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Sample records for left frontal convolution

  1. Variations in size, shape and asymmetries of the third frontal convolution in hominids: paleoneurological implications for hominin evolution and the origin of language.

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    Balzeau, Antoine; Gilissen, Emmanuel; Holloway, Ralph L; Prima, Sylvain; Grimaud-Hervé, Dominique

    2014-11-01

    The study of brain structural asymmetries as anatomical substrates of functional asymmetries in extant humans, great apes, and fossil hominins is of major importance in understanding the structural basis of modern human cognition. We propose methods to quantify the variation in size, shape and bilateral asymmetries of the third frontal convolution (or posterior inferior frontal gyrus) among recent modern humans, bonobos and chimpanzees, and fossil hominins using actual and virtual endocasts. These methodological improvements are necessary to extend previous qualitative studies of these features. We demonstrate both an absolute and relative bilateral increase in the size of the third frontal convolution in width and length between Pan species, as well as in hominins. We also observed a global bilateral increase in the size of the third frontal convolution across all species during hominin evolution, but also non-allometric intra-group variations independent of brain size within the fossil samples. Finally, our results show that the commonly accepted leftward asymmetry of Broca's cap is biased by qualitative observation of individual specimens. The trend during hominin evolution seems to be a reduction in size on the left compared with the right side, and also a clearer definition of the area. The third frontal convolution considered as a whole projects more laterally and antero-posteriorly in the right hemisphere. As a result, the left 'Broca's cap' looks more globular and better defined. Our results also suggest that the pattern of brain asymmetries is similar between Pan paniscus and hominins, leaving the gradient of the degree of asymmetry as the only relevant structural parameter. As the anatomical substrate related to brain asymmetry has been present since the appearance of the hominin lineage, it is not possible to prove a direct relationship between the extent of variations in the size, shape, and asymmetries of the third frontal convolution and the origin of

  2. Using Convolutional Neural Network Filters to Measure Left-Right Mirror Symmetry in Images

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    Anselm Brachmann

    2016-12-01

    Full Text Available We propose a method for measuring symmetry in images by using filter responses from Convolutional Neural Networks (CNNs. The aim of the method is to model human perception of left/right symmetry as closely as possible. Using the Convolutional Neural Network (CNN approach has two main advantages: First, CNN filter responses closely match the responses of neurons in the human visual system; they take information on color, edges and texture into account simultaneously. Second, we can measure higher-order symmetry, which relies not only on color, edges and texture, but also on the shapes and objects that are depicted in images. We validated our algorithm on a dataset of 300 music album covers, which were rated according to their symmetry by 20 human observers, and compared results with those from a previously proposed method. With our method, human perception of symmetry can be predicted with high accuracy. Moreover, we demonstrate that the inclusion of features from higher CNN layers, which encode more abstract image content, increases the performance further. In conclusion, we introduce a model of left/right symmetry that closely models human perception of symmetry in CD album covers.

  3. Role of the left frontal aslant tract in stuttering: a brain stimulation and tractographic study.

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    Kemerdere, Rahsan; de Champfleur, Nicolas Menjot; Deverdun, Jérémy; Cochereau, Jérôme; Moritz-Gasser, Sylvie; Herbet, Guillaume; Duffau, Hugues

    2016-01-01

    The neural correlates of stuttering are to date incompletely understood. Although the possible involvement of the basal ganglia, the cerebellum and certain parts of the cerebral cortex in this speech disorder has previously been reported, there are still not many studies investigating the role of white matter fibers in stuttering. Axonal stimulation during awake surgery provides a unique opportunity to study the functional role of structural connectivity. Here, our goal was to investigate the white matter tracts implicated in stuttering, by combining direct electrostimulation mapping and postoperative tractography imaging, with a special focus on the left frontal aslant tract. Eight patients with no preoperative stuttering underwent awake surgery for a left frontal low-grade glioma. Intraoperative cortical and axonal electrical mapping was used to interfere in speech processing and subsequently provoke stuttering. We further assessed the relationship between the subcortical sites leading to stuttering and the spatial course of the frontal aslant tract. All patients experienced intraoperative stuttering during axonal electrostimulation. On postsurgical tractographies, the subcortical distribution of stimulated sites matched the topographical position of the left frontal aslant tract. This white matter pathway was preserved during surgery, and no patients had postoperative stuttering. For the first time to our knowledge, by using direct axonal stimulation combined with postoperative tractography, we provide original data supporting a pivotal role of the left frontal aslant tract in stuttering. We propose that this speech disorder could be the result of a disconnection within a large-scale cortico-subcortical circuit subserving speech motor control.

  4. Origin of human motor readiness field linked to left middle frontal gyrus by MEG and PET

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    Pedersen, Jane Rygaard; Johannsen, P; Bak, Christen Kjeldahl

    1998-01-01

    Combined magnetoencephalography and positron emission tomography identified a prior source of activity in the left middle frontal gyrus duping uncued movements of the right index finger Voluntary movements gave rise to a change in the cortical electrical potential known as the Bereitschaftspotent...

  5. Segmentation of Fetal Left Ventricle in Echocardiographic Sequences Based on Dynamic Convolutional Neural Networks.

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    Yu, Li; Guo, Yi; Wang, Yuanyuan; Yu, Jinhua; Chen, Ping

    2017-08-01

    Segmentation of fetal left ventricle (LV) in echocardiographic sequences is important for further quantitative analysis of fetal cardiac function. However, image gross inhomogeneities and fetal random movements make the segmentation a challenging problem. In this paper, a dynamic convolutional neural networks (CNN) based on multiscale information and fine-tuning is proposed for fetal LV segmentation. The CNN is pretrained by amount of labeled training data. In the segmentation, the first frame of each echocardiographic sequence is delineated manually. The dynamic CNN is fine-tuned by deep tuning with the first frame and shallow tuning with the rest of frames, respectively, to adapt to the individual fetus. Additionally, to separate the connection region between LV and left atrium (LA), a matching approach, which consists of block matching and line matching, is used for mitral valve (MV) base points tracking. Advantages of our proposed method are compared with an active contour model (ACM), a dynamical appearance model (DAM), and a fixed multiscale CNN method. Experimental results in 51 echocardiographic sequences show that the segmentation results agree well with the ground truth, especially in the cases with leakage, blurry boundaries, and subject-to-subject variations. The CNN architecture can be simple, and the dynamic fine-tuning is efficient.

  6. Memory of music: roles of right hippocampus and left inferior frontal gyrus.

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    Watanabe, Takamitsu; Yagishita, Sho; Kikyo, Hideyuki

    2008-01-01

    We investigated neural correlates of retrieval success for music memory using event-related functional magnetic resonance imaging. To minimize the interference from MRI scan noise, we used sparse temporal sampling technique. Newly composed music materials were employed as stimuli, which enabled us to detect regions in absence of effects of experience with the music stimuli in this study. Whole brain analyses demonstrated significant retrieval success activities in the right hippocampus, bilateral lateral temporal regions, left inferior frontal gyrus and left precuneus. Anatomically defined region-of-interests analyses showed that the activity of the right hippocampus was stronger than that of the left, while the activities of the inferior frontal gyri showed the reverse pattern. Furthermore, performance-based analyses demonstrated that the retrieval success activity of the right hippocampus was positively correlated with the corrected recognition rate, suggesting that the right hippocampus contributes to the accuracy of music retrieval outcome.

  7. Mirror Neurons, the Representation of Word meaning, and the Foot of the Third Left Frontal Convolution

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    de Zubicaray, Greig; Postle, Natasha; McMahon, Katie; Meredith, Matthew; Ashton, Roderick

    2010-01-01

    Previous neuroimaging research has attempted to demonstrate a preferential involvement of the human mirror neuron system (MNS) in the comprehension of effector-related action word (verb) meanings. These studies have assumed that Broca's area (or Brodmann's area 44) is the homologue of a monkey premotor area (F5) containing mouth and hand mirror…

  8. Behavioral approach system sensitivity and risk taking interact to predict left-frontal EEG asymmetry.

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    Black, Chelsea L; Goldstein, Kim E; LaBelle, Denise R; Brown, Christopher W; Harmon-Jones, Eddie; Abramson, Lyn Y; Alloy, Lauren B

    2014-09-01

    The Behavioral Approach System (BAS) hypersensitivity theory of bipolar disorder (BD; Alloy & Abramson, 2010; Depue & Iacono, 1989) suggests that hyperreactivity in the BAS results in the extreme fluctuations of mood characteristic of BD. In addition to risk conferred by BAS hypersensitivity, cognitive and personality variables may play a role in determining risk. We evaluated relationships among BAS sensitivity, risk taking, and an electrophysiological correlate of approach motivation, relative left-frontal electroencephalography (EEG) asymmetry. BAS sensitivity moderated the relationship between risk taking and EEG asymmetry. More specifically, individuals who were high in BAS sensitivity showed left-frontal EEG asymmetry regardless of their level of risk-taking behavior. However, among individuals who were moderate in BAS sensitivity, risk taking was positively associated with asymmetry. These findings suggest that cognitive and personality correlates of bipolar risk may evidence unique contributions to a neural measure of trait-approach motivation. Clinical implications of these findings are discussed. Copyright © 2014. Published by Elsevier Ltd.

  9. Statistical parametric mapping for analyzing interictal magnetoencephalography in patients with left frontal lobe epilepsy.

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    Zhu, Haitao; Zhu, Jinlong; Bao, Forrest Sheng; Liu, Hongyi; Zhu, Xuchuang; Wu, Ting; Yang, Lu; Zou, Yuanjie; Zhang, Rui; Zheng, Gang

    2016-01-01

    Frontal lobe epilepsy is a common epileptic disorder and is characterized by recurring seizures that arise in the frontal lobes. The purpose of this study is to identify the epileptogenic regions and other abnormal regions in patients with left frontal lobe epilepsy (LFLE) based on the magnetoencephalogram (MEG), and to understand the effects of clinical variables on brain activities in patients with LFLE. Fifteen patients with LFLE (23.20 ± 8.68 years, 6 female and 9 male) and 16 healthy controls (23.13 ± 7.66 years, 6 female and 10 male) were included in resting-stage MEG examinations. Epileptogenic regions of LFLE patients were confirmed by surgery. Regional brain activations were quantified using statistical parametric mapping (SPM). The correlation between the activations of the abnormal brain regions and the clinical seizure parameters were computed for LFLE patients. Brain activations of LFLE patients were significantly elevated in left superior/middle/inferior frontal gyri, postcentral gyrus, inferior temporal gyrus, insula, parahippocampal gyrus and amygdala, including the epileptogenic regions. Remarkable decreased activations were found mainly in the left parietal gyrus and precuneus. There is a positive correlation between the duration of the epilepsy (in month) and activations of the abnormal regions, while no relation was found between age of seizure onset (year), seizure frequency and the regions of the abnormal activity of the epileptic patients. Our findings suggest that the aberrant brain activities of LFLE patients were not restricted to the epileptogenic zones. Long duration of epilepsy might induce further functional damage in patients with LFLE. Copyright © 2015 British Epilepsy Association. Published by Elsevier Ltd. All rights reserved.

  10. Elevated left mid-frontal cortical activity prospectively predicts conversion to bipolar I disorder

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    Nusslock, Robin; Harmon-Jones, Eddie; Alloy, Lauren B.; Urosevic, Snezana; Goldstein, Kim; Abramson, Lyn Y.

