

2017-11-01

Task-based analysis of medical imaging performance underlies many ongoing efforts in the development of new imaging systems. In statistical image reconstruction, regularization is often formulated in terms to encourage smoothness and/or sharpness (e.g. a linear, quadratic, or Huber penalty) but without explicit formulation of the task. We propose an alternative regularization approach in which a spatially varying penalty is determined that maximizes task-based imaging performance at every location in a 3D image. We apply the method to model-based image reconstruction (MBIR—viz., penalized weighted least-squares, PWLS) in cone-beam CT (CBCT) of the head, focusing on the task of detecting a small, low-contrast intracranial hemorrhage (ICH), and we test the performance of the algorithm in the context of a recently developed CBCT prototype for point-of-care imaging of brain injury. Theoretical predictions of local spatial resolution and noise are computed via an optimization by which regularization (specifically, the quadratic penalty strength) is allowed to vary throughout the image to maximize local task-based detectability index ({{d}\\prime} ). Simulation studies and test-bench experiments were performed using an anthropomorphic head phantom. Three PWLS implementations were tested: conventional (constant) penalty; a certainty-based penalty derived to enforce constant point-spread function, PSF; and the task-based penalty derived to maximize local detectability at each location. Conventional (constant) regularization exhibited a fairly strong degree of spatial variation in {{d}\\prime} , and the certainty-based method achieved uniform PSF, but each exhibited a reduction in detectability compared to the task-based method, which improved detectability up to ~15%. The improvement was strongest in areas of high attenuation (skull base), where the conventional and certainty-based methods tended to over-smooth the data. The task-driven reconstruction method presents a

Reliability of the Cooking Task in adults with acquired brain injury.

Science.gov (United States)

Poncet, Frédérique; Swaine, Bonnie; Taillefer, Chantal; Lamoureux, Julie; Pradat-Diehl, Pascale; Chevignard, Mathilde

2015-01-01

Acquired brain injury (ABI) often leads to deficits in executive functioning (EF) responsible for severe and long-standing disabilities in daily life activities. The Cooking Task is an ecological and valid test of EF involving multi-tasking in a real environment. Given its complex scoring system, it is important to establish the tool's reliability. The objective of the study was to examine the reliability of the Cooking Task (internal consistency, inter-rater and test-retest reliability). A total of 160 patients with ABI (113 men, mean age 37 years, SD = 14.3) were tested using the Cooking Task. For test-retest reliability, patients were assessed by the same rater on two occasions (mean interval 11 days) while two raters independently and simultaneously observed and scored patients' performances to estimate inter-rater reliability. Internal consistency was high for the global scale (Cronbach α = .74). Inter-rater reliability (n = 66) for total errors was also high (ICC = .93), however the test-retest reliability (n = 11) was poor (ICC = .36). In general the Cooking Task appears to be a reliable tool. The low test-retest results were expected given the importance of EF in the performance of novel tasks.

Brain activity during divided and selective attention to auditory and visual sentence comprehension tasks

OpenAIRE

Moisala, Mona; Salmela, Viljami; Salo, Emma; Carlson, Synnove; Vuontela, Virve; Salonen, Oili; Alho, Kimmo

2015-01-01

Using functional magnetic resonance imaging (fMRI), we measured brain activity of human participants while they performed a sentence congruence judgment task in either the visual or auditory modality separately, or in both modalities simultaneously. Significant performance decrements were observed when attention was divided between the two modalities compared with when one modality was selectively attended. Compared with selective attention (i.e., single tasking), divided attention (i.e., dua...

SegAN: Adversarial Network with Multi-scale L1 Loss for Medical Image Segmentation.

Science.gov (United States)

Xue, Yuan; Xu, Tao; Zhang, Han; Long, L Rodney; Huang, Xiaolei

2018-05-03

Inspired by classic Generative Adversarial Networks (GANs), we propose a novel end-to-end adversarial neural network, called SegAN, for the task of medical image segmentation. Since image segmentation requires dense, pixel-level labeling, the single scalar real/fake output of a classic GAN's discriminator may be ineffective in producing stable and sufficient gradient feedback to the networks. Instead, we use a fully convolutional neural network as the segmentor to generate segmentation label maps, and propose a novel adversarial critic network with a multi-scale L 1 loss function to force the critic and segmentor to learn both global and local features that capture long- and short-range spatial relationships between pixels. In our SegAN framework, the segmentor and critic networks are trained in an alternating fashion in a min-max game: The critic is trained by maximizing a multi-scale loss function, while the segmentor is trained with only gradients passed along by the critic, with the aim to minimize the multi-scale loss function. We show that such a SegAN framework is more effective and stable for the segmentation task, and it leads to better performance than the state-of-the-art U-net segmentation method. We tested our SegAN method using datasets from the MICCAI BRATS brain tumor segmentation challenge. Extensive experimental results demonstrate the effectiveness of the proposed SegAN with multi-scale loss: on BRATS 2013 SegAN gives performance comparable to the state-of-the-art for whole tumor and tumor core segmentation while achieves better precision and sensitivity for Gd-enhance tumor core segmentation; on BRATS 2015 SegAN achieves better performance than the state-of-the-art in both dice score and precision.

Feature-based Alignment of Volumetric Multi-modal Images

Science.gov (United States)

Toews, Matthew; Zöllei, Lilla; Wells, William M.

2014-01-01

This paper proposes a method for aligning image volumes acquired from different imaging modalities (e.g. MR, CT) based on 3D scale-invariant image features. A novel method for encoding invariant feature geometry and appearance is developed, based on the assumption of locally linear intensity relationships, providing a solution to poor repeatability of feature detection in different image modalities. The encoding method is incorporated into a probabilistic feature-based model for multi-modal image alignment. The model parameters are estimated via a group-wise alignment algorithm, that iteratively alternates between estimating a feature-based model from feature data, then realigning feature data to the model, converging to a stable alignment solution with few pre-processing or pre-alignment requirements. The resulting model can be used to align multi-modal image data with the benefits of invariant feature correspondence: globally optimal solutions, high efficiency and low memory usage. The method is tested on the difficult RIRE data set of CT, T1, T2, PD and MP-RAGE brain images of subjects exhibiting significant inter-subject variability due to pathology. PMID:24683955

Videogame training strategy-induced change in brain function during a complex visuomotor task.

Science.gov (United States)

Lee, Hyunkyu; Voss, Michelle W; Prakash, Ruchika Shaurya; Boot, Walter R; Vo, Loan T K; Basak, Chandramallika; Vanpatter, Matt; Gratton, Gabriele; Fabiani, Monica; Kramer, Arthur F

2012-07-01

Although changes in brain function induced by cognitive training have been examined, functional plasticity associated with specific training strategies is still relatively unexplored. In this study, we examined changes in brain function during a complex visuomotor task following training using the Space Fortress video game. To assess brain function, participants completed functional magnetic resonance imaging (fMRI) before and after 30 h of training with one of two training regimens: Hybrid Variable-Priority Training (HVT), with a focus on improving specific skills and managing task priority, or Full Emphasis Training (FET), in which participants simply practiced the game to obtain the highest overall score. Control participants received only 6 h of FET. Compared to FET, HVT learners reached higher performance on the game and showed less brain activation in areas related to visuo-spatial attention and goal-directed movement after training. Compared to the control group, HVT exhibited less brain activation in right dorsolateral prefrontal cortex (DLPFC), coupled with greater performance improvement. Region-of-interest analysis revealed that the reduction in brain activation was correlated with improved performance on the task. This study sheds light on the neurobiological mechanisms of improved learning from directed training (HVT) over non-directed training (FET), which is related to visuo-spatial attention and goal-directed motor planning, while separating the practice-based benefit, which is related to executive control and rule management. Copyright © 2012 Elsevier B.V. All rights reserved.

Brain activation during dual-task processing is associated with cardiorespiratory fitness and performance in older adults

Directory of Open Access Journals (Sweden)

Chelsea N Wong

2015-08-01

Full Text Available Higher cardiorespiratory fitness is associated with better cognitive performance and enhanced brain activation. Yet, the extent to which cardiorespiratory fitness-related brain activation is associated with better cognitive performance is not well understood. In this cross-sectional study, we examined whether the association between cardiorespiratory fitness and executive function was mediated by greater prefrontal cortex activation in healthy older adults. Brain activation was measured during dual-task performance with functional magnetic resonance imaging in a sample of 128 healthy older adults (59-80 years. Higher cardiorespiratory fitness was associated with greater activation during dual-task processing in several brain areas including the anterior cingulate and supplementary motor cortex (ACC/SMA, thalamus and basal ganglia, right motor/somatosensory cortex and middle frontal gyrus, and left somatosensory cortex, controlling for age, sex, education, and gray matter volume. Of these regions, greater ACC/SMA activation mediated the association between cardiorespiratory fitness and dual-task performance. We provide novel evidence that cardiorespiratory fitness may support cognitive performance by facilitating brain activation in a core region critical for executive function.

Neural correlates of interference resolution in the multi-source interference task: a meta-analysis of functional neuroimaging studies.

Science.gov (United States)

Deng, Yuqin; Wang, Xiaochun; Wang, Yan; Zhou, Chenglin

2018-04-10

Interference resolution refers to cognitive control processes enabling one to focus on task-related information while filtering out unrelated information. But the exact neural areas, which underlie a specific cognitive task on interference resolution, are still equivocal. The multi-source interference task (MSIT), as a particular cognitive task, is a well-established experimental paradigm used to evaluate interference resolution. Studies combining the MSIT with functional magnetic resonance imaging (fMRI) have shown that the MSIT evokes the dorsal anterior cingulate cortex (dACC) and cingulate-frontal-parietal cognitive-attentional networks. However, these brain areas have not been evaluated quantitatively and these findings have not been replicated. In the current study, we firstly report a voxel-based meta-analysis of functional brain activation associated with the MSIT so as to identify the localization of interference resolution in such a specific cognitive task. Articles on MSIT-related fMRI published between 2003 and July 2017 were eligible. The electronic databases searched included PubMed, Web of Knowledge, and Google Scholar. Differential BOLD activation patterns between the incongruent and congruent condition were meta-analyzed in anisotropic effect-size signed differential mapping software. Robustness meta-analysis indicated that two significant activation clusters were shown to have reliable functional activity in comparisons between incongruent and congruent conditions. The first reliable activation cluster, which included the dACC, medial prefrontal cortex, supplementary motor area, replicated the previous MSIT-related fMRI study results. Furthermore, we found another reliable activation cluster comprising areas of the right insula, right inferior frontal gyrus, and right lenticular nucleus-putamen, which were not typically discussed in previous MSIT-related fMRI studies. The current meta-analysis study presents the reliable brain activation patterns

Image quality at synthetic brain magnetic resonance imaging in children

Energy Technology Data Exchange (ETDEWEB)

Lee, So Mi; Cho, Seung Hyun; Kim, Won Hwa; Kim, Hye Jung [Kyungpook National University Hospital, Department of Radiology, Daegu (Korea, Republic of); Choi, Young Hun; Cheon, Jung-Eun; Kim, In-One [Seoul National University College of Medicine, Department of Radiology and Institute of Radiation Medicine, Seoul (Korea, Republic of); Cho, Hyun-Hae [Ewha Womans University Mokdong Hospital, Department of Radiology, Seoul (Korea, Republic of); You, Sun-Kyoung [Chungnam National University Hospital, Department of Radiology, Daejeon (Korea, Republic of); Park, Sook-Hyun [Kyungpook National University Hospital, Department of Pediatrics, Daegu (Korea, Republic of); Hwang, Moon Jung [GE Healthcare, MR Applications and Workflow, Seoul (Korea, Republic of)

2017-11-15

The clinical application of the multi-echo, multi-delay technique of synthetic magnetic resonance imaging (MRI) generates multiple sequences in a single acquisition but has mainly been used in adults. To evaluate the image quality of synthetic brain MR in children compared with that of conventional images. Twenty-nine children (median age: 6 years, range: 0-16 years) underwent synthetic and conventional imaging. Synthetic (T2-weighted, T1-weighted and fluid-attenuated inversion recovery [FLAIR]) images with settings matching those of the conventional images were generated. The overall image quality, gray/white matter differentiation, lesion conspicuity and image degradations were rated on a 5-point scale. The relative contrasts were assessed quantitatively and acquisition times for the two imaging techniques were compared. Synthetic images were inferior due to more pronounced image degradations; however, there were no significant differences for T1- and T2-weighted images in children <2 years old. The quality of T1- and T2-weighted images were within the diagnostically acceptable range. FLAIR images showed greatly reduced quality. Gray/white matter differentiation was comparable or better in synthetic T1- and T2-weighted images, but poorer in FLAIR images. There was no effect on lesion conspicuity. Synthetic images had equal or greater relative contrast. Acquisition time was approximately two-thirds of that for conventional sequences. Synthetic T1- and T2-weighted images were diagnostically acceptable, but synthetic FLAIR images were not. Lesion conspicuity and gray/white matter differentiation were comparable to conventional MRI. (orig.)

Brain imaging

International Nuclear Information System (INIS)

Mishkin, F.S.

1978-01-01

The techniques of brain imaging and results in perfusion studies and delayed images are outlined. An analysis of the advantages and disadvantages of the brain scan in a variety of common problems is discussed, especially as compared with other available procedures. Both nonneoplastic and neoplastic lesions are considered. (Auth/C.F.)

Low cost light-sheet microscopy for whole brain imaging

Science.gov (United States)

Kumar, Manish; Nasenbeny, Jordan; Kozorovitskiy, Yevgenia

2018-02-01

Light-sheet microscopy has evolved as an indispensable tool in imaging biological samples. It can image 3D samples at fast speed, with high-resolution optical sectioning, and with reduced photobleaching effects. These properties make light-sheet microscopy ideal for imaging fluorophores in a variety of biological samples and organisms, e.g. zebrafish, drosophila, cleared mouse brains, etc. While most commercial turnkey light-sheet systems are expensive, the existing lower cost implementations, e.g. OpenSPIM, are focused on achieving high-resolution imaging of small samples or organisms like zebrafish. In this work, we substantially reduce the cost of light-sheet microscope system while targeting to image much larger samples, i.e. cleared mouse brains, at single-cell resolution. The expensive components of a lightsheet system - excitation laser, water-immersion objectives, and translation stage - are replaced with an incoherent laser diode, dry objectives, and a custom-built Arduino-controlled translation stage. A low-cost CUBIC protocol is used to clear fixed mouse brain samples. The open-source platforms of μManager and Fiji support image acquisition, processing, and visualization. Our system can easily be extended to multi-color light-sheet microscopy.

Multi-view Multi-sparsity Kernel Reconstruction for Multi-class Image Classification

KAUST Repository

Zhu, Xiaofeng; Xie, Qing; Zhu, Yonghua; Liu, Xingyi; Zhang, Shichao

2015-01-01

This paper addresses the problem of multi-class image classification by proposing a novel multi-view multi-sparsity kernel reconstruction (MMKR for short) model. Given images (including test images and training images) representing with multiple

Human brain imaging

International Nuclear Information System (INIS)

Kuhar, M.J.

1987-01-01

Just as there have been dramatic advances in the molecular biology of the human brain in recent years, there also have been remarkable advances in brain imaging. This paper reports on the development and broad application of microscopic imaging techniques which include the autoradiographic localization of receptors and the measurement of glucose utilization by autoradiography. These approaches provide great sensitivity and excellent anatomical resolution in exploring brain organization and function. The first noninvasive external imaging of receptor distributions in the living human brain was achieved by positron emission tomography (PET) scanning. Developments, techniques and applications continue to progress. Magnetic resonance imaging (MRI) is also becoming important. Its initial clinical applications were in examining the structure and anatomy of the brain. However, more recent uses, such as MRI spectroscopy, indicate the feasibility of exploring biochemical pathways in the brain, the metabolism of drugs in the brain, and also of examining some of these procedures at an anatomical resolution which is substantially greater than that obtainable by PET scanning. The issues will be discussed in greater detail

Robust visual tracking via structured multi-task sparse learning

KAUST Repository

Zhang, Tianzhu; Ghanem, Bernard; Liu, Si; Ahuja, Narendra

2012-01-01

In this paper, we formulate object tracking in a particle filter framework as a structured multi-task sparse learning problem, which we denote as Structured Multi-Task Tracking (S-MTT). Since we model particles as linear combinations of dictionary

Multi-task feature learning by using trace norm regularization

Directory of Open Access Journals (Sweden)

Jiangmei Zhang

2017-11-01

Full Text Available Multi-task learning can extract the correlation of multiple related machine learning problems to improve performance. This paper considers applying the multi-task learning method to learn a single task. We propose a new learning approach, which employs the mixture of expert model to divide a learning task into several related sub-tasks, and then uses the trace norm regularization to extract common feature representation of these sub-tasks. A nonlinear extension of this approach by using kernel is also provided. Experiments conducted on both simulated and real data sets demonstrate the advantage of the proposed approach.

Toward valid and reliable brain imaging results in eating disorders.

Science.gov (United States)

Frank, Guido K W; Favaro, Angela; Marsh, Rachel; Ehrlich, Stefan; Lawson, Elizabeth A

2018-03-01

Human brain imaging can help improve our understanding of mechanisms underlying brain function and how they drive behavior in health and disease. Such knowledge may eventually help us to devise better treatments for psychiatric disorders. However, the brain imaging literature in psychiatry and especially eating disorders has been inconsistent, and studies are often difficult to replicate. The extent or severity of extremes of eating and state of illness, which are often associated with differences in, for instance hormonal status, comorbidity, and medication use, commonly differ between studies and likely add to variation across study results. Those effects are in addition to the well-described problems arising from differences in task designs, data quality control procedures, image data preprocessing and analysis or statistical thresholds applied across studies. Which of those factors are most relevant to improve reproducibility is still a question for debate and further research. Here we propose guidelines for brain imaging research in eating disorders to acquire valid results that are more reliable and clinically useful. © 2018 Wiley Periodicals, Inc.

Grid Computing Application for Brain Magnetic Resonance Image Processing

International Nuclear Information System (INIS)

Valdivia, F; Crépeault, B; Duchesne, S

2012-01-01

This work emphasizes the use of grid computing and web technology for automatic post-processing of brain magnetic resonance images (MRI) in the context of neuropsychiatric (Alzheimer's disease) research. Post-acquisition image processing is achieved through the interconnection of several individual processes into pipelines. Each process has input and output data ports, options and execution parameters, and performs single tasks such as: a) extracting individual image attributes (e.g. dimensions, orientation, center of mass), b) performing image transformations (e.g. scaling, rotation, skewing, intensity standardization, linear and non-linear registration), c) performing image statistical analyses, and d) producing the necessary quality control images and/or files for user review. The pipelines are built to perform specific sequences of tasks on the alphanumeric data and MRIs contained in our database. The web application is coded in PHP and allows the creation of scripts to create, store and execute pipelines and their instances either on our local cluster or on high-performance computing platforms. To run an instance on an external cluster, the web application opens a communication tunnel through which it copies the necessary files, submits the execution commands and collects the results. We present result on system tests for the processing of a set of 821 brain MRIs from the Alzheimer's Disease Neuroimaging Initiative study via a nonlinear registration pipeline composed of 10 processes. Our results show successful execution on both local and external clusters, and a 4-fold increase in performance if using the external cluster. However, the latter's performance does not scale linearly as queue waiting times and execution overhead increase with the number of tasks to be executed.

On the role of cost-sensitive learning in multi-class brain-computer interfaces.

Science.gov (United States)

Devlaminck, Dieter; Waegeman, Willem; Wyns, Bart; Otte, Georges; Santens, Patrick

2010-06-01

Brain-computer interfaces (BCIs) present an alternative way of communication for people with severe disabilities. One of the shortcomings in current BCI systems, recently put forward in the fourth BCI competition, is the asynchronous detection of motor imagery versus resting state. We investigated this extension to the three-class case, in which the resting state is considered virtually lying between two motor classes, resulting in a large penalty when one motor task is misclassified into the other motor class. We particularly focus on the behavior of different machine-learning techniques and on the role of multi-class cost-sensitive learning in such a context. To this end, four different kernel methods are empirically compared, namely pairwise multi-class support vector machines (SVMs), two cost-sensitive multi-class SVMs and kernel-based ordinal regression. The experimental results illustrate that ordinal regression performs better than the other three approaches when a cost-sensitive performance measure such as the mean-squared error is considered. By contrast, multi-class cost-sensitive learning enables us to control the number of large errors made between two motor tasks.

Sensation seeking predicts brain responses in the old-new task: converging multimodal neuroimaging evidence.

Science.gov (United States)

Lawson, Adam L; Liu, Xun; Joseph, Jane; Vagnini, Victoria L; Kelly, Thomas H; Jiang, Yang

2012-06-01

Novel images and message content enhance visual attention and memory for high sensation seekers, but the neural mechanisms associated with this effect are unclear. To investigate the individual differences in brain responses to new and old (studied) visual stimuli, we utilized event-related potentials (ERP) and functional Magnetic Resonance Imaging (fMRI) measures to examine brain reactivity among high and low sensation seekers during a classic old-new memory recognition task. Twenty low and 20 high sensation seekers completed separate, but parallel, ERP and fMRI sessions. For each session, participants initially studied drawings of common images, and then performed an old-new recognition task during scanning. High sensation seekers showed greater ERP responses to new objects at the frontal N2 ERP component, compared to low sensation seekers. The ERP Novelty-N2 responses were correlated with fMRI responses in the orbitofrontal gyrus. Sensation seeking status also modulated the FN400 ERP component indexing familiarity and conceptual learning, along with fMRI responses in the caudate nucleus, which correlated with FN400 activity. No group differences were found in the late ERP positive components indexing classic old-new amplitude effects. Our combined ERP and fMRI results suggest that sensation-seeking personality affects the early brain responses to visual processing, but not the later stage of memory recognition. Copyright © 2012 Elsevier B.V. All rights reserved.

Deep multi-scale convolutional neural network for hyperspectral image classification

Science.gov (United States)

Zhang, Feng-zhe; Yang, Xia

2018-04-01

In this paper, we proposed a multi-scale convolutional neural network for hyperspectral image classification task. Firstly, compared with conventional convolution, we utilize multi-scale convolutions, which possess larger respective fields, to extract spectral features of hyperspectral image. We design a deep neural network with a multi-scale convolution layer which contains 3 different convolution kernel sizes. Secondly, to avoid overfitting of deep neural network, dropout is utilized, which randomly sleeps neurons, contributing to improve the classification accuracy a bit. In addition, new skills like ReLU in deep learning is utilized in this paper. We conduct experiments on University of Pavia and Salinas datasets, and obtained better classification accuracy compared with other methods.

Intrinsic and task-evoked network architectures of the human brain

Science.gov (United States)

Cole, Michael W.; Bassett, Danielle S.; Power, Jonathan D.; Braver, Todd S.; Petersen, Steven E.

2014-01-01

Summary Many functional network properties of the human brain have been identified during rest and task states, yet it remains unclear how the two relate. We identified a whole-brain network architecture present across dozens of task states that was highly similar to the resting-state network architecture. The most frequent functional connectivity strengths across tasks closely matched the strengths observed at rest, suggesting this is an “intrinsic”, standard architecture of functional brain organization. Further, a set of small but consistent changes common across tasks suggests the existence of a task-general network architecture distinguishing task states from rest. These results indicate the brain’s functional network architecture during task performance is shaped primarily by an intrinsic network architecture that is also present during rest, and secondarily by evoked task-general and task-specific network changes. This establishes a strong relationship between resting-state functional connectivity and task-evoked functional connectivity – areas of neuroscientific inquiry typically considered separately. PMID:24991964

Cross-domain and multi-task transfer learning of deep convolutional neural network for breast cancer diagnosis in digital breast tomosynthesis

Science.gov (United States)

Samala, Ravi K.; Chan, Heang-Ping; Hadjiiski, Lubomir; Helvie, Mark A.; Richter, Caleb; Cha, Kenny

2018-02-01

We propose a cross-domain, multi-task transfer learning framework to transfer knowledge learned from non-medical images by a deep convolutional neural network (DCNN) to medical image recognition task while improving the generalization by multi-task learning of auxiliary tasks. A first stage cross-domain transfer learning was initiated from ImageNet trained DCNN to mammography trained DCNN. 19,632 regions-of-interest (ROI) from 2,454 mass lesions were collected from two imaging modalities: digitized-screen film mammography (SFM) and full-field digital mammography (DM), and split into training and test sets. In the multi-task transfer learning, the DCNN learned the mass classification task simultaneously from the training set of SFM and DM. The best transfer network for mammography was selected from three transfer networks with different number of convolutional layers frozen. The performance of single-task and multitask transfer learning on an independent SFM test set in terms of the area under the receiver operating characteristic curve (AUC) was 0.78+/-0.02 and 0.82+/-0.02, respectively. In the second stage cross-domain transfer learning, a set of 12,680 ROIs from 317 mass lesions on DBT were split into validation and independent test sets. We first studied the data requirements for the first stage mammography trained DCNN by varying the mammography training data from 1% to 100% and evaluated its learning on the DBT validation set in inference mode. We found that the entire available mammography set provided the best generalization. The DBT validation set was then used to train only the last four fully connected layers, resulting in an AUC of 0.90+/-0.04 on the independent DBT test set.

Intersection Based Motion Correction of Multi-Slice MRI for 3D in utero Fetal Brain Image Formation

Science.gov (United States)

Kim, Kio; Habas, Piotr A.; Rousseau, Francois; Glenn, Orit A.; Barkovich, Anthony J.; Studholme, Colin

2012-01-01

In recent years post-processing of fast multi-slice MR imaging to correct fetal motion has provided the first true 3D MR images of the developing human brain in utero. Early approaches have used reconstruction based algorithms, employing a two step iterative process, where slices from the acquired data are re-aligned to an approximate 3D reconstruction of the fetal brain, which is then refined further using the improved slice alignment. This two step slice-to-volume process, although powerful, is computationally expensive in needing a 3D reconstruction, and is limited in its ability to recover sub-voxel alignment. Here, we describe an alternative approach which we term slice intersection motion correction (SIMC), that seeks to directly co-align multiple slice stacks by considering the matching structure along all intersecting slice pairs in all orthogonally planned slices that are acquired in clinical imaging studies. A collective update scheme for all slices is then derived, to simultaneously drive slices into a consistent match along their lines of intersection. We then describe a 3D reconstruction algorithm that, using the final motion corrected slice locations, suppresses through-plane partial volume effects to provide a single high isotropic resolution 3D image. The method is tested on simulated data with known motions and is applied to retrospectively reconstruct 3D images from a range of clinically acquired imaging studies. The quantitative evaluation of the registration accuracy for the simulated data sets demonstrated a significant improvement over previous approaches. An initial application of the technique to studying clinical pathology is included, where the proposed method recovered up to 15 mm of translation and 30 degrees of rotation for individual slices, and produced full 3D reconstructions containing clinically useful additional information not visible in the original 2D slices. PMID:19744911

The impact of verbal framing on brain activity evoked by emotional images.

Science.gov (United States)

Kisley, Michael A; Campbell, Alana M; Larson, Jenna M; Naftz, Andrea E; Regnier, Jesse T; Davalos, Deana B

2011-12-01

Emotional stimuli generally command more brain processing resources than non-emotional stimuli, but the magnitude of this effect is subject to voluntary control. Cognitive reappraisal represents one type of emotion regulation that can be voluntarily employed to modulate responses to emotional stimuli. Here, the late positive potential (LPP), a specific event-related brain potential (ERP) component, was measured in response to neutral, positive and negative images while participants performed an evaluative categorization task. One experimental group adopted a "negative frame" in which images were categorized as negative or not. The other adopted a "positive frame" in which the exact same images were categorized as positive or not. Behavioral performance confirmed compliance with random group assignment, and peak LPP amplitude to negative images was affected by group membership: brain responses to negative images were significantly reduced in the "positive frame" group. This suggests that adopting a more positive appraisal frame can modulate brain activity elicited by negative stimuli in the environment.

Diffusion MRI processing for multi-compartment characterization of brain pathology

International Nuclear Information System (INIS)

Hedouin, Renaud

2017-01-01

Diffusion weighted imaging (DWI) is a specific type of MRI acquisition based on the direction of diffusion of the brain water molecules. It allows, through several acquisitions, to model the brain microstructure, as white matter, which is significantly smaller than the voxel-resolution. To acquire a large number of images in a clinical setting, very-fast acquisition techniques are required as single-shot imaging. However these acquisitions suffer locally large distortions. We propose a block-matching registration method based on the acquisition of images with opposite phase-encoding directions (PED). This technique specially designed for Echo-Planar Images (EPI) robustly correct images and provides a deformation field. This field is applicable to an entire DWI series from only one reversed EPI allowing distortion correction with a minimal acquisition time cost. This registration algorithm has been validated both on phantom and on in vivo data and is available in our source medical image processing toolbox Anima. From these diffusion images, we are able to construct multi-compartments models (MCM) which can represent complex brain microstructure. Doing registration, averaging and atlas creation on these MCM images is required to perform studies and statistic analyses. We propose a general method to interpolate MCM as a simplification problem based on spectral clustering. This technique, which is adaptable for any MCM, has been validated on both synthetic and real data. Then, from a registered dataset, we performed a patient to population analysis at a voxel-level computing statistics on MCM parameters. Specifically designed tractography can also be used to make analysis, following tracks, based on individual anisotropic compartments. All these tools are designed and used on real data and contribute to the search of bio-markers for brain diseases such as multiple sclerosis. (author)

Automatic structural parcellation of mouse brain MRI using multi-atlas label fusion.

Directory of Open Access Journals (Sweden)

Da Ma

Full Text Available Multi-atlas segmentation propagation has evolved quickly in recent years, becoming a state-of-the-art methodology for automatic parcellation of structural images. However, few studies have applied these methods to preclinical research. In this study, we present a fully automatic framework for mouse brain MRI structural parcellation using multi-atlas segmentation propagation. The framework adopts the similarity and truth estimation for propagated segmentations (STEPS algorithm, which utilises a locally normalised cross correlation similarity metric for atlas selection and an extended simultaneous truth and performance level estimation (STAPLE framework for multi-label fusion. The segmentation accuracy of the multi-atlas framework was evaluated using publicly available mouse brain atlas databases with pre-segmented manually labelled anatomical structures as the gold standard, and optimised parameters were obtained for the STEPS algorithm in the label fusion to achieve the best segmentation accuracy. We showed that our multi-atlas framework resulted in significantly higher segmentation accuracy compared to single-atlas based segmentation, as well as to the original STAPLE framework.

Brain activity during divided and selective attention to auditory and visual sentence comprehension tasks.

Science.gov (United States)

Moisala, Mona; Salmela, Viljami; Salo, Emma; Carlson, Synnöve; Vuontela, Virve; Salonen, Oili; Alho, Kimmo

2015-01-01

Using functional magnetic resonance imaging (fMRI), we measured brain activity of human participants while they performed a sentence congruence judgment task in either the visual or auditory modality separately, or in both modalities simultaneously. Significant performance decrements were observed when attention was divided between the two modalities compared with when one modality was selectively attended. Compared with selective attention (i.e., single tasking), divided attention (i.e., dual-tasking) did not recruit additional cortical regions, but resulted in increased activity in medial and lateral frontal regions which were also activated by the component tasks when performed separately. Areas involved in semantic language processing were revealed predominantly in the left lateral prefrontal cortex by contrasting incongruent with congruent sentences. These areas also showed significant activity increases during divided attention in relation to selective attention. In the sensory cortices, no crossmodal inhibition was observed during divided attention when compared with selective attention to one modality. Our results suggest that the observed performance decrements during dual-tasking are due to interference of the two tasks because they utilize the same part of the cortex. Moreover, semantic dual-tasking did not appear to recruit additional brain areas in comparison with single tasking, and no crossmodal inhibition was observed during intermodal divided attention.

Brain activity during divided and selective attention to auditory and visual sentence comprehension tasks

Science.gov (United States)

Moisala, Mona; Salmela, Viljami; Salo, Emma; Carlson, Synnöve; Vuontela, Virve; Salonen, Oili; Alho, Kimmo

2015-01-01

Using functional magnetic resonance imaging (fMRI), we measured brain activity of human participants while they performed a sentence congruence judgment task in either the visual or auditory modality separately, or in both modalities simultaneously. Significant performance decrements were observed when attention was divided between the two modalities compared with when one modality was selectively attended. Compared with selective attention (i.e., single tasking), divided attention (i.e., dual-tasking) did not recruit additional cortical regions, but resulted in increased activity in medial and lateral frontal regions which were also activated by the component tasks when performed separately. Areas involved in semantic language processing were revealed predominantly in the left lateral prefrontal cortex by contrasting incongruent with congruent sentences. These areas also showed significant activity increases during divided attention in relation to selective attention. In the sensory cortices, no crossmodal inhibition was observed during divided attention when compared with selective attention to one modality. Our results suggest that the observed performance decrements during dual-tasking are due to interference of the two tasks because they utilize the same part of the cortex. Moreover, semantic dual-tasking did not appear to recruit additional brain areas in comparison with single tasking, and no crossmodal inhibition was observed during intermodal divided attention. PMID:25745395

Hierarchical organization of brain functional networks during visual tasks.

Science.gov (United States)

Zhuo, Zhao; Cai, Shi-Min; Fu, Zhong-Qian; Zhang, Jie

2011-09-01

The functional network of the brain is known to demonstrate modular structure over different hierarchical scales. In this paper, we systematically investigated the hierarchical modular organizations of the brain functional networks that are derived from the extent of phase synchronization among high-resolution EEG time series during a visual task. In particular, we compare the modular structure of the functional network from EEG channels with that of the anatomical parcellation of the brain cortex. Our results show that the modular architectures of brain functional networks correspond well to those from the anatomical structures over different levels of hierarchy. Most importantly, we find that the consistency between the modular structures of the functional network and the anatomical network becomes more pronounced in terms of vision, sensory, vision-temporal, motor cortices during the visual task, which implies that the strong modularity in these areas forms the functional basis for the visual task. The structure-function relationship further reveals that the phase synchronization of EEG time series in the same anatomical group is much stronger than that of EEG time series from different anatomical groups during the task and that the hierarchical organization of functional brain network may be a consequence of functional segmentation of the brain cortex.

Multi-Task Convolutional Neural Network for Pose-Invariant Face Recognition

Science.gov (United States)

Yin, Xi; Liu, Xiaoming

2018-02-01

This paper explores multi-task learning (MTL) for face recognition. We answer the questions of how and why MTL can improve the face recognition performance. First, we propose a multi-task Convolutional Neural Network (CNN) for face recognition where identity classification is the main task and pose, illumination, and expression estimations are the side tasks. Second, we develop a dynamic-weighting scheme to automatically assign the loss weight to each side task, which is a crucial problem in MTL. Third, we propose a pose-directed multi-task CNN by grouping different poses to learn pose-specific identity features, simultaneously across all poses. Last but not least, we propose an energy-based weight analysis method to explore how CNN-based MTL works. We observe that the side tasks serve as regularizations to disentangle the variations from the learnt identity features. Extensive experiments on the entire Multi-PIE dataset demonstrate the effectiveness of the proposed approach. To the best of our knowledge, this is the first work using all data in Multi-PIE for face recognition. Our approach is also applicable to in-the-wild datasets for pose-invariant face recognition and achieves comparable or better performance than state of the art on LFW, CFP, and IJB-A datasets.

Advance preparation in task-switching: converging evidence from behavioral, brain activation and model-based approaches

NARCIS (Netherlands)

Karayanidis, F.; Jamadar, S.; Ruge, H.; Phillips, N.; Heathcote, A.; Forstmann, B.U.

2010-01-01

Recent research has taken advantage of the temporal and spatial resolution of event-related brain potentials (ERPs) and functional magnetic resonance imaging (fMRI) to identify the time course and neural circuitry of preparatory processes required to switch between different tasks. Here we overview

Cognitive impairment after traumatic brain injury: a functional magnetic resonance imaging study using the Stroop task

International Nuclear Information System (INIS)

Soeda, Akio; Iwama, Toru; Nakashima, Toshihiko; Okumura, Ayumi; Shinoda, Jun; Kuwata, Kazuo

2005-01-01

The anterior cingulate cortex (ACC) plays a key role in cognition, motor function, and emotion processing. However, little is known about how traumatic brain injury (TBI) affects the ACC system. Our purpose was to compare, by functional magnetic resonance imaging (fMRI) studies, the patterns of cortical activation in patients with cognitive impairment after TBI and those of normal subjects. Cortical activation maps of 11 right-handed healthy control subjects and five TBI patients with cognitive impairment were recorded in response to a Stroop task during a block-designed fMRI experiment. Statistical parametric mapping (SPM99) was used for individual subjects and group analysis. In TBI patients and controls, cortical activation, found in similar regions of the frontal, occipital, and parietal lobes, resembled patterns of activation documented in previous neuroimaging studies of the Stroop task in healthy controls. However, the TBI patients showed a relative decrease in ACC activity compared with the controls. Cognitive impairment in TBI patients seems to be associated with alterations in functional cerebral activity, especially less activation of the ACC. These changes are probably the result of destruction of neural networks after diffuse axonal injury and may reflect cortical disinhibition attributable to disconnection or compensation for an inefficient cognitive process. (orig.)

Cognitive impairment after traumatic brain injury: a functional magnetic resonance imaging study using the Stroop task

Energy Technology Data Exchange (ETDEWEB)

Soeda, Akio; Iwama, Toru [Gifu University School of Medicine, Department of Neurosurgery, Gifu City (Japan); Nakashima, Toshihiko; Okumura, Ayumi; Shinoda, Jun [Kizawa Memorial Hospital, Chubu Medical Center for Prolonged Traumatic Brain Dysfunction, Department of Neurosurgery, Minokamo (Japan); Kuwata, Kazuo [Gifu University School of Medicine, Department of Biochemistry and Biophysics, Gifu (Japan)

2005-07-01

The anterior cingulate cortex (ACC) plays a key role in cognition, motor function, and emotion processing. However, little is known about how traumatic brain injury (TBI) affects the ACC system. Our purpose was to compare, by functional magnetic resonance imaging (fMRI) studies, the patterns of cortical activation in patients with cognitive impairment after TBI and those of normal subjects. Cortical activation maps of 11 right-handed healthy control subjects and five TBI patients with cognitive impairment were recorded in response to a Stroop task during a block-designed fMRI experiment. Statistical parametric mapping (SPM99) was used for individual subjects and group analysis. In TBI patients and controls, cortical activation, found in similar regions of the frontal, occipital, and parietal lobes, resembled patterns of activation documented in previous neuroimaging studies of the Stroop task in healthy controls. However, the TBI patients showed a relative decrease in ACC activity compared with the controls. Cognitive impairment in TBI patients seems to be associated with alterations in functional cerebral activity, especially less activation of the ACC. These changes are probably the result of destruction of neural networks after diffuse axonal injury and may reflect cortical disinhibition attributable to disconnection or compensation for an inefficient cognitive process. (orig.)

Persistency and flexibility of complex brain networks underlie dual-task interference.

Science.gov (United States)

Alavash, Mohsen; Hilgetag, Claus C; Thiel, Christiane M; Gießing, Carsten

2015-09-01

Previous studies on multitasking suggest that performance decline during concurrent task processing arises from interfering brain modules. Here, we used graph-theoretical network analysis to define functional brain modules and relate the modular organization of complex brain networks to behavioral dual-task costs. Based on resting-state and task fMRI we explored two organizational aspects potentially associated with behavioral interference when human subjects performed a visuospatial and speech task simultaneously: the topological overlap between persistent single-task modules, and the flexibility of single-task modules in adaptation to the dual-task condition. Participants showed a significant decline in visuospatial accuracy in the dual-task compared with single visuospatial task. Global analysis of topological similarity between modules revealed that the overlap between single-task modules significantly correlated with the decline in visuospatial accuracy. Subjects with larger overlap between single-task modules showed higher behavioral interference. Furthermore, lower flexible reconfiguration of single-task modules in adaptation to the dual-task condition significantly correlated with larger decline in visuospatial accuracy. Subjects with lower modular flexibility showed higher behavioral interference. At the regional level, higher overlap between single-task modules and less modular flexibility in the somatomotor cortex positively correlated with the decline in visuospatial accuracy. Additionally, higher modular flexibility in cingulate and frontal control areas and lower flexibility in right-lateralized nodes comprising the middle occipital and superior temporal gyri supported dual-tasking. Our results suggest that persistency and flexibility of brain modules are important determinants of dual-task costs. We conclude that efficient dual-tasking benefits from a specific balance between flexibility and rigidity of functional brain modules. © 2015 Wiley

Ranking Performance Measures in Multi-Task Agencies

DEFF Research Database (Denmark)

Christensen, Peter Ove; Sabac, Florin; Tian, Joyce

2010-01-01

We derive sufficient conditions for ranking performance evaluation systems in multi-task agency models (using both optimal and linear contracts) in terms of a second-order stochastic dominance (SSD) condition on the likelihood ratios. The SSD condition can be replaced by a variance-covariance mat......We derive sufficient conditions for ranking performance evaluation systems in multi-task agency models (using both optimal and linear contracts) in terms of a second-order stochastic dominance (SSD) condition on the likelihood ratios. The SSD condition can be replaced by a variance...

Large-field-of-view imaging by multi-pupil adaptive optics.

Science.gov (United States)

Park, Jung-Hoon; Kong, Lingjie; Zhou, Yifeng; Cui, Meng

2017-06-01

Adaptive optics can correct for optical aberrations. We developed multi-pupil adaptive optics (MPAO), which enables simultaneous wavefront correction over a field of view of 450 × 450 μm 2 and expands the correction area to nine times that of conventional methods. MPAO's ability to perform spatially independent wavefront control further enables 3D nonplanar imaging. We applied MPAO to in vivo structural and functional imaging in the mouse brain.

When global rule reversal meets local task switching: The neural mechanisms of coordinated behavioral adaptation to instructed multi-level demand changes.

Science.gov (United States)

Shi, Yiquan; Wolfensteller, Uta; Schubert, Torsten; Ruge, Hannes

2018-02-01

Cognitive flexibility is essential to cope with changing task demands and often it is necessary to adapt to combined changes in a coordinated manner. The present fMRI study examined how the brain implements such multi-level adaptation processes. Specifically, on a "local," hierarchically lower level, switching between two tasks was required across trials while the rules of each task remained unchanged for blocks of trials. On a "global" level regarding blocks of twelve trials, the task rules could reverse or remain the same. The current task was cued at the start of each trial while the current task rules were instructed before the start of a new block. We found that partly overlapping and partly segregated neural networks play different roles when coping with the combination of global rule reversal and local task switching. The fronto-parietal control network (FPN) supported the encoding of reversed rules at the time of explicit rule instruction. The same regions subsequently supported local task switching processes during actual implementation trials, irrespective of rule reversal condition. By contrast, a cortico-striatal network (CSN) including supplementary motor area and putamen was increasingly engaged across implementation trials and more so for rule reversal than for nonreversal blocks, irrespective of task switching condition. Together, these findings suggest that the brain accomplishes the coordinated adaptation to multi-level demand changes by distributing processing resources either across time (FPN for reversed rule encoding and later for task switching) or across regions (CSN for reversed rule implementation and FPN for concurrent task switching). © 2017 Wiley Periodicals, Inc.

MR image-guided portal verification for brain treatment field

International Nuclear Information System (INIS)

Yin, F.-F.; Gao, Q.H.; Xie, H.; Nelson, D.F.; Yu, Y.; Kwok, W.E.; Totterman, S.; Schell, M.C.; Rubin, P.

1996-01-01

Purpose/Objective: Although MR images have been extensively used for the treatment planning of radiation therapy of cancers, especially for brain cancers, they are not effectively used for the portal verification due to lack of bone/air information in MR images and geometric distortions. Typically, MR images are utilized through correlation with CT images, and this procedure is usually very labor and time consuming. For many brain cancer patients to be treated using conventional external beam radiation, MR images with proper distortion correction provide sufficient information for treatment planning and dose calculation, and a projection images may be generated for each specific treatment port and to be used as a reference image for treatment verification. The question is how to transfer anatomical features in MR images to the projection image as landmarks which could be correlated automatically to those in the portal image. The goal of this study is to generate digitally reconstructed projection images from MR brain images with some important anatomical features (brain contour, skull and gross tumor) as well as their relative locations to be used as references for the development of computerized portal verification scheme. Materials/Methods: Compared to conventional digital reconstructed radiograph from CT images, generation of digitally reconstructed projection images from MR images is heavily involved with pixel manipulation of MR images to correlate information from two types of images (MR, portal x-ray images) which are produced based on totally different imaging principles. Initially a wavelet based multi-resolution adaptive thresholding method is used to segment the skull slice-by-slice in MR brain axial images, and identified skull pixels are re-assigned to relatively higher intensities so that projection images will have comparable grey-level information as that in typical brain portal images. Both T1- and T2-weighted images are utilized to eliminate fat

Similar brain activation during false belief tasks in a large sample of adults with and without autism.

Science.gov (United States)

Dufour, Nicholas; Redcay, Elizabeth; Young, Liane; Mavros, Penelope L; Moran, Joseph M; Triantafyllou, Christina; Gabrieli, John D E; Saxe, Rebecca

2013-01-01

Reading about another person's beliefs engages 'Theory of Mind' processes and elicits highly reliable brain activation across individuals and experimental paradigms. Using functional magnetic resonance imaging, we examined activation during a story task designed to elicit Theory of Mind processing in a very large sample of neurotypical (N = 462) individuals, and a group of high-functioning individuals with autism spectrum disorders (N = 31), using both region-of-interest and whole-brain analyses. This large sample allowed us to investigate group differences in brain activation to Theory of Mind tasks with unusually high sensitivity. There were no differences between neurotypical participants and those diagnosed with autism spectrum disorder. These results imply that the social cognitive impairments typical of autism spectrum disorder can occur without measurable changes in the size, location or response magnitude of activity during explicit Theory of Mind tasks administered to adults.

Similar brain activation during false belief tasks in a large sample of adults with and without autism.

Directory of Open Access Journals (Sweden)

Nicholas Dufour

Full Text Available Reading about another person's beliefs engages 'Theory of Mind' processes and elicits highly reliable brain activation across individuals and experimental paradigms. Using functional magnetic resonance imaging, we examined activation during a story task designed to elicit Theory of Mind processing in a very large sample of neurotypical (N = 462 individuals, and a group of high-functioning individuals with autism spectrum disorders (N = 31, using both region-of-interest and whole-brain analyses. This large sample allowed us to investigate group differences in brain activation to Theory of Mind tasks with unusually high sensitivity. There were no differences between neurotypical participants and those diagnosed with autism spectrum disorder. These results imply that the social cognitive impairments typical of autism spectrum disorder can occur without measurable changes in the size, location or response magnitude of activity during explicit Theory of Mind tasks administered to adults.

Imaging gait analysis: An fMRI dual task study.

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Bürki, Céline N; Bridenbaugh, Stephanie A; Reinhardt, Julia; Stippich, Christoph; Kressig, Reto W; Blatow, Maria

2017-08-01

In geriatric clinical diagnostics, gait analysis with cognitive-motor dual tasking is used to predict fall risk and cognitive decline. To date, the neural correlates of cognitive-motor dual tasking processes are not fully understood. To investigate these underlying neural mechanisms, we designed an fMRI paradigm to reproduce the gait analysis. We tested the fMRI paradigm's feasibility in a substudy with fifteen young adults and assessed 31 healthy older adults in the main study. First, gait speed and variability were quantified using the GAITRite © electronic walkway. Then, participants lying in the MRI-scanner were stepping on pedals of an MRI-compatible stepping device used to imitate gait during functional imaging. In each session, participants performed cognitive and motor single tasks as well as cognitive-motor dual tasks. Behavioral results showed that the parameters of both gait analyses, GAITRite © and fMRI, were significantly positively correlated. FMRI results revealed significantly reduced brain activation during dual task compared to single task conditions. Functional ROI analysis showed that activation in the superior parietal lobe (SPL) decreased less from single to dual task condition than activation in primary motor cortex and in supplementary motor areas. Moreover, SPL activation was increased during dual tasks in subjects exhibiting lower stepping speed and lower executive control. We were able to simulate walking during functional imaging with valid results that reproduce those from the GAITRite © gait analysis. On the neural level, SPL seems to play a crucial role in cognitive-motor dual tasking and to be linked to divided attention processes, particularly when motor activity is involved.

Transcranial direct current stimulation facilitates cognitive multi-task performance differentially depending on anode location and subtask.

Directory of Open Access Journals (Sweden)

Melissa eScheldrup

2014-09-01

Full Text Available There is a need to facilitate acquisition of real world cognitive multi-tasks that require long periods of training (e.g., air traffic control, intelligence analysis, medicine. Non-invasive brain stimulation – specifically transcranial Direct Current Stimulation (tDCS – has promise as a method to speed multi-task training. We hypothesized that during acquisition of the complex multi-task Space Fortress, subtasks that require focused attention on ship control would benefit from tDCS aimed at the dorsal attention network while subtasks that require redirection of attention would benefit from tDCS aimed at the right hemisphere ventral attention network. We compared effects of 30 min prefrontal and parietal stimulation to right and left hemispheres on subtask performance during the first 45 min of training. The strongest effects both overall and for ship flying (control and velocity subtasks were seen with a right parietal (C4 to left shoulder montage, shown by modeling to induce an electric field that includes nodes in both dorsal and ventral attention networks. This is consistent with the re-orienting hypothesis that the ventral attention network is activated along with the dorsal attention network if a new, task-relevant event occurs while visuospatial attention is focused (Corbetta et al., 2008. No effects were seen with anodes over sites that stimulated only dorsal (C3 or only ventral (F10 attention networks. The speed subtask (update memory for symbols benefited from an F9 anode over left prefrontal cortex. These results argue for development of tDCS as a training aid in real world settings where multi-tasking is critical.

Transcranial direct current stimulation facilitates cognitive multi-task performance differentially depending on anode location and subtask.

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Scheldrup, Melissa; Greenwood, Pamela M; McKendrick, Ryan; Strohl, Jon; Bikson, Marom; Alam, Mahtab; McKinley, R Andy; Parasuraman, Raja

2014-01-01

There is a need to facilitate acquisition of real world cognitive multi-tasks that require long periods of training (e.g., air traffic control, intelligence analysis, medicine). Non-invasive brain stimulation-specifically transcranial Direct Current Stimulation (tDCS)-has promise as a method to speed multi-task training. We hypothesized that during acquisition of the complex multi-task Space Fortress, subtasks that require focused attention on ship control would benefit from tDCS aimed at the dorsal attention network while subtasks that require redirection of attention would benefit from tDCS aimed at the right hemisphere ventral attention network. We compared effects of 30 min prefrontal and parietal stimulation to right and left hemispheres on subtask performance during the first 45 min of training. The strongest effects both overall and for ship flying (control and velocity subtasks) were seen with a right parietal (C4, reference to left shoulder) montage, shown by modeling to induce an electric field that includes nodes in both dorsal and ventral attention networks. This is consistent with the re-orienting hypothesis that the ventral attention network is activated along with the dorsal attention network if a new, task-relevant event occurs while visuospatial attention is focused (Corbetta et al., 2008). No effects were seen with anodes over sites that stimulated only dorsal (C3) or only ventral (F10) attention networks. The speed subtask (update memory for symbols) benefited from an F9 anode over left prefrontal cortex. These results argue for development of tDCS as a training aid in real world settings where multi-tasking is critical.

Final Report on LDRD project 130784 : functional brain imaging by tunable multi-spectral Event-Related Optical Signal (EROS).

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Speed, Ann Elizabeth; Spahn, Olga Blum; Hsu, Alan Yuan-Chun

2009-09-01

Functional brain imaging is of great interest for understanding correlations between specific cognitive processes and underlying neural activity. This understanding can provide the foundation for developing enhanced human-machine interfaces, decision aides, and enhanced cognition at the physiological level. The functional near infrared spectroscopy (fNIRS) based event-related optical signal (EROS) technique can provide direct, high-fidelity measures of temporal and spatial characteristics of neural networks underlying cognitive behavior. However, current EROS systems are hampered by poor signal-to-noise-ratio (SNR) and depth of measure, limiting areas of the brain and associated cognitive processes that can be investigated. We propose to investigate a flexible, tunable, multi-spectral fNIRS EROS system which will provide up to 10x greater SNR as well as improved spatial and temporal resolution through significant improvements in electronics, optoelectronics and optics, as well as contribute to the physiological foundation of higher-order cognitive processes and provide the technical foundation for miniaturized portable neuroimaging systems.

Specialization in the default mode: Task-induced brain deactivations dissociate between visual working memory and attention.

Science.gov (United States)

Mayer, Jutta S; Roebroeck, Alard; Maurer, Konrad; Linden, David E J

2010-01-01

The idea of an organized mode of brain function that is present as default state and suspended during goal-directed behaviors has recently gained much interest in the study of human brain function. The default mode hypothesis is based on the repeated observation that certain brain areas show task-induced deactivations across a wide range of cognitive tasks. In this event-related functional resonance imaging study we tested the default mode hypothesis by comparing common and selective patterns of BOLD deactivation in response to the demands on visual attention and working memory (WM) that were independently modulated within one task. The results revealed task-induced deactivations within regions of the default mode network (DMN) with a segregation of areas that were additively deactivated by an increase in the demands on both attention and WM, and areas that were selectively deactivated by either high attentional demand or WM load. Attention-selective deactivations appeared in the left ventrolateral and medial prefrontal cortex and the left lateral temporal cortex. Conversely, WM-selective deactivations were found predominantly in the right hemisphere including the medial-parietal, the lateral temporo-parietal, and the medial prefrontal cortex. Moreover, during WM encoding deactivated regions showed task-specific functional connectivity. These findings demonstrate that task-induced deactivations within parts of the DMN depend on the specific characteristics of the attention and WM components of the task. The DMN can thus be subdivided into a set of brain regions that deactivate indiscriminately in response to cognitive demand ("the core DMN") and a part whose deactivation depends on the specific task. 2009 Wiley-Liss, Inc.

Brain spect imaging

International Nuclear Information System (INIS)

Lee, R.G.L.; Hill, T.C.; Holman, B.L.

1989-01-01

This paper discusses how the rapid development of single-photon radiopharmaceuticals has given new life to tomographic brain imaging in nuclear medicine. Further developments in radiopharmaceuticals and refinements in neuro-SPECT (single-photon emission computed tomography) instrumentation should help to reinstate brain scintigraphy as an important part of neurologic diagnosis. SPECT of the brain evolved from experimentation using prototype instrumentation during the early 1960s. Although tomographic studies provided superior diagnostic accuracy when compared to planar techniques, the arrival of X-ray CT of the head resulted in the rapid demise of technetium brain imaging

An open, multi-vendor, multi-field-strength brain MR dataset and analysis of publicly available skull stripping methods agreement.

Science.gov (United States)

Souza, Roberto; Lucena, Oeslle; Garrafa, Julia; Gobbi, David; Saluzzi, Marina; Appenzeller, Simone; Rittner, Letícia; Frayne, Richard; Lotufo, Roberto

2018-04-15

This paper presents an open, multi-vendor, multi-field strength magnetic resonance (MR) T1-weighted volumetric brain imaging dataset, named Calgary-Campinas-359 (CC-359). The dataset is composed of images of older healthy adults (29-80 years) acquired on scanners from three vendors (Siemens, Philips and General Electric) at both 1.5 T and 3 T. CC-359 is comprised of 359 datasets, approximately 60 subjects per vendor and magnetic field strength. The dataset is approximately age and gender balanced, subject to the constraints of the available images. It provides consensus brain extraction masks for all volumes generated using supervised classification. Manual segmentation results for twelve randomly selected subjects performed by an expert are also provided. The CC-359 dataset allows investigation of 1) the influences of both vendor and magnetic field strength on quantitative analysis of brain MR; 2) parameter optimization for automatic segmentation methods; and potentially 3) machine learning classifiers with big data, specifically those based on deep learning methods, as these approaches require a large amount of data. To illustrate the utility of this dataset, we compared to the results of a supervised classifier, the results of eight publicly available skull stripping methods and one publicly available consensus algorithm. A linear mixed effects model analysis indicated that vendor (p-valuefield strength (p-value<0.001) have statistically significant impacts on skull stripping results. Copyright © 2017 Elsevier Inc. All rights reserved.

Cloud-based processing of multi-spectral imaging data

Science.gov (United States)

Bernat, Amir S.; Bolton, Frank J.; Weiser, Reuven; Levitz, David

2017-03-01

Multispectral imaging holds great promise as a non-contact tool for the assessment of tissue composition. Performing multi - spectral imaging on a hand held mobile device would allow to bring this technology and with it knowledge to low resource settings to provide a state of the art classification of tissue health. This modality however produces considerably larger data sets than white light imaging and requires preliminary image analysis for it to be used. The data then needs to be analyzed and logged, while not requiring too much of the system resource or a long computation time and battery use by the end point device. Cloud environments were designed to allow offloading of those problems by allowing end point devices (smartphones) to offload computationally hard tasks. For this end we present a method where the a hand held device based around a smartphone captures a multi - spectral dataset in a movie file format (mp4) and compare it to other image format in size, noise and correctness. We present the cloud configuration used for segmenting images to frames where they can later be used for further analysis.

Differences between child and adult large-scale functional brain networks for reading tasks.

Science.gov (United States)

Liu, Xin; Gao, Yue; Di, Qiqi; Hu, Jiali; Lu, Chunming; Nan, Yun; Booth, James R; Liu, Li

2018-02-01

Reading is an important high-level cognitive function of the human brain, requiring interaction among multiple brain regions. Revealing differences between children's large-scale functional brain networks for reading tasks and those of adults helps us to understand how the functional network changes over reading development. Here we used functional magnetic resonance imaging data of 17 adults (19-28 years old) and 16 children (11-13 years old), and graph theoretical analyses to investigate age-related changes in large-scale functional networks during rhyming and meaning judgment tasks on pairs of visually presented Chinese characters. We found that: (1) adults had stronger inter-regional connectivity and nodal degree in occipital regions, while children had stronger inter-regional connectivity in temporal regions, suggesting that adults rely more on visual orthographic processing whereas children rely more on auditory phonological processing during reading. (2) Only adults showed between-task differences in inter-regional connectivity and nodal degree, whereas children showed no task differences, suggesting the topological organization of adults' reading network is more specialized. (3) Children showed greater inter-regional connectivity and nodal degree than adults in multiple subcortical regions; the hubs in children were more distributed in subcortical regions while the hubs in adults were more distributed in cortical regions. These findings suggest that reading development is manifested by a shift from reliance on subcortical to cortical regions. Taken together, our study suggests that Chinese reading development is supported by developmental changes in brain connectivity properties, and some of these changes may be domain-general while others may be specific to the reading domain. © 2017 Wiley Periodicals, Inc.

MULTI-TEMPORAL AND MULTI-SENSOR IMAGE MATCHING BASED ON LOCAL FREQUENCY INFORMATION

Directory of Open Access Journals (Sweden)

X. Liu

2012-08-01

Full Text Available Image Matching is often one of the first tasks in many Photogrammetry and Remote Sensing applications. This paper presents an efficient approach to automated multi-temporal and multi-sensor image matching based on local frequency information. Two new independent image representations, Local Average Phase (LAP and Local Weighted Amplitude (LWA, are presented to emphasize the common scene information, while suppressing the non-common illumination and sensor-dependent information. In order to get the two representations, local frequency information is firstly obtained from Log-Gabor wavelet transformation, which is similar to that of the human visual system; then the outputs of odd and even symmetric filters are used to construct the LAP and LWA. The LAP and LWA emphasize on the phase and amplitude information respectively. As these two representations are both derivative-free and threshold-free, they are robust to noise and can keep as much of the image details as possible. A new Compositional Similarity Measure (CSM is also presented to combine the LAP and LWA with the same weight for measuring the similarity of multi-temporal and multi-sensor images. The CSM can make the LAP and LWA compensate for each other and can make full use of the amplitude and phase of local frequency information. In many image matching applications, the template is usually selected without consideration of its matching robustness and accuracy. In order to overcome this problem, a local best matching point detection is presented to detect the best matching template. In the detection method, we employ self-similarity analysis to identify the template with the highest matching robustness and accuracy. Experimental results using some real images and simulation images demonstrate that the presented approach is effective for matching image pairs with significant scene and illumination changes and that it has advantages over other state-of-the-art approaches, which include: the

Multi-view Multi-sparsity Kernel Reconstruction for Multi-class Image Classification

KAUST Repository

Zhu, Xiaofeng

2015-05-28

This paper addresses the problem of multi-class image classification by proposing a novel multi-view multi-sparsity kernel reconstruction (MMKR for short) model. Given images (including test images and training images) representing with multiple visual features, the MMKR first maps them into a high-dimensional space, e.g., a reproducing kernel Hilbert space (RKHS), where test images are then linearly reconstructed by some representative training images, rather than all of them. Furthermore a classification rule is proposed to classify test images. Experimental results on real datasets show the effectiveness of the proposed MMKR while comparing to state-of-the-art algorithms.

Multi-task feature selection in microarray data by binary integer programming.

Science.gov (United States)

Lan, Liang; Vucetic, Slobodan

2013-12-20

A major challenge in microarray classification is that the number of features is typically orders of magnitude larger than the number of examples. In this paper, we propose a novel feature filter algorithm to select the feature subset with maximal discriminative power and minimal redundancy by solving a quadratic objective function with binary integer constraints. To improve the computational efficiency, the binary integer constraints are relaxed and a low-rank approximation to the quadratic term is applied. The proposed feature selection algorithm was extended to solve multi-task microarray classification problems. We compared the single-task version of the proposed feature selection algorithm with 9 existing feature selection methods on 4 benchmark microarray data sets. The empirical results show that the proposed method achieved the most accurate predictions overall. We also evaluated the multi-task version of the proposed algorithm on 8 multi-task microarray datasets. The multi-task feature selection algorithm resulted in significantly higher accuracy than when using the single-task feature selection methods.

CQPSO scheduling algorithm for heterogeneous multi-core DAG task model

Science.gov (United States)

Zhai, Wenzheng; Hu, Yue-Li; Ran, Feng

2017-07-01

Efficient task scheduling is critical to achieve high performance in a heterogeneous multi-core computing environment. The paper focuses on the heterogeneous multi-core directed acyclic graph (DAG) task model and proposes a novel task scheduling method based on an improved chaotic quantum-behaved particle swarm optimization (CQPSO) algorithm. A task priority scheduling list was built. A processor with minimum cumulative earliest finish time (EFT) was acted as the object of the first task assignment. The task precedence relationships were satisfied and the total execution time of all tasks was minimized. The experimental results show that the proposed algorithm has the advantage of optimization abilities, simple and feasible, fast convergence, and can be applied to the task scheduling optimization for other heterogeneous and distributed environment.

Image processing and Quality Control for the first 10,000 brain imaging datasets from UK Biobank.

Science.gov (United States)

Alfaro-Almagro, Fidel; Jenkinson, Mark; Bangerter, Neal K; Andersson, Jesper L R; Griffanti, Ludovica; Douaud, Gwenaëlle; Sotiropoulos, Stamatios N; Jbabdi, Saad; Hernandez-Fernandez, Moises; Vallee, Emmanuel; Vidaurre, Diego; Webster, Matthew; McCarthy, Paul; Rorden, Christopher; Daducci, Alessandro; Alexander, Daniel C; Zhang, Hui; Dragonu, Iulius; Matthews, Paul M; Miller, Karla L; Smith, Stephen M

2018-02-01

UK Biobank is a large-scale prospective epidemiological study with all data accessible to researchers worldwide. It is currently in the process of bringing back 100,000 of the original participants for brain, heart and body MRI, carotid ultrasound and low-dose bone/fat x-ray. The brain imaging component covers 6 modalities (T1, T2 FLAIR, susceptibility weighted MRI, Resting fMRI, Task fMRI and Diffusion MRI). Raw and processed data from the first 10,000 imaged subjects has recently been released for general research access. To help convert this data into useful summary information we have developed an automated processing and QC (Quality Control) pipeline that is available for use by other researchers. In this paper we describe the pipeline in detail, following a brief overview of UK Biobank brain imaging and the acquisition protocol. We also describe several quantitative investigations carried out as part of the development of both the imaging protocol and the processing pipeline. Copyright © 2017 The Authors. Published by Elsevier Inc. All rights reserved.

Deploying swarm intelligence in medical imaging identifying metastasis, micro-calcifications and brain image segmentation.

Science.gov (United States)

al-Rifaie, Mohammad Majid; Aber, Ahmed; Hemanth, Duraiswamy Jude

2015-12-01

This study proposes an umbrella deployment of swarm intelligence algorithm, such as stochastic diffusion search for medical imaging applications. After summarising the results of some previous works which shows how the algorithm assists in the identification of metastasis in bone scans and microcalcifications on mammographs, for the first time, the use of the algorithm in assessing the CT images of the aorta is demonstrated along with its performance in detecting the nasogastric tube in chest X-ray. The swarm intelligence algorithm presented in this study is adapted to address these particular tasks and its functionality is investigated by running the swarms on sample CT images and X-rays whose status have been determined by senior radiologists. In addition, a hybrid swarm intelligence-learning vector quantisation (LVQ) approach is proposed in the context of magnetic resonance (MR) brain image segmentation. The particle swarm optimisation is used to train the LVQ which eliminates the iteration-dependent nature of LVQ. The proposed methodology is used to detect the tumour regions in the abnormal MR brain images.

Wide-area mapping of resting state hemodynamic correlations at microvascular resolution with multi-contrast optical imaging (Conference Presentation)

Science.gov (United States)

Senarathna, Janaka; Hadjiabadi, Darian; Gil, Stacy; Thakor, Nitish V.; Pathak, Arvind P.

2017-02-01

Different brain regions exhibit complex information processing even at rest. Therefore, assessing temporal correlations between regions permits task-free visualization of their `resting state connectivity'. Although functional MRI (fMRI) is widely used for mapping resting state connectivity in the human brain, it is not well suited for `microvascular scale' imaging in rodents because of its limited spatial resolution. Moreover, co-registered cerebral blood flow (CBF) and total hemoglobin (HbT) data are often unavailable in conventional fMRI experiments. Therefore, we built a customized system that combines laser speckle contrast imaging (LSCI), intrinsic optical signal (IOS) imaging and fluorescence imaging (FI) to generate multi-contrast functional connectivity maps at a spatial resolution of 10 μm. This system comprised of three illumination sources: a 632 nm HeNe laser (for LSCI), a 570 nm ± 5 nm filtered white light source (for IOS), and a 473 nm blue laser (for FI), as well as a sensitive CCD camera operating at 10 frames per second for image acquisition. The acquired data enabled visualization of changes in resting state neurophysiology at microvascular spatial scales. Moreover, concurrent mapping of CBF and HbT-based temporal correlations enabled in vivo mapping of how resting brain regions were linked in terms of their hemodynamics. Additionally, we complemented this approach by exploiting the transit times of a fluorescent tracer (Dextran-FITC) to distinguish arterial from venous perfusion. Overall, we demonstrated the feasibility of wide area mapping of resting state connectivity at microvascular resolution and created a new toolbox for interrogating neurovascular function.

Towards brain-activity-controlled information retrieval: Decoding image relevance from MEG signals.

Science.gov (United States)

Kauppi, Jukka-Pekka; Kandemir, Melih; Saarinen, Veli-Matti; Hirvenkari, Lotta; Parkkonen, Lauri; Klami, Arto; Hari, Riitta; Kaski, Samuel

2015-05-15

We hypothesize that brain activity can be used to control future information retrieval systems. To this end, we conducted a feasibility study on predicting the relevance of visual objects from brain activity. We analyze both magnetoencephalographic (MEG) and gaze signals from nine subjects who were viewing image collages, a subset of which was relevant to a predetermined task. We report three findings: i) the relevance of an image a subject looks at can be decoded from MEG signals with performance significantly better than chance, ii) fusion of gaze-based and MEG-based classifiers significantly improves the prediction performance compared to using either signal alone, and iii) non-linear classification of the MEG signals using Gaussian process classifiers outperforms linear classification. These findings break new ground for building brain-activity-based interactive image retrieval systems, as well as for systems utilizing feedback both from brain activity and eye movements. Copyright © 2015 Elsevier Inc. All rights reserved.

Brain Activity in Patients With Adductor Spasmodic Dysphonia Detected by Functional Magnetic Resonance Imaging.

Science.gov (United States)

Kiyuna, Asanori; Kise, Norimoto; Hiratsuka, Munehisa; Kondo, Shunsuke; Uehara, Takayuki; Maeda, Hiroyuki; Ganaha, Akira; Suzuki, Mikio

2017-05-01

Spasmodic dysphonia (SD) is considered a focal dystonia. However, the detailed pathophysiology of SD remains unclear, despite the detection of abnormal activity in several brain regions. The aim of this study was to clarify the pathophysiological background of SD. This is a case-control study. Both task-related brain activity measured by functional magnetic resonance imaging by reading the five-digit numbers and resting-state functional connectivity (FC) measured by 150 T2-weighted echo planar images acquired without any task were investigated in 12 patients with adductor SD and in 16 healthy controls. The patients with SD showed significantly higher task-related brain activation in the left middle temporal gyrus, left thalamus, bilateral primary motor area, bilateral premotor area, bilateral cerebellum, bilateral somatosensory area, right insula, and right putamen compared with the controls. Region of interest voxel FC analysis revealed many FC changes within the cerebellum-basal ganglia-thalamus-cortex loop in the patients with SD. Of the significant connectivity changes between the patients with SD and the controls, the FC between the left thalamus and the left caudate nucleus was significantly correlated with clinical parameters in SD. The higher task-related brain activity in the insula and cerebellum was consistent with previous neuroimaging studies, suggesting that these areas are one of the unique characteristics of phonation-induced brain activity in SD. Based on FC analysis and their significant correlations with clinical parameters, the basal ganglia network plays an important role in the pathogenesis of SD. Copyright © 2017 The Voice Foundation. Published by Elsevier Inc. All rights reserved.

Development of magneto-plasmonic nanoparticles for multimodal image-guided therapy to the brain.

Science.gov (United States)

Tomitaka, Asahi; Arami, Hamed; Raymond, Andrea; Yndart, Adriana; Kaushik, Ajeet; Jayant, Rahul Dev; Takemura, Yasushi; Cai, Yong; Toborek, Michal; Nair, Madhavan

2017-01-05

Magneto-plasmonic nanoparticles are one of the emerging multi-functional materials in the field of nanomedicine. Their potential for targeting and multi-modal imaging is highly attractive. In this study, magnetic core/gold shell (MNP@Au) magneto-plasmonic nanoparticles were synthesized by citrate reduction of Au ions on magnetic nanoparticle seeds. Hydrodynamic size and optical properties of magneto-plasmonic nanoparticles synthesized with the variation of Au ions and reducing agent concentrations were evaluated. The synthesized magneto-plasmonic nanoparticles exhibited superparamagnetic properties, and their magnetic properties contributed to the concentration-dependent contrast in magnetic resonance imaging (MRI). The imaging contrast from the gold shell part of the magneto-plasmonic nanoparticles was also confirmed by X-ray computed tomography (CT). The transmigration study of the magneto-plasmonic nanoparticles using an in vitro blood-brain barrier (BBB) model proved enhanced transmigration efficiency without disrupting the integrity of the BBB, and showed potential to be used for brain diseases and neurological disorders.