    2013-01-01

    Bipolar disorder is characterized by a hypersensitivity to reward-relevant cues and a propensity to experience an excessive increase in approach-related affect, which may be reflected in hypo/manic symptoms. The present study examined the relationship between relative left-frontal electroencephalographic (EEG) activity, a proposed neurophysiological index of approach-system sensitivity and approach/reward-related affect, and bipolar course and state-related variables. Fifty-eight individuals with cyclothymia or bipolar II disorder and 59 healthy control participants with no affective psychopathology completed resting EEG recordings. Alpha power was obtained and asymmetry indices computed for homologous electrodes. Bipolar spectrum participants were classified as being in a major/minor depressive episode, a hypomanic episode, or a euthymic/remitted state at EEG recording. Participants were then followed prospectively for an average 4.7 year follow-up period with diagnostic interview assessments every four-months. Sixteen bipolar spectrum participants converted to bipolar I disorder during follow-up. Consistent with hypotheses, elevated relative left-frontal EEG activity at baseline 1) prospectively predicted a greater likelihood of converting from cyclothymia or bipolar II disorder to bipolar I disorder over the 4.7 year follow-up period, 2) was associated with an earlier age-of-onset of first bipolar spectrum episode, and 3) was significantly elevated in bipolar spectrum individuals in a hypomanic episode at EEG recording. This is the first study to identify a neurophysiological marker that prospectively predicts conversion to bipolar I disorder. The fact that unipolar depression is characterized by decreased relative left-frontal EEG activity suggests that unipolar depression and vulnerability to hypo/mania may be characterized by different profiles of frontal EEG asymmetry. PMID:22775582

  11. Distinct roles of left inferior frontal regions that explain individual differences in second language acquisition.

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    Sakai, Kuniyoshi L; Nauchi, Arihito; Tatsuno, Yoshinori; Hirano, Kazuyoshi; Muraishi, Yukimasa; Kimura, Masakazu; Bostwick, Mike; Yusa, Noriaki

    2009-08-01

    Second language (L2) acquisition is more susceptible to environmental and idiosyncratic factors than first language acquisition. Here, we used functional magnetic resonance imaging for L2 learners of different ages of first exposure (mean: 12.6 and 5.6 years) in a formal school environment, and compared the cortical activations involved in processing English sentences containing either syntactic or spelling errors, where the testing ages and task performances of both groups were matched. We found novel activation patterns in two regions of the left inferior frontal gyrus (IFG) that correlated differentially with the performances of the late and early learners. Specifically, activations of the dorsal and ventral triangular part (F3t) of the left IFG correlated positively with the accuracy of the syntactic task for the late learners, whereas activations of the left ventral F3t correlated negatively with the accuracy for the early learners. In contrast, other cortical regions exhibited differential correlation patterns with the reaction times (RTs) of the syntactic task. Namely, activations of the orbital part (F3O) of the left IFG, as well as those of the left angular gyrus, correlated positively with the RTs for the late learners, whereas those activations correlated negatively with the RTs for the early learners. Moreover, the task-selective activation of the left F3O was maintained for both the late and early learners. These results explain individual differences in L2 acquisition, such that the acquisition of linguistic knowledge in L2 is subserved by at least two distinct inferior frontal regions of the left F3t and F3O. (c) 2008 Wiley-Liss, Inc.

  12. Reasoning by analogy requires the left frontal pole: lesion-deficit mapping and clinical implications.

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    Urbanski, Marika; Bréchemier, Marie-Laure; Garcin, Béatrice; Bendetowicz, David; Thiebaut de Schotten, Michel; Foulon, Chris; Rosso, Charlotte; Clarençon, Frédéric; Dupont, Sophie; Pradat-Diehl, Pascale; Labeyrie, Marc-Antoine; Levy, Richard; Volle, Emmanuelle

    2016-06-01

    SEE BURGESS DOI101093/BRAIN/AWW092 FOR A SCIENTIFIC COMMENTARY ON THIS ARTICLE  : Analogical reasoning is at the core of the generalization and abstraction processes that enable concept formation and creativity. The impact of neurological diseases on analogical reasoning is poorly known, despite its importance in everyday life and in society. Neuroimaging studies of healthy subjects and the few studies that have been performed on patients have highlighted the importance of the prefrontal cortex in analogical reasoning. However, the critical cerebral bases for analogical reasoning deficits remain elusive. In the current study, we examined analogical reasoning abilities in 27 patients with focal damage in the frontal lobes and performed voxel-based lesion-behaviour mapping and tractography analyses to investigate the structures critical for analogical reasoning. The findings revealed that damage to the left rostrolateral prefrontal region (or some of its long-range connections) specifically impaired the ability to reason by analogies. A short version of the analogy task predicted the existence of a left rostrolateral prefrontal lesion with good accuracy. Experimental manipulations of the analogy tasks suggested that this region plays a role in relational matching or integration. The current lesion approach demonstrated that the left rostrolateral prefrontal region is a critical node in the analogy network. Our results also suggested that analogy tasks should be translated to clinical practice to refine the neuropsychological assessment of patients with frontal lobe lesions. © The Author (2016). Published by Oxford University Press on behalf of the Guarantors of Brain. All rights reserved. For Permissions, please email: journals.permissions@oup.com.

  13. Generating predictions: lesion evidence on the role of left inferior frontal cortex in rapid syntactic analysis.

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    Jakuszeit, Maria; Kotz, Sonja A; Hasting, Anna S

    2013-01-01

    A well-documented phenomenon in event-related electroencephalography (EEG) and magnetoencephalography (MEG) studies on language processing is that syntactic violations of different types elicit negativities as early as 100 msec after the violation point. Recently, these responses have been associated with activations in or very close to sensory cortices, suggesting the involvement of basic sensory mechanisms in the detection of syntactic violations. The present study investigated whether intact auditory cortices and adjacent temporal regions are sufficient to generate early syntactic negativities in the auditory event-related potential (ERP). We tested ten clinically non-aphasic patients with left inferior frontal lesions, but intact temporal cortices in a passive auditory ERP paradigm that had reliably elicited early negativities in response to violations of subject-verb agreement and word category in the past. Subject-verb agreement violations failed to elicit early grammaticality effects in these patients, whereas a group of ten age-matched controls showed a reliable early negativity. This finding supports the idea that sensory aspects of syntactic analysis as reflected in early syntactic negativities critically depend on top-down predictions generated by the left inferior frontal cortex. In contrast, word category violations elicited a small, marginally significant early negativity both in controls and patients, suggesting an additional involvement of temporal regions in early phrase structure processing. In an additional auditory oddball experiment patients showed a regular P300, but no N2b component in response to deviant tones, indicating that their deficit in generating sensory predictions extends beyond the language domain. Copyright © 2013 Elsevier Ltd. All rights reserved.

  14. Left inferior frontal gyrus mediates morphosyntax: ERP evidence from verb processing in left-hemisphere damaged patients.

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    Regel, Stefanie; Kotz, Sonja A; Henseler, Ilona; Friederici, Angela D

    2017-01-01

    Neurocognitive models of language comprehension have proposed different mechanisms with different neural substrates mediating human language processing. Whether the left inferior frontal gyrus (LIFG) is engaged in morpho-syntactic information processing is currently still controversially debated. The present study addresses this issue by examining the processing of irregular verb inflection in real words (e.g., swim > swum > swam) and pseudowords (e.g., frim > frum > fram) by using event-related brain potentials (ERPs) in neurological patients with lesions in the LIFG involving Broca's area as well as healthy controls. Different ERP patterns in response to the grammatical violations were observed in both groups. Controls showed a biphasic negativity-P600 pattern in response to incorrect verb inflections whereas patients with LIFG lesions displayed a N400. For incorrect pseudoword inflections, a late positivity was found in controls, while no ERP effects were obtained in patients. These findings of different ERP patterns in the two groups strongly indicate an involvement of LIFG in morphosyntactic processing, thereby suggesting brain regions' specialization for different language functions. Copyright © 2016 Elsevier Ltd. All rights reserved.

  15. Early-latency categorical speech sound representations in the left inferior frontal gyrus.

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    Alho, Jussi; Green, Brannon M; May, Patrick J C; Sams, Mikko; Tiitinen, Hannu; Rauschecker, Josef P; Jääskeläinen, Iiro P

    2016-04-01

    Efficient speech perception requires the mapping of highly variable acoustic signals to distinct phonetic categories. How the brain overcomes this many-to-one mapping problem has remained unresolved. To infer the cortical location, latency, and dependency on attention of categorical speech sound representations in the human brain, we measured stimulus-specific adaptation of neuromagnetic responses to sounds from a phonetic continuum. The participants attended to the sounds while performing a non-phonetic listening task and, in a separate recording condition, ignored the sounds while watching a silent film. Neural adaptation indicative of phoneme category selectivity was found only during the attentive condition in the pars opercularis (POp) of the left inferior frontal gyrus, where the degree of selectivity correlated with the ability of the participants to categorize the phonetic stimuli. Importantly, these category-specific representations were activated at an early latency of 115-140 ms, which is compatible with the speed of perceptual phonetic categorization. Further, concurrent functional connectivity was observed between POp and posterior auditory cortical areas. These novel findings suggest that when humans attend to speech, the left POp mediates phonetic categorization through integration of auditory and motor information via the dorsal auditory stream. Copyright © 2016 Elsevier Inc. All rights reserved.

  16. Positive association of video game playing with left frontal cortical thickness in adolescents.

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    Simone Kühn

    Full Text Available Playing video games is a common recreational activity of adolescents. Recent research associated frequent video game playing with improvements in cognitive functions. Improvements in cognition have been related to grey matter changes in prefrontal cortex. However, a fine-grained analysis of human brain structure in relation to video gaming is lacking. In magnetic resonance imaging scans of 152 14-year old adolescents, FreeSurfer was used to estimate cortical thickness. Cortical thickness across the whole cortical surface was correlated with self-reported duration of video gaming (hours per week. A robust positive association between cortical thickness and video gaming duration was observed in left dorsolateral prefrontal cortex (DLPFC and left frontal eye fields (FEFs. No regions showed cortical thinning in association with video gaming frequency. DLPFC is the core correlate of executive control and strategic planning which in turn are essential cognitive domains for successful video gaming. The FEFs are a key region involved in visuo-motor integration important for programming and execution of eye movements and allocation of visuo-spatial attention, processes engaged extensively in video games. The results may represent the biological basis of previously reported cognitive improvements due to video game play. Whether or not these results represent a-priori characteristics or consequences of video gaming should be studied in future longitudinal investigations.

  17. Transcranial Magnetic Stimulation over Left Inferior Frontal and Posterior Temporal Cortex Disrupts Gesture-Speech Integration.