Multi-dimensional imaging

CERN Document Server

Javidi, Bahram; Andres, Pedro

2014-01-01

Provides a broad overview of advanced multidimensional imaging systems with contributions from leading researchers in the field Multi-dimensional Imaging takes the reader from the introductory concepts through to the latest applications of these techniques. Split into 3 parts covering 3D image capture, processing, visualization and display, using 1) a Multi-View Approach and 2.) a Holographic Approach, followed by a 3rd part addressing other 3D systems approaches, applications and signal processing for advanced 3D imaging. This book describes recent developments, as well as the prospects and

MRI reconstruction of multi-image acquisitions using a rank regularizer with data reordering

Energy Technology Data Exchange (ETDEWEB)

Adluru, Ganesh, E-mail: gadluru@gmail.com; Anderson, Jeffrey [UCAIR, Department of Radiology, University of Utah, Salt Lake City, Utah 84108 (United States); Gur, Yaniv [IBM Almaden Research Center, San Jose, California 95120 (United States); Chen, Liyong; Feinberg, David [Advanced MRI Technologies, Sebastpool, California, 95472 (United States); DiBella, Edward V. R. [UCAIR, Department of Radiology, University of Utah, Salt Lake City, Utah 84108 and Department of Bioengineering, University of Utah, Salt Lake City, Utah 84112 (United States)

2015-08-15

Purpose: To improve rank constrained reconstructions for undersampled multi-image MRI acquisitions. Methods: Motivated by the recent developments in low-rank matrix completion theory and its applicability to rapid dynamic MRI, a new reordering-based rank constrained reconstruction of undersampled multi-image data that uses prior image information is proposed. Instead of directly minimizing the nuclear norm of a matrix of estimated images, the nuclear norm of reordered matrix values is minimized. The reordering is based on the prior image estimates. The method is tested on brain diffusion imaging data and dynamic contrast enhanced myocardial perfusion data. Results: Good quality images from data undersampled by a factor of three for diffusion imaging and by a factor of 3.5 for dynamic cardiac perfusion imaging with respiratory motion were obtained. Reordering gave visually improved image quality over standard nuclear norm minimization reconstructions. Root mean squared errors with respect to ground truth images were improved by ∼18% and ∼16% with reordering for diffusion and perfusion applications, respectively. Conclusions: The reordered low-rank constraint is a way to inject prior image information that offers improvements over a standard low-rank constraint for undersampled multi-image MRI reconstructions.

MRI reconstruction of multi-image acquisitions using a rank regularizer with data reordering

International Nuclear Information System (INIS)

Adluru, Ganesh; Anderson, Jeffrey; Gur, Yaniv; Chen, Liyong; Feinberg, David; DiBella, Edward V. R.

2015-01-01

Purpose: To improve rank constrained reconstructions for undersampled multi-image MRI acquisitions. Methods: Motivated by the recent developments in low-rank matrix completion theory and its applicability to rapid dynamic MRI, a new reordering-based rank constrained reconstruction of undersampled multi-image data that uses prior image information is proposed. Instead of directly minimizing the nuclear norm of a matrix of estimated images, the nuclear norm of reordered matrix values is minimized. The reordering is based on the prior image estimates. The method is tested on brain diffusion imaging data and dynamic contrast enhanced myocardial perfusion data. Results: Good quality images from data undersampled by a factor of three for diffusion imaging and by a factor of 3.5 for dynamic cardiac perfusion imaging with respiratory motion were obtained. Reordering gave visually improved image quality over standard nuclear norm minimization reconstructions. Root mean squared errors with respect to ground truth images were improved by ∼18% and ∼16% with reordering for diffusion and perfusion applications, respectively. Conclusions: The reordered low-rank constraint is a way to inject prior image information that offers improvements over a standard low-rank constraint for undersampled multi-image MRI reconstructions

A pilot study of three dimensional color CT images of brain diseases to improve informed consent

International Nuclear Information System (INIS)

Tanizaki, Yoshio; Akiyama, Takenori; Hiraga, Kenji; Akaji, Kazunori

2005-01-01

We have described brain diseases to patients and their family using monochrome CT images. It is thought that patients have difficulties in giving their consent to our conventional explanation because their understanding of brain diseases is based on three dimensional and color images, however, standard CT images are two dimensional and gray scale images. We have been trying to use three dimensional color CT images to improve the typical patient's comprehension of brain diseases. We also try to simulate surgery using these images. Multi-slice CT accumulates precise isotropic voxel data within a half minute. These two dimensional and monochrome data are converted to three dimensional color CT images by 3D workstation. Three dimensional color CT images of each brain structures (e.g. scalp, skull, brain, ventricles and lesions) are created separately. Then, selected structures are fused together for different purposes. These images are able to rotate around any axis. Because the methods to generate three-dimensional color images have not established, we neurosurgeons must create these images. In particular, when an operation is required, the surgeon should create the images. In this paper, we demonstrate how three-dimensional color CT images can improve informed consent. (author)

Brain perfusion imaging with iodinated amines

International Nuclear Information System (INIS)

Kung, H.F.

1989-01-01

Traditional nuclear medicine brain study using 99m Tc pertechnetate, glucoheptonate or diethlenetriaminepentacetic acid (DTPA) and planar imaging has experienced a significant decline in the past 10 years. This is mainly due to the introduction of X-ray CT and more recently the nuclear magnetic resonance (NMR) imaging, by which detailed morphology of the brain, including the detection of breakdown of the blood-brain barrier, can be obtained. The nuclear medicine brain imaging is only prescribed as a complementary test when X-ray CT is negative or equivocal and clinical suspicion remains. The attention of nuclear medicine brain imaging has been shifted from the detection of the breakdown of the blood-brain barrier to the study of brain function-perfusion, metabolism, and receptor binding, etc. The functional brain imaging provides diagnostic information usually unattainable by other radiological techniques. In this article, the iodinated amines as brain perfusion imaging agents are reviewed. Potential clinical application of these agents is discussed

Brain imaging and schizophrenia

International Nuclear Information System (INIS)

Martinot, J.L.; Dao-Castellana, M.H.

1991-01-01

Brain structures and brain function have been investigated by the new brain imaging techniques for more than ten years. In Psychiatry, these techniques could afford a new understanding of mental diseases. In schizophrenic patients, CAT scanner and RMI pointed out statistically significant ventricular enlargments which are presently considered as evidence for abnormalities in brain maturation. Functional imaging techniques reported metabolic dysfunctions in the cortical associative areas which are probably linked to the cognitive features of schizophrenics [fr

Searching for Conservation Laws in Brain Dynamics—BOLD Flux and Source Imaging

Directory of Open Access Journals (Sweden)

Henning U. Voss

2014-07-01

Full Text Available Blood-oxygen-level-dependent (BOLD imaging is the most important noninvasive tool to map human brain function. It relies on local blood-flow changes controlled by neurovascular coupling effects, usually in response to some cognitive or perceptual task. In this contribution we ask if the spatiotemporal dynamics of the BOLD signal can be modeled by a conservation law. In analogy to the description of physical laws, which often can be derived from some underlying conservation law, identification of conservation laws in the brain could lead to new models for the functional organization of the brain. Our model is independent of the nature of the conservation law, but we discuss possible hints and motivations for conservation laws. For example, globally limited blood supply and local competition between brain regions for blood might restrict the large scale BOLD signal in certain ways that could be observable. One proposed selective pressure for the evolution of such conservation laws is the closed volume of the skull limiting the expansion of brain tissue by increases in blood volume. These ideas are demonstrated on a mental motor imagery fMRI experiment, in which functional brain activation was mapped in a group of volunteers imagining themselves swimming. In order to search for local conservation laws during this complex cognitive process, we derived maps of quantities resulting from spatial interaction of the BOLD amplitudes. Specifically, we mapped fluxes and sources of the BOLD signal, terms that would appear in a description by a continuity equation. Whereas we cannot present final answers with the particular analysis of this particular experiment, some results seem to be non-trivial. For example, we found that during task the group BOLD flux covered more widespread regions than identified by conventional BOLD mapping and was always increasing during task. It is our hope that these results motivate more work towards the search for conservation

Multi-level discriminative dictionary learning with application to large scale image classification.

Science.gov (United States)

Shen, Li; Sun, Gang; Huang, Qingming; Wang, Shuhui; Lin, Zhouchen; Wu, Enhua

2015-10-01

The sparse coding technique has shown flexibility and capability in image representation and analysis. It is a powerful tool in many visual applications. Some recent work has shown that incorporating the properties of task (such as discrimination for classification task) into dictionary learning is effective for improving the accuracy. However, the traditional supervised dictionary learning methods suffer from high computation complexity when dealing with large number of categories, making them less satisfactory in large scale applications. In this paper, we propose a novel multi-level discriminative dictionary learning method and apply it to large scale image classification. Our method takes advantage of hierarchical category correlation to encode multi-level discriminative information. Each internal node of the category hierarchy is associated with a discriminative dictionary and a classification model. The dictionaries at different layers are learnt to capture the information of different scales. Moreover, each node at lower layers also inherits the dictionary of its parent, so that the categories at lower layers can be described with multi-scale information. The learning of dictionaries and associated classification models is jointly conducted by minimizing an overall tree loss. The experimental results on challenging data sets demonstrate that our approach achieves excellent accuracy and competitive computation cost compared with other sparse coding methods for large scale image classification.

Rheem: Enabling Multi-Platform Task Execution

KAUST Repository

Agrawal, Divy; Kruse, Sebastian; Ouzzani, Mourad; Papotti, Paolo; Quiane-Ruiz, Jorge-Arnulfo; Tang, Nan; Zaki, Mohammed J.; Ba, Lamine; Berti-Equille, Laure; Chawla, Sanjay; Elmagarmid, Ahmed; Hammady, Hossam; Idris, Yasser; Kaoudi, Zoi; Khayyat, Zuhair

2016-01-01

Many emerging applications, from domains such as healthcare and oil & gas, require several data processing systems for complex analytics. This demo paper showcases Rheem, a framework that provides multi-platform task execution for such applications. It features a three-layer data processing abstraction and a new query optimization approach for multi-platform settings. We will demonstrate the strengths of Rheem by using real-world scenarios from three different applications, namely, machine learning, data cleaning, and data fusion. © 2016 ACM.

Rheem: Enabling Multi-Platform Task Execution

KAUST Repository

Agrawal, Divy

2016-06-16

Many emerging applications, from domains such as healthcare and oil & gas, require several data processing systems for complex analytics. This demo paper showcases Rheem, a framework that provides multi-platform task execution for such applications. It features a three-layer data processing abstraction and a new query optimization approach for multi-platform settings. We will demonstrate the strengths of Rheem by using real-world scenarios from three different applications, namely, machine learning, data cleaning, and data fusion. © 2016 ACM.

Three-dimensional reconstruction of functional brain images

International Nuclear Information System (INIS)

Inoue, Masato; Shoji, Kazuhiko; Kojima, Hisayoshi; Hirano, Shigeru; Naito, Yasushi; Honjo, Iwao

1999-01-01

We consider PET (positron emission tomography) measurement with SPM (Statistical Parametric Mapping) analysis to be one of the most useful methods to identify activated areas of the brain involved in language processing. SPM is an effective analytical method that detects markedly activated areas over the whole brain. However, with the conventional presentations of these functional brain images, such as horizontal slices, three directional projection, or brain surface coloring, makes understanding and interpreting the positional relationships among various brain areas difficult. Therefore, we developed three-dimensionally reconstructed images from these functional brain images to improve the interpretation. The subjects were 12 normal volunteers. The following three types of images were constructed: routine images by SPM, three-dimensional static images, and three-dimensional dynamic images, after PET images were analyzed by SPM during daily dialog listening. The creation of images of both the three-dimensional static and dynamic types employed the volume rendering method by VTK (The Visualization Toolkit). Since the functional brain images did not include original brain images, we synthesized SPM and MRI brain images by self-made C++ programs. The three-dimensional dynamic images were made by sequencing static images with available software. Images of both the three-dimensional static and dynamic types were processed by a personal computer system. Our newly created images showed clearer positional relationships among activated brain areas compared to the conventional method. To date, functional brain images have been employed in fields such as neurology or neurosurgery, however, these images may be useful even in the field of otorhinolaryngology, to assess hearing and speech. Exact three-dimensional images based on functional brain images are important for exact and intuitive interpretation, and may lead to new developments in brain science. Currently, the surface

Three-dimensional reconstruction of functional brain images

Energy Technology Data Exchange (ETDEWEB)

Inoue, Masato; Shoji, Kazuhiko; Kojima, Hisayoshi; Hirano, Shigeru; Naito, Yasushi; Honjo, Iwao [Kyoto Univ. (Japan)

1999-08-01

We consider PET (positron emission tomography) measurement with SPM (Statistical Parametric Mapping) analysis to be one of the most useful methods to identify activated areas of the brain involved in language processing. SPM is an effective analytical method that detects markedly activated areas over the whole brain. However, with the conventional presentations of these functional brain images, such as horizontal slices, three directional projection, or brain surface coloring, makes understanding and interpreting the positional relationships among various brain areas difficult. Therefore, we developed three-dimensionally reconstructed images from these functional brain images to improve the interpretation. The subjects were 12 normal volunteers. The following three types of images were constructed: routine images by SPM, three-dimensional static images, and three-dimensional dynamic images, after PET images were analyzed by SPM during daily dialog listening. The creation of images of both the three-dimensional static and dynamic types employed the volume rendering method by VTK (The Visualization Toolkit). Since the functional brain images did not include original brain images, we synthesized SPM and MRI brain images by self-made C++ programs. The three-dimensional dynamic images were made by sequencing static images with available software. Images of both the three-dimensional static and dynamic types were processed by a personal computer system. Our newly created images showed clearer positional relationships among activated brain areas compared to the conventional method. To date, functional brain images have been employed in fields such as neurology or neurosurgery, however, these images may be useful even in the field of otorhinolaryngology, to assess hearing and speech. Exact three-dimensional images based on functional brain images are important for exact and intuitive interpretation, and may lead to new developments in brain science. Currently, the surface

Accurate Learning with Few Atlases (ALFA): an algorithm for MRI neonatal brain extraction and comparison with 11 publicly available methods.

Science.gov (United States)

Serag, Ahmed; Blesa, Manuel; Moore, Emma J; Pataky, Rozalia; Sparrow, Sarah A; Wilkinson, A G; Macnaught, Gillian; Semple, Scott I; Boardman, James P

2016-03-24

Accurate whole-brain segmentation, or brain extraction, of magnetic resonance imaging (MRI) is a critical first step in most neuroimage analysis pipelines. The majority of brain extraction algorithms have been developed and evaluated for adult data and their validity for neonatal brain extraction, which presents age-specific challenges for this task, has not been established. We developed a novel method for brain extraction of multi-modal neonatal brain MR images, named ALFA (Accurate Learning with Few Atlases). The method uses a new sparsity-based atlas selection strategy that requires a very limited number of atlases 'uniformly' distributed in the low-dimensional data space, combined with a machine learning based label fusion technique. The performance of the method for brain extraction from multi-modal data of 50 newborns is evaluated and compared with results obtained using eleven publicly available brain extraction methods. ALFA outperformed the eleven compared methods providing robust and accurate brain extraction results across different modalities. As ALFA can learn from partially labelled datasets, it can be used to segment large-scale datasets efficiently. ALFA could also be applied to other imaging modalities and other stages across the life course.

A multi-atlas based method for automated anatomical rat brain MRI segmentation and extraction of PET activity.

Science.gov (United States)

Lancelot, Sophie; Roche, Roxane; Slimen, Afifa; Bouillot, Caroline; Levigoureux, Elise; Langlois, Jean-Baptiste; Zimmer, Luc; Costes, Nicolas

2014-01-01

Preclinical in vivo imaging requires precise and reproducible delineation of brain structures. Manual segmentation is time consuming and operator dependent. Automated segmentation as usually performed via single atlas registration fails to account for anatomo-physiological variability. We present, evaluate, and make available a multi-atlas approach for automatically segmenting rat brain MRI and extracting PET activies. High-resolution 7T 2DT2 MR images of 12 Sprague-Dawley rat brains were manually segmented into 27-VOI label volumes using detailed protocols. Automated methods were developed with 7/12 atlas datasets, i.e. the MRIs and their associated label volumes. MRIs were registered to a common space, where an MRI template and a maximum probability atlas were created. Three automated methods were tested: 1/registering individual MRIs to the template, and using a single atlas (SA), 2/using the maximum probability atlas (MP), and 3/registering the MRIs from the multi-atlas dataset to an individual MRI, propagating the label volumes and fusing them in individual MRI space (propagation & fusion, PF). Evaluation was performed on the five remaining rats which additionally underwent [18F]FDG PET. Automated and manual segmentations were compared for morphometric performance (assessed by comparing volume bias and Dice overlap index) and functional performance (evaluated by comparing extracted PET measures). Only the SA method showed volume bias. Dice indices were significantly different between methods (PF>MP>SA). PET regional measures were more accurate with multi-atlas methods than with SA method. Multi-atlas methods outperform SA for automated anatomical brain segmentation and PET measure's extraction. They perform comparably to manual segmentation for FDG-PET quantification. Multi-atlas methods are suitable for rapid reproducible VOI analyses.

Effect of inter-tissue inductive coupling on multi-frequency imaging of intracranial hemorrhage by magnetic induction tomography

Science.gov (United States)

Xiao, Zhili; Tan, Chao; Dong, Feng

2017-08-01

Magnetic induction tomography (MIT) is a promising technique for continuous monitoring of intracranial hemorrhage due to its contactless nature, low cost and capacity to penetrate the high-resistivity skull. The inter-tissue inductive coupling increases with frequency, which may lead to errors in multi-frequency imaging at high frequency. The effect of inter-tissue inductive coupling was investigated to improve the multi-frequency imaging of hemorrhage. An analytical model of inter-tissue inductive coupling based on the equivalent circuit was established. A set of new multi-frequency decomposition equations separating the phase shift of hemorrhage from other brain tissues was derived by employing the coupling information to improve the multi-frequency imaging of intracranial hemorrhage. The decomposition error and imaging error are both decreased after considering the inter-tissue inductive coupling information. The study reveals that the introduction of inter-tissue inductive coupling can reduce the errors of multi-frequency imaging, promoting the development of intracranial hemorrhage monitoring by multi-frequency MIT.

Automated, non-linear registration between 3-dimensional brain map and medical head image

International Nuclear Information System (INIS)

Mizuta, Shinobu; Urayama, Shin-ichi; Zoroofi, R.A.; Uyama, Chikao

1998-01-01

In this paper, we propose an automated, non-linear registration method between 3-dimensional medical head image and brain map in order to efficiently extract the regions of interest. In our method, input 3-dimensional image is registered into a reference image extracted from a brain map. The problems to be solved are automated, non-linear image matching procedure, and cost function which represents the similarity between two images. Non-linear matching is carried out by dividing the input image into connected partial regions, transforming the partial regions preserving connectivity among the adjacent images, evaluating the image similarity between the transformed regions of the input image and the correspondent regions of the reference image, and iteratively searching the optimal transformation of the partial regions. In order to measure the voxelwise similarity of multi-modal images, a cost function is introduced, which is based on the mutual information. Some experiments using MR images presented the effectiveness of the proposed method. (author)

Action video gaming and the brain: fMRI effects without behavioral effects in visual and verbal cognitive tasks.

Science.gov (United States)

Richlan, Fabio; Schubert, Juliane; Mayer, Rebecca; Hutzler, Florian; Kronbichler, Martin

2018-01-01

In this functional magnetic resonance imaging (fMRI) study, we compared task performance together with brain activation in a visuospatial task (VST) and a letter detection task (LDT) between longtime action video gamers ( N  =   14) and nongamers ( N  =   14) in order to investigate possible effects of gaming on cognitive and brain abilities. Based on previous research, we expected advantages in performance for experienced action video gamers accompanied by less activation (due to higher efficiency) as measured by fMRI in the frontoparietal attention network. Contrary to these expectations, we did not find differences in overall task performance, nor in brain activation during the VST. We identified, however, a significantly different increase in the BOLD signal from a baseline task to the LDT in action video gamers compared with nongamers. This increased activation was evident in a number of frontoparietal regions including the left middle paracingulate cortex, the left superior frontal sulcus, the opercular part of the left inferior frontal gyrus, and the left and right posterior parietal cortex. Furthermore, we found increased activation in the triangular part of the left inferior frontal gyrus in gamers relative to nongamers when activation during the LDT was compared with activation during the VST. In sum, the expected positive relation between action video game experience and cognitive performance could not be confirmed. Despite their comparable task performance, however, gamers and nongamers exhibited clear-cut differences in brain activation patterns presumably reflecting differences in neural engagement, especially during verbal cognitive tasks.

Visual mismatch negativity indicates automatic, task-independent detection of artistic image composition in abstract artworks.

Science.gov (United States)

Menzel, Claudia; Kovács, Gyula; Amado, Catarina; Hayn-Leichsenring, Gregor U; Redies, Christoph

2018-05-06

In complex abstract art, image composition (i.e., the artist's deliberate arrangement of pictorial elements) is an important aesthetic feature. We investigated whether the human brain detects image composition in abstract artworks automatically (i.e., independently of the experimental task). To this aim, we studied whether a group of 20 original artworks elicited a visual mismatch negativity when contrasted with a group of 20 images that were composed of the same pictorial elements as the originals, but in shuffled arrangements, which destroy artistic composition. We used a passive oddball paradigm with parallel electroencephalogram recordings to investigate the detection of image type-specific properties. We observed significant deviant-standard differences for the shuffled and original images, respectively. Furthermore, for both types of images, differences in amplitudes correlated with the behavioral ratings of the images. In conclusion, we show that the human brain can detect composition-related image properties in visual artworks in an automatic fashion. Copyright © 2018 Elsevier B.V. All rights reserved.

Synchronized 2D/3D optical mapping for interactive exploration and real-time visualization of multi-function neurological images.

Science.gov (United States)

Zhang, Qi; Alexander, Murray; Ryner, Lawrence

2013-01-01

Efficient software with the ability to display multiple neurological image datasets simultaneously with full real-time interactivity is critical for brain disease diagnosis and image-guided planning. In this paper, we describe the creation and function of a new comprehensive software platform that integrates novel algorithms and functions for multiple medical image visualization, processing, and manipulation. We implement an opacity-adjustment algorithm to build 2D lookup tables for multiple slice image display and fusion, which achieves a better visual result than those of using VTK-based methods. We also develop a new real-time 2D and 3D data synchronization scheme for multi-function MR volume and slice image optical mapping and rendering simultaneously through using the same adjustment operation. All these methodologies are integrated into our software framework to provide users with an efficient tool for flexibly, intuitively, and rapidly exploring and analyzing the functional and anatomical MR neurological data. Finally, we validate our new techniques and software platform with visual analysis and task-specific user studies. Copyright © 2013 Elsevier Ltd. All rights reserved.

Multi-Modality Cascaded Convolutional Neural Networks for Alzheimer's Disease Diagnosis.

Science.gov (United States)

Liu, Manhua; Cheng, Danni; Wang, Kundong; Wang, Yaping

2018-03-23

Accurate and early diagnosis of Alzheimer's disease (AD) plays important role for patient care and development of future treatment. Structural and functional neuroimages, such as magnetic resonance images (MRI) and positron emission tomography (PET), are providing powerful imaging modalities to help understand the anatomical and functional neural changes related to AD. In recent years, machine learning methods have been widely studied on analysis of multi-modality neuroimages for quantitative evaluation and computer-aided-diagnosis (CAD) of AD. Most existing methods extract the hand-craft imaging features after image preprocessing such as registration and segmentation, and then train a classifier to distinguish AD subjects from other groups. This paper proposes to construct cascaded convolutional neural networks (CNNs) to learn the multi-level and multimodal features of MRI and PET brain images for AD classification. First, multiple deep 3D-CNNs are constructed on different local image patches to transform the local brain image into more compact high-level features. Then, an upper high-level 2D-CNN followed by softmax layer is cascaded to ensemble the high-level features learned from the multi-modality and generate the latent multimodal correlation features of the corresponding image patches for classification task. Finally, these learned features are combined by a fully connected layer followed by softmax layer for AD classification. The proposed method can automatically learn the generic multi-level and multimodal features from multiple imaging modalities for classification, which are robust to the scale and rotation variations to some extent. No image segmentation and rigid registration are required in pre-processing the brain images. Our method is evaluated on the baseline MRI and PET images of 397 subjects including 93 AD patients, 204 mild cognitive impairment (MCI, 76 pMCI +128 sMCI) and 100 normal controls (NC) from Alzheimer's Disease Neuroimaging

Quality assessment of brain images by Hoffman phantom

International Nuclear Information System (INIS)

Karimian, A.R.; Saddad, F.; Mosalla, B.; Moradkhani, S.; Degbankhan, R.; Pouladi, M.

2002-01-01

The purpose of this investigation is using Hoffman brain phantom for quality assessment of brian images in SPECT system. There are the following standards for quality control in nuclear medicine: American Association of Physicists in Medicine, National Electrical Manufacturers Association, International Electromechanical Commission, International Atomic Energy Agency. Each of the above standards has the following important orders: Physical inspection, Acceptance and Reference Testing, Periodic Q C tests (Daily, Weekly, Monthly, Quarterly, Annually). The above tests are simple physics measures. To more meaningful ones based on performance of some tasks related to clinical application it is better to use from organs' phantoms, such as: brain, cardiac, etc. In this research we made a comparison between normal and abnormal states of Hoffman brain phantom. Methods of Hoffman brain phantom was filled with a solution of Tc- 99 m (5 mCi) and water (1300 cc). this results: The investigation of small abnormalities strongly related to the operating conditions and deviation from best tuning state of the system

Brain activity patterns uniquely supporting visual feature integration after traumatic brain injury

Directory of Open Access Journals (Sweden)

Anjali eRaja Beharelle

2011-12-01

Full Text Available Traumatic brain injury (TBI patients typically respond more slowly and with more variability than controls during tasks of attention requiring speeded reaction time. These behavioral changes are attributable, at least in part, to diffuse axonal injury (DAI, which affects integrated processing in distributed systems. Here we use a multivariate method sensitive to distributed neural activity to compare brain activity patterns of patients with chronic phase moderate-to-severe TBI to those of controls during performance on a visual feature-integration task assessing complex attentional processes that has previously shown sensitivity to TBI. The TBI patients were carefully screened to be free of large focal lesions that can affect performance and brain activation independently of DAI. The task required subjects to hold either one or three features of a target in mind while suppressing responses to distracting information. In controls, the multi-feature condition activated a distributed network including limbic, prefrontal, and medial temporal structures. TBI patients engaged this same network in the single-feature and baseline conditions. In multi-feature presentations, TBI patients alone activated additional frontal, parietal, and occipital regions. These results are consistent with neuroimaging studies using tasks assessing different cognitive domains, where increased spread of brain activity changes was associated with TBI. Our results also extend previous findings that brain activity for relatively moderate task demands in TBI patients is similar to that associated with of high task demands in controls.

Brain connectivity study of joint attention using frequency-domain optical imaging technique

Science.gov (United States)

Chaudhary, Ujwal; Zhu, Banghe; Godavarty, Anuradha

2010-02-01

Autism is a socio-communication brain development disorder. It is marked by degeneration in the ability to respond to joint attention skill task, from as early as 12 to 18 months of age. This trait is used to distinguish autistic from nonautistic populations. In this study, diffuse optical imaging is being used to study brain connectivity for the first time in response to joint attention experience in normal adults. The prefrontal region of the brain was non-invasively imaged using a frequency-domain based optical imager. The imaging studies were performed on 11 normal right-handed adults and optical measurements were acquired in response to joint-attention based video clips. While the intensity-based optical data provides information about the hemodynamic response of the underlying neural process, the time-dependent phase-based optical data has the potential to explicate the directional information on the activation of the brain. Thus brain connectivity studies are performed by computing covariance/correlations between spatial units using this frequency-domain based optical measurements. The preliminary results indicate that the extent of synchrony and directional variation in the pattern of activation varies in the left and right frontal cortex. The results have significant implication for research in neural pathways associated with autism that can be mapped using diffuse optical imaging tools in the future.

Introduction to machine learning for brain imaging.

Science.gov (United States)

Lemm, Steven; Blankertz, Benjamin; Dickhaus, Thorsten; Müller, Klaus-Robert

2011-05-15

Machine learning and pattern recognition algorithms have in the past years developed to become a working horse in brain imaging and the computational neurosciences, as they are instrumental for mining vast amounts of neural data of ever increasing measurement precision and detecting minuscule signals from an overwhelming noise floor. They provide the means to decode and characterize task relevant brain states and to distinguish them from non-informative brain signals. While undoubtedly this machinery has helped to gain novel biological insights, it also holds the danger of potential unintentional abuse. Ideally machine learning techniques should be usable for any non-expert, however, unfortunately they are typically not. Overfitting and other pitfalls may occur and lead to spurious and nonsensical interpretation. The goal of this review is therefore to provide an accessible and clear introduction to the strengths and also the inherent dangers of machine learning usage in the neurosciences. Copyright © 2010 Elsevier Inc. All rights reserved.

Inter-subject FDG PET Brain Networks Exhibit Multi-scale Community Structure with Different Normalization Techniques.

Science.gov (United States)

Sperry, Megan M; Kartha, Sonia; Granquist, Eric J; Winkelstein, Beth A

2018-07-01

Inter-subject networks are used to model correlations between brain regions and are particularly useful for metabolic imaging techniques, like 18F-2-deoxy-2-(18F)fluoro-D-glucose (FDG) positron emission tomography (PET). Since FDG PET typically produces a single image, correlations cannot be calculated over time. Little focus has been placed on the basic properties of inter-subject networks and if they are affected by group size and image normalization. FDG PET images were acquired from rats (n = 18), normalized by whole brain, visual cortex, or cerebellar FDG uptake, and used to construct correlation matrices. Group size effects on network stability were investigated by systematically adding rats and evaluating local network connectivity (node strength and clustering coefficient). Modularity and community structure were also evaluated in the differently normalized networks to assess meso-scale network relationships. Local network properties are stable regardless of normalization region for groups of at least 10. Whole brain-normalized networks are more modular than visual cortex- or cerebellum-normalized network (p network resolutions where modularity differs most between brain and randomized networks. Hierarchical analysis reveals consistent modules at different scales and clustering of spatially-proximate brain regions. Findings suggest inter-subject FDG PET networks are stable for reasonable group sizes and exhibit multi-scale modularity.

Gender differences in brain activation on a mental rotation task.

Science.gov (United States)

Semrud-Clikeman, Margaret; Fine, Jodene Goldenring; Bledsoe, Jesse; Zhu, David C

2012-10-01

Few neuroimaging studies have explored gender differences on mental rotation tasks. Most studies have utilized samples with both genders, samples mainly consisting of men, or samples with six or fewer females. Graduate students in science fields or liberal arts programs (20 males, 20 females) completed a mental rotation task during functional magnetic resonance imaging (fMRI). When a pair of cube figures was shown, the participant made a keypad response based on whether the pair is the same/similar or different. Regardless of gender, the bilateral middle frontal gyrus, bilateral intraparietal sulcus (IPS), and the left precuneus were activated when a subject tried to solve the mental rotation task. Increased activation in the right inferior frontal gyrus/middle frontal gyrus, the left precuneus/posterior cingulate cortex/cuneus region, and the left middle occipital gyrus was found for men as compared to women. Better accuracy and shorter response times were correlated with an increased activation in the bilateral intraparietal sulcus. No significant brain activity differences related to mental rotation were found between academic majors. These findings suggest that networks involved in visual attention appear to be more strongly activated in the mental rotation tasks in men as compared to women. It also suggests that men use a more automatic process when analyzing complex visual reasoning tasks while women use a more top-down process.

Electromagnetic brain imaging

International Nuclear Information System (INIS)

Sekihara, Kensuke

2008-01-01

Present imaging methods of cerebral neuro-activity like brain functional MRI and positron emission tomography (PET) secondarily measure only average activities within a time of the second-order (low time-resolution). In contrast, the electromagnetic brain imaging (EMBI) directly measures the faint magnetic field (10 -12 -10 -13 T) yielded by the cerebral activity with use of multiple arrayed sensors equipped on the head surface within a time of sub-millisecond order (high time-resolution). The sensor array technology to find the signal source from the measured data is common in wide areas like signal procession for radar, sonar, and epicenter detection by seismic wave. For estimating and reconstructing the active region in the brain in EMBI, the efficient method must be developed and this paper describes the direct and inverse problems concerned in signal and image processions of EMBI. The direct problem involves the cerebral magnetic field/lead field matrix and inverse problem for reconstruction of signal source, the MUSIC (multiple signal classification) algorithm, GLRT (generalized likelihood ratio test) scan, and adaptive beamformer. As an example, given are results of magnetic intensity changes (unit, fT) in the somatosensory cortex vs time (msec) measured by 160 sensors and of images reconstructed from EMBI and MRI during electric muscle afferent input from the hand. The real-time imaging is thus possible with EMBI and extremely, the EMBI image, the real-time cerebral signals, can inversely operate a machine, of which application directs toward the brain/machine interface development. (R.T.)

In vivo electrical conductivity imaging of a canine brain using a 3 T MREIT system

International Nuclear Information System (INIS)

Kim, Hyung Joong; Oh, Tong In; Kim, Young Tae; Lee, Byung Il; Woo, Eung Je; Lee, Soo Yeol; Seo, Jin Keun; Kwon, Ohin; Park, Chunjae; Kang, Byeong Teck; Park, Hee Myung

2008-01-01

Magnetic resonance electrical impedance tomography (MREIT) aims at producing high-resolution cross-sectional conductivity images of an electrically conducting object such as the human body. Following numerous phantom imaging experiments, the most recent study demonstrated successful conductivity image reconstructions of postmortem canine brains using a 3 T MREIT system with 40 mA imaging currents. Here, we report the results of in vivo animal imaging experiments using 5 mA imaging currents. To investigate any change of electrical conductivity due to brain ischemia, canine brains having a regional ischemic model were scanned along with separate scans of canine brains having no disease model. Reconstructed multi-slice conductivity images of in vivo canine brains with a pixel size of 1.4 mm showed a clear contrast between white and gray matter and also between normal and ischemic regions. We found that the conductivity value of an ischemic region decreased by about 10–14%. In a postmortem brain, conductivity values of white and gray matter decreased by about 4–8% compared to those in a live brain. Accumulating more experience of in vivo animal imaging experiments, we plan to move to human experiments. One of the important goals of our future work is the reduction of the imaging current to a level that a human subject can tolerate. The ability to acquire high-resolution conductivity images will find numerous clinical applications not supported by other medical imaging modalities. Potential applications in biology, chemistry and material science are also expected

Advantages of Task-Specific Multi-Objective Optimisation in Evolutionary Robotics.