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    Zhao, Wanying; Riggs, Kevin; Schindler, Igor; Holle, Henning

    2018-02-21

    Language and action naturally occur together in the form of cospeech gestures, and there is now convincing evidence that listeners display a strong tendency to integrate semantic information from both domains during comprehension. A contentious question, however, has been which brain areas are causally involved in this integration process. In previous neuroimaging studies, left inferior frontal gyrus (IFG) and posterior middle temporal gyrus (pMTG) have emerged as candidate areas; however, it is currently not clear whether these areas are causally or merely epiphenomenally involved in gesture-speech integration. In the present series of experiments, we directly tested for a potential critical role of IFG and pMTG by observing the effect of disrupting activity in these areas using transcranial magnetic stimulation in a mixed gender sample of healthy human volunteers. The outcome measure was performance on a Stroop-like gesture task (Kelly et al., 2010a), which provides a behavioral index of gesture-speech integration. Our results provide clear evidence that disrupting activity in IFG and pMTG selectively impairs gesture-speech integration, suggesting that both areas are causally involved in the process. These findings are consistent with the idea that these areas play a joint role in gesture-speech integration, with IFG regulating strategic semantic access via top-down signals acting upon temporal storage areas. SIGNIFICANCE STATEMENT Previous neuroimaging studies suggest an involvement of inferior frontal gyrus and posterior middle temporal gyrus in gesture-speech integration, but findings have been mixed and due to methodological constraints did not allow inferences of causality. By adopting a virtual lesion approach involving transcranial magnetic stimulation, the present study provides clear evidence that both areas are causally involved in combining semantic information arising from gesture and speech. These findings support the view that, rather than being

  18. Greater Activity in the Frontal Cortex on Left Curves: A Vector-Based fNIRS Study of Left and Right Curve Driving.

    Directory of Open Access Journals (Sweden)

    Noriyuki Oka

    Full Text Available In the brain, the mechanisms of attention to the left and the right are known to be different. It is possible that brain activity when driving also differs with different horizontal road alignments (left or right curves, but little is known about this. We found driver brain activity to be different when driving on left and right curves, in an experiment using a large-scale driving simulator and functional near-infrared spectroscopy (fNIRS.The participants were fifteen healthy adults. We created a course simulating an expressway, comprising straight line driving and gentle left and right curves, and monitored the participants under driving conditions, in which they drove at a constant speed of 100 km/h, and under non-driving conditions, in which they simply watched the screen (visual task. Changes in hemoglobin concentrations were monitored at 48 channels including the prefrontal cortex, the premotor cortex, the primary motor cortex and the parietal cortex. From orthogonal vectors of changes in deoxyhemoglobin and changes in oxyhemoglobin, we calculated changes in cerebral oxygen exchange, reflecting neural activity, and statistically compared the resulting values from the right and left curve sections.Under driving conditions, there were no sites where cerebral oxygen exchange increased significantly more during right curves than during left curves (p > 0.05, but cerebral oxygen exchange increased significantly more during left curves (p < 0.05 in the right premotor cortex, the right frontal eye field and the bilateral prefrontal cortex. Under non-driving conditions, increases were significantly greater during left curves (p < 0.05 only in the right frontal eye field.Left curve driving was thus found to require more brain activity at multiple sites, suggesting that left curve driving may require more visual attention than right curve driving. The right frontal eye field was activated under both driving and non-driving conditions.

  19. Left Frontal Hub Connectivity during Memory Performance Supports Reserve in Aging and Mild Cognitive Impairment.

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    Franzmeier, Nicolai; Hartmann, Julia C; Taylor, Alexander N W; Araque Caballero, Miguel Á; Simon-Vermot, Lee; Buerger, Katharina; Kambeitz-Ilankovic, Lana M; Ertl-Wagner, Birgit; Mueller, Claudia; Catak, Cihan; Janowitz, Daniel; Stahl, Robert; Dichgans, Martin; Duering, Marco; Ewers, Michael

    2017-01-01

    Reserve in aging and Alzheimer's disease (AD) is defined as maintaining cognition at a relatively high level in the presence of neurodegeneration, an ability often associated with higher education among other life factors. Recent evidence suggests that higher resting-state functional connectivity within the frontoparietal control network, specifically the left frontal cortex (LFC) hub, contributes to higher reserve. Following up these previous resting-state fMRI findings, we probed memory-task related functional connectivity of the LFC hub as a neural substrate of reserve. In elderly controls (CN, n = 37) and patients with mild cognitive impairment (MCI, n = 17), we assessed global connectivity of the LFC hub during successful face-name association learning, using generalized psychophysiological interaction analyses. Reserve was quantified as residualized memory performance, accounted for gender and proxies of neurodegeneration (age, hippocampus atrophy, and APOE genotype). We found that greater education was associated with higher LFC-connectivity in both CN and MCI during successful memory. Furthermore, higher LFC-connectivity predicted higher residualized memory (i.e., reserve). These results suggest that higher LFC-connectivity contributes to reserve in both healthy and pathological aging.

  20. Transcortical mixed aphasia due to cerebral infarction in left inferior frontal lobe and temporo-parietal lobe

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    Maeshima, S.; Matsumoto, T.; Ueyoshi, A.; Toshiro, H.; Sekiguchi, E.; Okita, R.; Yamaga, H.; Ozaki, F.; Moriwaki, H.; Roger, P.

    2002-01-01

    We present a case of transcortical mixed aphasia caused by a cerebral embolism. A 77-year-old right-handed man was admitted to our hospital with speech disturbance and a right hemianopia. His spontaneous speech was remarkably reduced, and object naming, word fluency, comprehension, reading and writing were all severely disturbed. However, repetition of phonemes and sentences and reading aloud were fully preserved. Although magnetic resonance imaging (MRI) showed cerebral infarcts in the left frontal and parieto-occipital lobe which included the inferior frontal gyrus and angular gyrus, single photon emission CT revealed a wider area of low perfusion over the entire left hemisphere except for part of the left perisylvian language areas. The amytal (Wada) test, which was performed via the left internal carotid artery, revealed that the left hemisphere was dominant for language. Hence, it appears that transcortical mixed aphasia may be caused by the isolation of perisylvian speech areas, even if there is a lesion in the inferior frontal gyrus, due to disconnection from surrounding areas. (orig.)

  1. Transcortical mixed aphasia due to cerebral infarction in left inferior frontal lobe and temporo-parietal lobe

    Energy Technology Data Exchange (ETDEWEB)

    Maeshima, S.; Matsumoto, T.; Ueyoshi, A. [Department of Physical Medicine and Rehabilitation, Wakayama Medical University, Wakayama (Japan); Toshiro, H.; Sekiguchi, E.; Okita, R.; Yamaga, H.; Ozaki, F.; Moriwaki, H. [Department of Neurological Surgery, Hidaka General Hospital, Wakayama (Japan); Roger, P. [School of Communication Sciences and Disorders, University of Sydney, Sydney, NSW (Australia)

    2002-02-01

    We present a case of transcortical mixed aphasia caused by a cerebral embolism. A 77-year-old right-handed man was admitted to our hospital with speech disturbance and a right hemianopia. His spontaneous speech was remarkably reduced, and object naming, word fluency, comprehension, reading and writing were all severely disturbed. However, repetition of phonemes and sentences and reading aloud were fully preserved. Although magnetic resonance imaging (MRI) showed cerebral infarcts in the left frontal and parieto-occipital lobe which included the inferior frontal gyrus and angular gyrus, single photon emission CT revealed a wider area of low perfusion over the entire left hemisphere except for part of the left perisylvian language areas. The amytal (Wada) test, which was performed via the left internal carotid artery, revealed that the left hemisphere was dominant for language. Hence, it appears that transcortical mixed aphasia may be caused by the isolation of perisylvian speech areas, even if there is a lesion in the inferior frontal gyrus, due to disconnection from surrounding areas. (orig.)

  2. The left inferior frontal gyrus: A neural crossroads between abstract and concrete knowledge.

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    Della Rosa, Pasquale Anthony; Catricalà, Eleonora; Canini, Matteo; Vigliocco, Gabriella; Cappa, Stefano F

    2018-04-12

    Evidence from both neuropsychology and neuroimaging suggests that different types of information are necessary for representing and processing concrete and abstract word meanings. Both abstract and concrete concepts, however, conjointly rely on perceptual, verbal and contextual knowledge, with abstract concepts characterized by low values of imageability (IMG) (low sensory-motor grounding) and low context availability (CA) (more difficult to contextualize). Imaging studies supporting differences between abstract and concrete concepts show a greater recruitment of the left inferior frontal gyrus (LIFG) for abstract concepts, which has been attributed either to the representation of abstract-specific semantic knowledge or to the request for more executive control than in the case of concrete concepts. We conducted an fMRI study on 27 participants, using a lexical decision task involving both abstract and concrete words, whose IMG and CA values were explicitly modelled in separate parametric analyses. The LIFG was significantly more activated for abstract than for concrete words, and a conjunction analysis showed a common activation for words with low IMG or low CA only in the LIFG, in the same area reported for abstract words. A regional template map of brain activations was then traced for words with low IMG or low CA, and BOLD regional time-series were extracted and correlated with the specific LIFG neural activity elicited for abstract words. The regions associated to low IMG, which were functionally correlated with LIFG, were mainly in the left hemisphere, while those associated with low CA were in the right hemisphere. Finally, in order to reveal which LIFG-related network increased its connectivity with decreases of IMG or CA, we conducted generalized psychophysiological interaction analyses. The connectivity strength values extracted from each region connected with the LIFG were correlated with specific LIFG neural activity for abstract words, and a regression

  3. Enhancing verbal creativity: modulating creativity by altering the balance between right and left inferior frontal gyrus with tDCS.

    Science.gov (United States)

    Mayseless, N; Shamay-Tsoory, S G

    2015-04-16

    Creativity is the production of novel ideas that have value. Previous research indicated that while regions in the right hemisphere are implicated in the production of new ideas, damage to the left inferior frontal gyrus (IFG) is associated with increased creativity, indicating that the left IFG damage may have a "releasing" effect on creativity. To examine this, in the present study we used transcranial direct current stimulation (tDCS) to modulate activity of the right and the left IFG. In the first experiment we show that whereas anodal tDCS over the right IFG coupled with cathodal tDCS over the left IFG increases creativity as measured by a verbal divergent thinking task, the reverse stimulation does not affect creative production. To further confirm that only altering the balance between the two hemispheres is crucial in modulating creativity, in the second experiment we show that stimulation targeting separately the left IFG (cathodal stimulation) or the right IFG (anodal stimulation) did not result in changes in creativity as measured by verbal divergent thinking. These findings support the balance hypothesis, according to which verbal creativity requires a balance of activation between the right and the left frontal lobes, and more specifically, between the right and the left IFG. Copyright © 2015 IBRO. Published by Elsevier Ltd. All rights reserved.

  4. Maintaining the feelings of others in working memory is associated with activation of the left anterior insula and left frontal-parietal control network.

    Science.gov (United States)

    Smith, Ryan; Lane, Richard D; Alkozei, Anna; Bao, Jennifer; Smith, Courtney; Sanova, Anna; Nettles, Matthew; Killgore, William D S

    2017-05-01

    The maintenance of social/emotional information in working memory (SWM/EWM) has recently been the topic of multiple neuroimaging studies. However, some studies find that SWM/EWM involves a medial frontal-parietal network while others instead find lateral frontal-parietal activations similar to studies of verbal and visuospatial WM. In this study, we asked 26 healthy volunteers to complete an EWM task designed to examine whether different cognitive strategies- maintaining emotional images, words, or feelings- might account for these discrepant results. We also examined whether differences in EWM performance were related to general intelligence (IQ), emotional intelligence (EI), and emotional awareness (EA). We found that maintaining emotional feelings, even when accounting for neural activation attributable to maintaining emotional images/words, still activated a left lateral frontal-parietal network (including the anterior insula and posterior dorsomedial frontal cortex). We also found that individual differences in the ability to maintain feelings were positively associated with IQ and EA, but not with EI. These results suggest that maintaining the feelings of others (at least when perceived exteroceptively) involves similar frontal-parietal control networks to exteroceptive WM, and that it is similarly linked to IQ, but that it also may be an important component of EA. © The Author (2017). Published by Oxford University Press.

  5. Left frontal cortical activation and spreading of alternatives: tests of the action-based model of dissonance.

    Science.gov (United States)

    Harmon-Jones, Eddie; Harmon-Jones, Cindy; Fearn, Meghan; Sigelman, Jonathan D; Johnson, Peter

    2008-01-01

    The action-based model of dissonance predicts that following decisional commitment, approach-oriented motivational processes occur to assist in translating the decision into effective and unconflicted behavior. Therefore, the modulation of these approach-oriented processes should affect the degree to which individuals change their attitudes to be more consistent with the decisional commitment (spreading of alternatives). Experiment 1 demonstrated that a neurofeedback-induced decrease in relative left frontal cortical activation, which has been implicated in approach motivational processes, caused a reduction in spreading of alternatives. Experiment 2 manipulated an action-oriented mindset following a decision and demonstrated that the action-oriented mindset caused increased activation in the left frontal cortical region as well as increased spreading of alternatives. Discussion focuses on how this integration of neuroscience and dissonance theory benefits both parent literatures. Copyright 2008 APA, all rights reserved.