Science.gov (United States)

Trianni, Vito; López-Ibáñez, Manuel

2015-01-01

The application of multi-objective optimisation to evolutionary robotics is receiving increasing attention. A survey of the literature reveals the different possibilities it offers to improve the automatic design of efficient and adaptive robotic systems, and points to the successful demonstrations available for both task-specific and task-agnostic approaches (i.e., with or without reference to the specific design problem to be tackled). However, the advantages of multi-objective approaches over single-objective ones have not been clearly spelled out and experimentally demonstrated. This paper fills this gap for task-specific approaches: starting from well-known results in multi-objective optimisation, we discuss how to tackle commonly recognised problems in evolutionary robotics. In particular, we show that multi-objective optimisation (i) allows evolving a more varied set of behaviours by exploring multiple trade-offs of the objectives to optimise, (ii) supports the evolution of the desired behaviour through the introduction of objectives as proxies, (iii) avoids the premature convergence to local optima possibly introduced by multi-component fitness functions, and (iv) solves the bootstrap problem exploiting ancillary objectives to guide evolution in the early phases. We present an experimental demonstration of these benefits in three different case studies: maze navigation in a single robot domain, flocking in a swarm robotics context, and a strictly collaborative task in collective robotics.

Advantages of Task-Specific Multi-Objective Optimisation in Evolutionary Robotics.

Directory of Open Access Journals (Sweden)

Vito Trianni

Full Text Available The application of multi-objective optimisation to evolutionary robotics is receiving increasing attention. A survey of the literature reveals the different possibilities it offers to improve the automatic design of efficient and adaptive robotic systems, and points to the successful demonstrations available for both task-specific and task-agnostic approaches (i.e., with or without reference to the specific design problem to be tackled. However, the advantages of multi-objective approaches over single-objective ones have not been clearly spelled out and experimentally demonstrated. This paper fills this gap for task-specific approaches: starting from well-known results in multi-objective optimisation, we discuss how to tackle commonly recognised problems in evolutionary robotics. In particular, we show that multi-objective optimisation (i allows evolving a more varied set of behaviours by exploring multiple trade-offs of the objectives to optimise, (ii supports the evolution of the desired behaviour through the introduction of objectives as proxies, (iii avoids the premature convergence to local optima possibly introduced by multi-component fitness functions, and (iv solves the bootstrap problem exploiting ancillary objectives to guide evolution in the early phases. We present an experimental demonstration of these benefits in three different case studies: maze navigation in a single robot domain, flocking in a swarm robotics context, and a strictly collaborative task in collective robotics.

Transfer learning improves supervised image segmentation across imaging protocols

DEFF Research Database (Denmark)

van Opbroek, Annegreet; Ikram, M. Arfan; Vernooij, Meike W.

2015-01-01

with slightly different characteristics. The performance of the four transfer classifiers was compared to that of standard supervised classification on two MRI brain-segmentation tasks with multi-site data: white matter, gray matter, and CSF segmentation; and white-matter- /MS-lesion segmentation......The variation between images obtained with different scanners or different imaging protocols presents a major challenge in automatic segmentation of biomedical images. This variation especially hampers the application of otherwise successful supervised-learning techniques which, in order to perform...... well, often require a large amount of labeled training data that is exactly representative of the target data. We therefore propose to use transfer learning for image segmentation. Transfer-learning techniques can cope with differences in distributions between training and target data, and therefore...

Subclinical cognitive decline in middle-age is associated with reduced task-induced deactivation of the brain's default mode network

DEFF Research Database (Denmark)

Hansen, Naja Liv; Lauritzen, Martin; Mortensen, Erik Lykke

2014-01-01

range of neurodegenerative diseases involving cognitive symptoms, in conditions with increased risk of Alzheimer's disease, and even in advanced but healthy aging. Here, we investigated brain activation and deactivation during a visual-motor task in 185 clinically healthy males from a Danish birth......Cognitive abilities decline with age, but with considerable individual variation. The neurobiological correlate of this variation is not well described. Functional brain imaging studies have demonstrated reduced task-induced deactivation (TID) of the brain's default mode network (DMN) in a wide...... cohort, whose cognitive function was assessed in youth and midlife. Using each individual as his own control, we defined a group with a large degree of cognitive decline, and a control group. When correcting for effects of total cerebral blood flow and hemoglobin level, we found reduced TID...

Neuroticism modulates brain visuo-vestibular and anxiety systems during a virtual rollercoaster task.

Science.gov (United States)

Riccelli, Roberta; Indovina, Iole; Staab, Jeffrey P; Nigro, Salvatore; Augimeri, Antonio; Lacquaniti, Francesco; Passamonti, Luca

2017-02-01

Different lines of research suggest that anxiety-related personality traits may influence the visual and vestibular control of balance, although the brain mechanisms underlying this effect remain unclear. To our knowledge, this is the first functional magnetic resonance imaging (fMRI) study that investigates how individual differences in neuroticism and introversion, two key personality traits linked to anxiety, modulate brain regional responses and functional connectivity patterns during a fMRI task simulating self-motion. Twenty-four healthy individuals with variable levels of neuroticism and introversion underwent fMRI while performing a virtual reality rollercoaster task that included two main types of trials: (1) trials simulating downward or upward self-motion (vertical motion), and (2) trials simulating self-motion in horizontal planes (horizontal motion). Regional brain activity and functional connectivity patterns when comparing vertical versus horizontal motion trials were correlated with personality traits of the Five Factor Model (i.e., neuroticism, extraversion-introversion, openness, agreeableness, and conscientiousness). When comparing vertical to horizontal motion trials, we found a positive correlation between neuroticism scores and regional activity in the left parieto-insular vestibular cortex (PIVC). For the same contrast, increased functional connectivity between the left PIVC and right amygdala was also detected as a function of higher neuroticism scores. Together, these findings provide new evidence that individual differences in personality traits linked to anxiety are significantly associated with changes in the activity and functional connectivity patterns within visuo-vestibular and anxiety-related systems during simulated vertical self-motion. Hum Brain Mapp 38:715-726, 2017. © 2016 The Authors Human Brain Mapping Published by Wiley Periodicals, Inc. © 2016 The Authors Human Brain Mapping Published by Wiley Periodicals, Inc.

Inducing task-relevant responses to speech in the sleeping brain.

Science.gov (United States)

Kouider, Sid; Andrillon, Thomas; Barbosa, Leonardo S; Goupil, Louise; Bekinschtein, Tristan A

2014-09-22

Falling asleep leads to a loss of sensory awareness and to the inability to interact with the environment [1]. While this was traditionally thought as a consequence of the brain shutting down to external inputs, it is now acknowledged that incoming stimuli can still be processed, at least to some extent, during sleep [2]. For instance, sleeping participants can create novel sensory associations between tones and odors [3] or reactivate existing semantic associations, as evidenced by event-related potentials [4-7]. Yet, the extent to which the brain continues to process external stimuli remains largely unknown. In particular, it remains unclear whether sensory information can be processed in a flexible and task-dependent manner by the sleeping brain, all the way up to the preparation of relevant actions. Here, using semantic categorization and lexical decision tasks, we studied task-relevant responses triggered by spoken stimuli in the sleeping brain. Awake participants classified words as either animals or objects (experiment 1) or as either words or pseudowords (experiment 2) by pressing a button with their right or left hand, while transitioning toward sleep. The lateralized readiness potential (LRP), an electrophysiological index of response preparation, revealed that task-specific preparatory responses are preserved during sleep. These findings demonstrate that despite the absence of awareness and behavioral responsiveness, sleepers can still extract task-relevant information from external stimuli and covertly prepare for appropriate motor responses. Copyright © 2014 The Authors. Published by Elsevier Inc. All rights reserved.

Improving our understanding of multi-tasking in healthcare: Drawing together the cognitive psychology and healthcare literature.

Science.gov (United States)

Douglas, Heather E; Raban, Magdalena Z; Walter, Scott R; Westbrook, Johanna I

2017-03-01

Multi-tasking is an important skill for clinical work which has received limited research attention. Its impacts on clinical work are poorly understood. In contrast, there is substantial multi-tasking research in cognitive psychology, driver distraction, and human-computer interaction. This review synthesises evidence of the extent and impacts of multi-tasking on efficiency and task performance from health and non-healthcare literature, to compare and contrast approaches, identify implications for clinical work, and to develop an evidence-informed framework for guiding the measurement of multi-tasking in future healthcare studies. The results showed healthcare studies using direct observation have focused on descriptive studies to quantify concurrent multi-tasking and its frequency in different contexts, with limited study of impact. In comparison, non-healthcare studies have applied predominantly experimental and simulation designs, focusing on interleaved and concurrent multi-tasking, and testing theories of the mechanisms by which multi-tasking impacts task efficiency and performance. We propose a framework to guide the measurement of multi-tasking in clinical settings that draws together lessons from these siloed research efforts. Copyright © 2016 The Authors. Published by Elsevier Ltd.. All rights reserved.

Cranial nerve clock. Part II: functional MR imaging of brain activation during a declarative memory task.

Science.gov (United States)

Weiss, K L; Welsh, R C; Eldevik, P; Bieliauskas, L A; Steinberg, B A

2001-12-01

The authors performed this study to assess brain activation during encoding and successful recall with a declarative memory paradigm that has previously been demonstrated to be effective for teaching students about the cranial nerves. Twenty-four students underwent functional magnetic resonance (MR) imaging during encoding and recall of the name, number, and function of the 12 cranial nerves. The students viewed mnemonic graphic and text slides related to individual nerves, as well as their respective control slides. For the recall paradigm, students were prompted with the numbers 1-12 (test condition) intermixed with the number 14 (control condition). Subjects were tested about their knowledge of cranial nerves outside the MR unit before and after functional MR imaging. Students learned about the cranial nerves while undergoing functional MR imaging (mean post- vs preparadigm score, 8.1 +/- 3.4 [of a possible 12] vs 0.75 +/- 0.94, bilateral prefrontal cortex, left greater than right; P brain activation. Encoding revealed statistically significant activation in the bilateral prefrontal cortex, left greater than right [corrected]; bilateral occipital and parietal associative cortices, parahippocampus region, fusiform gyri, and cerebellum. Successful recall activated the left much more than the right prefrontal, parietal associative, and anterior cingulate cortices; bilateral precuneus and cerebellum; and right more than the left posterior cingulate. A predictable pattern of brain activation at functional MR imaging accompanies the encoding and successful recall of the cranial nerves with this declarative memory paradigm.

Brain tumor segmentation using holistically nested neural networks in MRI images.

Science.gov (United States)

Zhuge, Ying; Krauze, Andra V; Ning, Holly; Cheng, Jason Y; Arora, Barbara C; Camphausen, Kevin; Miller, Robert W

2017-10-01

Gliomas are rapidly progressive, neurologically devastating, largely fatal brain tumors. Magnetic resonance imaging (MRI) is a widely used technique employed in the diagnosis and management of gliomas in clinical practice. MRI is also the standard imaging modality used to delineate the brain tumor target as part of treatment planning for the administration of radiation therapy. Despite more than 20 yr of research and development, computational brain tumor segmentation in MRI images remains a challenging task. We are presenting a novel method of automatic image segmentation based on holistically nested neural networks that could be employed for brain tumor segmentation of MRI images. Two preprocessing techniques were applied to MRI images. The N4ITK method was employed for correction of bias field distortion. A novel landmark-based intensity normalization method was developed so that tissue types have a similar intensity scale in images of different subjects for the same MRI protocol. The holistically nested neural networks (HNN), which extend from the convolutional neural networks (CNN) with a deep supervision through an additional weighted-fusion output layer, was trained to learn the multiscale and multilevel hierarchical appearance representation of the brain tumor in MRI images and was subsequently applied to produce a prediction map of the brain tumor on test images. Finally, the brain tumor was obtained through an optimum thresholding on the prediction map. The proposed method was evaluated on both the Multimodal Brain Tumor Image Segmentation (BRATS) Benchmark 2013 training datasets, and clinical data from our institute. A dice similarity coefficient (DSC) and sensitivity of 0.78 and 0.81 were achieved on 20 BRATS 2013 training datasets with high-grade gliomas (HGG), based on a two-fold cross-validation. The HNN model built on the BRATS 2013 training data was applied to ten clinical datasets with HGG from a locally developed database. DSC and sensitivity of

Multi-Scale Factor Analysis of High-Dimensional Brain Signals

KAUST Repository

Ting, Chee-Ming

2017-05-18

In this paper, we develop an approach to modeling high-dimensional networks with a large number of nodes arranged in a hierarchical and modular structure. We propose a novel multi-scale factor analysis (MSFA) model which partitions the massive spatio-temporal data defined over the complex networks into a finite set of regional clusters. To achieve further dimension reduction, we represent the signals in each cluster by a small number of latent factors. The correlation matrix for all nodes in the network are approximated by lower-dimensional sub-structures derived from the cluster-specific factors. To estimate regional connectivity between numerous nodes (within each cluster), we apply principal components analysis (PCA) to produce factors which are derived as the optimal reconstruction of the observed signals under the squared loss. Then, we estimate global connectivity (between clusters or sub-networks) based on the factors across regions using the RV-coefficient as the cross-dependence measure. This gives a reliable and computationally efficient multi-scale analysis of both regional and global dependencies of the large networks. The proposed novel approach is applied to estimate brain connectivity networks using functional magnetic resonance imaging (fMRI) data. Results on resting-state fMRI reveal interesting modular and hierarchical organization of human brain networks during rest.

Multi-task learning with group information for human action recognition

Science.gov (United States)

Qian, Li; Wu, Song; Pu, Nan; Xu, Shulin; Xiao, Guoqiang

2018-04-01

Human action recognition is an important and challenging task in computer vision research, due to the variations in human motion performance, interpersonal differences and recording settings. In this paper, we propose a novel multi-task learning framework with group information (MTL-GI) for accurate and efficient human action recognition. Specifically, we firstly obtain group information through calculating the mutual information according to the latent relationship between Gaussian components and action categories, and clustering similar action categories into the same group by affinity propagation clustering. Additionally, in order to explore the relationships of related tasks, we incorporate group information into multi-task learning. Experimental results evaluated on two popular benchmarks (UCF50 and HMDB51 datasets) demonstrate the superiority of our proposed MTL-GI framework.

Cerebellum segmentation in MRI using atlas registration and local multi-scale image descriptors

DEFF Research Database (Denmark)

van der Lijn, F.; de Bruijne, M.; Hoogendam, Y.Y.

2009-01-01

We propose a novel cerebellum segmentation method for MRI, based on a combination of statistical models of the structure's expected location in the brain and its local appearance. The appearance model is obtained from a k-nearest-neighbor classifier, which uses a set of multi-scale local image...

TECHNOLOGIES OF BRAIN IMAGES PROCESSING

Directory of Open Access Journals (Sweden)

O.M. Klyuchko

2017-12-01

Full Text Available The purpose of present research was to analyze modern methods of processing biological images implemented before storage in databases for biotechnological purposes. The databases further were incorporated into web-based digital systems. Examples of such information systems were described in the work for two levels of biological material organization; databases for storing data of histological analysis and of whole brain were described. Methods of neuroimaging processing for electronic brain atlas were considered. It was shown that certain pathological features can be revealed in histological image processing. Several medical diagnostic techniques (for certain brain pathologies, etc. as well as a few biotechnological methods are based on such effects. Algorithms of image processing were suggested. Electronic brain atlas was conveniently for professionals in different fields described in details. Approaches of brain atlas elaboration, “composite” scheme for large deformations as well as several methods of mathematic images processing were described as well.

Brain Tumor Image Segmentation in MRI Image

Science.gov (United States)

Peni Agustin Tjahyaningtijas, Hapsari

2018-04-01

Brain tumor segmentation plays an important role in medical image processing. Treatment of patients with brain tumors is highly dependent on early detection of these tumors. Early detection of brain tumors will improve the patient’s life chances. Diagnosis of brain tumors by experts usually use a manual segmentation that is difficult and time consuming because of the necessary automatic segmentation. Nowadays automatic segmentation is very populer and can be a solution to the problem of tumor brain segmentation with better performance. The purpose of this paper is to provide a review of MRI-based brain tumor segmentation methods. There are number of existing review papers, focusing on traditional methods for MRI-based brain tumor image segmentation. this paper, we focus on the recent trend of automatic segmentation in this field. First, an introduction to brain tumors and methods for brain tumor segmentation is given. Then, the state-of-the-art algorithms with a focus on recent trend of full automatic segmentaion are discussed. Finally, an assessment of the current state is presented and future developments to standardize MRI-based brain tumor segmentation methods into daily clinical routine are addressed.

Prediction of standard-dose brain PET image by using MRI and low-dose brain [{sup 18}F]FDG PET images

Energy Technology Data Exchange (ETDEWEB)

Kang, Jiayin [School of Electronics Engineering, Huaihai Institute of Technology, Lianyungang, Jiangsu 222005, China and IDEA Laboratory, Department of Radiology and BRIC, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina 27599 (United States); Gao, Yaozong [IDEA Laboratory, Department of Radiology and BRIC, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina 27599 and Department of Computer Science, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina 27599 (United States); Shi, Feng [IDEA Laboratory, Department of Radiology and BRIC, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina 27599 (United States); Lalush, David S. [Joint UNC-NCSU Department of Biomedical Engineering, North Carolina State University, Raleigh, North Carolina 27695 (United States); Lin, Weili [MRI Laboratory, Department of Radiology and BRIC, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina 27599 (United States); Shen, Dinggang, E-mail: dgshen@med.unc.edu [IDEA Laboratory, Department of Radiology and BRIC, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina 27599 and Department of Brain and Cognitive Engineering, Korea University, Seoul 136-713 (Korea, Republic of)

2015-09-15

Purpose: Positron emission tomography (PET) is a nuclear medical imaging technology that produces 3D images reflecting tissue metabolic activity in human body. PET has been widely used in various clinical applications, such as in diagnosis of brain disorders. High-quality PET images play an essential role in diagnosing brain diseases/disorders. In practice, in order to obtain high-quality PET images, a standard-dose radionuclide (tracer) needs to be used and injected into a living body. As a result, it will inevitably increase the patient’s exposure to radiation. One solution to solve this problem is predicting standard-dose PET images using low-dose PET images. As yet, no previous studies with this approach have been reported. Accordingly, in this paper, the authors propose a regression forest based framework for predicting a standard-dose brain [{sup 18}F]FDG PET image by using a low-dose brain [{sup 18}F]FDG PET image and its corresponding magnetic resonance imaging (MRI) image. Methods: The authors employ a regression forest for predicting the standard-dose brain [{sup 18}F]FDG PET image by low-dose brain [{sup 18}F]FDG PET and MRI images. Specifically, the proposed method consists of two main steps. First, based on the segmented brain tissues (i.e., cerebrospinal fluid, gray matter, and white matter) in the MRI image, the authors extract features for each patch in the brain image from both low-dose PET and MRI images to build tissue-specific models that can be used to initially predict standard-dose brain [{sup 18}F]FDG PET images. Second, an iterative refinement strategy, via estimating the predicted image difference, is used to further improve the prediction accuracy. Results: The authors evaluated their algorithm on a brain dataset, consisting of 11 subjects with MRI, low-dose PET, and standard-dose PET images, using leave-one-out cross-validations. The proposed algorithm gives promising results with well-estimated standard-dose brain [{sup 18}F]FDG PET

Prediction of standard-dose brain PET image by using MRI and low-dose brain ["1"8F]FDG PET images

International Nuclear Information System (INIS)

Kang, Jiayin; Gao, Yaozong; Shi, Feng; Lalush, David S.; Lin, Weili; Shen, Dinggang

2015-01-01

Purpose: Positron emission tomography (PET) is a nuclear medical imaging technology that produces 3D images reflecting tissue metabolic activity in human body. PET has been widely used in various clinical applications, such as in diagnosis of brain disorders. High-quality PET images play an essential role in diagnosing brain diseases/disorders. In practice, in order to obtain high-quality PET images, a standard-dose radionuclide (tracer) needs to be used and injected into a living body. As a result, it will inevitably increase the patient’s exposure to radiation. One solution to solve this problem is predicting standard-dose PET images using low-dose PET images. As yet, no previous studies with this approach have been reported. Accordingly, in this paper, the authors propose a regression forest based framework for predicting a standard-dose brain ["1"8F]FDG PET image by using a low-dose brain ["1"8F]FDG PET image and its corresponding magnetic resonance imaging (MRI) image. Methods: The authors employ a regression forest for predicting the standard-dose brain ["1"8F]FDG PET image by low-dose brain ["1"8F]FDG PET and MRI images. Specifically, the proposed method consists of two main steps. First, based on the segmented brain tissues (i.e., cerebrospinal fluid, gray matter, and white matter) in the MRI image, the authors extract features for each patch in the brain image from both low-dose PET and MRI images to build tissue-specific models that can be used to initially predict standard-dose brain ["1"8F]FDG PET images. Second, an iterative refinement strategy, via estimating the predicted image difference, is used to further improve the prediction accuracy. Results: The authors evaluated their algorithm on a brain dataset, consisting of 11 subjects with MRI, low-dose PET, and standard-dose PET images, using leave-one-out cross-validations. The proposed algorithm gives promising results with well-estimated standard-dose brain ["1"8F]FDG PET image and substantially

Robust Estimation of Electron Density From Anatomic Magnetic Resonance Imaging of the Brain Using a Unifying Multi-Atlas Approach

Energy Technology Data Exchange (ETDEWEB)

Ren, Shangjie [Tianjin Key Laboratory of Process Measurement and Control, School of Electrical Engineering and Automation, Tianjin University, Tianjin (China); Department of Radiation Oncology, Stanford University School of Medicine, Palo Alto, California (United States); Hara, Wendy; Wang, Lei; Buyyounouski, Mark K.; Le, Quynh-Thu; Xing, Lei [Department of Radiation Oncology, Stanford University School of Medicine, Palo Alto, California (United States); Li, Ruijiang, E-mail: rli2@stanford.edu [Department of Radiation Oncology, Stanford University School of Medicine, Palo Alto, California (United States)

2017-03-15

Purpose: To develop a reliable method to estimate electron density based on anatomic magnetic resonance imaging (MRI) of the brain. Methods and Materials: We proposed a unifying multi-atlas approach for electron density estimation based on standard T1- and T2-weighted MRI. First, a composite atlas was constructed through a voxelwise matching process using multiple atlases, with the goal of mitigating effects of inherent anatomic variations between patients. Next we computed for each voxel 2 kinds of conditional probabilities: (1) electron density given its image intensity on T1- and T2-weighted MR images; and (2) electron density given its spatial location in a reference anatomy, obtained by deformable image registration. These were combined into a unifying posterior probability density function using the Bayesian formalism, which provided the optimal estimates for electron density. We evaluated the method on 10 patients using leave-one-patient-out cross-validation. Receiver operating characteristic analyses for detecting different tissue types were performed. Results: The proposed method significantly reduced the errors in electron density estimation, with a mean absolute Hounsfield unit error of 119, compared with 140 and 144 (P<.0001) using conventional T1-weighted intensity and geometry-based approaches, respectively. For detection of bony anatomy, the proposed method achieved an 89% area under the curve, 86% sensitivity, 88% specificity, and 90% accuracy, which improved upon intensity and geometry-based approaches (area under the curve: 79% and 80%, respectively). Conclusion: The proposed multi-atlas approach provides robust electron density estimation and bone detection based on anatomic MRI. If validated on a larger population, our work could enable the use of MRI as a primary modality for radiation treatment planning.

A Spatial Queuing-Based Algorithm for Multi-Robot Task Allocation

Directory of Open Access Journals (Sweden)

William Lenagh

2015-08-01

Full Text Available Multi-robot task allocation (MRTA is an important area of research in autonomous multi-robot systems. The main problem in MRTA is to allocate a set of tasks to a set of robots so that the tasks can be completed by the robots while ensuring that a certain metric, such as the time required to complete all tasks, or the distance traveled, or the energy expended by the robots is reduced. We consider a scenario where tasks can appear dynamically and a task needs to be performed by multiple robots to be completed. We propose a new algorithm called SQ-MRTA (Spatial Queueing-MRTA that uses a spatial queue-based model to allocate tasks between robots in a distributed manner. We have implemented the SQ-MRTA algorithm on accurately simulated models of Corobot robots within the Webots simulator for different numbers of robots and tasks and compared its performance with other state-of-the-art MRTA algorithms. Our results show that the SQ-MRTA algorithm is able to scale up with the number of tasks and robots in the environment, and it either outperforms or performs comparably with respect to other distributed MRTA algorithms.

A proposal toward a possibilistic multi-robot task allocation

Energy Technology Data Exchange (ETDEWEB)

Guerrero, J.

2017-07-01

One of the main problems to solve in multi-agent (or multi-robot) systems is to select the best robot or group of robots to carry out a specific task. This problem, referenced as Multi-Agent (robot) task allocation (MRTA), is still an open issue in real environments. Swarm intelligence methods provide very simple solutions for the MRTA problem. One of the most widely used swarm methods are the so-called Response Threshold algorithms, where the behavior of the systems is modeled as a Markov chain and the robots in each time step select the next task to execute according to a transition probability function. Among other factors, this probability depends on a stimulus (for example the distance between the robot and the task). This classical probabilistic approach presents a lot of disadvantages:the transition function must meet constraints of a probabilistic distribution, the system only convergences to a stationary asymptotically, and so on. In order to overcome these problems, a new theoretical framework based on fuzzy (possibilistic) Markov chains was proposed [2]. As was proved, the possibilistic Markov chains outperform the classical probabilistic when a Max-Min algebra is considered for matrix composition. For example, fuzzy Markov chains convergence to a stable state in a finite number of steps 10 times faster than its probability counter part. Moreover, they improve the predictions of the system under imprecise information. Firstly, this paper will review relevant work in MRTA, from theoretical and experimental point of view. Then it will be summarized the aforementioned recent advances given toward a new possibilistic swarm multi-robot task allocation framework. It will be seen how the possibilistic Markov chains behave when other algebras are considered for matrix composition [1] and how the possibility transition function impacts on the system's performance [3]. Finally, it will be proposed new future works in this field. (Author)

A proposal toward a possibilistic multi-robot task allocation

International Nuclear Information System (INIS)

Guerrero, J.

2017-01-01

One of the main problems to solve in multi-agent (or multi-robot) systems is to select the best robot or group of robots to carry out a specific task. This problem, referenced as Multi-Agent (robot) task allocation (MRTA), is still an open issue in real environments. Swarm intelligence methods provide very simple solutions for the MRTA problem. One of the most widely used swarm methods are the so-called Response Threshold algorithms, where the behavior of the systems is modeled as a Markov chain and the robots in each time step select the next task to execute according to a transition probability function. Among other factors, this probability depends on a stimulus (for example the distance between the robot and the task). This classical probabilistic approach presents a lot of disadvantages:the transition function must meet constraints of a probabilistic distribution, the system only convergences to a stationary asymptotically, and so on. In order to overcome these problems, a new theoretical framework based on fuzzy (possibilistic) Markov chains was proposed [2]. As was proved, the possibilistic Markov chains outperform the classical probabilistic when a Max-Min algebra is considered for matrix composition. For example, fuzzy Markov chains convergence to a stable state in a finite number of steps 10 times faster than its probability counter part. Moreover, they improve the predictions of the system under imprecise information. Firstly, this paper will review relevant work in MRTA, from theoretical and experimental point of view. Then it will be summarized the aforementioned recent advances given toward a new possibilistic swarm multi-robot task allocation framework. It will be seen how the possibilistic Markov chains behave when other algebras are considered for matrix composition [1] and how the possibility transition function impacts on the system's performance [3]. Finally, it will be proposed new future works in this field. (Author)

The Wikipedia Image Retrieval Task

NARCIS (Netherlands)

T. Tsikrika (Theodora); J. Kludas

2010-01-01

htmlabstractThe wikipedia image retrieval task at ImageCLEF provides a testbed for the system-oriented evaluation of visual information retrieval from a collection of Wikipedia images. The aim is to investigate the effectiveness of retrieval approaches that exploit textual and visual evidence in the

Task-oriented control of Single-Master Multi-Slave Manipulator System

International Nuclear Information System (INIS)

Kosuge, Kazuhiro; Ishikawa, Jun; Furuta, Katsuhisa; Hariki, Kazuo; Sakai, Masaru.

1994-01-01

A master-slave manipulator system, in general, consists of a master arm manipulated by a human and a slave arm used for real tasks. Some tasks, such as manipulation of a heavy object, etc., require two or more slave arms operated simultaneously. A Single-Master Multi-Slave Manipulator System consists of a master arm with six degrees of freedom and two or more slave arms, each of which has six or more degrees of freedom. In this system, a master arm controls the task-oriented variables using Virtual Internal Model (VIM) based on the concept of 'Task-Oriented Control'. VIM is a reference model driven by sensory information and used to describe the desired relation between the motion of a master arm and task-oriented variables. The motion of slave arms are controlled based on the task oriented variables generated by VIM and tailors the system to meet specific tasks. A single-master multi-slave manipulator system, having two slave arms, is experimentally developed and illustrates the concept. (author)

Development of the Young Brain

Medline Plus

Full Text Available ... items) Institute Announcements (24 items) Development of the Young Brain May 2, 2011 For more than twenty ... Announcer: Our brains have been challenged by the effects of multi-tasking in many ways brought on ...

Modeling decision-making in single- and multi-modal medical images

Science.gov (United States)

Canosa, R. L.; Baum, K. G.

2009-02-01

This research introduces a mode-specific model of visual saliency that can be used to highlight likely lesion locations and potential errors (false positives and false negatives) in single-mode PET and MRI images and multi-modal fused PET/MRI images. Fused-modality digital images are a relatively recent technological improvement in medical imaging; therefore, a novel component of this research is to characterize the perceptual response to these fused images. Three different fusion techniques were compared to single-mode displays in terms of observer error rates using synthetic human brain images generated from an anthropomorphic phantom. An eye-tracking experiment was performed with naÃve (non-radiologist) observers who viewed the single- and multi-modal images. The eye-tracking data allowed the errors to be classified into four categories: false positives, search errors (false negatives never fixated), recognition errors (false negatives fixated less than 350 milliseconds), and decision errors (false negatives fixated greater than 350 milliseconds). A saliency model consisting of a set of differentially weighted low-level feature maps is derived from the known error and ground truth locations extracted from a subset of the test images for each modality. The saliency model shows that lesion and error locations attract visual attention according to low-level image features such as color, luminance, and texture.

Structural Correlates of Skilled Performance on a Motor Sequence Task

Directory of Open Access Journals (Sweden)

Christopher J Steele

2012-10-01

Full Text Available The brain regions functionally engaged in motor sequence performance are well established, but the structural characteristics of these regions and the fibre pathways involved have been less well studied. In addition, relatively few studies have combined multiple magnetic resonance imaging (MRI and behavioural performance measures in the same sample. Therefore, the current study used diffusion tensor imaging, probabilistic tractography, and voxel-based morphometry to determine the structural correlates of skilled motor performance. Further, we compared these findings with fMRI results in the same sample. We correlated final performance and rate of improvement measures on a temporal motor sequence task with skeletonised fractional anisotropy (FA and whole brain grey matter (GM volume. Final synchronisation performance was negatively correlated with FA in white matter underlying bilateral sensorimotor cortex – an effect that was mediated by a positive correlation with radial diffusivity. Multi-fibre tractography indicated that this region contained crossing fibres from the corticospinal tract and superior longitudinal fasciculus (SLF. The identified SLF pathway linked parietal and auditory cortical regions that have been shown to be functionally engaged in this task. Thus, we hypothesise that enhanced synchronisation performance on this task may be related to greater fibre integrity of the SLF. Rate of improvement on synchronisation was positively correlated with GM volume in cerebellar lobules HVI and V – regions that showed training-related decreases in activity in the same sample. Taken together, our results link individual differences in brain structure and function to motor sequence performance on the same task. Further, our study illustrates the utility of using multiple MR measures and analysis techniques to specify the interpretation of structural findings.

Non-FDG PET imaging of brain tumors

Institute of Scientific and Technical Information of China (English)

HUANG Zemin; GUAN Yihui; ZUO Chuantao; ZHANG Zhengwei; XUE Fangping; LIN Xiangtong

2007-01-01

Due to relatively high uptake of glucose in the brain cortex, the use of FDG PET imaging is greatly limited in brain tumor imaging, especially for low-grade gliomas and some metastatic tumours. More and more tracers with higher specificity were developed lately for brain tumor imaging. There are 3 main types of non-FDG PET tracers:amino acid tracers, choline tracers and nucleic acid tracers. These tracers are now widely applied in many aspects of brain tumor imaging. This article summarized the general use of non-FDG PET in different aspects of brain tumor imaging.

VoxelStats: A MATLAB Package for Multi-Modal Voxel-Wise Brain Image Analysis.

Science.gov (United States)

Mathotaarachchi, Sulantha; Wang, Seqian; Shin, Monica; Pascoal, Tharick A; Benedet, Andrea L; Kang, Min Su; Beaudry, Thomas; Fonov, Vladimir S; Gauthier, Serge; Labbe, Aurélie; Rosa-Neto, Pedro

2016-01-01

In healthy individuals, behavioral outcomes are highly associated with the variability on brain regional structure or neurochemical phenotypes. Similarly, in the context of neurodegenerative conditions, neuroimaging reveals that cognitive decline is linked to the magnitude of atrophy, neurochemical declines, or concentrations of abnormal protein aggregates across brain regions. However, modeling the effects of multiple regional abnormalities as determinants of cognitive decline at the voxel level remains largely unexplored by multimodal imaging research, given the high computational cost of estimating regression models for every single voxel from various imaging modalities. VoxelStats is a voxel-wise computational framework to overcome these computational limitations and to perform statistical operations on multiple scalar variables and imaging modalities at the voxel level. VoxelStats package has been developed in Matlab(®) and supports imaging formats such as Nifti-1, ANALYZE, and MINC v2. Prebuilt functions in VoxelStats enable the user to perform voxel-wise general and generalized linear models and mixed effect models with multiple volumetric covariates. Importantly, VoxelStats can recognize scalar values or image volumes as response variables and can accommodate volumetric statistical covariates as well as their interaction effects with other variables. Furthermore, this package includes built-in functionality to perform voxel-wise receiver operating characteristic analysis and paired and unpaired group contrast analysis. Validation of VoxelStats was conducted by comparing the linear regression functionality with existing toolboxes such as glim_image and RMINC. The validation results were identical to existing methods and the additional functionality was demonstrated by generating feature case assessments (t-statistics, odds ratio, and true positive rate maps). In summary, VoxelStats expands the current methods for multimodal imaging analysis by allowing the

A Magnetic Resonance Compatible Soft Wearable Robotic Glove for Hand Rehabilitation and Brain Imaging.

Science.gov (United States)

Hong Kai Yap; Kamaldin, Nazir; Jeong Hoon Lim; Nasrallah, Fatima A; Goh, James Cho Hong; Chen-Hua Yeow

2017-06-01

In this paper, we present the design, fabrication and evaluation of a soft wearable robotic glove, which can be used with functional Magnetic Resonance imaging (fMRI) during the hand rehabilitation and task specific training. The soft wearable robotic glove, called MR-Glove, consists of two major components: a) a set of soft pneumatic actuators and b) a glove. The soft pneumatic actuators, which are made of silicone elastomers, generate bending motion and actuate finger joints upon pressurization. The device is MR-compatible as it contains no ferromagnetic materials and operates pneumatically. Our results show that the device did not cause artifacts to fMRI images during hand rehabilitation and task-specific exercises. This study demonstrated the possibility of using fMRI and MR-compatible soft wearable robotic device to study brain activities and motor performances during hand rehabilitation, and to unravel the functional effects of rehabilitation robotics on brain stimulation.

MULTI-TEMPORAL REMOTE SENSING IMAGE CLASSIFICATION - A MULTI-VIEW APPROACH

Data.gov (United States)

National Aeronautics and Space Administration — MULTI-TEMPORAL REMOTE SENSING IMAGE CLASSIFICATION - A MULTI-VIEW APPROACH VARUN CHANDOLA AND RANGA RAJU VATSAVAI Abstract. Multispectral remote sensing images have...

Ranking Performance Measures in Multi-Task Agencies

DEFF Research Database (Denmark)

Christensen, Peter Ove; Sabac, Florin; Tian, Joyce

We derive sufficient conditions for ranking performance evaluation systems in multi-task agency models using both optimal and linear contracts in terms of a second-order stochastic dominance (SSD) condition on the likelihood ratios. The SSD condition can be replaced by a variance-covariance matrix...

The Multi-Feature Hypothesis: Connectionist Guidelines for L2 Task Design

Science.gov (United States)

Moonen, Machteld; de Graaff, Rick; Westhoff, Gerard; Brekelmans, Mieke

2014-01-01

This study focuses on the effects of task type on the retention and ease of activation of second language (L2) vocabulary, based on the multi-feature hypothesis (Moonen, De Graaff, & Westhoff, 2006). Two tasks were compared: a writing task and a list-learning task. It was hypothesized that performing the writing task would yield higher…

SPECT brain perfusion imaging in mild traumatic brain injury

International Nuclear Information System (INIS)

Li Juan; Liu Baojun; Zhao Feng; He Lirong; Xia Yucheng

2003-01-01

Objective: To study the clinical value of SPECT brain perfusion imaging after mild traumatic brain injury and to evaluate the mechanism of brain blood flow changes in the brain traumatic symptoms. Methods: SPECT 99 Tc m -ethylene cysteinate dimer (ECD) brain perfusion imaging was performed on 39 patients with normal consciousness and normal computed tomography. The study was performed on 23 patients within 3 months after the accidental injury and on 16 patients at more than 3 months post-injury. The cerebellum was used as the reference site (100% maximum value). Any decrease in cerebral perfusion in cortex or basal ganglia to below 70%, or even to below 50% in the medial temporal lobe, compared to the cerebellar reference was considered abnormal. Results: The results of 23 patients (59%) were abnormal. Among them, 20 patients showed 74 focal lesions with an average of 3.7 per patient (15 studies performed within 3 months and 8 studies performed more than 3 months after injury). The remaining 3 showed diffuse hypoperfusion (two at the early stage and one at more than 3 months after the injury). The 13 abnormal studies performed at the early stage showed 58 lesions (average, 4.5 per patient), whereas there was a reduction to an average of 2.3 per patient in the 7 patients (total 16 lesions) at more than 3 months post-injury. In the 20 patients with focal lesions, mainly the following regions were involved: frontal lobes 43.2% (32/74), basal ganglia 24.3% (18/74) and temporal lobes 17.6% (13/74). Conclusions: 1) SPECT brain perfusion imaging is more sensitive than computed tomography in detecting brain lesions of mild traumatic brain injury. 2) SPECT brain perfusion imaging is more sensitive at early stage than at late stage after injury. 3) The most common complaints were headache, dizziness, memory deficit. The patients without loss of consciousness may present brain hypoperfusion, too. 4) The changes may explain a neurological component of the patient symptoms in

Brain water mapping with MR imaging

International Nuclear Information System (INIS)

Laine, F.J.; Fatouros, P.P.; Kraft, K.A.

1990-01-01

This paper reports on a recently developed MR imaging technique to determine the spatial distribution of brain water to healthy volunteers. A noninvasive MR imaging technique to obtain absolute measurements of brain water has been developed and validated with phantom and animal studies. Patient confirmation was obtained from independent gravimetric measurements of brain tissue samples harvested by biopsy. This approach entails the production of accurate T1 maps from multiple inversion recovery images of a selected anatomic section and their subsequent conversion into an absolute water image by means of a previously determined calibration curve. Twenty healthy volunteers were studied and their water distribution was determined in a standard section. The following brain water values means and SD grams of water per gram of tissue) were obtained for selected brain regions; white matter, 68.9% ± 1.0; corpus callosum, 67.4% ± 1.1; thalamus, 75.3% ± 1.4; and caudate nucleus, 80.3% ± 1.4. MR imaging water mapping is a valid means of determining water content in a variety of brain tissues

Identifying beneficial task relations for multi-task learning in deep neural networks

DEFF Research Database (Denmark)

Bingel, Joachim; Søgaard, Anders

2017-01-01

Multi-task learning (MTL) in deep neural networks for NLP has recently received increasing interest due to some compelling benefits, including its potential to efficiently regularize models and to reduce the need for labeled data. While it has brought significant improvements in a number of NLP...

Auto-Context Convolutional Neural Network (Auto-Net) for Brain Extraction in Magnetic Resonance Imaging.

Science.gov (United States)

Mohseni Salehi, Seyed Sadegh; Erdogmus, Deniz; Gholipour, Ali

2017-11-01

resonance imaging (MRI) data sets. In this application, our voxelwise auto-context CNN performed much better than the other methods (Dice coefficient: 95.97%), where the other methods performed poorly due to the non-standard orientation and geometry of the fetal brain in MRI. Through training, our method can provide accurate brain extraction in challenging applications. This, in turn, may reduce the problems associated with image registration in segmentation tasks.

A novel structure-aware sparse learning algorithm for brain imaging genetics.

Science.gov (United States)

Du, Lei; Jingwen, Yan; Kim, Sungeun; Risacher, Shannon L; Huang, Heng; Inlow, Mark; Moore, Jason H; Saykin, Andrew J; Shen, Li

2014-01-01

Brain imaging genetics is an emergent research field where the association between genetic variations such as single nucleotide polymorphisms (SNPs) and neuroimaging quantitative traits (QTs) is evaluated. Sparse canonical correlation analysis (SCCA) is a bi-multivariate analysis method that has the potential to reveal complex multi-SNP-multi-QT associations. Most existing SCCA algorithms are designed using the soft threshold strategy, which assumes that the features in the data are independent from each other. This independence assumption usually does not hold in imaging genetic data, and thus inevitably limits the capability of yielding optimal solutions. We propose a novel structure-aware SCCA (denoted as S2CCA) algorithm to not only eliminate the independence assumption for the input data, but also incorporate group-like structure in the model. Empirical comparison with a widely used SCCA implementation, on both simulated and real imaging genetic data, demonstrated that S2CCA could yield improved prediction performance and biologically meaningful findings.

Multi-robot Task Allocation for Search and Rescue Missions

International Nuclear Information System (INIS)

Hussein, Ahmed; Adel, Mohamed; Bakr, Mohamed; Shehata, Omar M; Khamis, Alaa

2014-01-01

Many researchers from academia and industry are attracted to investigate how to design and develop robust versatile multi-robot systems by solving a number of challenging and complex problems such as task allocation, group formation, self-organization and much more. In this study, the problem of multi-robot task allocation (MRTA) is tackled. MRTA is the problem of optimally allocating a set of tasks to a group of robots to optimize the overall system performance while being subjected to a set of constraints. A generic market-based approach is proposed in this paper to solve this problem. The efficacy of the proposed approach is quantitatively evaluated through simulation and real experimentation using heterogeneous Khepera-III mobile robots. The results from both simulation and experimentation indicate the high performance of the proposed algorithms and their applicability in search and rescue missions

Higher media multi-tasking activity is associated with smaller gray-matter density in the anterior cingulate cortex.

Directory of Open Access Journals (Sweden)

Kep Kee Loh

Full Text Available Media multitasking, or the concurrent consumption of multiple media forms, is increasingly prevalent in today's society and has been associated with negative psychosocial and cognitive impacts. Individuals who engage in heavier media-multitasking are found to perform worse on cognitive control tasks and exhibit more socio-emotional difficulties. However, the neural processes associated with media multi-tasking remain unexplored. The present study investigated relationships between media multitasking activity and brain structure. Research has demonstrated that brain structure can be altered upon prolonged exposure to novel environments and experience. Thus, we expected differential engagements in media multitasking to correlate with brain structure variability. This was confirmed via Voxel-Based Morphometry (VBM analyses: Individuals with higher Media Multitasking Index (MMI scores had smaller gray matter density in the anterior cingulate cortex (ACC. Functional connectivity between this ACC region and the precuneus was negatively associated with MMI. Our findings suggest a possible structural correlate for the observed decreased cognitive control performance and socio-emotional regulation in heavy media-multitaskers. While the cross-sectional nature of our study does not allow us to specify the direction of causality, our results brought to light novel associations between individual media multitasking behaviors and ACC structure differences.

Multi-tasking and Arduino : why and how?

NARCIS (Netherlands)

Feijs, L.M.G.; Chen, L.L.; Djajadiningrat, T.; Feijs, L.M.G.; Fraser, S.; Hu, J.; Kyffin, S.; Steffen, D.

2013-01-01

In this article I argue that it is important to develop experiential prototypes which have multi-tasking capabilities. At the same time I show that for embedded prototype software based on the popular Arduino platform this is not too difficult. The approach is explained and illustrated using

Brain imaging

International Nuclear Information System (INIS)

Greenfield, L.D.; Bennett, L.R.

1976-01-01

Imaging with radionuclides should be used in a complementary fashion with other neuroradiologic techniques. It is useful in the early detection and evaluation of intracranial neoplasm, cerebrovascular accident and abscess, and in postsurgical follow-up. Cisternography yields useful information about the functional status of cerebrospinal fluid pathways. Computerized axial tomography is a new technique of great promise that produced a cross-sectional image of the brain

Image enhancement of digital periapical radiographs according to diagnostic tasks

International Nuclear Information System (INIS)

Choi, Jin Woo; Han, Won Jeong; Kim, Eun Kyung

2014-01-01

his study was performed to investigate the effect of image enhancement of periapical radiographs according to the diagnostic task. Eighty digital intraoral radiographs were obtained from patients and classified into four groups according to the diagnostic tasks of dental caries, periodontal diseases, periapical lesions, and endodontic files. All images were enhanced differently by using five processing techniques. Three radiologists blindly compared the subjective image quality of the original images and the processed images using a 5-point scale. There were significant differences between the image quality of the processed images and that of the original images (P<0.01) in all the diagnostic task groups. Processing techniques showed significantly different efficacy according to the diagnostic task (P<0.01). Image enhancement affects the image quality differently depending on the diagnostic task. And the use of optimal parameters is important for each diagnostic task.

Image enhancement of digital periapical radiographs according to diagnostic tasks

Energy Technology Data Exchange (ETDEWEB)

Choi, Jin Woo; Han, Won Jeong; Kim, Eun Kyung [Dept. of Oral and Maxillofacial Radiology, Dankook University College of Dentistry, Cheonan (Korea, Republic of)

2014-03-15

his study was performed to investigate the effect of image enhancement of periapical radiographs according to the diagnostic task. Eighty digital intraoral radiographs were obtained from patients and classified into four groups according to the diagnostic tasks of dental caries, periodontal diseases, periapical lesions, and endodontic files. All images were enhanced differently by using five processing techniques. Three radiologists blindly compared the subjective image quality of the original images and the processed images using a 5-point scale. There were significant differences between the image quality of the processed images and that of the original images (P<0.01) in all the diagnostic task groups. Processing techniques showed significantly different efficacy according to the diagnostic task (P<0.01). Image enhancement affects the image quality differently depending on the diagnostic task. And the use of optimal parameters is important for each diagnostic task.

Neural congruency effects in the multi-source interference task vanish in healthy youth after controlling for conditional differences in mean RT.

Directory of Open Access Journals (Sweden)

Kamin Kim

Full Text Available According to the conflict monitoring model of cognitive control, reaction time (RT in distracter interference tasks (e.g., the Stroop task is a more precise index of response conflict than stimulus congruency (incongruent vs. congruent. The model therefore predicts that RT should be a reliable predictor of activity in regions of the posterior medial frontal cortex (pMFC that are posited to detect response conflict. In particular, pMFC activity should be (a greater in slow-RT than in fast-RT trials within a given task condition (e.g., congruent and (b equivalent in RT-matched trials from different conditions (i.e., congruent and incongruent trials. Both of these effects have been observed in functional magnetic resonance imaging (MRI studies of adults. However, neither effect was observed in a recent study of healthy youth, suggesting that (a the model does not accurately describe the relationship between RT and pMFC activity in this population or (b the recent study was characterized by high variability due to a relatively small sample size. To distinguish between these possibilities, we asked a relatively large group of healthy youth (n = 28 to perform a distracter interference task - the multi-source interference task (MSIT - while we recorded their brain activity with functional MRI. In this relatively large sample, both of the model's predictions were confirmed. We conclude that the model accurately describes the relationship between pMFC activity and RT in healthy youth, but that additional research is needed to determine whether processes unrelated to response conflict contribute to this relationship.

Brain imaging during seizure: ictal brain SPECT

International Nuclear Information System (INIS)

Kottamasu, Sambasiva Rao

1997-01-01

The role of single photon computed tomography (SPECT) in presurgical localization of medically intractable complex partial epilepsy (CPE) in children is reviewed. 99m Technetium neurolite, a newer lipophylic agent with a high first pass brain extraction and little or no redistribution is injected during a seizure, while the child is monitored with a video recording and continuous EEG and SPECT imaging is performed in the next 1-3 hours with the images representing regional cerebral profusion at the time of injection. On SPECT studies performed with radiopharmaceutical injected during a seizure, ictal focus is generally hypervascular. Other findings on ictal brain SPECT include hypoperfusion of adjacent cerebral cortex and white matter, hyperperfusion of contralateral motor cortex, hyperperfusion of ipsilateral basal ganglia and thalamus, brain stem and contralateral cerebellum. Ictal brain SPECT is non-invasive, cost effective and highly sensitive for localization of epileptic focus in patients with intractable CPE. (author)

Robust visual tracking via structured multi-task sparse learning

KAUST Repository

Zhang, Tianzhu

2012-11-09

In this paper, we formulate object tracking in a particle filter framework as a structured multi-task sparse learning problem, which we denote as Structured Multi-Task Tracking (S-MTT). Since we model particles as linear combinations of dictionary templates that are updated dynamically, learning the representation of each particle is considered a single task in Multi-Task Tracking (MTT). By employing popular sparsity-inducing lp,q mixed norms (specifically p∈2,∞ and q=1), we regularize the representation problem to enforce joint sparsity and learn the particle representations together. As compared to previous methods that handle particles independently, our results demonstrate that mining the interdependencies between particles improves tracking performance and overall computational complexity. Interestingly, we show that the popular L1 tracker (Mei and Ling, IEEE Trans Pattern Anal Mach Intel 33(11):2259-2272, 2011) is a special case of our MTT formulation (denoted as the L11 tracker) when p=q=1. Under the MTT framework, some of the tasks (particle representations) are often more closely related and more likely to share common relevant covariates than other tasks. Therefore, we extend the MTT framework to take into account pairwise structural correlations between particles (e.g. spatial smoothness of representation) and denote the novel framework as S-MTT. The problem of learning the regularized sparse representation in MTT and S-MTT can be solved efficiently using an Accelerated Proximal Gradient (APG) method that yields a sequence of closed form updates. As such, S-MTT and MTT are computationally attractive. We test our proposed approach on challenging sequences involving heavy occlusion, drastic illumination changes, and large pose variations. Experimental results show that S-MTT is much better than MTT, and both methods consistently outperform state-of-the-art trackers. © 2012 Springer Science+Business Media New York.

101 labeled brain images and a consistent human cortical labeling protocol

Directory of Open Access Journals (Sweden)

Arno eKlein

2012-12-01

Full Text Available We introduce the Mindboggle-101 dataset, the largest and most complete set of free, publicly accessible, manually labeled human brain images. To manually label the macroscopic anatomy in magnetic resonance images of 101 healthy participants, we created a new cortical labeling protocol that relies on robust anatomical landmarks and minimal manual edits after initialization with automated labels. The Desikan-Killiany-Tourville (DKT protocol is intended to improve the ease, consistency, and accuracy of labeling human cortical areas. Given how difficult it is to label brains, the Mindboggle-101 dataset is intended to serve as brain atlases for use in labeling other brains, as a normative dataset to establish morphometric variation in a healthy population for comparison against clinical populations, and contribute to the development, training, testing, and evaluation of automated registration and labeling algorithms. To this end, we also introduce benchmarks for the evaluation of such algorithms by comparing our manual labels with labels automatically generated by probabilistic and multi-atlas registration-based approaches. All data and related software and updated information are available on the http://www.mindboggle.info/data/ website.

101 Labeled Brain Images and a Consistent Human Cortical Labeling Protocol

Science.gov (United States)

Klein, Arno; Tourville, Jason

2012-01-01

We introduce the Mindboggle-101 dataset, the largest and most complete set of free, publicly accessible, manually labeled human brain images. To manually label the macroscopic anatomy in magnetic resonance images of 101 healthy participants, we created a new cortical labeling protocol that relies on robust anatomical landmarks and minimal manual edits after initialization with automated labels. The “Desikan–Killiany–Tourville” (DKT) protocol is intended to improve the ease, consistency, and accuracy of labeling human cortical areas. Given how difficult it is to label brains, the Mindboggle-101 dataset is intended to serve as brain atlases for use in labeling other brains, as a normative dataset to establish morphometric variation in a healthy population for comparison against clinical populations, and contribute to the development, training, testing, and evaluation of automated registration and labeling algorithms. To this end, we also introduce benchmarks for the evaluation of such algorithms by comparing our manual labels with labels automatically generated by probabilistic and multi-atlas registration-based approaches. All data and related software and updated information are available on the http://mindboggle.info/data website. PMID:23227001

Brain Activations for Vestibular Stimulation and Dual Tasking Change with Spaceflight

Science.gov (United States)

Yuan, Peng; Koppelmans, Vincent; Reuter-Lorenz, Patricia; De Dios, Yiri; Gadd, Nichole; Wood, Scott; Riascos, Roy; Kofman, Igor; Bloomberg, Jacob; Mulavara, Ajitkumar;

Rough Sets and Stomped Normal Distribution for Simultaneous Segmentation and Bias Field Correction in Brain MR Images.

Science.gov (United States)

Banerjee, Abhirup; Maji, Pradipta

2015-12-01

The segmentation of brain MR images into different tissue classes is an important task for automatic image analysis technique, particularly due to the presence of intensity inhomogeneity artifact in MR images. In this regard, this paper presents a novel approach for simultaneous segmentation and bias field correction in brain MR images. It integrates judiciously the concept of rough sets and the merit of a novel probability distribution, called stomped normal (SN) distribution. The intensity distribution of a tissue class is represented by SN distribution, where each tissue class consists of a crisp lower approximation and a probabilistic boundary region. The intensity distribution of brain MR image is modeled as a mixture of finite number of SN distributions and one uniform distribution. The proposed method incorporates both the expectation-maximization and hidden Markov random field frameworks to provide an accurate and robust segmentation. The performance of the proposed approach, along with a comparison with related methods, is demonstrated on a set of synthetic and real brain MR images for different bias fields and noise levels.

FLAIR images of brain diseases

International Nuclear Information System (INIS)

Segawa, Fuminori; Kinoshita, Masao; Kishibayashi, Jun; Kamada, Kazuhiko; Sunohara, Nobuhiko.

1994-01-01

The present study was designed to assess the usefulness of fluid-attenuated inversion recovery (FLAIR) images in diagnosing brain diseases. The subjects were 20 patients with multiple cerebral infarction, multiple sclerosis, temporal epilepsy, or brain trauma, and 20 other healthy adults. FLAIR images, with a long repetitive time of 6000 msec and a long inversion time of 1400-1600 msec, showed low signal intensity in the cerebrospinal fluid in the lateral ventricles and the cerebral sulci, and high signal intensity in brain tissues. Signal intensity on FLAIR images correlated well with T2 relaxation times under 100 msec. For multiple sclerosis and cerebral infarction, cystic lesions, which were shown on T2-weighted images with long relaxation times over 100 msec, appeared as low-signal areas; and the lesions surrounding the cystic lesions appeared as high-signal areas. For temporal lobe epilepsy, the hippocampus was visualized as a high-signal area. Hippocampal lesions were demonstrated better with FLAIR images than with conventional T2-weighted and proton-density images. In a patient with cerebral trauma, FLAIR images revealed the lobulated structure with the residual cortex shown as a high signal area. The lesions surrounding the cystic change were imaged as high signal areas. These structural changes were demonstrated better with FLAIR images than with conventional T2-weighted sequences. FLAIR images were useful in detecting white matter lesions surrounding the lateral ventricles and cortical and subcortical lesions near the brain surface, which were unclear on conventional T2-weighted and proton-density images. (N.K.)

Automatic registration of imaging mass spectrometry data to the Allen Brain Atlas transcriptome

Science.gov (United States)

Abdelmoula, Walid M.; Carreira, Ricardo J.; Shyti, Reinald; Balluff, Benjamin; Tolner, Else; van den Maagdenberg, Arn M. J. M.; Lelieveldt, B. P. F.; McDonnell, Liam; Dijkstra, Jouke

2014-03-01

Imaging Mass Spectrometry (IMS) is an emerging molecular imaging technology that provides spatially resolved information on biomolecular structures; each image pixel effectively represents a molecular mass spectrum. By combining the histological images and IMS-images, neuroanatomical structures can be distinguished based on their biomolecular features as opposed to morphological features. The combination of IMS data with spatially resolved gene expression maps of the mouse brain, as provided by the Allen Mouse Brain atlas, would enable comparative studies of spatial metabolic and gene expression patterns in life-sciences research and biomarker discovery. As such, it would be highly desirable to spatially register IMS slices to the Allen Brain Atlas (ABA). In this paper, we propose a multi-step automatic registration pipeline to register ABA histology to IMS- images. Key novelty of the method is the selection of the best reference section from the ABA, based on pre-processed histology sections. First, we extracted a hippocampus-specific geometrical feature from the given experimental histological section to initially localize it among the ABA sections. Then, feature-based linear registration is applied to the initially localized section and its two neighbors in the ABA to select the most similar reference section. A non-rigid registration yields a one-to-one mapping of the experimental IMS slice to the ABA. The pipeline was applied on 6 coronal sections from two mouse brains, showing high anatomical correspondence, demonstrating the feasibility of complementing biomolecule distributions from individual mice with the genome-wide ABA transcriptome.

Multi-pinhole collimator design for small-object imaging with SiliSPECT: a high-resolution SPECT

International Nuclear Information System (INIS)

Shokouhi, S; Peterson, T E; Metzler, S D; Wilson, D W

2009-01-01

We have designed a multi-pinhole collimator for a dual-headed, stationary SPECT system that incorporates high-resolution silicon double-sided strip detectors. The compact camera design of our system enables imaging at source-collimator distances between 20 and 30 mm. Our analytical calculations show that using knife-edge pinholes with small-opening angles or cylindrically shaped pinholes in a focused, multi-pinhole configuration in combination with this camera geometry can generate narrow sensitivity profiles across the field of view that can be useful for imaging small objects at high sensitivity and resolution. The current prototype system uses two collimators each containing 127 cylindrically shaped pinholes that are focused toward a target volume. Our goal is imaging objects such as a mouse brain, which could find potential applications in molecular imaging.

Effects of caffeine and maltodextrin mouth rinsing on P300, brain imaging, and cognitive performance.

Science.gov (United States)

De Pauw, K; Roelands, B; Knaepen, K; Polfliet, M; Stiens, J; Meeusen, R

2015-03-15

Caffeine (CAF) and maltodextrin (MALT) mouth rinses (MR) improve exercise performance. The current experiment aims to determine the effect of CAF and MALT MR on cognitive performance and brain activity. Ten healthy male subjects (age 27 ± 3 yr) completed three experimental trials. Each trial included four Stroop tasks: two familiarization tasks, and one task before and one task after an MR period. The reaction time (in milliseconds) and accuracy (percent) of simple, congruent, and incongruent stimuli were assessed. Electroencephalography was applied throughout the experiment to record brain activity. The amplitudes and latencies of the P300 were determined during the Stroop tasks before and after the MR period. Subjects received MR with CAF (0.3 g/25 ml), MALT (1.6 g/25 ml), or placebo (PLAC) in a randomized, double-blind, crossover design. During MR, the brain imaging technique standardized low-resolution brain electromagnetic tomography was applied. Magnitude-based inferences showed that CAF MR is likely trivial (63.5%) and likely beneficial (36.4%) compared with PLAC MR, and compared with MALT MR likely beneficial to reaction time on incongruent stimuli (61.6%). Additionally, both the orbitofrontal and dorsolateral prefrontal cortex were activated only during CAF MR, potentially explaining the likely beneficial effect on reaction times. MALT MR increased brain activity only within the orbitofrontal cortex. However, this brain activation did not alter the reaction time. Furthermore, no significant differences in the accuracy of stimuli responses were observed between conditions. In conclusion, only CAF MR exerted a likely beneficial effect on reaction time due to the subsequent activation of both the orbitofrontal and dorsolateral prefrontal cortexes. Copyright © 2015 the American Physiological Society.

Functional mapping of language networks in the normal brain using a word-association task

International Nuclear Information System (INIS)

Ghosh, Shantanu; Basu, Amrita; Kumaran, Senthil S; Khushu, Subash

2010-01-01

Language functions are known to be affected in diverse neurological conditions, including ischemic stroke, traumatic brain injury, and brain tumors. Because language networks are extensive, interpretation of functional data depends on the task completed during evaluation. The aim was to map the hemodynamic consequences of word association using functional magnetic resonance imaging (fMRI) in normal human subjects. Ten healthy subjects underwent fMRI scanning with a postlexical access semantic association task vs lexical processing task. The fMRI protocol involved a T2*-weighted gradient-echo echo-planar imaging (GE-EPI) sequence (TR 4523 ms, TE 64 ms, flip angle 90°) with alternate baseline and activation blocks. A total of 78 scans were taken (interscan interval = 3 s) with a total imaging time of 587 s. Functional data were processed in Statistical Parametric Mapping software (SPM2) with 8-mm Gaussian kernel by convolving the blood oxygenation level-dependent (BOLD) signal with an hemodynamic response function estimated by general linear method to generate SPM{t} and SPM{F} maps. Single subject analysis of the functional data (FWE-corrected, P≤0.001) revealed extensive activation in the frontal lobes, with overlaps among middle frontal gyrus (MFG), superior, and inferior frontal gyri. BOLD activity was also found in the medial frontal gyrus, middle occipital gyrus (MOG), anterior fusiform gyrus, superior and inferior parietal lobules, and to a smaller extent, the thalamus and right anterior cerebellum. Group analysis (FWE-corrected, P≤0.001) revealed neural recruitment of bilateral lingual gyri, left MFG, bilateral MOG, left superior occipital gyrus, left fusiform gyrus, bilateral thalami, and right cerebellar areas. Group data analysis revealed a cerebellar–occipital–fusiform–thalamic network centered around bilateral lingual gyri for word association, thereby indicating how these areas facilitate language comprehension by activating a semantic

Functional mapping of language networks in the normal brain using a word-association task

Directory of Open Access Journals (Sweden)

Ghosh Shantanu

2010-01-01

Full Text Available Background: Language functions are known to be affected in diverse neurological conditions, including ischemic stroke, traumatic brain injury, and brain tumors. Because language networks are extensive, interpretation of functional data depends on the task completed during evaluation. Aim: The aim was to map the hemodynamic consequences of word association using functional magnetic resonance imaging (fMRI in normal human subjects. Materials and Methods: Ten healthy subjects underwent fMRI scanning with a postlexical access semantic association task vs lexical processing task. The fMRI protocol involved a T2FNx01-weighted gradient-echo echo-planar imaging (GE-EPI sequence (TR 4523 ms, TE 64 ms, flip angle 90º with alternate baseline and activation blocks. A total of 78 scans were taken (interscan interval = 3 s with a total imaging time of 587 s. Functional data were processed in Statistical Parametric Mapping software (SPM2 with 8-mm Gaussian kernel by convolving the blood oxygenation level-dependent (BOLD signal with an hemodynamic response function estimated by general linear method to generate SPM{t} and SPM{F} maps. Results: Single subject analysis of the functional data (FWE-corrected, P≤0.001 revealed extensive activation in the frontal lobes, with overlaps among middle frontal gyrus (MFG, superior, and inferior frontal gyri. BOLD activity was also found in the medial frontal gyrus, middle occipital gyrus (MOG, anterior fusiform gyrus, superior and inferior parietal lobules, and to a smaller extent, the thalamus and right anterior cerebellum. Group analysis (FWE-corrected, P≤0.001 revealed neural recruitment of bilateral lingual gyri, left MFG, bilateral MOG, left superior occipital gyrus, left fusiform gyrus, bilateral thalami, and right cerebellar areas. Conclusions: Group data analysis revealed a cerebellar-occipital-fusiform-thalamic network centered around bilateral lingual gyri for word association, thereby indicating how these

A computational study of whole-brain connectivity in resting state and task fMRI

Science.gov (United States)

Goparaju, Balaji; Rana, Kunjan D.; Calabro, Finnegan J.; Vaina, Lucia Maria

2014-01-01

Background We compared the functional brain connectivity produced during resting-state in which subjects were not actively engaged in a task with that produced while they actively performed a visual motion task (task-state). Material/Methods In this paper we employed graph-theoretical measures and network statistics in novel ways to compare, in the same group of human subjects, functional brain connectivity during resting-state fMRI with brain connectivity during performance of a high level visual task. We performed a whole-brain connectivity analysis to compare network statistics in resting and task states among anatomically defined Brodmann areas to investigate how brain networks spanning the cortex changed when subjects were engaged in task performance. Results In the resting state, we found strong connectivity among the posterior cingulate cortex (PCC), precuneus, medial prefrontal cortex (MPFC), lateral parietal cortex, and hippocampal formation, consistent with previous reports of the default mode network (DMN). The connections among these areas were strengthened while subjects actively performed an event-related visual motion task, indicating a continued and strong engagement of the DMN during task processing. Regional measures such as degree (number of connections) and betweenness centrality (number of shortest paths), showed that task performance induces stronger inter-regional connections, leading to a denser processing network, but that this does not imply a more efficient system as shown by the integration measures such as path length and global efficiency, and from global measures such as small-worldness. Conclusions In spite of the maintenance of connectivity and the “hub-like” behavior of areas, our results suggest that the network paths may be rerouted when performing the task condition. PMID:24947491

Integration of sparse multi-modality representation and geometrical constraint for isointense infant brain segmentation.

Science.gov (United States)

Wang, Li; Shi, Feng; Li, Gang; Lin, Weili; Gilmore, John H; Shen, Dinggang

2013-01-01

Segmentation of infant brain MR images is challenging due to insufficient image quality, severe partial volume effect, and ongoing maturation and myelination process. During the first year of life, the signal contrast between white matter (WM) and gray matter (GM) in MR images undergoes inverse changes. In particular, the inversion of WM/GM signal contrast appears around 6-8 months of age, where brain tissues appear isointense and hence exhibit extremely low tissue contrast, posing significant challenges for automated segmentation. In this paper, we propose a novel segmentation method to address the above-mentioned challenge based on the sparse representation of the complementary tissue distribution information from T1, T2 and diffusion-weighted images. Specifically, we first derive an initial segmentation from a library of aligned multi-modality images with ground-truth segmentations by using sparse representation in a patch-based fashion. The segmentation is further refined by the integration of the geometrical constraint information. The proposed method was evaluated on 22 6-month-old training subjects using leave-one-out cross-validation, as well as 10 additional infant testing subjects, showing superior results in comparison to other state-of-the-art methods.

Analysis of Time-Dependent Brain Network on Active and MI Tasks for Chronic Stroke Patients.

Directory of Open Access Journals (Sweden)

Da-Hye Kim

Full Text Available Several researchers have analyzed brain activities by investigating brain networks. However, there is a lack of the research on the temporal characteristics of the brain network during a stroke by EEG and the comparative studies between motor execution and imagery, which became known to have similar motor functions and pathways. In this study, we proposed the possibility of temporal characteristics on the brain networks of a stroke. We analyzed the temporal properties of the brain networks for nine chronic stroke patients by the active and motor imagery tasks by EEG. High beta band has a specific role in the brain network during motor tasks. In the high beta band, for the active task, there were significant characteristics of centrality and small-worldness on bilateral primary motor cortices at the initial motor execution. The degree centrality significantly increased on the contralateral primary motor cortex, and local efficiency increased on the ipsilateral primary motor cortex. These results indicate that the ipsilateral primary motor cortex constructed a powerful subnetwork by influencing the linked channels as compensatory effect, although the contralateral primary motor cortex organized an inefficient network by using the connected channels due to lesions. For the MI task, degree centrality and local efficiency significantly decreased on the somatosensory area at the initial motor imagery. Then, there were significant correlations between the properties of brain networks and motor function on the contralateral primary motor cortex and somatosensory area for each motor execution/imagery task. Our results represented that the active and MI tasks have different mechanisms of motor acts. Based on these results, we indicated the possibility of customized rehabilitation according to different motor tasks. We expect these results to help in the construction of the customized rehabilitation system depending on motor tasks by understanding temporal

Real-time multi-task operators support system

International Nuclear Information System (INIS)

Wang He; Peng Minjun; Wang Hao; Cheng Shouyu

2005-01-01

The development in computer software and hardware technology and information processing as well as the accumulation in the design and feedback from Nuclear Power Plant (NPP) operation created a good opportunity to develop an integrated Operator Support System. The Real-time Multi-task Operator Support System (RMOSS) has been built to support the operator's decision making process during normal and abnormal operations. RMOSS consists of five system subtasks such as Data Collection and Validation Task (DCVT), Operation Monitoring Task (OMT), Fault Diagnostic Task (FDT), Operation Guideline Task (OGT) and Human Machine Interface Task (HMIT). RMOSS uses rule-based expert system and Artificial Neural Network (ANN). The rule-based expert system is used to identify the predefined events in static conditions and track the operation guideline through data processing. In dynamic status, Back-Propagation Neural Network is adopted for fault diagnosis, which is trained with the Genetic Algorithm. Embedded real-time operation system VxWorks and its integrated environment Tornado II are used as the RMOSS software cross-development. VxGUI is used to design HMI. All of the task programs are designed in C language. The task tests and function evaluation of RMOSS have been done in one real-time full scope simulator. Evaluation results show that each task of RMOSS is capable of accomplishing its functions. (authors)

The imaging diagnosis of diffuse brain swelling due to severe brain trauma

International Nuclear Information System (INIS)

Shen Jianqiang; Hu Jiawang

2008-01-01

Objective: To discuss the clinical and pathological characteristics and the imaging types of the diffuse brain swelling due to severe brain trauma. Methods: The clinical data and CT and MR images on 48 cases with diffuse brain swelling due to severe brain trauma were analyzed. Results: Among these 48 cases of the diffuse brain swelling due to severe brain trauma, 33 cases were complicated with brain contusions (including 12 cases brain diffuse axonal injury, 1 case infarct of the right basal ganglion), 31 cases were complicated with hematoma (epidural, subdural or intracerebral), 27 cases were complicated with skull base fracture, and 10 cases were complicated with subarachnoid hematoma. The CT and MR imaging of the diffuse brain swelling included as followed: (1) Symmetrically diffuse brain swelling in both cerebral hemispheres with cerebral ventricles decreased or disappeared, without median line shift. (2)Diffuse brain swelling in one side cerebral hemisphere with cerebral ventricles decreased or disappeared at same side, and median line shift to other side. (3) Subarachnoid hematoma or little subcortex intracerebral hematoma were complicated. (4) The CT value of the cerebral could be equal, lower or higher comparing with normal. Conclusion: The pathological reason of diffuse brain swelling was the brain vessel expanding resulting from hypothalamus and brainstem injured in severe brain trauma. There were four CT and MR imaging findings in diffuse brain swelling. The diffuse brain swelling without hematoma may be caused by ischemical reperfusion injury. (authors)

Formal Derivation of Lotka-Volterra-Haken Amplitude Equations of Task-Related Brain Activity in Multiple, Consecutively Performed Tasks

Science.gov (United States)

Frank, T. D.