  6. The effect of commitment on relative left frontal cortical activity: tests of the action-based model of dissonance.

    Science.gov (United States)

    Harmon-Jones, Eddie; Harmon-Jones, Cindy; Serra, Raymond; Gable, Philip A

    2011-03-01

    The action-based model of dissonance and recent advances in neuroscience suggest that commitment to action should cause greater relative left frontal cortical activity. Two experiments were conducted in which electroencephalographic activity was recorded following commitment to action, operationalized with a perceived choice manipulation. Perceived high as compared to low choice to engage in the action, regardless of whether it was counterattitudinal or proattitudinal, caused greater relative left frontal cortical activity. Moreover, perceived high as compared to low choice caused attitudes to be more consistent with the action. These results broaden the theoretical reach of the action-based model by suggesting that similar neural and motivational processes are involved in attitudinal responses to counterattitudinal and proattitudinal commitments.

  7. Correlations between measures of executive attention and cortical thickness of left posterior middle frontal gyrus - a dichotic listening study

    Directory of Open Access Journals (Sweden)

    Lundervold Arvid

    2009-10-01

    Full Text Available Abstract Background The frontal lobe has been associated to a wide range of cognitive control functions and is also vulnerable to degeneration in old age. A recent study by Thomsen and colleagues showed a difference between a young and old sample in grey matter density and activation in the left middle frontal cortex (MFC and performance on a dichotic listening task. The present study investigated this brain behaviour association within a sample of healthy older individuals, and predicted a positive correlation between performance in a condition requiring executive attention and measures of grey matter structure of the posterior left MFC. Methods A dichotic listening forced attention paradigm was used to measure attention control functions. Subjects were instructed to report only the left or the right ear syllable of a dichotically presented consonant-vowel syllable pair. A conflict situation appears when subjects are instructed to report the left ear stimulus, caused by the conflict with the bottom-up, stimulus-driven right ear advantage. Overcoming this processing conflict was used as a measure of executive attention. Thickness and volumes of frontal lobe regions were derived from automated segmentation of 3D magnetic resonance image acquisitions. Results The results revealed a statistically significant positive correlation between the thickness measure of the left posterior MFC and performance on the dichotic listening measures of executive attention. Follow-up analyses showed that this correlation was only statistically significant in the subgroup that showed the typical bottom-up, stimulus-driven right ear advantage. Conclusion The results suggest that the left MFC is a part of an executive attention network, and that the dichotic listening forced attention paradigm may be a feasible tool for assessing subtle attentional dysfunctions in older adults.

  8. Neural substrates of semantic relationships: common and distinct left-frontal activities for generation of synonyms vs. antonyms.

    Science.gov (United States)

    Jeon, Hyeon-Ae; Lee, Kyoung-Min; Kim, Young-Bo; Cho, Zang-Hee

    2009-11-01

    Synonymous and antonymous relationships among words may reflect the organization and/or processing in the mental lexicon and its implementation in the brain. In this study, functional magnetic resonance imaging (fMRI) is employed to compare brain activities during generation of synonyms (SYN) and antonyms (ANT) prompted by the same words. Both SYN and ANT, when compared with reading nonwords (NW), activated a region in the left middle frontal gyrus (BA 46). Neighboring this region, there was a dissociation observed in that the ANT activation extended more anteriorly and laterally to the SYN activation. The activations in the left middle frontal gyrus may be related to mental processes that are shared in the SYN and ANT generations, such as engaging semantically related parts of mental lexicon for the word search, whereas the distinct activations unique for either SYN or ANT generation may reflect the additional component of antonym retrieval, namely, reversing the polarity of semantic relationship in one crucial dimension. These findings suggest that specific components in the semantic processing, such as the polarity reversal for antonym generation and the similarity assessment for synonyms, are separately and systematically laid out in the left-frontal cortex.

  9. The left frontal cortex supports reserve in aging by enhancing functional network efficiency.

    Science.gov (United States)

    Franzmeier, Nicolai; Hartmann, Julia; Taylor, Alexander N W; Araque-Caballero, Miguel Á; Simon-Vermot, Lee; Kambeitz-Ilankovic, Lana; Bürger, Katharina; Catak, Cihan; Janowitz, Daniel; Müller, Claudia; Ertl-Wagner, Birgit; Stahl, Robert; Dichgans, Martin; Duering, Marco; Ewers, Michael

    2018-03-06

    Recent evidence derived from functional magnetic resonance imaging (fMRI) studies suggests that functional hubs (i.e., highly connected brain regions) are important for mental health. We found recently that global connectivity of a hub in the left frontal cortex (LFC connectivity) is associated with relatively preserved memory abilities and higher levels of protective factors (education, IQ) in normal aging and Alzheimer's disease. These results suggest that LFC connectivity supports reserve capacity, alleviating memory decline. An open question, however, is why LFC connectivity is beneficial and supports memory function in the face of neurodegeneration. We hypothesized that higher LFC connectivity is associated with enhanced efficiency in connected major networks involved in episodic memory. We further hypothesized that higher LFC-related network efficiency predicts higher memory abilities. We assessed fMRI during a face-name association learning task performed by 26 healthy, cognitively normal elderly participants. Using beta-series correlation analysis, we computed task-related LFC connectivity to key memory networks, including the default mode network (DMN) and dorsal attention network (DAN). Network efficiency within the DMN and DAN was estimated by the graph theoretical small-worldness statistic. We applied linear regression analyses to test the association between LFC connectivity with the DMN/DAN and small-worldness of these networks. Mediation analysis was applied to test LFC connectivity to the DMN and DAN as a mediator of the association between education and higher DMN and DAN small-worldness. Last, we tested network small-worldness as a predictor of memory performance. We found that higher LFC connectivity to the DMN and DAN during successful memory encoding and recognition was associated with higher small-worldness of those networks. Higher task-related LFC connectivity mediated the association between education and higher small-worldness in the DMN

  10. Training and Validating a Deep Convolutional Neural Network for Computer-Aided Detection and Classification of Abnormalities on Frontal Chest Radiographs.

    Science.gov (United States)

    Cicero, Mark; Bilbily, Alexander; Colak, Errol; Dowdell, Tim; Gray, Bruce; Perampaladas, Kuhan; Barfett, Joseph

    2017-05-01

    Convolutional neural networks (CNNs) are a subtype of artificial neural network that have shown strong performance in computer vision tasks including image classification. To date, there has been limited application of CNNs to chest radiographs, the most frequently performed medical imaging study. We hypothesize CNNs can learn to classify frontal chest radiographs according to common findings from a sufficiently large data set. Our institution's research ethics board approved a single-center retrospective review of 35,038 adult posterior-anterior chest radiographs and final reports performed between 2005 and 2015 (56% men, average age of 56, patient type: 24% inpatient, 39% outpatient, 37% emergency department) with a waiver for informed consent. The GoogLeNet CNN was trained using 3 graphics processing units to automatically classify radiographs as normal (n = 11,702) or into 1 or more of cardiomegaly (n = 9240), consolidation (n = 6788), pleural effusion (n = 7786), pulmonary edema (n = 1286), or pneumothorax (n = 1299). The network's performance was evaluated using receiver operating curve analysis on a test set of 2443 radiographs with the criterion standard being board-certified radiologist interpretation. Using 256 × 256-pixel images as input, the network achieved an overall sensitivity and specificity of 91% with an area under the curve of 0.964 for classifying a study as normal (n = 1203). For the abnormal categories, the sensitivity, specificity, and area under the curve, respectively, were 91%, 91%, and 0.962 for pleural effusion (n = 782), 82%, 82%, and 0.868 for pulmonary edema (n = 356), 74%, 75%, and 0.850 for consolidation (n = 214), 81%, 80%, and 0.875 for cardiomegaly (n = 482), and 78%, 78%, and 0.861 for pneumothorax (n = 167). Current deep CNN architectures can be trained with modest-sized medical data sets to achieve clinically useful performance at detecting and excluding common pathology on chest radiographs.

  11. Left dorsolateral prefrontal cortex atrophy is associated with frontal lobe function in Alzheimer's disease and contributes to caregiver burden.

    Science.gov (United States)

    Matsuoka, Kiwamu; Yasuno, Fumihiko; Hashimoto, Akiko; Miyasaka, Toshiteru; Takahashi, Masato; Kiuchi, Kuniaki; Iida, Junzo; Kichikawa, Kimihiko; Kishimoto, Toshifumi

    2017-12-27

    Caregivers of patients with dementia experience physical and mental deterioration. We have previously reported a correlation between caregiver burden and the Frontal Assessment Battery (FAB) total scores of patients with Alzheimer's disease (AD), especially regarding the dependency factor from the Zarit Burden Interview. The present study aimed to identify an objective biomarker for predicting caregiver burden. The participants were 26 pairs of caregivers and patients with AD and mild-to-moderate dementia. Correlations between regional gray matter volumes in the patients with AD and the FAB total scores were explored by using whole-brain voxel-based morphometric analysis. Path analysis was used to estimate the relationships between regional gray matter volumes, FAB total scores, and caregiver burden based on the Zarit Burden Interview. The voxel-based morphometric revealed a significant positive correlation between the FAB total scores and the volume of the left dorsolateral prefrontal cortex. This positive correlation persisted after controlling for the effect of general cognitive dysfunction, which was assessed by using the Mini-Mental State Examination. Path analysis revealed that decreases in FAB scores, caused by reduced frontal lobe volumes, negatively affected caregiver burden. The present study revealed that frontal lobe function, based on FAB scores, was affected by the volume of the left dorsolateral prefrontal cortex. Decreased scores were associated with greater caregiver burden, especially for the dependency factor. These findings may facilitate the development of an objective biomarker for predicting caregiver burden. Copyright © 2017 John Wiley & Sons, Ltd.

  12. Hostile Attribution Bias Mediates the Relationship Between Structural Variations in the Left Middle Frontal Gyrus and Trait Angry Rumination

    Directory of Open Access Journals (Sweden)

    Yueyue Wang

    2018-04-01

    Full Text Available Angry rumination is a common mental phenomenon which may lead to negative social behaviors such as aggression. Although numerous neuroimaging studies have focused on brain area activation during angry rumination, to our knowledge no study has examined the neuroanatomical and cognitive mechanisms of this process. In this study, we conducted a voxel-based morphometry analysis, using a region of interest analysis to identify the structural and cognitive mechanisms underlying individual differences in trait angry rumination (as measured by the Angry Rumination Scale in a sample of 82 undergraduate students. We found that angry rumination was positively correlated with gray matter density in the left middle frontal gyrus (left-MFG, which is implicated in inhibition control, working memory, and emotional regulation. The mediation analysis further revealed that hostile attribution bias (as measured by the Social Information Processing–Attribution Bias Questionnaire acted as a cognitive mechanism underlying the positive association between the left-MFG gray matter density and trait angry rumination. These findings suggest that hostile attribution bias may contribute to trait angry rumination, while the left-MFG may play an important role in the development of hostile attribution bias and trait angry rumination. The study reveals the brain mechanisms of trait angry rumination and plays a role in revealing the cognitive mechanisms of the development of trait angry rumination.

  13. Bihemispheric stimulation over left and right inferior frontal region enhances recovery from apraxia of speech in chronic aphasia.

    Science.gov (United States)

    Marangolo, Paola; Fiori, Valentina; Cipollari, Susanna; Campana, Serena; Razzano, Carmelina; Di Paola, Margherita; Koch, Giacomo; Caltagirone, Carlo

    2013-11-01

    Several studies have already shown that transcranial direct current stimulation (tDCS) is a useful tool for enhancing recovery in aphasia. However, all tDCS studies have previously investigated the effects using unihemisperic stimulation. No reports to date have examined the role of bihemispheric tDCS on aphasia recovery. Here, eight aphasic persons with apraxia of speech underwent intensive language therapy in two different conditions: real bihemispheric anodic ipsilesional stimulation over the left Broca's area and cathodic contralesional stimulation over the right homologue of Broca's area, and a sham condition. In both conditions, patients underwent concurrent language therapy for their apraxia of speech. The language treatment lasted 10 days (Monday to Friday, then weekend off, then Monday to Friday). There was a 14-day intersession interval between the real and the sham conditions. In all patients, language measures were collected before (T0), at the end of (T10) and 1 week after the end of (F/U) treatment. Results showed that after simultaneous excitatory stimulation to the left frontal hemisphere and inhibitory stimulation to the right frontal hemisphere regions, patients exhibited a significant recovery not only in terms of better accuracy and speed in articulating the treated stimuli but also in other language tasks (picture description, noun and verb naming, word repetition, word reading) which persisted in the follow-up session. Taken together, these data suggest that bihemispheric anodic ipsilesional and cathodic contralesional stimulation in chronic aphasia patients may affect the treated function, resulting in a positive influence on different language tasks. © 2013 Federation of European Neuroscience Societies and John Wiley & Sons Ltd.

  14. Left frontal meningioangiomatosis associated with type IIIc focal cortical dysplasia causing refractory epilepsy and literature review.

    Science.gov (United States)

    Roux, Alexandre; Mellerio, Charles; Lechapt-Zalcman, Emmanuelle; Still, Megan; Zerah, Michel; Bourgeois, Marie; Pallud, Johan

    2018-03-29

    We report the surgical management of a lesional drug-resistant epilepsy caused by a meningioangiomatosis associated with a type IIIc focal cortical dysplasia located in the left supplementary motor area in a young male patient. A first anatomical-based partial surgical resection was performed at 11 years old under general anaesthesia without intraoperative mapping, which allowed for postoperative seizure control (Engel IA) for six years. The patient then presented with intractable right sensatory and aphasic focal onset seizures despite two appropriate antiepileptic drugs. A second functional-based surgical resection was performed using intraoperative cortico-subcortical functional mapping with direct electrical stimulation under awake conditions. A complete surgical resection was performed and a left partial supplementary motor area syndrome was observed. At six postoperative months, the patient is seizure free (Engel IA) with an ongoing decrease in antiepileptic drug therapy. Intraoperative functional brain mapping can be applied to preserve the brain function and networks around a meningioangiomatosis to facilitate the resection of potentially epileptogenic perilesional dysplastic cortex and to tailor the extent of resection to functional boundaries. Copyright © 2018. Published by Elsevier Inc.

  15. Morphometry of Left Frontal and Temporal Poles Predicts Analogical Reasoning Abilities.

    Science.gov (United States)

    Aichelburg, Clarisse; Urbanski, Marika; Thiebaut de Schotten, Michel; Humbert, Frederic; Levy, Richard; Volle, Emmanuelle

    2016-03-01

    Analogical reasoning is critical for making inferences and adapting to novelty. It can be studied experimentally using tasks that require creating similarities between situations or concepts, i.e., when their constituent elements share a similar organization or structure. Brain correlates of analogical reasoning have mostly been explored using functional imaging that has highlighted the involvement of the left rostrolateral prefrontal cortex (rlPFC) in healthy subjects. However, whether inter-individual variability in analogical reasoning ability in a healthy adult population is related to differences in brain architecture is unknown. We investigated this question by employing linear regression models of performance in analogy tasks and voxel-based morphometry in 54 healthy subjects. Our results revealed that the ability to reason by analogy was associated with structural variability in the left rlPFC and the anterior part of the inferolateral temporal cortex. Tractography of diffusion-weighted images suggested that these 2 regions have a different set of connections but may exchange information via the arcuate fasciculus. These results suggest that enhanced integrative and semantic abilities supported by structural variation in these areas (or their connectivity) may lead to more efficient analogical reasoning. © The Author 2014. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.

  16. The effect of left frontal transcranial direct-current stimulation on propranolol-induced fear memory acquisition and consolidation deficits.

    Science.gov (United States)

    Nasehi, Mohammad; Khani-Abyaneh, Mozhgan; Ebrahimi-Ghiri, Mohaddeseh; Zarrindast, Mohammad-Reza

    2017-07-28

    Accumulating evidence supports the efficacy of transcranial direct current stimulation (tDCS) in modulating numerous cognitive functions. Despite the fact that tDCS has been used for the enhancement of memory and cognition, very few animal studies have addressed its impact on the modulation of fear memory. This study was designed to determine whether pre/post-training frontal tDCS application would alter fear memory acquisition and/or consolidation deficits induced by propranolol in NMRI mice. Results indicated that administration of β1-adrenoceptor blocker propranolol (0.1mg/kg) impaired fear memory retrieval. Pre/post-training application of anodal tDCS when propranolol was administered prior to training reversed contextual memory retrieval whereas only the anodal application prior to training could induce the same result in the auditory test. Meanwhile, anodal stimulation had no effect on fear memories by itself. Moreover, regardless of when cathode was applied and propranolol administered, their combination restored contextual memory retrieval, while only cathodal stimulation prior to training facilitated the contextual memory retrieval. Also, auditory memory retrieval was restored when cathodal stimulation and propranolol occurred prior to training but it was abolished when stimulation occurred after training and propranolol was administered prior to training. Collectively, our findings show that tDCS applied on the left frontal cortex of mice affects fear memory performance. This alteration seems to be task-dependent and varies depending on the nature and timing of the stimulation. In certain conditions, tDCS reverses the effect of propranolol. These results provide initial evidence to support the timely use of tDCS for the modulation of fear-related memories. Copyright © 2017 Elsevier B.V. All rights reserved.

  17. Comparison of Metabolite Concentrations in the Left Dorsolateral Prefrontal Cortex, the Left Frontal White Matter, and the Left Hippocampus in Patients in Stable Schizophrenia Treated with Antipsychotics with or without Antidepressants. ¹H-NMR Spectroscopy Study.

    Science.gov (United States)

    Strzelecki, Dominik; Grzelak, Piotr; Podgórski, Michał; Kałużyńska, Olga; Stefańczyk, Ludomir; Kotlicka-Antczak, Magdalena; Gmitrowicz, Agnieszka

    2015-10-15

    Managing affective, negative, and cognitive symptoms remains the most difficult therapeutic problem in stable phase of schizophrenia. Efforts include administration of antidepressants. Drugs effects on brain metabolic parameters can be evaluated by means of proton nuclear magnetic resonance (¹H-NMR) spectroscopy. We compared spectroscopic parameters in the left prefrontal cortex (DLPFC), the left frontal white matter (WM) and the left hippocampus and assessed the relationship between treatment and the spectroscopic parameters in both groups. We recruited 25 patients diagnosed with schizophrenia (DSM-IV-TR), with dominant negative symptoms and in stable clinical condition, who were treated with antipsychotic and antidepressive medication for minimum of three months. A group of 25 patients with schizophrenia, who were taking antipsychotic drugs but not antidepressants, was matched. We compared metabolic parameters (N-acetylaspartate (NAA), myo-inositol (mI), glutamatergic parameters (Glx), choline (Cho), and creatine (Cr)) between the two groups. All patients were also assessed with the Positive and Negative Syndrome Scale (PANSS) and the Calgary Depression Scale for Schizophrenia (CDSS). In patients receiving antidepressants we observed significantly higher NAA/Cr and NAA/Cho ratios within the DLPFC, as well as significantly higher mI/Cr within the frontal WM. Moreover, we noted significantly lower values of parameters associated with the glutamatergic transmission--Glx/Cr and Glx/Cho in the hippocampus. Doses of antipsychotic drugs in the group treated with antidepressants were also significantly lower in the patients showing similar severity of psychopathology.

  18. Comparison of Metabolite Concentrations in the Left Dorsolateral Prefrontal Cortex, the Left Frontal White Matter, and the Left Hippocampus in Patients in Stable Schizophrenia Treated with Antipsychotics with or without Antidepressants. 1H-NMR Spectroscopy Study

    Science.gov (United States)

    Strzelecki, Dominik; Grzelak, Piotr; Podgórski, Michał; Kałużyńska, Olga; Stefańczyk, Ludomir; Kotlicka-Antczak, Magdalena; Gmitrowicz, Agnieszka

    2015-01-01

    Managing affective, negative, and cognitive symptoms remains the most difficult therapeutic problem in stable phase of schizophrenia. Efforts include administration of antidepressants. Drugs effects on brain metabolic parameters can be evaluated by means of proton nuclear magnetic resonance (1H-NMR) spectroscopy. We compared spectroscopic parameters in the left prefrontal cortex (DLPFC), the left frontal white matter (WM) and the left hippocampus and assessed the relationship between treatment and the spectroscopic parameters in both groups. We recruited 25 patients diagnosed with schizophrenia (DSM-IV-TR), with dominant negative symptoms and in stable clinical condition, who were treated with antipsychotic and antidepressive medication for minimum of three months. A group of 25 patients with schizophrenia, who were taking antipsychotic drugs but not antidepressants, was matched. We compared metabolic parameters (N-acetylaspartate (NAA), myo-inositol (mI), glutamatergic parameters (Glx), choline (Cho), and creatine (Cr)) between the two groups. All patients were also assessed with the Positive and Negative Syndrome Scale (PANSS) and the Calgary Depression Scale for Schizophrenia (CDSS). In patients receiving antidepressants we observed significantly higher NAA/Cr and NAA/Cho ratios within the DLPFC, as well as significantly higher mI/Cr within the frontal WM. Moreover, we noted significantly lower values of parameters associated with the glutamatergic transmission—Glx/Cr and Glx/Cho in the hippocampus. Doses of antipsychotic drugs in the group treated with antidepressants were also significantly lower in the patients showing similar severity of psychopathology. PMID:26501256

  19. Left Inferior Frontal Gyrus Sensitivity to Phonetic Competition in Receptive Language Processing: A Comparison of Clear and Conversational Speech.

    Science.gov (United States)

    Xie, Xin; Myers, Emily

    2018-03-01

    The speech signal is rife with variations in phonetic ambiguity. For instance, when talkers speak in a conversational register, they demonstrate less articulatory precision, leading to greater potential for confusability at the phonetic level compared with a clear speech register. Current psycholinguistic models assume that ambiguous speech sounds activate more than one phonological category and that competition at prelexical levels cascades to lexical levels of processing. Imaging studies have shown that the left inferior frontal gyrus (LIFG) is modulated by phonetic competition between simultaneously activated categories, with increases in activation for more ambiguous tokens. Yet, these studies have often used artificially manipulated speech and/or metalinguistic tasks, which arguably may recruit neural regions that are not critical for natural speech recognition. Indeed, a prominent model of speech processing, the dual-stream model, posits that the LIFG is not involved in prelexical processing in receptive language processing. In the current study, we exploited natural variation in phonetic competition in the speech signal to investigate the neural systems sensitive to phonetic competition as listeners engage in a receptive language task. Participants heard nonsense sentences spoken in either a clear or conversational register as neural activity was monitored using fMRI. Conversational sentences contained greater phonetic competition, as estimated by measures of vowel confusability, and these sentences also elicited greater activation in a region in the LIFG. Sentence-level phonetic competition metrics uniquely correlated with LIFG activity as well. This finding is consistent with the hypothesis that the LIFG responds to competition at multiple levels of language processing and that recruitment of this region does not require an explicit phonological judgment.