The Lotka-Volterra-Haken equations have been frequently used in ecology and pattern formation. Recently, the equations have been proposed by several research groups as amplitude equations for task-related patterns of brain activity. In this theoretical study, the focus is on the circular causality aspect of pattern formation systems as formulated within the framework of synergetics. Accordingly, the stable modes of a pattern formation system inhibit the unstable modes, whereas the unstable modes excite the stable modes. Using this circular causality principle it is shown that under certain conditions the Lotka-Volterra-Haken amplitude equations can be derived from a general model of brain activity akin to the Wilson-Cowan model. The model captures the amplitude dynamics for brain activity patterns in experiments involving several consecutively performed multiple-choice tasks. This is explicitly demonstrated for two-choice tasks involving grasping and walking. A comment on the relevance of the theoretical framework for clinical psychology and schizophrenia is given as well.

Introduction of a novel ultrahigh sensitivity collimator for brain SPECT imaging

Energy Technology Data Exchange (ETDEWEB)

Park, Mi-Ae, E-mail: miaepark@bwh.harvard.edu; Kijewski, Marie Foley; Lyon, Morgan C.; Horky, Laura; Moore, Stephen C. [Department of Radiology, Brigham and Women’s Hospital and Harvard Medical School, Boston, Massachusetts 02115 (United States); Keijzers, Ronnie; Keijzers, Mark [Nuclear Fields USA, Des Plaines, Illinois 60018 (United States)

2016-08-15

Purpose: Noise levels of brain SPECT images are highest in central regions, due to preferential attenuation of photons emitted from deep structures. To address this problem, the authors have designed a novel collimator for brain SPECT imaging that yields greatly increased sensitivity near the center of the brain without loss of resolution. This hybrid collimator consisted of ultrashort cone-beam holes in the central regions and slant-holes in the periphery (USCB). We evaluated this collimator for quantitative brain imaging tasks. Methods: Owing to the uniqueness of the USCB collimation, the hole pattern required substantial variations in collimator parameters. To utilize the lead-casting technique, the authors designed two supporting plates to position about 37 000 hexagonal, slightly tapered pins. The holes in the supporting plates were modeled to yield the desired focal length, hole length, and septal thickness. To determine the properties of the manufactured collimator and to compute the system matrix, the authors prepared an array of point sources that covered the entire detector area. Each point source contained 32 μCi of Tc-99m at the first scan time. The array was imaged for 5 min at each of the 64 shifted locations to yield a 2-mm sampling distance, and hole parameters were calculated. The sensitivity was also measured using a point source placed along the central ray at several distances from the collimator face. High-count projection data from a five-compartment brain phantom were acquired with the three collimators on a dual-head SPECT/CT system. The authors calculated Cramer-Rao bounds on the precision of estimates of striatal and background activity concentration. In order to assess the new collimation system to detect changes in striatal activity, the authors evaluated the precision of measuring a 5% decrease in right putamen activity. The authors also reconstructed images of projection data obtained by summing data from the individual phantom

Brain MR image segmentation based on an improved active contour model.

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Xiangrui Meng

Full Text Available It is often a difficult task to accurately segment brain magnetic resonance (MR images with intensity in-homogeneity and noise. This paper introduces a novel level set method for simultaneous brain MR image segmentation and intensity inhomogeneity correction. To reduce the effect of noise, novel anisotropic spatial information, which can preserve more details of edges and corners, is proposed by incorporating the inner relationships among the neighbor pixels. Then the proposed energy function uses the multivariate Student's t-distribution to fit the distribution of the intensities of each tissue. Furthermore, the proposed model utilizes Hidden Markov random fields to model the spatial correlation between neigh-boring pixels/voxels. The means of the multivariate Student's t-distribution can be adaptively estimated by multiplying a bias field to reduce the effect of intensity inhomogeneity. In the end, we reconstructed the energy function to be convex and calculated it by using the Split Bregman method, which allows our framework for random initialization, thereby allowing fully automated applications. Our method can obtain the final result in less than 1 second for 2D image with size 256 × 256 and less than 300 seconds for 3D image with size 256 × 256 × 171. The proposed method was compared to other state-of-the-art segmentation methods using both synthetic and clinical brain MR images and increased the accuracies of the results more than 3%.

OBJECT-SPACE MULTI-IMAGE MATCHING OF MOBILE-MAPPING-SYSTEM IMAGE SEQUENCES

Directory of Open Access Journals (Sweden)

Y. C. Chen

2012-07-01

Full Text Available This paper proposes an object-space multi-image matching procedure of terrestrial MMS (Mobile Mapping System image sequences to determine the coordinates of an object point automatically and reliably. This image matching procedure can be applied to find conjugate points of MMS image sequences efficiently. Conventional area-based image matching methods are not reliable to deliver accurate matching results for this application due to image scale variations, viewing angle variations, and object occlusions. In order to deal with these three matching problems, an object space multi-image matching is proposed. A modified NCC (Normalized Cross Correlation coefficient is proposed to measure the similarity of image patches. A modified multi-window matching procedure will also be introduced to solve the problem of object occlusion. A coarse-to-fine procedure with a combination of object-space multi-image matching and multi-window matching is adopted. The proposed procedure has been implemented for the purpose of matching terrestrial MMS image sequences. The ratio of correct matches of this experiment was about 80 %. By providing an approximate conjugate point in an overlapping image manually, most of the incorrect matches could be fixed properly and the ratio of correct matches was improved up to 98 %.

Multi-modal MRI of mild traumatic brain injury

Directory of Open Access Journals (Sweden)

Ponnada A. Narayana

2015-01-01

Full Text Available Multi-modal magnetic resonance imaging (MRI that included high resolution structural imaging, diffusion tensor imaging (DTI, magnetization transfer ratio (MTR imaging, and magnetic resonance spectroscopic imaging (MRSI were performed in mild traumatic brain injury (mTBI patients with negative computed tomographic scans and in an orthopedic-injured (OI group without concomitant injury to the brain. The OI group served as a comparison group for mTBI. MRI scans were performed both in the acute phase of injury (~24 h and at follow-up (~90 days. DTI data was analyzed using tract based spatial statistics (TBSS. Global and regional atrophies were calculated using tensor-based morphometry (TBM. MTR values were calculated using the standard method. MRSI was analyzed using LC Model. At the initial scan, the mean diffusivity (MD was significantly higher in the mTBI cohort relative to the comparison group in several white matter (WM regions that included internal capsule, external capsule, superior corona radiata, anterior corona radiata, posterior corona radiata, inferior fronto-occipital fasciculus, inferior longitudinal fasciculus, forceps major and forceps minor of the corpus callosum, superior longitudinal fasciculus, and corticospinal tract in the right hemisphere. TBSS analysis failed to detect significant differences in any DTI measures between the initial and follow-up scans either in the mTBI or OI group. No significant differences were found in MRSI, MTR or morphometry between the mTBI and OI cohorts either at the initial or follow-up scans with or without family wise error (FWE correction. Our study suggests that a number of WM tracts are affected in mTBI in the acute phase of injury and that these changes disappear by 90 days. This study also suggests that none of the MRI-modalities used in this study, with the exception of DTI, is sensitive in detecting changes in the acute phase of mTBI.

Functional Magnetic Resonance Imaging Correlates of First-Episode Psychoses during Attentional and Memory Task Performance.

Science.gov (United States)

Del Casale, Antonio; Kotzalidis, Georgios D; Rapinesi, Chiara; Sorice, Serena; Girardi, Nicoletta; Ferracuti, Stefano; Girardi, Paolo

2016-01-01

The nature of the alteration of the response to cognitive tasks in first-episode psychosis (FEP) still awaits clarification. We used activation likelihood estimation, an increasingly used method in evaluating normal and pathological brain function, to identify activation changes in functional magnetic resonance imaging (fMRI) studies of FEP during attentional and memory tasks. We included 11 peer-reviewed fMRI studies assessing FEP patients versus healthy controls (HCs) during performance of attentional and memory tasks. Our database comprised 290 patients with FEP, matched with 316 HCs. Between-group analyses showed that HCs, compared to FEP patients, exhibited hyperactivation of the right middle frontal gyrus (Brodmann area, BA, 9), right inferior parietal lobule (BA 40), and right insula (BA 13) during attentional task performances and hyperactivation of the left insula (BA 13) during memory task performances. Right frontal, parietal, and insular dysfunction during attentional task performance and left insular dysfunction during memory task performance are significant neural functional FEP correlates. © 2016 S. Karger AG, Basel.

Brain-computer interface analysis of a dynamic visuo-motor task.

Science.gov (United States)

Logar, Vito; Belič, Aleš

2011-01-01

The area of brain-computer interfaces (BCIs) represents one of the more interesting fields in neurophysiological research, since it investigates the development of the machines that perform different transformations of the brain's "thoughts" to certain pre-defined actions. Experimental studies have reported some successful implementations of BCIs; however, much of the field still remains unexplored. According to some recent reports the phase coding of informational content is an important mechanism in the brain's function and cognition, and has the potential to explain various mechanisms of the brain's data transfer, but it has yet to be scrutinized in the context of brain-computer interface. Therefore, if the mechanism of phase coding is plausible, one should be able to extract the phase-coded content, carried by brain signals, using appropriate signal-processing methods. In our previous studies we have shown that by using a phase-demodulation-based signal-processing approach it is possible to decode some relevant information on the current motor action in the brain from electroencephalographic (EEG) data. In this paper the authors would like to present a continuation of their previous work on the brain-information-decoding analysis of visuo-motor (VM) tasks. The present study shows that EEG data measured during more complex, dynamic visuo-motor (dVM) tasks carries enough information about the currently performed motor action to be successfully extracted by using the appropriate signal-processing and identification methods. The aim of this paper is therefore to present a mathematical model, which by means of the EEG measurements as its inputs predicts the course of the wrist movements as applied by each subject during the task in simulated or real time (BCI analysis). However, several modifications to the existing methodology are needed to achieve optimal decoding results and a real-time, data-processing ability. The information extracted from the EEG could

Advantages in functional imaging of the brain.

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Mier, Walter; Mier, Daniela

2015-01-01

As neuronal pathologies cause only minor morphological alterations, molecular imaging techniques are a prerequisite for the study of diseases of the brain. The development of molecular probes that specifically bind biochemical markers and the advances of instrumentation have revolutionized the possibilities to gain insight into the human brain organization and beyond this-visualize structure-function and brain-behavior relationships. The review describes the development and current applications of functional brain imaging techniques with a focus on applications in psychiatry. A historical overview of the development of functional imaging is followed by the portrayal of the principles and applications of positron emission tomography (PET) and functional magnetic resonance imaging (fMRI), two key molecular imaging techniques that have revolutionized the ability to image molecular processes in the brain. We conclude that the juxtaposition of PET and fMRI in hybrid PET/MRI scanners enhances the significance of both modalities for research in neurology and psychiatry and might pave the way for a new area of personalized medicine.

Comparison of Tc-99m HM-PAO and I-123 IMP cerebral SPECT images in Alzheimer's disease and multi-infarct dementia

Energy Technology Data Exchange (ETDEWEB)

Gemmell, H G; Sharp, P F; Besson, J A.O.; Ebmeier, K P; Smith, F W

1988-10-01

SPECT images of the brain can be obtained using either /sup 123/I labelled amines or /sup 99m/Tc-hexamethylpropyleneamineoxime (HM-PAO). Both materials produce images which are blood flow dominated and so appear similar in normal subjects, although the respective mechanisms of uptake are not yet finally established. It seems likely, however, that the different mechanisms of uptake are responsible for recent reports of some differences seen in images obtained with the two types of agent in patients with cerebral pathology, mainly cerebrovascular disease. In this study, 12 demented patients, 6 with dementia of the Alzheimer type (DAT) and 6 with multi infarct dementia (MID), were imaged with /sup 123/I-isopropylamphetamine (IMP) and /sup 99m/Tc-HM-PAO and the images compared. Significantly more lesions were seen with IMP than HM-PAO (P < 0.02); out of a possible 120 sites, 41 lesions were seen with IMP compared to 28 with HM-PAO, 23 being seen with both agents. However, it is concluded that either agent can be used for the differential diagnosis of dementia, a task for which the new cerebral blood flow agents seem well suited.

Robust visual tracking via multi-task sparse learning

KAUST Repository

Zhang, Tianzhu

2012-06-01

In this paper, we formulate object tracking in a particle filter framework as a multi-task sparse learning problem, which we denote as Multi-Task Tracking (MTT). Since we model particles as linear combinations of dictionary templates that are updated dynamically, learning the representation of each particle is considered a single task in MTT. By employing popular sparsity-inducing p, q mixed norms (p D; 1), we regularize the representation problem to enforce joint sparsity and learn the particle representations together. As compared to previous methods that handle particles independently, our results demonstrate that mining the interdependencies between particles improves tracking performance and overall computational complexity. Interestingly, we show that the popular L 1 tracker [15] is a special case of our MTT formulation (denoted as the L 11 tracker) when p q 1. The learning problem can be efficiently solved using an Accelerated Proximal Gradient (APG) method that yields a sequence of closed form updates. As such, MTT is computationally attractive. We test our proposed approach on challenging sequences involving heavy occlusion, drastic illumination changes, and large pose variations. Experimental results show that MTT methods consistently outperform state-of-the-art trackers. © 2012 IEEE.

Sea-land segmentation for infrared remote sensing images based on superpixels and multi-scale features

Science.gov (United States)

Lei, Sen; Zou, Zhengxia; Liu, Dunge; Xia, Zhenghuan; Shi, Zhenwei

2018-06-01

Sea-land segmentation is a key step for the information processing of ocean remote sensing images. Traditional sea-land segmentation algorithms ignore the local similarity prior of sea and land, and thus fail in complex scenarios. In this paper, we propose a new sea-land segmentation method for infrared remote sensing images to tackle the problem based on superpixels and multi-scale features. Considering the connectivity and local similarity of sea or land, we interpret the sea-land segmentation task in view of superpixels rather than pixels, where similar pixels are clustered and the local similarity are explored. Moreover, the multi-scale features are elaborately designed, comprising of gray histogram and multi-scale total variation. Experimental results on infrared bands of Landsat-8 satellite images demonstrate that the proposed method can obtain more accurate and more robust sea-land segmentation results than the traditional algorithms.

PyDBS: an automated image processing workflow for deep brain stimulation surgery.

Science.gov (United States)

D'Albis, Tiziano; Haegelen, Claire; Essert, Caroline; Fernández-Vidal, Sara; Lalys, Florent; Jannin, Pierre

2015-02-01

Deep brain stimulation (DBS) is a surgical procedure for treating motor-related neurological disorders. DBS clinical efficacy hinges on precise surgical planning and accurate electrode placement, which in turn call upon several image processing and visualization tasks, such as image registration, image segmentation, image fusion, and 3D visualization. These tasks are often performed by a heterogeneous set of software tools, which adopt differing formats and geometrical conventions and require patient-specific parameterization or interactive tuning. To overcome these issues, we introduce in this article PyDBS, a fully integrated and automated image processing workflow for DBS surgery. PyDBS consists of three image processing pipelines and three visualization modules assisting clinicians through the entire DBS surgical workflow, from the preoperative planning of electrode trajectories to the postoperative assessment of electrode placement. The system's robustness, speed, and accuracy were assessed by means of a retrospective validation, based on 92 clinical cases. The complete PyDBS workflow achieved satisfactory results in 92 % of tested cases, with a median processing time of 28 min per patient. The results obtained are compatible with the adoption of PyDBS in clinical practice.

Brain noise is task dependent and region specific.

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Misić, Bratislav; Mills, Travis; Taylor, Margot J; McIntosh, Anthony R

2010-11-01

The emerging organization of anatomical and functional connections during human brain development is thought to facilitate global integration of information. Recent empirical and computational studies have shown that this enhanced capacity for information processing enables a diversified dynamic repertoire that manifests in neural activity as irregularity and noise. However, transient functional networks unfold over multiple time, scales and the embedding of a particular region depends not only on development, but also on the manner in which sensory and cognitive systems are engaged. Here we show that noise is a facet of neural activity that is also sensitive to the task context and is highly region specific. Children (6-16 yr) and adults (20-41 yr) performed a one-back face recognition task with inverted and upright faces. Neuromagnetic activity was estimated at several hundred sources in the brain by applying a beamforming technique to the magnetoencephalogram (MEG). During development, neural activity became more variable across the whole brain, with most robust increases in medial parietal regions, such as the precuneus and posterior cingulate cortex. For young children and adults, activity evoked by upright faces was more variable and noisy compared with inverted faces, and this effect was reliable only in the right fusiform gyrus. These results are consistent with the notion that upright faces engender a variety of integrative neural computations, such as the relations among facial features and their holistic constitution. This study shows that transient changes in functional integration modulated by task demand are evident in the variability of regional neural activity.

Usage of fMRI for pre-surgical planning in brain tumor and vascular lesion patients: Task and statistical threshold effects on language lateralization☆☆☆

Science.gov (United States)

Nadkarni, Tanvi N.; Andreoli, Matthew J.; Nair, Veena A.; Yin, Peng; Young, Brittany M.; Kundu, Bornali; Pankratz, Joshua; Radtke, Andrew; Holdsworth, Ryan; Kuo, John S.; Field, Aaron S.; Baskaya, Mustafa K.; Moritz, Chad H.; Meyerand, M. Elizabeth; Prabhakaran, Vivek

2014-01-01

Background and purpose Functional magnetic resonance imaging (fMRI) is a non-invasive pre-surgical tool used to assess localization and lateralization of language function in brain tumor and vascular lesion patients in order to guide neurosurgeons as they devise a surgical approach to treat these lesions. We investigated the effect of varying the statistical thresholds as well as the type of language tasks on functional activation patterns and language lateralization. We hypothesized that language lateralization indices (LIs) would be threshold- and task-dependent. Materials and methods Imaging data were collected from brain tumor patients (n = 67, average age 48 years) and vascular lesion patients (n = 25, average age 43 years) who received pre-operative fMRI scanning. Both patient groups performed expressive (antonym and/or letter-word generation) and receptive (tumor patients performed text-reading; vascular lesion patients performed text-listening) language tasks. A control group (n = 25, average age 45 years) performed the letter-word generation task. Results Brain tumor patients showed left-lateralization during the antonym-word generation and text-reading tasks at high threshold values and bilateral activation during the letter-word generation task, irrespective of the threshold values. Vascular lesion patients showed left-lateralization during the antonym and letter-word generation, and text-listening tasks at high threshold values. Conclusion Our results suggest that the type of task and the applied statistical threshold influence LI and that the threshold effects on LI may be task-specific. Thus identifying critical functional regions and computing LIs should be conducted on an individual subject basis, using a continuum of threshold values with different tasks to provide the most accurate information for surgical planning to minimize post-operative language deficits. PMID:25685705

Functional near infrared spectroscopy of the sensory and motor brain regions with simultaneous kinematic and EMG monitoring during motor tasks.

Science.gov (United States)

Sukal-Moulton, Theresa; de Campos, Ana Carolina; Stanley, Christopher J; Damiano, Diane L

2014-12-05

There are several advantages that functional near-infrared spectroscopy (fNIRS) presents in the study of the neural control of human movement. It is relatively flexible with respect to participant positioning and allows for some head movements during tasks. Additionally, it is inexpensive, light weight, and portable, with very few contraindications to its use. This presents a unique opportunity to study functional brain activity during motor tasks in individuals who are typically developing, as well as those with movement disorders, such as cerebral palsy. An additional consideration when studying movement disorders, however, is the quality of actual movements performed and the potential for additional, unintended movements. Therefore, concurrent monitoring of both blood flow changes in the brain and actual movements of the body during testing is required for appropriate interpretation of fNIRS results. Here, we show a protocol for the combination of fNIRS with muscle and kinematic monitoring during motor tasks. We explore gait, a unilateral multi-joint movement (cycling), and two unilateral single-joint movements (isolated ankle dorsiflexion, and isolated hand squeezing). The techniques presented can be useful in studying both typical and atypical motor control, and can be modified to investigate a broad range of tasks and scientific questions.

Multi-robot task allocation based on two dimensional artificial fish swarm algorithm

Science.gov (United States)

Zheng, Taixiong; Li, Xueqin; Yang, Liangyi

2007-12-01

The problem of task allocation for multiple robots is to allocate more relative-tasks to less relative-robots so as to minimize the processing time of these tasks. In order to get optimal multi-robot task allocation scheme, a twodimensional artificial swarm algorithm based approach is proposed in this paper. In this approach, the normal artificial fish is extended to be two dimension artificial fish. In the two dimension artificial fish, each vector of primary artificial fish is extended to be an m-dimensional vector. Thus, each vector can express a group of tasks. By redefining the distance between artificial fish and the center of artificial fish, the behavior of two dimension fish is designed and the task allocation algorithm based on two dimension artificial swarm algorithm is put forward. At last, the proposed algorithm is applied to the problem of multi-robot task allocation and comparer with GA and SA based algorithm is done. Simulation and compare result shows the proposed algorithm is effective.

Imaging Human Brain Perfusion with Inhaled Hyperpolarized 129Xe MR Imaging.

Science.gov (United States)

Rao, Madhwesha R; Stewart, Neil J; Griffiths, Paul D; Norquay, Graham; Wild, Jim M

2018-02-01

Purpose To evaluate the feasibility of directly imaging perfusion of human brain tissue by using magnetic resonance (MR) imaging with inhaled hyperpolarized xenon 129 ( 129 Xe). Materials and Methods In vivo imaging with 129 Xe was performed in three healthy participants. The combination of a high-yield spin-exchange optical pumping 129 Xe polarizer, custom-built radiofrequency coils, and an optimized gradient-echo MR imaging protocol was used to achieve signal sensitivity sufficient to directly image hyperpolarized 129 Xe dissolved in the human brain. Conventional T1-weighted proton (hydrogen 1 [ 1 H]) images and perfusion images by using arterial spin labeling were obtained for comparison. Results Images of 129 Xe uptake were obtained with a signal-to-noise ratio of 31 ± 9 and demonstrated structural similarities to the gray matter distribution on conventional T1-weighted 1 H images and to perfusion images from arterial spin labeling. Conclusion Hyperpolarized 129 Xe MR imaging is an injection-free means of imaging the perfusion of cerebral tissue. The proposed method images the uptake of inhaled xenon gas to the extravascular brain tissue compartment across the intact blood-brain barrier. This level of sensitivity is not readily available with contemporary MR imaging methods. © RSNA, 2017.

An Initial Investigation of Factors Affecting Multi-Task Performance

National Research Council Canada - National Science Library

Branscome, Tersa A; Swoboda, Jennifer C; Fatkin, Linda T

2007-01-01

This report presents the results of the first in a series of investigations designed to increase fundamental knowledge and understanding of the factors affecting multi-task performance in a military environment...

Emerging imaging tools for use with traumatic brain injury research.

Science.gov (United States)

Hunter, Jill V; Wilde, Elisabeth A; Tong, Karen A; Holshouser, Barbara A

2012-03-01

This article identifies emerging neuroimaging measures considered by the inter-agency Pediatric Traumatic Brain Injury (TBI) Neuroimaging Workgroup. This article attempts to address some of the potential uses of more advanced forms of imaging in TBI as well as highlight some of the current considerations and unresolved challenges of using them. We summarize emerging elements likely to gain more widespread use in the coming years, because of 1) their utility in diagnosis, prognosis, and understanding the natural course of degeneration or recovery following TBI, and potential for evaluating treatment strategies; 2) the ability of many centers to acquire these data with scanners and equipment that are readily available in existing clinical and research settings; and 3) advances in software that provide more automated, readily available, and cost-effective analysis methods for large scale data image analysis. These include multi-slice CT, volumetric MRI analysis, susceptibility-weighted imaging (SWI), diffusion tensor imaging (DTI), magnetization transfer imaging (MTI), arterial spin tag labeling (ASL), functional MRI (fMRI), including resting state and connectivity MRI, MR spectroscopy (MRS), and hyperpolarization scanning. However, we also include brief introductions to other specialized forms of advanced imaging that currently do require specialized equipment, for example, single photon emission computed tomography (SPECT), positron emission tomography (PET), encephalography (EEG), and magnetoencephalography (MEG)/magnetic source imaging (MSI). Finally, we identify some of the challenges that users of the emerging imaging CDEs may wish to consider, including quality control, performing multi-site and longitudinal imaging studies, and MR scanning in infants and children.

Visual tracking for multi-modality computer-assisted image guidance

Science.gov (United States)

Basafa, Ehsan; Foroughi, Pezhman; Hossbach, Martin; Bhanushali, Jasmine; Stolka, Philipp

2017-03-01

With optical cameras, many interventional navigation tasks previously relying on EM, optical, or mechanical guidance can be performed robustly, quickly, and conveniently. We developed a family of novel guidance systems based on wide-spectrum cameras and vision algorithms for real-time tracking of interventional instruments and multi-modality markers. These navigation systems support the localization of anatomical targets, support placement of imaging probe and instruments, and provide fusion imaging. The unique architecture - low-cost, miniature, in-hand stereo vision cameras fitted directly to imaging probes - allows for an intuitive workflow that fits a wide variety of specialties such as anesthesiology, interventional radiology, interventional oncology, emergency medicine, urology, and others, many of which see increasing pressure to utilize medical imaging and especially ultrasound, but have yet to develop the requisite skills for reliable success. We developed a modular system, consisting of hardware (the Optical Head containing the mini cameras) and software (components for visual instrument tracking with or without specialized visual features, fully automated marker segmentation from a variety of 3D imaging modalities, visual observation of meshes of widely separated markers, instant automatic registration, and target tracking and guidance on real-time multi-modality fusion views). From these components, we implemented a family of distinct clinical and pre-clinical systems (for combinations of ultrasound, CT, CBCT, and MRI), most of which have international regulatory clearance for clinical use. We present technical and clinical results on phantoms, ex- and in-vivo animals, and patients.

Simultaneous MRI and PET imaging of a rat brain

Energy Technology Data Exchange (ETDEWEB)

Raylman, Raymond R [Center for Advanced Imaging, Department of Radiology, Box 9236, West Virginia University, Morgantown, WV (United States); Majewski, Stan [Thomas Jefferson National Accelerator Facility, 12000 Jefferson Ave., Newport News, VA (United States); Lemieux, Susan K [Center for Advanced Imaging, Department of Radiology, Box 9236, West Virginia University, Morgantown, WV (United States); Velan, S Sendhil [Center for Advanced Imaging, Department of Radiology, Box 9236, West Virginia University, Morgantown, WV (United States); Kross, Brian [Thomas Jefferson National Accelerator Facility, 12000 Jefferson Ave., Newport News, VA (United States); Popov, Vladimir [Thomas Jefferson National Accelerator Facility, 12000 Jefferson Ave., Newport News, VA (United States); Smith, Mark F [Thomas Jefferson National Accelerator Facility, 12000 Jefferson Ave., Newport News, VA (United States); Weisenberger, Andrew G [Thomas Jefferson National Accelerator Facility, 12000 Jefferson Ave., Newport News, VA (United States); Zorn, Carl [Thomas Jefferson National Accelerator Facility, 12000 Jefferson Ave., Newport News, VA (United States); Marano, Gary D [Center for Advanced Imaging, Department of Radiology, Box 9236, West Virginia University, Morgantown, WV (United States)

2006-12-21

Multi-modality imaging is rapidly becoming a valuable tool in the diagnosis of disease and in the development of new drugs. Functional images produced with PET fused with anatomical structure images created by MRI will allow the correlation of form with function. Our group is developing a system to acquire MRI and PET images contemporaneously. The prototype device consists of two opposed detector heads, operating in coincidence mode. Each MRI-PET detector module consists of an array of LSO detector elements coupled through a long fibre optic light guide to a single Hamamatsu flat panel position-sensitive photomultiplier tube (PSPMT). The use of light guides allows the PSPMTs to be positioned outside the bore of a 3T MRI scanner where the magnetic field is relatively small. To test the device, simultaneous MRI and PET images of the brain of a male Sprague Dawley rat injected with FDG were successfully obtained. The images revealed no noticeable artefacts in either image set. Future work includes the construction of a full ring PET scanner, improved light guides and construction of a specialized MRI coil to permit higher quality MRI imaging.

Simultaneous MRI and PET imaging of a rat brain

International Nuclear Information System (INIS)

Raylman, Raymond R; Majewski, Stan; Lemieux, Susan K; Velan, S Sendhil; Kross, Brian; Popov, Vladimir; Smith, Mark F; Weisenberger, Andrew G; Zorn, Carl; Marano, Gary D

2006-01-01

Multi-modality imaging is rapidly becoming a valuable tool in the diagnosis of disease and in the development of new drugs. Functional images produced with PET fused with anatomical structure images created by MRI will allow the correlation of form with function. Our group is developing a system to acquire MRI and PET images contemporaneously. The prototype device consists of two opposed detector heads, operating in coincidence mode. Each MRI-PET detector module consists of an array of LSO detector elements coupled through a long fibre optic light guide to a single Hamamatsu flat panel position-sensitive photomultiplier tube (PSPMT). The use of light guides allows the PSPMTs to be positioned outside the bore of a 3T MRI scanner where the magnetic field is relatively small. To test the device, simultaneous MRI and PET images of the brain of a male Sprague Dawley rat injected with FDG were successfully obtained. The images revealed no noticeable artefacts in either image set. Future work includes the construction of a full ring PET scanner, improved light guides and construction of a specialized MRI coil to permit higher quality MRI imaging

Multi-angle compound imaging

DEFF Research Database (Denmark)

Jespersen, Søren Kragh; Wilhjelm, Jens Erik; Sillesen, Henrik

1998-01-01

This paper reports on a scanning technique, denoted multi-angle compound imaging (MACI), using spatial compounding. The MACI method also contains elements of frequency compounding, as the transmit frequency is lowered for the highest beam angles in order to reduce grating lobes. Compared to conve......This paper reports on a scanning technique, denoted multi-angle compound imaging (MACI), using spatial compounding. The MACI method also contains elements of frequency compounding, as the transmit frequency is lowered for the highest beam angles in order to reduce grating lobes. Compared...... to conventional B-mode imaging MACI offers better defined tissue boundaries and lower variance of the speckle pattern, resulting in an image with reduced random variations. Design and implementation of a compound imaging system is described, images of rubber tubes and porcine aorta are shown and effects...... on visualization are discussed. The speckle reduction is analyzed numerically and the results are found to be in excellent agreement with existing theory. An investigation of detectability of low-contrast lesions shows significant improvements compared to conventional imaging. Finally, possibilities for improving...

Brain Image Motion Correction

DEFF Research Database (Denmark)

Jensen, Rasmus Ramsbøl; Benjaminsen, Claus; Larsen, Rasmus

2015-01-01

The application of motion tracking is wide, including: industrial production lines, motion interaction in gaming, computer-aided surgery and motion correction in medical brain imaging. Several devices for motion tracking exist using a variety of different methodologies. In order to use such devices...... offset and tracking noise in medical brain imaging. The data are generated from a phantom mounted on a rotary stage and have been collected using a Siemens High Resolution Research Tomograph for positron emission tomography. During acquisition the phantom was tracked with our latest tracking prototype...

Brain medical image diagnosis based on corners with importance-values.

Science.gov (United States)

Gao, Linlin; Pan, Haiwei; Li, Qing; Xie, Xiaoqin; Zhang, Zhiqiang; Han, Jinming; Zhai, Xiao

2017-11-21

Brain disorders are one of the top causes of human death. Generally, neurologists analyze brain medical images for diagnosis. In the image analysis field, corners are one of the most important features, which makes corner detection and matching studies essential. However, existing corner detection studies do not consider the domain information of brain. This leads to many useless corners and the loss of significant information. Regarding corner matching, the uncertainty and structure of brain are not employed in existing methods. Moreover, most corner matching studies are used for 3D image registration. They are inapplicable for 2D brain image diagnosis because of the different mechanisms. To address these problems, we propose a novel corner-based brain medical image classification method. Specifically, we automatically extract multilayer texture images (MTIs) which embody diagnostic information from neurologists. Moreover, we present a corner matching method utilizing the uncertainty and structure of brain medical images and a bipartite graph model. Finally, we propose a similarity calculation method for diagnosis. Brain CT and MRI image sets are utilized to evaluate the proposed method. First, classifiers are trained in N-fold cross-validation analysis to produce the best θ and K. Then independent brain image sets are tested to evaluate the classifiers. Moreover, the classifiers are also compared with advanced brain image classification studies. For the brain CT image set, the proposed classifier outperforms the comparison methods by at least 8% on accuracy and 2.4% on F1-score. Regarding the brain MRI image set, the proposed classifier is superior to the comparison methods by more than 7.3% on accuracy and 4.9% on F1-score. Results also demonstrate that the proposed method is robust to different intensity ranges of brain medical image. In this study, we develop a robust corner-based brain medical image classifier. Specifically, we propose a corner detection

Large-Scale Multi-Resolution Representations for Accurate Interactive Image and Volume Operations

KAUST Repository

Sicat, Ronell B.

2015-11-25

and voxel footprints in input images and volumes. We show that the continuous pdfs encoded in the sparse pdf map representation enable accurate multi-resolution non-linear image operations on gigapixel images. Similarly, we show that sparse pdf volumes enable more consistent multi-resolution volume rendering compared to standard approaches, on both artificial and real world large-scale volumes. The supplementary videos demonstrate our results. In the standard approach, users heavily rely on panning and zooming interactions to navigate the data within the limits of their display devices. However, panning across the whole spatial domain and zooming across all resolution levels of large-scale images to search for interesting regions is not practical. Assisted exploration techniques allow users to quickly narrow down millions to billions of possible regions to a more manageable number for further inspection. However, existing approaches are not fully user-driven because they typically already prescribe what being of interest means. To address this, we introduce the patch sets representation for large-scale images. Patches inside a patch set are grouped and encoded according to similarity via a permutohedral lattice (p-lattice) in a user-defined feature space. Fast set operations on p-lattices facilitate patch set queries that enable users to describe what is interesting. In addition, we introduce an exploration framework—GigaPatchExplorer—for patch set-based image exploration. We show that patch sets in our framework are useful for a variety of user-driven exploration tasks in gigapixel images and whole collections thereof.

Synthetic Synchronisation: From Attention and Multi-Tasking to Negative Capability and Judgment

Science.gov (United States)

Stables, Andrew

2013-01-01

Educational literature has tended to focus, explicitly and implicitly, on two kinds of task orientation: the ability either to focus on a single task, or to multi-task. A third form of orientation characterises many highly successful people. This is the ability to combine several tasks into one: to "kill two (or more) birds with one…

Brain MR imaging in dietarily treated phenylketonuria

Energy Technology Data Exchange (ETDEWEB)

Breysem, L. [Dept. of Radiology, University Hospitals, Leuven (Belgium); Smet, M.H. [Dept. of Radiology, University Hospitals, Leuven (Belgium); Johannik, K. [Dept. of Radiology, University Hospitals, Leuven (Belgium); Hecke, P. van [Dept. of Radiology, University Hospitals, Leuven (Belgium); Francois, B. [L. Willems Inst., Diepenbeek (Belgium); Wilms, G. [Dept. of Radiology, University Hospitals, Leuven (Belgium); Bosmans, H. [Dept. of Radiology, University Hospitals, Leuven (Belgium); Marchal, G. [Dept. of Radiology, University Hospitals, Leuven (Belgium); Jaeken, J. [Dept. of Pediatrics, University Hospitals, Leuven (Belgium); Demaerel, P. [Dept. of Radiology, University Hospitals, Leuven (Belgium)

1994-08-01

Magnetic resonance imaging is the most efficient imaging modality to evaluate brain gray and white matter of patients with metabolic diseases. The main purpose of our study was to investigate the relation between brain MRI abnormalities and the phenylalanine (phe) and tyrosine (tyr) blood levels in 38 phenylketonuria (PKU) patients. Increased periventricular white matter intensity on T2-weighted brain images was the only pathologic finding in 24 patients. Brain MRI abnormalities were scored (4) and correlated with the individual mean phe and phe/tyr levels during 1 year preceding MR examination and with phe tolerance. The residual activity of phenylalanine hydroxylase was defined for each patient by an oral phe tolerance. The appearance of MRI abnormalities on brain T2-weighted images correlates with a threshold mean phe level (averaged over the year preceding the examination). (orig.)