  20. Bilateral inferior frontal language-related activation correlates with verbal recall in patients with left temporal lobe epilepsy and typical language distribution.

    Science.gov (United States)

    Sanjuán, Ana; Bustamante, Juan Carlos; García-Porcar, María; Rodríguez-Pujadas, Aina; Forn, Cristina; Martínez, Juan Carlos; Campos, Anabel; Palau, Juan; Gutiérrez, Antonio; Villanueva, Vicente; Avila, César

    2013-03-01

    Language fMRI has been used in the presurgical evaluation of drug-resistant temporal lobe epilepsy patients. Previous studies have demonstrated that left temporal lobe epilepsy (LTLE) patients with atypical language lateralization are at lower risk of postsurgical verbal memory decline, hypothesizing co-occurrence of verbal memory and language reorganization presurgically. Furthermore, it has been proposed that the recruitment of right frontal language-related areas is associated with the preservation of verbal memory performance in these patients. However, less is known about the correlation between these functions specifically in LTLE patients with left language dominance, although they are more prone to postsurgical verbal memory decline. The aim of the present study was to investigate whether the relationship between verbal memory scores and frontal language activation is also observed in LTLE patients with typical language dominance. Eighteen healthy controls, 12 right temporal lobe epilepsy patients and 12 LTLE patients with typical language distribution as assessed by an fMRI verbal fluency task were selected. Verbal memory scores were obtained from the patients' neuropsychological presurgical evaluation. Our results showed a positive correlation between verbal recall and activation of bilateral inferior frontal areas in LTLE patients. These results support the hypothesis of a link between language representation in inferior frontal areas and hippocampal functioning, and indicate that both hemispheres are related to the preservation of verbal memory in patients with hippocampal damage and typical language dominance. Copyright © 2012 Elsevier B.V. All rights reserved.

  1. Motor Speech Apraxia in a 70-Year-Old Man with Left Dorsolateral Frontal Arachnoid Cyst: A [18F]FDG PET-CT Study

    Directory of Open Access Journals (Sweden)

    Nicolaas I. Bohnen

    2016-01-01

    Full Text Available Motor speech apraxia is a speech disorder of impaired syllable sequencing which, when seen with advancing age, is suggestive of a neurodegenerative process affecting cortical structures in the left frontal lobe. Arachnoid cysts can be associated with neurologic symptoms due to compression of underlying brain structures though indications for surgical intervention are unclear. We present the case of a 70-year-old man who presented with a two-year history of speech changes along with decreased initiation and talkativeness, shorter utterances, and dysnomia. [18F]Fluorodeoxyglucose (FDG Positron Emission and Computed Tomography (PET-CT and magnetic resonance imaging (MRI showed very focal left frontal cortical hypometabolism immediately adjacent to an arachnoid cyst but no specific evidence of a neurodegenerative process.

  2. Maintaining the feelings of others in working memory is associated with activation of the left anterior insula and left frontal-parietal control network

    OpenAIRE

    Smith, Ryan; Lane, Richard D.; Alkozei, Anna; Bao, Jennifer; Smith, Courtney; Sanova, Anna; Nettles, Matthew; Killgore, William D. S.

    2017-01-01

    Abstract The maintenance of social/emotional information in working memory (SWM/EWM) has recently been the topic of multiple neuroimaging studies. However, some studies find that SWM/EWM involves a medial frontal-parietal network while others instead find lateral frontal-parietal activations similar to studies of verbal and visuospatial WM. In this study, we asked 26 healthy volunteers to complete an EWM task designed to examine whether different cognitive strategies? maintaining emotional im...

  3. Differential activity in left inferior frontal gyrus for pseudo and real words: an event-related functional MRI study on auditory lexical decision

    International Nuclear Information System (INIS)

    Xiao Zhuangwei; Xu Weixiong; Zhang Xuexin; Wang Xiaoyi; Weng Xuchu; Wu Renhua; Wu Xiaoping

    2006-01-01

    Objective: To study lexical processing of pseudo words and real words by using a fast event-related functional MRI (ER-fMRI) design. Methods: Participants did an auditory lexical decision task on a list of pseudo-randomly intermixed real and pseudo Chinese two-character (or two-syllable) words. Pseudo words were constructed by recombining constituent characters of the real words to control for sublexical codes properties. Results: The behavioral performance of fourteen participants indicated that response to pseudowords was significantly slower and less accurate than to real words (mean error rate: 9.9% versus 3.9%, mean reaction time: 1618 ms versus 1143 ms). Processing of pseudo words and real words activated a highly comparable network of brain regions, including bilateral inferior frontal gyrus, superior, middle temporal gyrus, calcarine and lingual gyrus, and left supramarginal gyrus. Mirroring a behavioral lexical effect, left inferior frontal gyrus (IFG) was significantly more activated for pseudo words than for real words. Conclusion: The results indicate that the processing of left inferior frontal gyrus in judging pseudo words and real words is not related to grapheme-to-phoneme conversion, but rather to making positive versus negative responses in decision making. (authors)

  4. Spontaneous Activity Associated with Delusions of Schizophrenia in the Left Medial Superior Frontal Gyrus: A Resting-State fMRI Study.

    Directory of Open Access Journals (Sweden)

    Bin Gao

    Full Text Available Delusions of schizophrenia have been found to be associated with alterations of some brain regions in structure and task-induced activation. However, the relationship between spontaneously occurring symptoms and spontaneous brain activity remains unclear. In the current study, 14 schizophrenic patients with delusions and 14 healthy controls underwent a resting-state functional magnetic resonance imaging (RS-fMRI scan. Patients with delusions of schizophrenia patients were rated with Positive and Negative Syndrome Scale (PANSS and Characteristics of Delusional Rating Scale (CDRS. Regional homogeneity (ReHo was calculated to measure the local synchronization of the spontaneous activity in a voxel-wise way. A two-sample t-test showed that ReHo of the right anterior cingulate gyrus and left medial superior frontal gyrus were higher in patients, and ReHo of the left superior occipital gyrus was lower, compared to healthy controls. Further, among patients, correlation analysis showed a significant difference between delusion scores of CRDS and ReHo of brain regions. ReHo of the left medial superior frontal gyrus was negatively correlated with patients' CDRS scores but not with delusional PANSS scores. These results suggested that altered local synchronization of spontaneous brain activity may be related to the pathophysiology of delusion in schizophrenia.

  5. Perturbation of the left inferior frontal gyrus triggers adaptive plasticity in the right homologous area during speech production

    DEFF Research Database (Denmark)

    Hartwigsen, Gesa; Saur, Dorothee; Price, Cathy J

    2013-01-01

    The role of the right hemisphere in aphasia recovery after left hemisphere damage remains unclear. Increased activation of the right hemisphere has been observed after left hemisphere damage. This may simply reflect a release from transcallosal inhibition that does not contribute to language...... hemisphere lesion. Our findings lend further support to the notion that increased activation of homologous right hemisphere areas supports aphasia recovery after left hemisphere damage....

  6. Greater pre-stimulus effective connectivity from the left inferior frontal area to other areas is associated with better phonological decoding in dyslexic readers

    Directory of Open Access Journals (Sweden)

    Richard E Frye

    2010-12-01

    Full Text Available Functional neuroimaging studies suggest that neural networks that subserve reading are organized differently in dyslexic readers (DRs and typical readers (TRs, yet the hierarchical structure of these networks has not been well studied. We used Granger Causality (GC to examine the effective connectivity of the preparatory network that occurs prior to viewing a non-word stimulus that requires phonological decoding in 7 DRs and 10 TRs who were young adults. The neuromagnetic activity that occurred 500 ms prior to each rhyme trial was analyzed from sensors overlying the left and right inferior frontal areas (IFA, temporoparietal areas (TPA, and ventral occipitotemporal areas (VOTA within the low, medium, and high beta and gamma sub-bands. A mixed-model analysis determined whether connectivity to or from the left and right IFAs differed across connectivity direction (into vs. out of the IFAs, brain areas, reading group, and/or performance. Results indicated that greater connectivity in the low beta sub-band from the left IFA to other cortical areas was significantly related to better non-word rhyme discrimination in DRs but not TRs. This suggests that the left IFA is an important cortical area involved in compensating for poor phonological function in DRs. We suggest that the left IFA activates a wider-than usual network prior to each trial in the service of supporting otherwise effortful phonological decoding in DRs. The fact that the left IFA provides top-down activation to both posterior left hemispheres areas used by typical readers for phonological decoding and homologous right hemisphere areas is discussed. In contrast, within the high gamma sub-band, better performance was associated with decreased connectivity between the left IFA and other brain areas, in both reading groups. Overly strong gamma connectivity during the pre-stimulus period may interfere with subsequent transient activation and deactivation of sub-networks once the non

  7. Changes in theta activities in the left posterior temporal region, left occipital region and right frontal region related to mild cognitive impairment in Parkinson's disease patients.

    Science.gov (United States)

    He, Xuetao; Zhang, Yuhu; Chen, Jieling; Xie, Chunge; Gan, Rong; Wang, Limin; Wang, Lijuan

    2017-01-01

    The aim of this study was to investigate changes in brain activity associated with mild cognitive impairment (MCI) in a large sample of nondemented Parkinson's disease (PD) patients and its relationship with specific neuropsychological deficits. Electroencephalography (EEG) and neuropsychological assessment were performed in a sample of 135 nondemented PD patients and 44 healthy controls. All patients underwent a neuropsychological battery to assess global cognitive function. Patients were classified according to their cognitive status as PD patients with MCI (n = 61) and without MCI (n = 74). EEG data were used to analyze the relative band power parameters for the following frequency bands: delta (0.5-4 Hz), theta (4-8 Hz), alpha (8-13 Hz) and beta (13-30 Hz). In addition, relative band power parameters were compared between groups and examined for correlations with neuropsychological performance. The relative theta band powers in three regions (O1, T5 and F4) exhibited statistically significant increases in PD patients with MCI. Beta band powers also exhibited obvious decreases in five regions (T5, T6, P3, P4 and C3) in the PD-MCI group compared with the normal control group. Furthermore, correlation analyses revealed that attention, visuospatial and executive functions were associated with theta power in local regions, mainly in the frontal region (F4). The present study demonstrated that changes in brain activities limited to distinct cognitive domains, especially the theta power in the frontal region, could serve as an electrophysiological marker of cognitive impairment in nondemented PD patients.

  8. Fundamentals of convolutional coding

    CERN Document Server

    Johannesson, Rolf

    2015-01-01

    Fundamentals of Convolutional Coding, Second Edition, regarded as a bible of convolutional coding brings you a clear and comprehensive discussion of the basic principles of this field * Two new chapters on low-density parity-check (LDPC) convolutional codes and iterative coding * Viterbi, BCJR, BEAST, list, and sequential decoding of convolutional codes * Distance properties of convolutional codes * Includes a downloadable solutions manual

  9. Left ventricular hypertrophy index based on a combination of frontal and transverse planes in the ECG and VCG: Diagnostic utility of cardiac vectors

    Science.gov (United States)

    Bonomini, Maria Paula; Juan Ingallina, Fernando; Barone, Valeria; Antonucci, Ricardo; Valentinuzzi, Max; Arini, Pedro David

    2016-04-01

    The changes that left ventricular hypertrophy (LVH) induces in depolarization and repolarization vectors are well known. We analyzed the performance of the electrocardiographic and vectorcardiographic transverse planes (TP in the ECG and XZ in the VCG) and frontal planes (FP in the ECG and XY in the VCG) to discriminate LVH patients from control subjects. In an age-balanced set of 58 patients, the directions and amplitudes of QRS-complexes and T-wave vectors were studied. The repolarization vector significantly decreased in modulus from controls to LVH in the transverse plane (TP: 0.45±0.17mV vs. 0.24±0.13mV, p<0.0005 XZ: 0.43±0.16mV vs. 0.26±0.11mV, p<0.005) while the depolarization vector significantly changed in angle in the electrocardiographic frontal plane (Controls vs. LVH, FP: 48.24±33.66° vs. 46.84±35.44°, p<0.005, XY: 20.28±35.20° vs. 19.35±12.31°, NS). Several LVH indexes were proposed combining such information in both ECG and VCG spaces. A subset of all those indexes with AUC values greater than 0.7 was further studied. This subset comprised four indexes, with three of them belonging to the ECG space. Two out of the four indexes presented the best ROC curves (AUC values: 0.78 and 0.75, respectively). One index belonged to the ECG space and the other one to the VCG space. Both indexes showed a sensitivity of 86% and a specificity of 70%. In conclusion, the proposed indexes can favorably complement LVH diagnosis

  10. Fast Convolution Module (Fast Convolution Module)

    National Research Council Canada - National Science Library

    Bierens, L

    1997-01-01

    This report describes the design and realisation of a real-time range azimuth compression module, the so-called 'Fast Convolution Module', based on the fast convolution algorithm developed at TNO-FEL...

  11. The Sport Expert's Attention Superiority on Skill-related Scene Dynamic by The Activation of Left Medial Frontal Gyrus: An ERP and LORETA Study.

    Science.gov (United States)

    He, Mengyang; Qi, Changzhu; Lu, Yang; Song, Amanda; Hayat, Saba Z; Xu, Xia

    2018-03-07

    Extensive studies have shown that a sports expert is superior to a sports novice in visually perceptual-cognitive processes of sports scene information, however the attentional and neural basis of it has not been thoroughly explored. The present study examined whether a sport expert has the attentional superiority on scene information relevant to his/her sport skill, and explored what factor drives this superiority. To address this problem, EEGs were recorded as participants passively viewed sport scenes (tennis vs. non-tennis) and negative emotional faces in the context of a visual attention task, where the pictures of sport scenes or of negative emotional faces randomly followed the pictures with overlapping sport scenes and negative emotional faces. ERP results showed that for experts, the evoked potential of attentional competition elicited by the overlap of tennis scene was significantly larger than that evoked by the overlap of non-tennis scene, while this effect was absent for novices. The LORETA showed that the experts' left medial frontal gyrus (MFG) cortex was significantly more active as compared to the right MFG when processing the overlap of tennis scene, but the lateralization effect was not significant in novices. Those results indicate that experts have attentional superiority on skill-related scene information, despite intruding the scene through negative emotional faces that are prone to cause negativity bias towards their visual field as a strong distractor. This superiority is actuated by the activation of left MFG cortex and probably due to self-reference. Copyright © 2018. Published by Elsevier Ltd.

  12. Tell it to a child! A brain stimulation study of the role of left inferior frontal gyrus in emotion regulation during storytelling.

    Science.gov (United States)

    Urgesi, Cosimo; Mattiassi, Alan D A; Buiatti, Tania; Marini, Andrea

    2016-08-01

    In everyday life we need to continuously regulate our emotional responses according to their social context. Strategies of emotion regulation allow individuals to control time, intensity, nature and expression of emotional responses to environmental stimuli. The left inferior frontal gyrus (LIFG) is involved in the cognitive control of the selection of semantic content. We hypothesized that it might also be involved in the regulation of emotional feelings and expressions. We applied continuous theta burst stimulation (cTBS) over LIFG or a control site before a newly-developed ecological regulation task that required participants to produce storytelling of pictures with negative or neutral valence to either a peer (unregulated condition) or a child (regulated condition). Linguistic, expressive, and physiological responses were analyzed in order to assess the effects of LIFG-cTBS on emotion regulation. Results showed that the emotion regulation context modulated the emotional content of narrative productions, but not the physiologic orienting response or the early expressive behavior to negative stimuli. Furthermore, LIFG-cTBS disrupted the text-level structuring of negative picture storytelling and the early cardiac and muscular response to negative pictures; however, it did not affect the contextual emotional regulation of storytelling. These results may suggest that LIFG is involved in the initial detection of the affective arousal of emotional stimuli. Copyright © 2016 Elsevier Inc. All rights reserved.

  13. TMS interferes with lexical-semantic retrieval in left inferior frontal gyrus and posterior middle temporal gyrus: Evidence from cyclical picture naming.

    Science.gov (United States)

    Krieger-Redwood, Katya; Jefferies, Elizabeth

    2014-11-01

    We used TMS to investigate the contribution of left inferior frontal gyrus (LIFG) and posterior middle temporal gyrus (pMTG) to lexical/semantic selection and retrieval processes using a cyclical naming paradigm. Participants named pictures that were presented repeatedly across six cycles, either in semantically related or unrelated sets. Previous research has suggested that selection demands are higher for related sets, especially after repetition, since participants experience competition from the activation of semantic neighbours. In contrast, retrieval demands are greater for unrelated sets in the absence of semantic priming, particularly on the first cycle when the target names have not been previously activated. Therefore, this paradigm can reveal independent effects of (i) retrieval demands (i.e., the ease of accessing picture names from visual input) and (ii) selection/competition. We found that rTMS to LIFG and pMTG produced similar behavioural effects: stimulation of both sites disrupted picture naming performance on early cycles (when participants were less practised at producing the picture names) and for semantically-related sets (when there was the potential for increased competition and yet also facilitation from semantic neighbours). There were no effects of TMS when either retrieval or selection requirements were maximal on their own. The data therefore support the view that both LIFG and pMTG contribute to picture name retrieval, with both sites playing a critical role in mediating the semantic facilitation of naming when retrieval demands are high. Copyright © 2014 Elsevier Ltd. All rights reserved.

  14. Context-dependent lexical ambiguity resolution: MEG evidence for the time-course of activity in left inferior frontal gyrus and posterior middle temporal gyrus.

    Science.gov (United States)

    Mollo, Giovanna; Jefferies, Elizabeth; Cornelissen, Piers; Gennari, Silvia P

    An MEG study investigated the role of context in semantic interpretation by examining the comprehension of ambiguous words in contexts leading to different interpretations. We compared high-ambiguity words in minimally different contexts (to bowl, the bowl) to low-ambiguity counterparts (the tray, to flog). Whole brain beamforming revealed the engagement of left inferior frontal gyrus (LIFG) and posterior middle temporal gyrus (LPMTG). Points of interest analyses showed that both these sites showed a stronger response to verb-contexts by 200 ms post-stimulus and displayed overlapping ambiguity effects that were sustained from 300 ms onwards. The effect of context was stronger for high-ambiguity words than for low-ambiguity words at several different time points, including within the first 100 ms post-stimulus. Unlike LIFG, LPMTG also showed stronger responses to verb than noun contexts in low-ambiguity trials. We argue that different functional roles previously attributed to LIFG and LPMTG are in fact played out at different periods during processing. Crown Copyright © 2018. Published by Elsevier Inc. All rights reserved.

  15. Resting-State Connectivity of the Left Frontal Cortex to the Default Mode and Dorsal Attention Network Supports Reserve in Mild Cognitive Impairment.

    Science.gov (United States)

    Franzmeier, Nicolai; Göttler, Jens; Grimmer, Timo; Drzezga, Alexander; Áraque-Caballero, Miguel A; Simon-Vermot, Lee; Taylor, Alexander N W; Bürger, Katharina; Catak, Cihan; Janowitz, Daniel; Müller, Claudia; Duering, Marco; Sorg, Christian; Ewers, Michael

    2017-01-01

    Reserve refers to the phenomenon of relatively preserved cognition in disproportion to the extent of neuropathology, e.g., in Alzheimer's disease. A putative functional neural substrate underlying reserve is global functional connectivity of the left lateral frontal cortex (LFC, Brodmann Area 6/44). Resting-state fMRI-assessed global LFC-connectivity is associated with protective factors (education) and better maintenance of memory in mild cognitive impairment (MCI). Since the LFC is a hub of the fronto-parietal control network that regulates the activity of other networks, the question arises whether LFC-connectivity to specific networks rather than the whole-brain may underlie reserve. We assessed resting-state fMRI in 24 MCI and 16 healthy controls (HC) and in an independent validation sample (23 MCI/32 HC). Seed-based LFC-connectivity to seven major resting-state networks (i.e., fronto-parietal, limbic, dorsal-attention, somatomotor, default-mode, ventral-attention, visual) was computed, reserve was quantified as residualized memory performance after accounting for age and hippocampal atrophy. In both samples of MCI, LFC-activity was anti-correlated with the default-mode network (DMN), but positively correlated with the dorsal-attention network (DAN). Greater education predicted stronger LFC-DMN-connectivity (anti-correlation) and LFC-DAN-connectivity. Stronger LFC-DMN and LFC-DAN-connectivity each predicted higher reserve, consistently in both MCI samples. No associations were detected for LFC-connectivity to other networks. These novel results extend our previous findings on global functional connectivity of the LFC, showing that LFC-connectivity specifically to the DAN and DMN, two core memory networks, enhances reserve in the memory domain in MCI.

  16. Resting-State Connectivity of the Left Frontal Cortex to the Default Mode and Dorsal Attention Network Supports Reserve in Mild Cognitive Impairment

    Directory of Open Access Journals (Sweden)

    Nicolai Franzmeier

    2017-08-01

    Full Text Available Reserve refers to the phenomenon of relatively preserved cognition in disproportion to the extent of neuropathology, e.g., in Alzheimer’s disease. A putative functional neural substrate underlying reserve is global functional connectivity of the left lateral frontal cortex (LFC, Brodmann Area 6/44. Resting-state fMRI-assessed global LFC-connectivity is associated with protective factors (education and better maintenance of memory in mild cognitive impairment (MCI. Since the LFC is a hub of the fronto-parietal control network that regulates the activity of other networks, the question arises whether LFC-connectivity to specific networks rather than the whole-brain may underlie reserve. We assessed resting-state fMRI in 24 MCI and 16 healthy controls (HC and in an independent validation sample (23 MCI/32 HC. Seed-based LFC-connectivity to seven major resting-state networks (i.e., fronto-parietal, limbic, dorsal-attention, somatomotor, default-mode, ventral-attention, visual was computed, reserve was quantified as residualized memory performance after accounting for age and hippocampal atrophy. In both samples of MCI, LFC-activity was anti-correlated with the default-mode network (DMN, but positively correlated with the dorsal-attention network (DAN. Greater education predicted stronger LFC-DMN-connectivity (anti-correlation and LFC-DAN-connectivity. Stronger LFC-DMN and LFC-DAN-connectivity each predicted higher reserve, consistently in both MCI samples. No associations were detected for LFC-connectivity to other networks. These novel results extend our previous findings on global functional connectivity of the LFC, showing that LFC-connectivity specifically to the DAN and DMN, two core memory networks, enhances reserve in the memory domain in MCI.

  17. An unusual association of headache, epilepsy, and late-onset Kleist's pseudodepression syndrome in frontal lobe cavernoma of the cerebral left hemisphere.