Brain MR imaging in dietarily treated phenylketonuria

International Nuclear Information System (INIS)

Breysem, L.; Smet, M.H.; Johannik, K.; Hecke, P. van; Francois, B.; Wilms, G.; Bosmans, H.; Marchal, G.; Jaeken, J.; Demaerel, P.

1994-01-01

Magnetic resonance imaging is the most efficient imaging modality to evaluate brain gray and white matter of patients with metabolic diseases. The main purpose of our study was to investigate the relation between brain MRI abnormalities and the phenylalanine (phe) and tyrosine (tyr) blood levels in 38 phenylketonuria (PKU) patients. Increased periventricular white matter intensity on T2-weighted brain images was the only pathologic finding in 24 patients. Brain MRI abnormalities were scored (4) and correlated with the individual mean phe and phe/tyr levels during 1 year preceding MR examination and with phe tolerance. The residual activity of phenylalanine hydroxylase was defined for each patient by an oral phe tolerance. The appearance of MRI abnormalities on brain T2-weighted images correlates with a threshold mean phe level (averaged over the year preceding the examination). (orig.)

Studying variability in human brain aging in a population-based German cohort – Rationale and design of 1000BRAINS

Directory of Open Access Journals (Sweden)

Svenja eCaspers

2014-07-01

Full Text Available The ongoing 1000 brains study (1000BRAINS is an epidemiological and neuroscientific investigation of structural and functional variability in the human brain during aging. The two recruitment sources are the 10-year follow-up cohort of the German Heinz Nixdorf Recall (HNR Study, and the HNR MultiGeneration Study cohort, which comprises spouses and offspring of HNR subjects. The HNR is a longitudinal epidemiological investigation of cardiovascular risk factors, with a comprehensive collection of clinical, laboratory, socioeconomic, and environmental data from population-based subjects aged 45-75 years on inclusion. HNR subjects underwent detailed assessments in 2000, 2006, and 2011, and completed annual postal questionnaires on health status. 1000BRAINS accesses these HNR data and applies a separate protocol comprising: neuropsychological tests of attention, memory, executive functions & language; examination of motor skills; ratings of personality, life quality, mood & daily activities; analysis of laboratory and genetic data; and state-of-the-art magnetic resonance imaging (MRI, 3 Tesla of the brain. The latter includes (i 3D-T1- and 3D-T2-weighted scans for structural analyses and myelin mapping; (ii three diffusion imaging sequences optimized for diffusion tensor imaging, high-angular resolution diffusion imaging for detailed fibre tracking and for diffusion kurtosis imaging; (iii resting-state and task-based functional MRI; and (iv fluid-attenuated inversion recovery and MR angiography for the detection of vascular lesions and the mapping of white matter lesions. The unique design of 1000BRAINS allows: (i comprehensive investigation of various influences including genetics, environment and health status on variability in brain structure and function during aging; and (ii identification of the impact of selected influencing factors on specific cognitive subsystems and their anatomical correlates.

Work first then play: Prior task difficulty increases motivation-related brain responses in a risk game.

Science.gov (United States)

Schmidt, Barbara; Mussel, Patrick; Osinsky, Roman; Rasch, Björn; Debener, Stefan; Hewig, Johannes

2017-05-01

Task motivation depends on what we did before. A recent theory differentiates between tasks that we want to do and tasks that we have to do. After a have-to task, motivation shifts towards a want-to task. We measured this shift of motivation via brain responses to monetary feedback in a risk game that was used as want-to task in our study. We tested 20 healthy participants that were about 28 years old in a within-subjects design. Participants worked on a Stroop task (have-to task) or an easier version of the Stroop task as a control condition and played a risk game afterwards (want-to task). After the Stroop task, brain responses to monetary feedback in the risk game were larger compared to the easier control task, especially for feedback indicating higher monetary rewards. We conclude that higher amplitudes of feedback-related brain responses in the risk game reflect the shift of motivation after a have-to task towards a want-to task. Copyright © 2017 Elsevier B.V. All rights reserved.

Brain hypoxia imaging

Energy Technology Data Exchange (ETDEWEB)

Song, Ho Chun [Chonnam National University Medical School, Gwangju (Korea, Republic of)

2007-04-15

The measurement of pathologically low levels of tissue pO{sub 2} is an important diagnostic goal for determining the prognosis of many clinically important diseases including cardiovascular insufficiency, stroke and cancer. The target tissues nowadays have mostly been tumors or the myocardium, with less attention centered on the brain. Radiolabelled nitroimidazole or derivatives may be useful in identifying the hypoxic cells in cerebrovascular disease or traumatic brain injury, and hypoxic-ischemic encephalopathy. In acute stroke, the target of therapy is the severely hypoxic but salvageable tissue. {sup 18}F-MISO PET and {sup 99m}Tc-EC-metronidazole SPECT in patients with acute ischemic stroke identified hypoxic tissues and ischemic penumbra, and predicted its outcome. A study using {sup 123}I-IAZA in patient with closed head injury detected the hypoxic tissues after head injury. Up till now these radiopharmaceuticals have drawbacks due to its relatively low concentration with hypoxic tissues associated with/without low blood-brain barrier permeability and the necessity to wait a long time to achieve acceptable target to background ratios for imaging in acute ischemic stroke. It is needed to develop new hypoxic marker exhibiting more rapid localization in the hypoxic region in the brain. And then, the hypoxic brain imaging with imidazoles or non-imidazoles may be very useful in detecting the hypoxic tissues, determining therapeutic strategies and developing therapeutic drugs in several neurological disease, especially, in acute ischemic stroke.

Concurrent Learning of Control in Multi agent Sequential Decision Tasks

Science.gov (United States)

2018-04-17

Concurrent Learning of Control in Multi-agent Sequential Decision Tasks The overall objective of this project was to develop multi-agent reinforcement... learning (MARL) approaches for intelligent agents to autonomously learn distributed control policies in decentral- ized partially observable... learning of policies in Dec-POMDPs, established performance bounds, evaluated these algorithms both theoretically and empirically, The views

The two-pore domain K+ channel TASK-1 is closely associated with brain barriers and meninges.

Science.gov (United States)

Kanjhan, Refik; Pow, David V; Noakes, Peter G; Bellingham, Mark C

2010-12-01

Impairment of the blood-brain barrier (BBB), the blood-cerebrospinal fluid (CSF) barrier and brain-CSF barrier has been implicated in neuropathology of several brain disorders, such as amyotrophic lateral sclerosis, cerebral edema, multiple sclerosis, neural inflammation, ischemia and stroke. Two-pore domain weakly inward rectifying K+ channel (TWIK)-related acid-sensitive potassium (TASK)-1 channels (K2p3.1; KCNK3) are among the targets that contribute to the development of these pathologies. For example TASK-1 activity is inhibited by acidification, ischemia, hypoxia and several signaling molecules released under pathologic conditions. We have used immuno-histochemistry to examine the distribution of the TASK-1 protein in structures associated with the BBB, blood-CSF barrier, brain-CSF barrier, and in the meninges of adult rat. Dense TASK-1 immuno-reactivity (TASK-1-IR) was observed in ependymal cells lining the fourth ventricle at the brain-CSF interface, in glial cells that ensheath the walls of blood vessels at the glio-vascular interface, and in the meninges. In these structures, TASK-1-IR often co-localized with glial fibrillary associated protein (GFAP) or vimentin. This study provides anatomical evidence for localization of TASK-1 K+ channels in cells that segregate distinct fluid compartments within and surrounding the brain. We suggest that TASK-1 channels, in coordination with other ion channels (e.g., aquaporins and chloride channels) and transporters (e.g., Na+-K+-ATPase and Na+-K+-2Cl⁻ and by virtue of its heterogeneous distribution, may differentially contribute to the varying levels of K+ vital for cellular function in these compartments. Our findings are likely to be relevant to recently reported roles of TASK-1 in cerebral ischemia, stroke and inflammatory brain disorders.

Functional MR imaging of the primary motor area in patients with brain tumors of the motor cortex. Evaluation with echo-planer imaging on a clinical 1.0 T MR imager

International Nuclear Information System (INIS)

Hara, Yoshie; Nakamura, Mitsugu; Tamaki, Norihiko; Ehara, Kazumasa; Kitamura, Junji

1998-01-01

The study included 3 healthy volunteers and 8 patients with a brain tumor of the motor cortex. The fMRI study was based on the spin echo (SE) type single shot echo-planer technique. Ten contiguous axial slices consisted of 40-60 echo-planer images acquired during 80-120 seconds of repeated task performances and resting periods. Activation maps were calculated by a Z-score method with thresholding, and interpolated on T1 images and surface anatomy scans. In all cases, areas of a significant signal increase were detected as clusters of several pixels on the precentral gyrus contralateral to the motor task performance. The mean signal change was 3.6±0.9% in normal subjects, and 7.2±4.1% in brain tumor patients. There was no significant difference between the two groups. In 5 brain tumor patients significant displacement of the precentral gyrus was observed on T1- or T2-weighted SE images. Of these, 2 also had marked peritumoral edema spreading over the precentral gyrus. There was no significant difference in the size, or the degree, of signal change between patients with or without compression or edema, nor between patients with and without preoperative motor impairment. During surgical intervention, displacement of the precentral gyrus was observed as had been demonstrated on preoperative images of patients. In all patients the precentral gyrus was preserved in all cases, and no deterioration of motor function occurred. Resolution of the displacement and edema was detected on postoperative MRI. Using the echo-planer technique on a clinical 1.0 T imager fMRI localization of the primary motor cortex was obtained in normal and brain tumor subjects. The activated areas were detected on the precentral gyrus of both groups, and even when there was marked brain compression or edema. It is important to identify and preserve the precentral gyrus during surgery to avoid deterioration of motor function. (K.H.)

Advanced multi-dimensional imaging of gamma-ray radiation

International Nuclear Information System (INIS)

Woodring, Mitchell; Beddingfield, David; Souza, David; Entine, Gerald; Squillante, Michael; Christian, James; Kogan, Alex

2003-01-01

The tracking of radiation contamination and distribution has become a high-priority US DOE task. To support DOE needs, Radiation Monitoring Devices Inc. has been actively carrying out research and development on a gamma-radiation imager, RadCam 2000 TM . The imager is based upon a position-sensitive PMT coupled to a scintillator near a MURA coded aperture. The modulated gamma flux detected by the PSPMT is mathematically decoded to produce images that are computer displayed in near real time. Additionally, we have developed a data-manipulation scheme which allows a multi-dimensional data array, comprised of x position, y position, and energy, to be used in the imaging process. In the imager software a gate can be set on a specific isotope energy to reveal where in the field of view the gated data lies or, conversely, a gate can be set on an area in the field of view to examine what isotopes are present in that area. This process is complicated by the FFT decoding process used with the coded aperture; however, we have achieved excellent performance and results are presented here

Automatic detection of the hippocampal region associated with Alzheimer's disease from microscopic images of mice brain

Science.gov (United States)

Albaidhani, Tahseen; Hawkes, Cheryl; Jassim, Sabah; Al-Assam, Hisham

2016-05-01

The hippocampus is the region of the brain that is primarily associated with memory and spatial navigation. It is one of the first brain regions to be damaged when a person suffers from Alzheimer's disease. Recent research in this field has focussed on the assessment of damage to different blood vessels within the hippocampal region from a high throughput brain microscopic images. The ultimate aim of our research is the creation of an automatic system to count and classify different blood vessels such as capillaries, veins, and arteries in the hippocampus region. This work should provide biologists with efficient and accurate tools in their investigation of the causes of Alzheimer's disease. Locating the boundary of the Region of Interest in the hippocampus from microscopic images of mice brain is the first essential stage towards developing such a system. This task benefits from the variation in colour channels and texture between the two sides of the hippocampus and the boundary region. Accordingly, the developed initial step of our research to locating the hippocampus edge uses a colour-based segmentation of the brain image followed by Hough transforms on the colour channel that isolate the hippocampus region. The output is then used to split the brain image into two sides of the detected section of the boundary: the inside region and the outside region. Experimental results on a sufficiently number of microscopic images demonstrate the effectiveness of the developed solution.

Magnetic Resonance Imaging (MRI): Brain (For Parents)

Science.gov (United States)

... Staying Safe Videos for Educators Search English Español Magnetic Resonance Imaging (MRI): Brain KidsHealth / For Parents / Magnetic Resonance Imaging (MRI): Brain What's in this article? What ...

A non-contact time-domain scanning brain imaging system: first in-vivo results

Science.gov (United States)

Mazurenka, M.; Di Sieno, L.; Boso, G.; Contini, D.; Pifferi, A.; Dalla Mora, A.; Tosi, A.; Wabnitz, H.; Macdonald, R.

2013-06-01

We present results of first in-vivo tests of an optical non-contact scanning imaging system, intended to study oxidative metabolism related processes in biological tissue by means of time-resolved near-infrared spectroscopy. Our method is a novel realization of the short source-detector separation approach and based on a fast-gated single-photon avalanche diode to detect late photons only. The scanning system is built in quasi-confocal configuration and utilizes polarizationsensitive detection. It scans an area of 4×4 cm2, recording images with 32×32 pixels, thus creating a high density of source-detector pairs. To test the system we performed a range of in vivo measurements of hemodynamic changes in several types of biological tissues, i.e. skin (Valsalva maneuver), muscle (venous and arterial occlusions) and brain (motor and cognitive tasks). Task-related changes in hemoglobin concentrations were clearly detected in skin and muscle. The brain activation shows weaker, but yet detectable changes. These changes were localized in pixels near the motor cortex area (C3). However, it was found that even very short hair substantially impairs the measurement. Thus the applicability of the scanner is limited to hairless parts of body. The results of our first in-vivo tests prove the feasibility of non-contact scanning imaging as a first step towards development of a prototype for biological tissue imaging for various medical applications.

Synchrotron radiation imaging is a powerful tool to image brain microvasculature

International Nuclear Information System (INIS)

Zhang, Mengqi; Sun, Danni; Xie, Yuanyuan; Xia, Jian; Long, Hongyu; Hu, Kai; Xiao, Bo; Peng, Guanyun

2014-01-01

Synchrotron radiation (SR) imaging is a powerful experimental tool for micrometer-scale imaging of microcirculation in vivo. This review discusses recent methodological advances and findings from morphological investigations of cerebral vascular networks during several neurovascular pathologies. In particular, it describes recent developments in SR microangiography for real-time assessment of the brain microvasculature under various pathological conditions in small animal models. It also covers studies that employed SR-based phase-contrast imaging to acquire 3D brain images and provide detailed maps of brain vasculature. In addition, a brief introduction of SR technology and current limitations of SR sources are described in this review. In the near future, SR imaging could transform into a common and informative imaging modality to resolve subtle details of cerebrovascular function

Synchrotron radiation imaging is a powerful tool to image brain microvasculature

Energy Technology Data Exchange (ETDEWEB)

Zhang, Mengqi; Sun, Danni; Xie, Yuanyuan; Xia, Jian; Long, Hongyu; Hu, Kai; Xiao, Bo, E-mail: csuxiaobo123456@163.com [Department of Neurology, Xiangya Hospital, Central South University, Changsha, Hunan 410008 (China); Peng, Guanyun [Shanghai Institute of Applied Physics, Chinese Academy of Sciences, Shanghai 201800 (China)

2014-03-15

Synchrotron radiation (SR) imaging is a powerful experimental tool for micrometer-scale imaging of microcirculation in vivo. This review discusses recent methodological advances and findings from morphological investigations of cerebral vascular networks during several neurovascular pathologies. In particular, it describes recent developments in SR microangiography for real-time assessment of the brain microvasculature under various pathological conditions in small animal models. It also covers studies that employed SR-based phase-contrast imaging to acquire 3D brain images and provide detailed maps of brain vasculature. In addition, a brief introduction of SR technology and current limitations of SR sources are described in this review. In the near future, SR imaging could transform into a common and informative imaging modality to resolve subtle details of cerebrovascular function.

Single-trial effective brain connectivity patterns enhance discriminability of mental imagery tasks

Science.gov (United States)

Rathee, Dheeraj; Cecotti, Hubert; Prasad, Girijesh

2017-10-01

Objective. The majority of the current approaches of connectivity based brain-computer interface (BCI) systems focus on distinguishing between different motor imagery (MI) tasks. Brain regions associated with MI are anatomically close to each other, hence these BCI systems suffer from low performances. Our objective is to introduce single-trial connectivity feature based BCI system for cognition imagery (CI) based tasks wherein the associated brain regions are located relatively far away as compared to those for MI. Approach. We implemented time-domain partial Granger causality (PGC) for the estimation of the connectivity features in a BCI setting. The proposed hypothesis has been verified with two publically available datasets involving MI and CI tasks. Main results. The results support the conclusion that connectivity based features can provide a better performance than a classical signal processing framework based on bandpass features coupled with spatial filtering for CI tasks, including word generation, subtraction, and spatial navigation. These results show for the first time that connectivity features can provide a reliable performance for imagery-based BCI system. Significance. We show that single-trial connectivity features for mixed imagery tasks (i.e. combination of CI and MI) can outperform the features obtained by current state-of-the-art method and hence can be successfully applied for BCI applications.

The role of image registration in brain mapping

Science.gov (United States)

Toga, A.W.; Thompson, P.M.

2008-01-01

Image registration is a key step in a great variety of biomedical imaging applications. It provides the ability to geometrically align one dataset with another, and is a prerequisite for all imaging applications that compare datasets across subjects, imaging modalities, or across time. Registration algorithms also enable the pooling and comparison of experimental findings across laboratories, the construction of population-based brain atlases, and the creation of systems to detect group patterns in structural and functional imaging data. We review the major types of registration approaches used in brain imaging today. We focus on their conceptual basis, the underlying mathematics, and their strengths and weaknesses in different contexts. We describe the major goals of registration, including data fusion, quantification of change, automated image segmentation and labeling, shape measurement, and pathology detection. We indicate that registration algorithms have great potential when used in conjunction with a digital brain atlas, which acts as a reference system in which brain images can be compared for statistical analysis. The resulting armory of registration approaches is fundamental to medical image analysis, and in a brain mapping context provides a means to elucidate clinical, demographic, or functional trends in the anatomy or physiology of the brain. PMID:19890483

PET/MRI for Oncologic Brain Imaging

DEFF Research Database (Denmark)

Rausch, Ivo; Rischka, Lucas; Ladefoged, Claes N

2017-01-01

The aim of this study was to compare attenuation-correction (AC) approaches for PET/MRI in clinical neurooncology.Methods:Forty-nine PET/MRI brain scans were included: brain tumor studies using18F-fluoro-ethyl-tyrosine (18F-FET) (n= 31) and68Ga-DOTANOC (n= 7) and studies of healthy subjects using18...... by Siemens Healthcare). As a reference, AC maps were derived from patient-specific CT images (CTref). PET data were reconstructed using standard settings after AC with all 4 AC methods. We report changes in diagnosis for all brain tumor patients and the following relative differences values (RDs...... of the whole brain and 10 anatomic regions segmented on MR images.Results:For brain tumor imaging (A and B), the standard PET-based diagnosis was not affected by any of the 3 MR-AC methods. For A, the average RDs of SUVmeanwere -10%, -4%, and -3% and of the VOIs 1%, 2%, and 7% for DIXON, UTE, and BD...

Neural decoding of collective wisdom with multi-brain computing.

Science.gov (United States)

Eckstein, Miguel P; Das, Koel; Pham, Binh T; Peterson, Matthew F; Abbey, Craig K; Sy, Jocelyn L; Giesbrecht, Barry

2012-01-02

Group decisions and even aggregation of multiple opinions lead to greater decision accuracy, a phenomenon known as collective wisdom. Little is known about the neural basis of collective wisdom and whether its benefits arise in late decision stages or in early sensory coding. Here, we use electroencephalography and multi-brain computing with twenty humans making perceptual decisions to show that combining neural activity across brains increases decision accuracy paralleling the improvements shown by aggregating the observers' opinions. Although the largest gains result from an optimal linear combination of neural decision variables across brains, a simpler neural majority decision rule, ubiquitous in human behavior, results in substantial benefits. In contrast, an extreme neural response rule, akin to a group following the most extreme opinion, results in the least improvement with group size. Analyses controlling for number of electrodes and time-points while increasing number of brains demonstrate unique benefits arising from integrating neural activity across different brains. The benefits of multi-brain integration are present in neural activity as early as 200 ms after stimulus presentation in lateral occipital sites and no additional benefits arise in decision related neural activity. Sensory-related neural activity can predict collective choices reached by aggregating individual opinions, voting results, and decision confidence as accurately as neural activity related to decision components. Estimation of the potential for the collective to execute fast decisions by combining information across numerous brains, a strategy prevalent in many animals, shows large time-savings. Together, the findings suggest that for perceptual decisions the neural activity supporting collective wisdom and decisions arises in early sensory stages and that many properties of collective cognition are explainable by the neural coding of information across multiple brains. Finally

Multi-scale and multi-orientation medical image analysis

NARCIS (Netherlands)

Haar Romenij, ter B.M.; Deserno, T.M.

2011-01-01

Inspired by multi-scale and multi-orientation mechanisms recognized in the first stages of our visual system, this chapter gives a tutorial overview of the basic principles. Images are discrete, measured data. The optimal aperture for an observation with as little artefacts as possible, is derived

Effects of long-term practice and task complexity on brain activities when performing abacus-based mental calculations: a PET study

International Nuclear Information System (INIS)

Wu, Tung-Hsin; Chen, Chia-Lin; Huang, Yung-Hui; Liu, Ren-Shyan; Hsieh, Jen-Chuen; Lee, Jason J.S.

2009-01-01

The aim of this study was to examine the neural bases for the exceptional mental calculation ability possessed by Chinese abacus experts through PET imaging. We compared the different regional cerebral blood flow (rCBF) patterns using 15 O-water PET in 10 abacus experts and 12 non-experts while they were performing each of the following three tasks: covert reading, simple addition, and complex contiguous addition. All data collected were analyzed using SPM2 and MNI templates. For non-experts during the tasks of simple addition, the observed activation of brain regions were associated with coordination of language (inferior frontal network) and visuospatial processing (left parietal/frontal network). Similar activation patterns but with a larger visuospatial processing involvement were observed during complex contiguous addition tasks, suggesting the recruitment of more visuospatial memory for solving the complex problems. For abacus experts, however, the brain activation patterns showed slight differences when they were performing simple and complex addition tasks, both of which involve visuospatial processing (bilateral parietal/frontal network). These findings supported the notion that the experts were completing all the calculation process on a virtual mental abacus and relying on this same computational strategy in both simple and complex tasks, which required almost no increasing brain workload for solving the latter. In conclusion, after intensive training and practice, the neural pathways in an abacus expert have been connected more effectively for performing the number encoding and retrieval that are required in abacus tasks, resulting in exceptional mental computational ability. (orig.)

Enhanced task-related brain activation and resting perfusion in healthy older adults after chronic blueberry supplementation.

Science.gov (United States)

Bowtell, Joanna L; Aboo-Bakkar, Zainie; Conway, Myra E; Adlam, Anna-Lynne R; Fulford, Jonathan

2017-07-01

Blueberries are rich in flavonoids, which possess antioxidant and anti-inflammatory properties. High flavonoid intakes attenuate age-related cognitive decline, but data from human intervention studies are sparse. We investigated whether 12 weeks of blueberry concentrate supplementation improved brain perfusion, task-related activation, and cognitive function in healthy older adults. Participants were randomised to consume either 30 mL blueberry concentrate providing 387 mg anthocyanidins (5 female, 7 male; age 67.5 ± 3.0 y; body mass index, 25.9 ± 3.3 kg·m -2 ) or isoenergetic placebo (8 female, 6 male; age 69.0 ± 3.3 y; body mass index, 27.1 ± 4.0 kg·m -2 ). Pre- and postsupplementation, participants undertook a battery of cognitive function tests and a numerical Stroop test within a 1.5T magnetic resonance imaging scanner while functional magnetic resonance images were continuously acquired. Quantitative resting brain perfusion was determined using an arterial spin labelling technique, and blood biomarkers of inflammation and oxidative stress were measured. Significant increases in brain activity were observed in response to blueberry supplementation relative to the placebo group within Brodmann areas 4/6/10/21/40/44/45, precuneus, anterior cingulate, and insula/thalamus (p blueberry versus placebo supplementation (p = 0.05). Supplementation with an anthocyanin-rich blueberry concentrate improved brain perfusion and activation in brain areas associated with cognitive function in healthy older adults.

Indistinguishability Operators Applied to Task Allocation Problems in Multi-Agent Systems

Directory of Open Access Journals (Sweden)

José Guerrero

2017-09-01

Full Text Available In this paper we show an application of indistinguishability operators to model response functions. Such functions are used in the mathematical modeling of the task allocation problem in multi-agent systems when the stimulus, perceived by the agent, to perform a task is assessed by means of the response threshold model. In particular, we propose this kind of operators to represent a response function when the stimulus only depends on the distance between the agent and a determined task, since we prove that two celebrated response functions used in the literature can be reproduced by appropriate indistinguishability operators when the stimulus only depends on the distance to each task that must be carried out. Despite the fact there is currently no systematic method to generate response functions, this paper provides, for the first time, a theoretical foundation to generate them and study their properties. To validate the theoretical results, the aforementioned indistinguishability operators have been used to simulate, with MATLAB, the allocation of a set of tasks in a multi-robot system with fuzzy Markov chains.

Task-driven image acquisition and reconstruction in cone-beam CT

International Nuclear Information System (INIS)

Gang, Grace J; Stayman, J Webster; Siewerdsen, Jeffrey H; Ehtiati, Tina

2015-01-01

This work introduces a task-driven imaging framework that incorporates a mathematical definition of the imaging task, a model of the imaging system, and a patient-specific anatomical model to prospectively design image acquisition and reconstruction techniques to optimize task performance. The framework is applied to joint optimization of tube current modulation, view-dependent reconstruction kernel, and orbital tilt in cone-beam CT. The system model considers a cone-beam CT system incorporating a flat-panel detector and 3D filtered backprojection and accurately describes the spatially varying noise and resolution over a wide range of imaging parameters in the presence of a realistic anatomical model. Task-based detectability index (d′) is incorporated as the objective function in a task-driven optimization of image acquisition and reconstruction techniques. The orbital tilt was optimized through an exhaustive search across tilt angles ranging ±30°. For each tilt angle, the view-dependent tube current and reconstruction kernel (i.e. the modulation profiles) that maximized detectability were identified via an alternating optimization. The task-driven approach was compared with conventional unmodulated and automatic exposure control (AEC) strategies for a variety of imaging tasks and anthropomorphic phantoms. The task-driven strategy outperformed the unmodulated and AEC cases for all tasks. For example, d′ for a sphere detection task in a head phantom was improved by 30% compared to the unmodulated case by using smoother kernels for noisy views and distributing mAs across less noisy views (at fixed total mAs) in a manner that was beneficial to task performance. Similarly for detection of a line-pair pattern, the task-driven approach increased d′ by 80% compared to no modulation by means of view-dependent mA and kernel selection that yields modulation transfer function and noise-power spectrum optimal to the task. Optimization of orbital tilt identified the

Brain activity associated with memory and cognitive function during jaw-tapping movement in healthy subjects using functional magnetic resonance imaging.

Science.gov (United States)

Cho, Seung-Yeon; Shin, Ae-Sook; Na, Byung-Jo; Jahng, Geon-Ho; Park, Seong-Uk; Jung, Woo-Sang; Moon, Sang-Kwan; Park, Jung-Mi

2013-06-01

To determine whether jaw-tapping movement, a classically described as an indication of personal well-being and mental health, stimulates the memory and the cognitive regions of the brain and is associated with improved brain performance. Twelve healthy right-handed female subjects completed the study. Each patient performed a jaw-tapping task and an n-back task during functional magnetic resonance imaging (fMRI). The subjects were trained to carry out the jaw-tapping movement at home twice a day for 4 weeks. The fMRI was repeated when they returned. During the first and second jaw-tapping session, both sides of precentral gyrus and the right middle frontal gyrus (BA 6) were activated. And during the second session of the jaw-tapping task, parts of frontal lobe and temporal lobe related to memory function were more activated. In addition, the total percent task accuracy in n-back task significantly increased after 4 weeks of jawtapping movement. After jaw-tapping training for 4 weeks, brain areas related to memory showed significantly increased blood oxygen level dependent signals. Jaw-tapping movement might be a useful exercise for stimulating the memory and cognitive regions of the brain.

Nuclear magnetic resonance imaging and brain functional exploration

International Nuclear Information System (INIS)

Le Bihan, D.; CEA, 91 - Orsay

1997-01-01

The utilization of nuclear magnetic resonance imaging for functional analysis of the brain is presented: the oxygenated and deoxygenated blood flowing in the brain do not have the same effect on NMR images; the oxygenated blood, related to brain activity, may be detected and the corresponding activity zone in the brain, identified; functional NMR imaging could be used to gain a better understanding of functional troubles linked to neurological or psychiatric diseases

Brain-computer interface based on generation of visual images.

Directory of Open Access Journals (Sweden)

Pavel Bobrov

Full Text Available This paper examines the task of recognizing EEG patterns that correspond to performing three mental tasks: relaxation and imagining of two types of pictures: faces and houses. The experiments were performed using two EEG headsets: BrainProducts ActiCap and Emotiv EPOC. The Emotiv headset becomes widely used in consumer BCI application allowing for conducting large-scale EEG experiments in the future. Since classification accuracy significantly exceeded the level of random classification during the first three days of the experiment with EPOC headset, a control experiment was performed on the fourth day using ActiCap. The control experiment has shown that utilization of high-quality research equipment can enhance classification accuracy (up to 68% in some subjects and that the accuracy is independent of the presence of EEG artifacts related to blinking and eye movement. This study also shows that computationally-inexpensive bayesian classifier based on covariance matrix analysis yields similar classification accuracy in this problem as a more sophisticated Multi-class Common Spatial Patterns (MCSP classifier.

Dynamic Task Allocation in Multi-Hop Multimedia Wireless Sensor Networks with Low Mobility

Directory of Open Access Journals (Sweden)

Klaus Moessner

2013-10-01

Full Text Available This paper presents a task allocation-oriented framework to enable efficient in-network processing and cost-effective multi-hop resource sharing for dynamic multi-hop multimedia wireless sensor networks with low node mobility, e.g., pedestrian speeds. The proposed system incorporates a fast task reallocation algorithm to quickly recover from possible network service disruptions, such as node or link failures. An evolutional self-learning mechanism based on a genetic algorithm continuously adapts the system parameters in order to meet the desired application delay requirements, while also achieving a sufficiently long network lifetime. Since the algorithm runtime incurs considerable time delay while updating task assignments, we introduce an adaptive window size to limit the delay periods and ensure an up-to-date solution based on node mobility patterns and device processing capabilities. To the best of our knowledge, this is the first study that yields multi-objective task allocation in a mobile multi-hop wireless environment under dynamic conditions. Simulations are performed in various settings, and the results show considerable performance improvement in extending network lifetime compared to heuristic mechanisms. Furthermore, the proposed framework provides noticeable reduction in the frequency of missing application deadlines.

MR imaging of the brain: tumors

International Nuclear Information System (INIS)

Sartor, K.

1999-01-01

The radiologic modality that most likely provides the imaging information needed in a patient suspected of having a brain tumor is MR imaging. A brain tumor can be reliably ruled out if the MR examination is performed properly and experts interpret the results as negative. If there is a tumor, however, its exact location and topography must be determined. Important for therapy and prognosis are also tumor properties such as histologic type and grade, as well as effects on adjacent brain structures. Although potentially a noninvasive method of in vivo neuropathology, MR is still far from being sufficiently specific, as dissimilar lesions may look the same despite the use of refined imaging protocols. The evolution of MR imaging continues, however, making further methodologic improvement likely. Presently, advanced methods, such as diffusion- and perfusion-weighted MR imaging, functional MR imaging, neuronavigation based on MR imaging data, and the use of MR imaging during surgery (intraoperative MR imaging), influence the way patients are treated. Likewise, follow-up imaging (monitoring) of tumor patients by MR has become more effective, and experience has shown how to distinguish reactive changes from recurrent tumor. In the future, MR imaging may gain importance in the development of novel therapeutic concepts. (orig.)

Design and evaluation of two multi-pinhole collimators for brain SPECT.

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Chen, Ling; Tsui, Benjamin M W; Mok, Greta S P

2017-10-01

SPECT is a powerful tool for diagnosing or staging brain diseases such as Alzheimer's disease (AD) and Parkinson's disease (PD) but is limited by its inferior resolution and sensitivity. At the same time, pinhole SPECT provides superior resolution and detection efficiency trade-off as compared to the conventional parallel-hole collimator for imaging small field-of-view (FOV), which fits for the case of brain imaging. In this study, we propose to develop and evaluate two multi-pinhole (MPH) collimator designs to improve the imaging of cerebral blood flow and striatum. We set the target resolutions to be 12 and 8 mm, respectively, and the FOV at 200 mm which is large enough to cover the whole brain. The constraints for system optimization include maximum and minimum detector-to-center-of-FOV (CFOV) distances of 344 and 294 mm, respectively, and minimal radius-of-rotation (ROR) of 135 mm to accommodate patients' shoulder. According to the targeted FOV, resolutions, and constraints, we determined the pinhole number, ROR, focal length, aperture acceptance angle, and aperture diameter which maximized the system sensitivity. We then assessed the imaging performance of the proposed MPH and standard low-energy high-resolution (LEHR) collimators using analytical simulations of a digital NCAT brain phantom with 99m Tc-HMPAO/ 99m Tc-TRODAT-1 distributions; Monte Carlo simulations of a hot-rod phantom; and a Defrise phantom using GATE v6.1. Projections were generated over 360° and reconstructed using the 3D MPH/LEHR OS-EM methods with up to 720 updates. The normalized mean square error (NMSE) was calculated over the cerebral and striatal regions extracted from the reconstructed images for 99m Tc-HMPAO and 99m Tc-TRODAT-1 simulations, respectively, and average normalized standard deviation (NSD) based on 20 noise realizations was assessed on selected uniform 3D regions as the noise index. Visual assessment and image profiles were applied to the results of Monte Carlo

Three-dimensional brain mapping using fMRI

International Nuclear Information System (INIS)

Fukunaga, Masaki; Tanaka, Chuzo; Umeda, Masahiro; Ebisu, Toshihiko; Aoki, Ichio; Higuchi, Toshihiro; Naruse, Shoji.

1997-01-01

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)

Integration of intraoperative stereovision imaging for brain shift visualization during image-guided cranial procedures

Science.gov (United States)

Schaewe, Timothy J.; Fan, Xiaoyao; Ji, Songbai; Roberts, David W.; Paulsen, Keith D.; Simon, David A.

2014-03-01

Dartmouth and Medtronic Navigation have established an academic-industrial partnership to develop, validate, and evaluate a multi-modality neurosurgical image-guidance platform for brain tumor resection surgery that is capable of updating the spatial relationships between preoperative images and the current surgical field. A stereovision system has been developed and optimized for intraoperative use through integration with a surgical microscope and an image-guided surgery system. The microscope optics and stereovision CCD sensors are localized relative to the surgical field using optical tracking and can efficiently acquire stereo image pairs from which a localized 3D profile of the exposed surface is reconstructed. This paper reports the first demonstration of intraoperative acquisition, reconstruction and visualization of 3D stereovision surface data in the context of an industry-standard image-guided surgery system. The integrated system is capable of computing and presenting a stereovision-based update of the exposed cortical surface in less than one minute. Alternative methods for visualization of high-resolution, texture-mapped stereovision surface data are also investigated with the objective of determining the technical feasibility of direct incorporation of intraoperative stereo imaging into future iterations of Medtronic's navigation platform.

Taxonomy of multi-focal nematode image stacks by a CNN based image fusion approach.

Science.gov (United States)

Liu, Min; Wang, Xueping; Zhang, Hongzhong

2018-03-01

In the biomedical field, digital multi-focal images are very important for documentation and communication of specimen data, because the morphological information for a transparent specimen can be captured in form of a stack of high-quality images. Given biomedical image stacks containing multi-focal images, how to efficiently extract effective features from all layers to classify the image stacks is still an open question. We present to use a deep convolutional neural network (CNN) image fusion based multilinear approach for the taxonomy of multi-focal image stacks. A deep CNN based image fusion technique is used to combine relevant information of multi-focal images within a given image stack into a single image, which is more informative and complete than any single image in the given stack. Besides, multi-focal images within a stack are fused along 3 orthogonal directions, and multiple features extracted from the fused images along different directions are combined by canonical correlation analysis (CCA). Because multi-focal image stacks represent the effect of different factors - texture, shape, different instances within the same class and different classes of objects, we embed the deep CNN based image fusion method within a multilinear framework to propose an image fusion based multilinear classifier. The experimental results on nematode multi-focal image stacks demonstrated that the deep CNN image fusion based multilinear classifier can reach a higher classification rate (95.7%) than that by the previous multilinear based approach (88.7%), even we only use the texture feature instead of the combination of texture and shape features as in the previous work. The proposed deep CNN image fusion based multilinear approach shows great potential in building an automated nematode taxonomy system for nematologists. It is effective to classify multi-focal image stacks. Copyright © 2018 Elsevier B.V. All rights reserved.

Brain Imaging Using Hyperpolarized 129Xe Magnetic Resonance Imaging.

Science.gov (United States)

Chahal, Simrun; Prete, Braedan R J; Wade, Alanna; Hane, Francis T; Albert, Mitchell S

2018-01-01

Hyperpolarized (HP) 129 Xe magnetic resonance imaging (MRI) is a novel iteration of traditional MRI that relies on detecting the spins of 1 H. Since 129 Xe is a gaseous signal source, it can be used for lung imaging. Additionally, 129 Xe dissolves in the blood stream and can therefore be detectable in the brain parenchyma and vasculature. In this work, we provide detailed information on the protocols that we have developed to image 129 Xe within the brains of both rodents and human subjects. © 2018 Elsevier Inc. All rights reserved.

Patterns of brain and cardiovascular activation while solving rule-discovery and rule-application numeric tasks.

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Sosnowski, Tytus; Rynkiewicz, Andrzej; Wordecha, Małgorzata; Kępkowicz, Anna; Majewska, Adrianna; Pstrągowska, Aleksandra; Oleksy, Tomasz; Wypych, Marek; Marchewka, Artur

2017-07-01

It is known that solving mental tasks leads to tonic increase in cardiovascular activity. Our previous research showed that tasks involving rule application (RA) caused greater tonic increase in cardiovascular activity than tasks requiring rule discovery (RD). However, it is not clear what brain mechanisms are responsible for this difference. The aim of two experimental studies was to compare the patterns of brain and cardiovascular activity while both RD and the RA numeric tasks were being solved. The fMRI study revealed greater brain activation while solving RD tasks than while solving RA tasks. In particular, RD tasks evoked greater activation of the left inferior frontal gyrus and selected areas in the parietal, and temporal cortices, including the precuneus, supramarginal gyrus, angular gyrus, inferior parietal lobule, and the superior temporal gyrus, and the cingulate cortex. In addition, RA tasks caused larger increases in HR than RD tasks. The second study, carried out in a cardiovascular laboratory, showed greater increases in heart rate (HR), systolic blood pressure (SBP), diastolic blood pressure (DBP), and mean arterial pressure (MAP) while solving RA tasks than while solving RD tasks. The results support the hypothesis that RD and RA tasks involve different modes of information processing, but the neuronal mechanism responsible for the observed greater cardiovascular response to RA tasks than to RD tasks is not completely clear. Copyright © 2017. Published by Elsevier B.V.

Multimodal Task-Driven Dictionary Learning for Image Classification

Science.gov (United States)

2015-12-18

recognition, multi-view face recognition, multi-view action recognition, and multimodal biometric recognition. It is also shown that, compared to the...improvement in several multi-task learning applications such as target classification, biometric recognitions, and multiview face recognition [12], [14], [17...relevant samples from other modalities for a given unimodal query. However, α1 α2 …αS D1 … Index finger Thumb finger … Iris x1 x2 xS D2 DS … … … J o in

Development and experimentation of an eye/brain/task testbed

Science.gov (United States)

Harrington, Nora; Villarreal, James

1987-01-01

The principal objective is to develop a laboratory testbed that will provide a unique capability to elicit, control, record, and analyze the relationship of operator task loading, operator eye movement, and operator brain wave data in a computer system environment. The ramifications of an integrated eye/brain monitor to the man machine interface are staggering. The success of such a system would benefit users of space and defense, paraplegics, and the monitoring of boring screens (nuclear power plants, air defense, etc.)

My Body Looks Like That Girl's: Body Mass Index Modulates Brain Activity during Body Image Self-Reflection among Young Women.

Science.gov (United States)

Gao, Xiao; Deng, Xiao; Wen, Xin; She, Ying; Vinke, Petra Corianne; Chen, Hong

2016-01-01

Body image distress or body dissatisfaction is one of the most common consequences of obesity and overweight. We investigated the neural bases of body image processing in overweight and average weight young women to understand whether brain regions that were previously found to be involved in processing self-reflective, perspective and affective components of body image would show different activation between two groups. Thirteen overweight (O-W group, age = 20.31±1.70 years) and thirteen average weight (A-W group, age = 20.15±1.62 years) young women underwent functional magnetic resonance imaging while performing a body image self-reflection task. Among both groups, whole-brain analysis revealed activations of a brain network related to perceptive and affective components of body image processing. ROI analysis showed a main effect of group in ACC as well as a group by condition interaction within bilateral EBA, bilateral FBA, right IPL, bilateral DLPFC, left amygdala and left MPFC. For the A-W group, simple effect analysis revealed stronger activations in Thin-Control compared to Fat-Control condition within regions related to perceptive (including bilateral EBA, bilateral FBA, right IPL) and affective components of body image processing (including bilateral DLPFC, left amygdala), as well as self-reference (left MPFC). The O-W group only showed stronger activations in Fat-Control than in Thin-Control condition within regions related to the perceptive component of body image processing (including left EBA and left FBA). Path analysis showed that in the Fat-Thin contrast, body dissatisfaction completely mediated the group difference in brain response in left amygdala across the whole sample. Our data are the first to demonstrate differences in brain response to body pictures between average weight and overweight young females involved in a body image self-reflection task. These results provide insights for understanding the vulnerability to body image distress

Upper Limb Muscle and Brain Activity in Light Assembly Task on Different Load Levels

Science.gov (United States)

Zadry, Hilma Raimona; Dawal, Siti Zawiah Md.; Taha, Zahari

2010-10-01

A study was conducted to investigate the effect of load on upper limb muscles and brain activities in light assembly task. The task was conducted at two levels of load (Low and high). Surface electromyography (EMG) was used to measure upper limb muscle activities of twenty subjects. Electroencephalography (EEG) was simultaneously recorded with EMG to record brain activities from Fz, Pz, O1 and O2 channels. The EMG Mean Power Frequency (MPF) of the right brachioradialis and the left upper trapezius activities were higher on the high-load task compared to low-load task. The EMG MPF values also decrease as time increases, that reflects muscle fatigue. Mean power of the EEG alpha bands for the Fz-Pz channels were found to be higher on the high-load task compared to low-load task, while for the O1-O2 channels, they were higher on the low-load task than on the high-load task. These results indicated that the load levels effect the upper limb muscle and brain activities. The high-load task will increase muscle activities on the right brachioradialis and the left upper tapezius muscles, and will increase the awareness and motivation of the subjects. Whilst the low-load task can generate drowsiness earlier. It signified that the longer the time and the more heavy of the task, the subjects will be more fatigue physically and mentally.

High-field, high-resolution, susceptibility-weighted magnetic resonance imaging: improved image quality by addition of contrast agent and higher field strength in patients with brain tumors

International Nuclear Information System (INIS)

Pinker, K.; Noebauer-Huhmann, I.M.; Szomolanyi, P.; Weber, M.; Grabner, G.; Trattnig, S.; Stavrou, I.; Knosp, E.; Hoeftberger, R.; Stadlbauer, A.

2008-01-01

To demonstrate intratumoral susceptibility effects in malignant brain tumors and to assess visualization of susceptibility effects before and after administration of the paramagnetic contrast agent MultiHance (gadobenate dimeglumine; Bracco Imaging), an agent known to have high relaxivity, with respect to susceptibility effects, image quality, and reduction of scan time. Included in the study were 19 patients with malignant brain tumors who underwent high-resolution, susceptibility-weighted (SW) MR imaging at 3 T before and after administration of contrast agent. In all patients, Multihance was administered intravenously as a bolus (0.1 mmol/kg body weight). MR images were individually evaluated by two radiologists with previous experience in the evaluation of pre- and postcontrast 3-T SW MR images with respect to susceptibility effects, image quality, and reduction of scan time. In the 19 patients 21 tumors were diagnosed, of which 18 demonstrated intralesional susceptibility effects both in pre- and postcontrast SW images, and 19 demonstrated contrast enhancement in both SW images and T1-weighted spin-echo MR images. Conspicuity of susceptibility effects and image quality were improved in postcontrast images compared with precontrast images and the scan time was also reduced due to decreased TE values from 9 min (precontrast) to 7 min (postcontrast). The intravenous administration of MultiHance, an agent with high relaxivity, allowed a reduction of scan time from 9 min to 7 min while preserving excellent susceptibility effects and image quality in SW images obtained at 3 T. Contrast enhancement and intralesional susceptibility effects can be assessed in one sequence. (orig.)

Physics considerations in MV-CBCT multi-layer imager design.

Science.gov (United States)

Hu, Yue-Houng; Fueglistaller, Rony; Myronakis, Marios E; Rottmann, Joerg; Wang, Adam; Shedlock, Daniel; Morf, Daniel; Baturin, Paul; Huber, Pascal; Star-Lack, Josh M; Berbeco, Ross I

2018-05-30

Megavoltage (MV) cone-beam computed tomography (CBCT) using an electronic portal imaging (EPID) offers advantageous features, including 3D mapping, treatment beam registration, high-z artifact suppression, and direct radiation dose calculation. Adoption has been slowed by image quality limitations and concerns about imaging dose. Developments in imager design, including pixelated scintillators, structured phosphors, inexpensive scintillation materials, and multi-layer imager (MLI) architecture have been explored to improve EPID image quality and reduce imaging dose. The present study employs a hybrid Monte Carlo and linear systems model to determine the effect of detector design elements, such as multi-layer architecture and scintillation materials. We follow metrics of image quality including modulation transfer function (MTF) and noise power spectrum (NPS) from projection images to 3D reconstructions to in-plane slices and apply a task based figure-of-merit, the ideal observer signal-to-noise ratio (d') to determine the effect of detector design on object detectability. Generally, detectability was limited by detector noise performance. Deploying an MLI imager with a single scintillation material for all layers yields improvement in noise performance and d' linear with the number of layers. In general, improving x-ray absorption using thicker scintillators results in improved DQE(0). However, if light yield is low, performance will be affected by electronic noise at relatively high doses, resulting in rapid image quality degradation. Maximizing image quality in a heterogenous MLI detector (i.e. multiple different scintillation materials) is most affected by limiting imager noise. However, while a second-order effect, maximizing total spatial resolution of the MLI detector is a balance between the intensity contribution of each layer against its individual MTF. So, while a thinner scintillator may yield a maximal individual-layer MTF, its quantum efficiency will

Multi-target molecular imaging and its progress in research and application

International Nuclear Information System (INIS)

Tang Ganghua

2011-01-01

Multi-target molecular imaging (MMI) is an important field of research in molecular imaging. It includes multi-tracer multi-target molecular imaging(MTMI), fusion-molecule multi-target imaging (FMMI), coupling-molecule multi-target imaging (CMMI), and multi-target multifunctional molecular imaging(MMMI). In this paper,imaging modes of MMI are reviewed, and potential applications of positron emission tomography MMI in near future are discussed. (author)

Imaging brain tumour microstructure.

Science.gov (United States)

Nilsson, Markus; Englund, Elisabet; Szczepankiewicz, Filip; van Westen, Danielle; Sundgren, Pia C

2018-05-08

Imaging is an indispensable tool for brain tumour diagnosis, surgical planning, and follow-up. Definite diagnosis, however, often demands histopathological analysis of microscopic features of tissue samples, which have to be obtained by invasive means. A non-invasive alternative may be to probe corresponding microscopic tissue characteristics by MRI, or so called 'microstructure imaging'. The promise of microstructure imaging is one of 'virtual biopsy' with the goal to offset the need for invasive procedures in favour of imaging that can guide pre-surgical planning and can be repeated longitudinally to monitor and predict treatment response. The exploration of such methods is motivated by the striking link between parameters from MRI and tumour histology, for example the correlation between the apparent diffusion coefficient and cellularity. Recent microstructure imaging techniques probe even more subtle and specific features, providing parameters associated to cell shape, size, permeability, and volume distributions. However, the range of scenarios in which these techniques provide reliable imaging biomarkers that can be used to test medical hypotheses or support clinical decisions is yet unknown. Accurate microstructure imaging may moreover require acquisitions that go beyond conventional data acquisition strategies. This review covers a wide range of candidate microstructure imaging methods based on diffusion MRI and relaxometry, and explores advantages, challenges, and potential pitfalls in brain tumour microstructure imaging. Copyright © 2018. Published by Elsevier Inc.

Gender differences in brain activity generated by unpleasant word stimuli concerning body image: an fMRI study.

Science.gov (United States)

Shirao, Naoko; Okamoto, Yasumasa; Mantani, Tomoyuki; Okamoto, Yuri; Yamawaki, Shigeto

2005-01-01

We have previously reported that the temporomesial area, including the amygdala, is activated in women when processing unpleasant words concerning body image. To detect gender differences in brain activation during processing of these words. Functional magnetic resonance imaging was used to investigate 13 men and 13 women during an emotional decision task consisting of unpleasant words concerning body image and neutral words. The left medial prefrontal cortex and hippocampus were activated only among men, and the left amygdala was activated only among women during the task; activation in the apical prefrontal region was significantly greater in men than in women. Our data suggest that the prefrontal region is responsible for the gender differences in the processing of words concerning body image, and may also be responsible for gender differences in susceptibility to eating disorders.

Development of the Young Brain

Medline Plus

Full Text Available ... developing brain. Announcer: So how well are our children handing multi-tasking in a digital age that changes, seemingly, by the hour? Early evidence suggests -pretty well. In fact, the human ...

Algorithm Design of CPCI Backboard's Interrupts Management Based on VxWorks' Multi-Tasks

Science.gov (United States)

Cheng, Jingyuan; An, Qi; Yang, Junfeng

2006-09-01

This paper begins with a brief introduction of the embedded real-time operating system VxWorks and CompactPCI standard, then gives the programming interfaces of Peripheral Controller Interface (PCI) configuring, interrupts handling and multi-tasks programming interface under VxWorks, and then emphasis is placed on the software frameworks of CPCI interrupt management based on multi-tasks. This method is sound in design and easy to adapt, ensures that all possible interrupts are handled in time, which makes it suitable for data acquisition systems with multi-channels, a high data rate, and hard real-time high energy physics.

Imaging brain plasticity after trauma

Institute of Scientific and Technical Information of China (English)

Zhifeng Kou; Armin Iraji

2014-01-01

The brain is highly plastic after stroke or epilepsy;however, there is a paucity of brain plasticity investigation after traumatic brain injury (TBI). This mini review summarizes the most recent evidence of brain plasticity in human TBI patients from the perspective of advanced magnetic resonance imaging. Similar to other forms of acquired brain injury, TBI patients also demonstrat-ed both structural reorganization as well as functional compensation by the recruitment of other brain regions. However, the large scale brain network alterations after TBI are still unknown, and the ifeld is still short of proper means on how to guide the choice of TBI rehabilitation or treat-ment plan to promote brain plasticity. The authors also point out the new direction of brain plas-ticity investigation.

Functional magnetic resonance imaging study of neuronal activation during cognitive tasks related to frontal lobe functions in patients with obsessive-compulsive disorder

International Nuclear Information System (INIS)

Koizumi, Hazuki

2010-01-01

Previous neurological studies and brain activation studies using functional magnetic resonance imaging (f-MRI) have suggested frontal lobe dysfunctions in patients with obsessive-compulsive disorder (OCD). However, no f-MRI study has used cognitive tasks reflecting fluency of ideas and memory related to frontal lobe functions. The purposes of this study are to assess the neuropsychological examinations and brain activities of OCD patients using f-MRI, as well as, to investigate the relationship between the severity of obsessive-compulsive symptoms and frontal lobe functions. The subjects were 22 right-handed persons consisting of 11 outpatients who had received a diagnosis of OCD based on diagnostic and statistical manual of mental disorders-fourth edition (DMS-IV) and age- and sex-matched 11 healthy controls. All subjects were examined using Yale-Brown Obsessive-Compulsive Scale (Y-BOCS), Wechsler Adult Intelligence Scale-3 rd edition (WAIS-III), Wisconsin Card Sorting Test (WCST), Modified Stroop Test (MST), Verbal Fluency Test (VFT), Idea Fluency Test (IFT), and Rey-Auditory Verbal Learning Test (RAVLT). The brain activities were measured with f-MRI during three cognitive tasks; Task 1: idea generation (IFT), Task 2: word generation (VFT), and Task 3: remembrance of words (RAVLT). The block design was used in the trials, in which rest and activating tasks were alternated for five times in each task. The neuropsychological examinations revealed significant differences in the numbers of categories achieved and total errors in WCST, times of Part I in MST, scores of VFT and IFT, and the results of RAVLT between the OCD patients and healthy controls. Using functional brain imaging with f-MRI, noticeable activations were found in the superior, middle, inferior frontal gyri, and the cingulate gyrus during all tasks in both the OCD and control groups. The OCD patients had significantly higher activation in the cingulate gyrus than normal controls during Task 1 (IFT

Vessel Segmentation in Retinal Images Using Multi-scale Line Operator and K-Means Clustering.

Science.gov (United States)

Saffarzadeh, Vahid Mohammadi; Osareh, Alireza; Shadgar, Bita

2014-04-01

Detecting blood vessels is a vital task in retinal image analysis. The task is more challenging with the presence of bright and dark lesions in retinal images. Here, a method is proposed to detect vessels in both normal and abnormal retinal fundus images based on their linear features. First, the negative impact of bright lesions is reduced by using K-means segmentation in a perceptive space. Then, a multi-scale line operator is utilized to detect vessels while ignoring some of the dark lesions, which have intensity structures different from the line-shaped vessels in the retina. The proposed algorithm is tested on two publicly available STARE and DRIVE databases. The performance of the method is measured by calculating the area under the receiver operating characteristic curve and the segmentation accuracy. The proposed method achieves 0.9483 and 0.9387 localization accuracy against STARE and DRIVE respectively.

Identification and Analysis of Multi-tasking Product Information Search Sessions with Query Logs

Directory of Open Access Journals (Sweden)

Xiang Zhou

2016-09-01

Full Text Available Purpose: This research aims to identify product search tasks in online shopping and analyze the characteristics of consumer multi-tasking search sessions. Design/methodology/approach: The experimental dataset contains 8,949 queries of 582 users from 3,483 search sessions. A sequential comparison of the Jaccard similarity coefficient between two adjacent search queries and hierarchical clustering of queries is used to identify search tasks. Findings: (1 Users issued a similar number of queries (1.43 to 1.47 with similar lengths (7.3-7.6 characters per task in mono-tasking and multi-tasking sessions, and (2 Users spent more time on average in sessions with more tasks, but spent less time for each task when the number of tasks increased in a session. Research limitations: The task identification method that relies only on query terms does not completely reflect the complex nature of consumer shopping behavior. Practical implications: These results provide an exploratory understanding of the relationships among multiple shopping tasks, and can be useful for product recommendation and shopping task prediction. Originality/value: The originality of this research is its use of query clustering with online shopping task identification and analysis, and the analysis of product search session characteristics.

Brain MR Image Restoration Using an Automatic Trilateral Filter With GPU-Based Acceleration.

Science.gov (United States)

Chang, Herng-Hua; Li, Cheng-Yuan; Gallogly, Audrey Haihong

2018-02-01

Noise reduction in brain magnetic resonance (MR) images has been a challenging and demanding task. This study develops a new trilateral filter that aims to achieve robust and efficient image restoration. Extended from the bilateral filter, the proposed algorithm contains one additional intensity similarity funct-ion, which compensates for the unique characteristics of noise in brain MR images. An entropy function adaptive to intensity variations is introduced to regulate the contributions of the weighting components. To hasten the computation, parallel computing based on the graphics processing unit (GPU) strategy is explored with emphasis on memory allocations and thread distributions. To automate the filtration, image texture feature analysis associated with machine learning is investigated. Among the 98 candidate features, the sequential forward floating selection scheme is employed to acquire the optimal texture features for regularization. Subsequently, a two-stage classifier that consists of support vector machines and artificial neural networks is established to predict the filter parameters for automation. A speedup gain of 757 was reached to process an entire MR image volume of 256 × 256 × 256 pixels, which completed within 0.5 s. Automatic restoration results revealed high accuracy with an ensemble average relative error of 0.53 ± 0.85% in terms of the peak signal-to-noise ratio. This self-regulating trilateral filter outperformed many state-of-the-art noise reduction methods both qualitatively and quantitatively. We believe that this new image restoration algorithm is of potential in many brain MR image processing applications that require expedition and automation.

Research status of multi - robot systems task allocation and uncertainty treatment

Science.gov (United States)

Li, Dahui; Fan, Qi; Dai, Xuefeng

2017-08-01

The multi-robot coordination algorithm has become a hot research topic in the field of robotics in recent years. It has a wide range of applications and good application prospects. This paper analyzes and summarizes the current research status of multi-robot coordination algorithms at home and abroad. From task allocation and dealing with uncertainty, this paper discusses the multi-robot coordination algorithm and presents the advantages and disadvantages of each method commonly used.

PET image reconstruction using multi-parametric anato-functional priors

Science.gov (United States)

Mehranian, Abolfazl; Belzunce, Martin A.; Niccolini, Flavia; Politis, Marios; Prieto, Claudia; Turkheimer, Federico; Hammers, Alexander; Reader, Andrew J.

2017-08-01

In this study, we investigate the application of multi-parametric anato-functional (MR-PET) priors for the maximum a posteriori (MAP) reconstruction of brain PET data in order to address the limitations of the conventional anatomical priors in the presence of PET-MR mismatches. In addition to partial volume correction benefits, the suitability of these priors for reconstruction of low-count PET data is also introduced and demonstrated, comparing to standard maximum-likelihood (ML) reconstruction of high-count data. The conventional local Tikhonov and total variation (TV) priors and current state-of-the-art anatomical priors including the Kaipio, non-local Tikhonov prior with Bowsher and Gaussian similarity kernels are investigated and presented in a unified framework. The Gaussian kernels are calculated using both voxel- and patch-based feature vectors. To cope with PET and MR mismatches, the Bowsher and Gaussian priors are extended to multi-parametric priors. In addition, we propose a modified joint Burg entropy prior that by definition exploits all parametric information in the MAP reconstruction of PET data. The performance of the priors was extensively evaluated using 3D simulations and two clinical brain datasets of [18F]florbetaben and [18F]FDG radiotracers. For simulations, several anato-functional mismatches were intentionally introduced between the PET and MR images, and furthermore, for the FDG clinical dataset, two PET-unique active tumours were embedded in the PET data. Our simulation results showed that the joint Burg entropy prior far outperformed the conventional anatomical priors in terms of preserving PET unique lesions, while still reconstructing functional boundaries with corresponding MR boundaries. In addition, the multi-parametric extension of the Gaussian and Bowsher priors led to enhanced preservation of edge and PET unique features and also an improved bias-variance performance. In agreement with the simulation results, the clinical results

Assessment of brain damage in a geriatric population through use of a visual-searching task.

Science.gov (United States)

Turbiner, M; Derman, R M

1980-04-01

This study was designed to assess the discriminative capacity of a visual-searching task for brain damage, as described by Goldstein and Kyc (1978), for 10 hospitalized male, brain-damaged patients, 10 hospitalized male schizophrenic patients, and 10 normal subjects in a control group, all of whom were approximately 65 yr. old. The derived data indicated, at a statistically significant level, that the visual-searching task was effective in successfully classifying 80% of the brain-damaged sample when compared to the schizophrenic patients and discriminating 90% of the brain-damaged patients from normal subjects.

Do brain image databanks support understanding of normal ageing brain structure? A systematic review

International Nuclear Information System (INIS)

Dickie, David Alexander; Job, Dominic E.; Wardlaw, Joanna M.; Poole, Ian; Ahearn, Trevor S.; Staff, Roger T.; Murray, Alison D.

2012-01-01

To document accessible magnetic resonance (MR) brain images, metadata and statistical results from normal older subjects that may be used to improve diagnoses of dementia. We systematically reviewed published brain image databanks (print literature and Internet) concerned with normal ageing brain structure. From nine eligible databanks, there appeared to be 944 normal subjects aged ≥60 years. However, many subjects were in more than one databank and not all were fully representative of normal ageing clinical characteristics. Therefore, there were approximately 343 subjects aged ≥60 years with metadata representative of normal ageing, but only 98 subjects were openly accessible. No databank had the range of MR image sequences, e.g. T2*, fluid-attenuated inversion recovery (FLAIR), required to effectively characterise the features of brain ageing. No databank supported random subject retrieval; therefore, manual selection bias and errors may occur in studies that use these subjects as controls. Finally, no databank stored results from statistical analyses of its brain image and metadata that may be validated with analyses of further data. Brain image databanks require open access, more subjects, metadata, MR image sequences, searchability and statistical results to improve understanding of normal ageing brain structure and diagnoses of dementia. (orig.)

Algorithm-Dependent Generalization Bounds for Multi-Task Learning.

Science.gov (United States)

Liu, Tongliang; Tao, Dacheng; Song, Mingli; Maybank, Stephen J

2017-02-01

Often, tasks are collected for multi-task learning (MTL) because they share similar feature structures. Based on this observation, in this paper, we present novel algorithm-dependent generalization bounds for MTL by exploiting the notion of algorithmic stability. We focus on the performance of one particular task and the average performance over multiple tasks by analyzing the generalization ability of a common parameter that is shared in MTL. When focusing on one particular task, with the help of a mild assumption on the feature structures, we interpret the function of the other tasks as a regularizer that produces a specific inductive bias. The algorithm for learning the common parameter, as well as the predictor, is thereby uniformly stable with respect to the domain of the particular task and has a generalization bound with a fast convergence rate of order O(1/n), where n is the sample size of the particular task. When focusing on the average performance over multiple tasks, we prove that a similar inductive bias exists under certain conditions on the feature structures. Thus, the corresponding algorithm for learning the common parameter is also uniformly stable with respect to the domains of the multiple tasks, and its generalization bound is of the order O(1/T), where T is the number of tasks. These theoretical analyses naturally show that the similarity of feature structures in MTL will lead to specific regularizations for predicting, which enables the learning algorithms to generalize fast and correctly from a few examples.

Optical Methods and Instrumentation in Brain Imaging and Therapy

CERN Document Server

2013-01-01

This book provides a comprehensive up-to-date review of optical approaches used in brain imaging and therapy. It covers a variety of imaging techniques including diffuse optical imaging, laser speckle imaging, photoacoustic imaging and optical coherence tomography. A number of laser-based therapeutic approaches are reviewed, including photodynamic therapy, fluorescence guided resection and photothermal therapy. Fundamental principles and instrumentation are discussed for each imaging and therapeutic technique. Represents the first publication dedicated solely to optical diagnostics and therapeutics in the brain Provides a comprehensive review of the principles of each imaging/therapeutic modality Reviews the latest advances in instrumentation for optical diagnostics in the brain Discusses new optical-based therapeutic approaches for brain diseases

Brain MR imaging in child abuse

International Nuclear Information System (INIS)

Sato, Y.; Ellerbroek, C.J.; Alexander, R.; Kao, S.C.S.; Yuh, W.T.C.; Smith, W.L.

1988-01-01

Intracranial injuries represent the most severe manifestation of child abuse. CT of the brain is the current standard for evaluation of these infants; however, MR imaging offers several potential advantages. MR imaging and CT were performed in ten infants who suffered intracranial trauma owing to child abuse. CT was slightly better at demonstrating subarachnoid hemorrhage and had definite advantages for defining fractures. MR imaging was superior in the demonstration of subacute extraaxial hemorrhage, deep brain injuries owing to shearing effects from shaking, and anoxic injuries. MR imaging has a definite complementary role in the evaluation of acute intracranial trauma in child abuse victims

Brain's tumor image processing using shearlet transform

Science.gov (United States)

Cadena, Luis; Espinosa, Nikolai; Cadena, Franklin; Korneeva, Anna; Kruglyakov, Alexey; Legalov, Alexander; Romanenko, Alexey; Zotin, Alexander

2017-09-01

Brain tumor detection is well known research area for medical and computer scientists. In last decades there has been much research done on tumor detection, segmentation, and classification. Medical imaging plays a central role in the diagnosis of brain tumors and nowadays uses methods non-invasive, high-resolution techniques, especially magnetic resonance imaging and computed tomography scans. Edge detection is a fundamental tool in image processing, particularly in the areas of feature detection and feature extraction, which aim at identifying points in a digital image at which the image has discontinuities. Shearlets is the most successful frameworks for the efficient representation of multidimensional data, capturing edges and other anisotropic features which frequently dominate multidimensional phenomena. The paper proposes an improved brain tumor detection method by automatically detecting tumor location in MR images, its features are extracted by new shearlet transform.

Whole-brain functional magnetic resonance imaging of human brain during voluntary movements of dominant and subdominant hands

International Nuclear Information System (INIS)

Yu Wei; Yan Zixu; Ma Xiaohai; Zhang Zhaoqi; Lin Chongyu; Zang Yufeng; Weng Xuchu

2003-01-01

Objective: To identify the neural substrates of voluntary movements of dominant and subdominant hands by using the whole-brain functional magnetic resonance imaging. Methods: Seven right-handed healthy volunteers were scanned at a Sonata 1.5 Tesla magnetic resonance imaging scanner (Siemens) while they were performing the visually instructive movement tasks with their right and left index fingers. Image data were co-registered to correct head motion, spatially normalized according to the standard coordinates, and spatially smoothed with isotopic Guassian Kernel. Statistical parametric maps (activation maps) for right and left hands were generated respectively by cross-correlation analysis. Results: Voluntary movements of the right/dominant hand mainly activated contralateral primary motor cortex (MI), bilateral supplementary motor area (SMA), bilateral second motor area (MII), and ipsilateral cerebellum, whereas movements of the left/subdominant hand additionally elicited activation in contralateral premotor area (PMC). Moreover, activation volumes in SMA and MII during movements of the subdominant hand were significantly larger than those during movements of the dominant hand. Conclusion: A large set of structures in the cerebral cortex and cerebellum is involved in voluntary movements, as revealed by whole brain-based fMRI. Movements of the subdominant hand are more dependent on higher control areas, such as SMA and PMC, comparing to movements of the dominant hand

Multi-atlas attenuation correction supports full quantification of static and dynamic brain PET data in PET-MR

Science.gov (United States)

Mérida, Inés; Reilhac, Anthonin; Redouté, Jérôme; Heckemann, Rolf A.; Costes, Nicolas; Hammers, Alexander

2017-04-01

In simultaneous PET-MR, attenuation maps are not directly available. Essential for absolute radioactivity quantification, they need to be derived from MR or PET data to correct for gamma photon attenuation by the imaged object. We evaluate a multi-atlas attenuation correction method for brain imaging (MaxProb) on static [18F]FDG PET and, for the first time, on dynamic PET, using the serotoninergic tracer [18F]MPPF. A database of 40 MR/CT image pairs (atlases) was used. The MaxProb method synthesises subject-specific pseudo-CTs by registering each atlas to the target subject space. Atlas CT intensities are then fused via label propagation and majority voting. Here, we compared these pseudo-CTs with the real CTs in a leave-one-out design, contrasting the MaxProb approach with a simplified single-atlas method (SingleAtlas). We evaluated the impact of pseudo-CT accuracy on reconstructed PET images, compared to PET data reconstructed with real CT, at the regional and voxel levels for the following: radioactivity images; time-activity curves; and kinetic parameters (non-displaceable binding potential, BPND). On static [18F]FDG, the mean bias for MaxProb ranged between 0 and 1% for 73 out of 84 regions assessed, and exceptionally peaked at 2.5% for only one region. Statistical parametric map analysis of MaxProb-corrected PET data showed significant differences in less than 0.02% of the brain volume, whereas SingleAtlas-corrected data showed significant differences in 20% of the brain volume. On dynamic [18F]MPPF, most regional errors on BPND ranged from -1 to  +3% (maximum bias 5%) for the MaxProb method. With SingleAtlas, errors were larger and had higher variability in most regions. PET quantification bias increased over the duration of the dynamic scan for SingleAtlas, but not for MaxProb. We show that this effect is due to the interaction of the spatial tracer-distribution heterogeneity variation over time with the degree of accuracy of the attenuation maps. This

Functional Brain Imaging Synthesis Based on Image Decomposition and Kernel Modeling: Application to Neurodegenerative Diseases

Directory of Open Access Journals (Sweden)

Francisco J. Martinez-Murcia

2017-11-01

Full Text Available The rise of neuroimaging in research and clinical practice, together with the development of new machine learning techniques has strongly encouraged the Computer Aided Diagnosis (CAD of different diseases and disorders. However, these algorithms are often tested in proprietary datasets to which the access is limited and, therefore, a direct comparison between CAD procedures is not possible. Furthermore, the sample size is often small for developing accurate machine learning methods. Multi-center initiatives are currently a very useful, although limited, tool in the recruitment of large populations and standardization of CAD evaluation. Conversely, we propose a brain image synthesis procedure intended to generate a new image set that share characteristics with an original one. Our system focuses on nuclear imaging modalities such as PET or SPECT brain images. We analyze the dataset by applying PCA to the original dataset, and then model the distribution of samples in the projected eigenbrain space using a Probability Density Function (PDF estimator. Once the model has been built, we can generate new coordinates on the eigenbrain space belonging to the same class, which can be then projected back to the image space. The system has been evaluated on different functional neuroimaging datasets assessing the: resemblance of the synthetic images with the original ones, the differences between them, their generalization ability and the independence of the synthetic dataset with respect to the original. The synthetic images maintain the differences between groups found at the original dataset, with no significant differences when comparing them to real-world samples. Furthermore, they featured a similar performance and generalization capability to that of the original dataset. These results prove that these images are suitable for standardizing the evaluation of CAD pipelines, and providing data augmentation in machine learning systems -e.g. in deep