    Science.gov (United States)

    Chirchiglia, Domenico; Della Torre, Attilio; Murrone, Domenico; Chirchiglia, Pasquale; Marotta, Rosa

    2017-01-01

    Cerebral cavernous angioma or cavernoma is a benign vascular malformation, usually asymptomatic. It is infrequent and often its discovery is incidental, a so-called incidentaloma. However, these lesions can be symptomatic, causing headaches, epilepsy, cerebral hemorrhage and other neurological signs depending on the brain area involved. Frontal localization is responsible for psychiatric disorders, particularly the prefrontal region, leading to prefrontal syndrome, a condition common in all frontal lobe tumors. Psychopathological syndrome can be depression-type, pseudodepression syndrome or maniac-type, pseudomaniac syndrome. Surgical treatment of lesions like this may not always be possible due to their location in eloquent areas. In this study, we describe an unusual association of migraine-like headache, epilepsy and frontal lobe pseudodepression late-onset syndrome in the same patient. We have considered this case interesting mainly for the rarity of both a headache with migraine features and for the late onset of pseudodepression syndrome. Pathophysiology underlying migraine-like headache and that concerning the late-onset pseudodepression frontal lobe syndrome seems to be unclear. This case leads to further hypotheses about the mechanisms responsible for headache syndromes and psychopathological disorders, in the specific case when caused by a cerebral frontal lobe lesion.

  18. An unusual association of headache, epilepsy, and late-onset Kleist’s pseudodepression syndrome in frontal lobe cavernoma of the cerebral left hemisphere

    Directory of Open Access Journals (Sweden)

    Chirchiglia D

    2017-05-01

    Full Text Available Domenico Chirchiglia,1 Attilio Della Torre,1 Domenico Murrone,2 Pasquale Chirchiglia,3 Rosa Marotta4 1Department of Neurosurgery, Neurophysiopathology Unit, University of Catanzaro “Magna Graecia”, Catanzaro, 2Neurosurgery Department, Di Venere Hospital, Bari, 3School of Medicine, University of Catanzaro, Catanzaro, 4Department of Medical and Surgical Sciences, University of Catanzaro, Catanzaro, Italy Abstract: Cerebral cavernous angioma or cavernoma is a benign vascular malformation, usually asymptomatic. It is infrequent and often its discovery is incidental, a so-called incidentaloma. However, these lesions can be symptomatic, causing headaches, epilepsy, cerebral hemorrhage and other neurological signs depending on the brain area involved. Frontal localization is responsible for psychiatric disorders, particularly the prefrontal region, leading to prefrontal syndrome, a condition common in all frontal lobe tumors. Psychopathological syndrome can be depression-type, pseudodepression syndrome or maniac-type, pseudomaniac syndrome. Surgical treatment of lesions like this may not always be possible due to their location in eloquent areas. In this study, we describe an unusual association of migraine-like headache, epilepsy and frontal lobe pseudodepression late-onset syndrome in the same patient. We have considered this case interesting mainly for the rarity of both a headache with migraine features and for the late onset of pseudodepression syndrome. Pathophysiology underlying migraine-like headache and that concerning the late-onset pseudodepression frontal lobe syndrome seems to be unclear. This case leads to further hypotheses about the mechanisms responsible for headache syndromes and psychopathological disorders, in the specific case when caused by a cerebral frontal lobe lesion. Keywords: cerebral cavernoma, cavernous angioma, headache, frontal syndrome, pseudodepression syndrome 

  19. Supervised Convolutional Sparse Coding

    KAUST Repository

    Affara, Lama Ahmed

    2018-04-08

    Convolutional Sparse Coding (CSC) is a well-established image representation model especially suited for image restoration tasks. In this work, we extend the applicability of this model by proposing a supervised approach to convolutional sparse coding, which aims at learning discriminative dictionaries instead of purely reconstructive ones. We incorporate a supervised regularization term into the traditional unsupervised CSC objective to encourage the final dictionary elements to be discriminative. Experimental results show that using supervised convolutional learning results in two key advantages. First, we learn more semantically relevant filters in the dictionary and second, we achieve improved image reconstruction on unseen data.

  20. A Preliminary fMRI Study of a Novel Self-Paced Written Fluency Task: Observation of Left-Hemispheric Activation, and Increased Frontal Activation in Late vs. Early Task Phases

    Directory of Open Access Journals (Sweden)

    Laleh eGolestanirad

    2015-03-01

    Full Text Available Neuropsychological tests of verbal fluency are very widely used to characterize impaired cognitive function. For clinical neuroscience studies and potential medical applications, measuring the brain activity that underlies such tests with functional magnetic resonance imaging (fMRI is of significant interest - but a challenging proposition because overt speech can cause signal artifacts, which tend to worsen as the duration of speech tasks becomes longer. In a novel approach, we present the group brain activity of 12 subjects who performed a self-paced written version of phonemic fluency using fMRI-compatible tablet technology that recorded responses and provided task-related feedback on a projection screen display, over long-duration task blocks (60 s. As predicted, we observed robust activation in the left anterior inferior and medial frontal gyri, consisting with previously reported results of verbal fluency tasks which established the role of these areas in strategic word retrieval. In addition, the number of words produced in the late phase (last 30 s of written phonemic fluency was significantly less (p < 0.05 than the number produced in the early phase (first 30 s. Activation during the late phase vs. the early phase was also assessed from the first 20 s and last 20 s of task performance, which eliminated the possibility that the sluggish hemodynamic response from the early phase would affect the activation estimates of the late phase. The last 20 s produced greater activation maps covering extended areas in bilateral precuneus, cuneus, middle temporal gyrus, insula, middle frontal gyrus and cingulate gyrus. Among them, greater activation was observed in the bilateral middle frontal gyrus (Brodmann area BA 9 and cingulate gyrus (BA 24, 32 likely as part of the initiation, maintenance, and shifting of attentional resources.

  1. A preliminary fMRI study of a novel self-paced written fluency task: observation of left-hemispheric activation, and increased frontal activation in late vs. early task phases.

    Science.gov (United States)

    Golestanirad, Laleh; Das, Sunit; Schweizer, Tom A; Graham, Simon J

    2015-01-01

    Neuropsychological tests of verbal fluency are very widely used to characterize impaired cognitive function. For clinical neuroscience studies and potential medical applications, measuring the brain activity that underlies such tests with functional magnetic resonance imaging (fMRI) is of significant interest-but a challenging proposition because overt speech can cause signal artifacts, which tend to worsen as the duration of speech tasks becomes longer. In a novel approach, we present the group brain activity of 12 subjects who performed a self-paced written version of phonemic fluency using fMRI-compatible tablet technology that recorded responses and provided task-related feedback on a projection screen display, over long-duration task blocks (60 s). As predicted, we observed robust activation in the left anterior inferior and medial frontal gyri, consistent with previously reported results of verbal fluency tasks which established the role of these areas in strategic word retrieval. In addition, the number of words produced in the late phase (last 30 s) of written phonemic fluency was significantly less (p < 0.05) than the number produced in the early phase (first 30 s). Activation during the late phase vs. the early phase was also assessed from the first 20 s and last 20 s of task performance, which eliminated the possibility that the sluggish hemodynamic response from the early phase would affect the activation estimates of the late phase. The last 20 s produced greater activation maps covering extended areas in bilateral precuneus, cuneus, middle temporal gyrus, insula, middle frontal gyrus and cingulate gyrus. Among these areas, greater activation was observed in the bilateral middle frontal gyrus (Brodmann area BA 9) and cingulate gyrus (BA 24, 32) likely as part of the initiation, maintenance, and shifting of attentional resources. Consistent with previous pertinent fMRI literature involving overt and covert verbal responses, these findings highlight the

  2. Convolution based profile fitting

    International Nuclear Information System (INIS)

    Kern, A.; Coelho, A.A.; Cheary, R.W.

    2002-01-01

    Full text: In convolution based profile fitting, profiles are generated by convoluting functions together to form the observed profile shape. For a convolution of 'n' functions this process can be written as, Y(2θ)=F 1 (2θ)x F 2 (2θ)x... x F i (2θ)x....xF n (2θ). In powder diffractometry the functions F i (2θ) can be interpreted as the aberration functions of the diffractometer, but in general any combination of appropriate functions for F i (2θ) may be used in this context. Most direct convolution fitting methods are restricted to combinations of F i (2θ) that can be convoluted analytically (e.g. GSAS) such as Lorentzians, Gaussians, the hat (impulse) function and the exponential function. However, software such as TOPAS is now available that can accurately convolute and refine a wide variety of profile shapes numerically, including user defined profiles, without the need to convolute analytically. Some of the most important advantages of modern convolution based profile fitting are: 1) virtually any peak shape and angle dependence can normally be described using minimal profile parameters in laboratory and synchrotron X-ray data as well as in CW and TOF neutron data. This is possible because numerical convolution and numerical differentiation is used within the refinement procedure so that a wide range of functions can easily be incorporated into the convolution equation; 2) it can use physically based diffractometer models by convoluting the instrument aberration functions. This can be done for most laboratory based X-ray powder diffractometer configurations including conventional divergent beam instruments, parallel beam instruments, and diffractometers used for asymmetric diffraction. It can also accommodate various optical elements (e.g. multilayers and monochromators) and detector systems (e.g. point and position sensitive detectors) and has already been applied to neutron powder diffraction systems (e.g. ANSTO) as well as synchrotron based

  3. Multithreaded implicitly dealiased convolutions

    Science.gov (United States)

    Roberts, Malcolm; Bowman, John C.

    2018-03-01

    Implicit dealiasing is a method for computing in-place linear convolutions via fast Fourier transforms that decouples work memory from input data. It offers easier memory management and, for long one-dimensional input sequences, greater efficiency than conventional zero-padding. Furthermore, for convolutions of multidimensional data, the segregation of data and work buffers can be exploited to reduce memory usage and execution time significantly. This is accomplished by processing and discarding data as it is generated, allowing work memory to be reused, for greater data locality and performance. A multithreaded implementation of implicit dealiasing that accepts an arbitrary number of input and output vectors and a general multiplication operator is presented, along with an improved one-dimensional Hermitian convolution that avoids the loop dependency inherent in previous work. An alternate data format that can accommodate a Nyquist mode and enhance cache efficiency is also proposed.

  4. Convolution copula econometrics

    CERN Document Server

    Cherubini, Umberto; Mulinacci, Sabrina

    2016-01-01

    This book presents a novel approach to time series econometrics, which studies the behavior of nonlinear stochastic processes. This approach allows for an arbitrary dependence structure in the increments and provides a generalization with respect to the standard linear independent increments assumption of classical time series models. The book offers a solution to the problem of a general semiparametric approach, which is given by a concept called C-convolution (convolution of dependent variables), and the corresponding theory of convolution-based copulas. Intended for econometrics and statistics scholars with a special interest in time series analysis and copula functions (or other nonparametric approaches), the book is also useful for doctoral students with a basic knowledge of copula functions wanting to learn about the latest research developments in the field.

  5. Efficient convolutional sparse coding

    Energy Technology Data Exchange (ETDEWEB)

    Wohlberg, Brendt

    2017-06-20

    Computationally efficient algorithms may be applied for fast dictionary learning solving the convolutional sparse coding problem in the Fourier domain. More specifically, efficient convolutional sparse coding may be derived within an alternating direction method of multipliers (ADMM) framework that utilizes fast Fourier transforms (FFT) to solve the main linear system in the frequency domain. Such algorithms may enable a significant reduction in computational cost over conventional approaches by implementing a linear solver for the most critical and computationally expensive component of the conventional iterative algorithm. The theoretical computational cost of the algorithm may be reduced from O(M.sup.3N) to O(MN log N), where N is the dimensionality of the data and M is the number of elements in the dictionary. This significant improvement in efficiency may greatly increase the range of problems that can practically be addressed via convolutional sparse representations.

  6. Convolution Operators on Groups

    CERN Document Server

    Derighetti, Antoine

    2011-01-01

    This volume is devoted to a systematic study of the Banach algebra of the convolution operators of a locally compact group. Inspired by classical Fourier analysis we consider operators on Lp spaces, arriving at a description of these operators and Lp versions of the theorems of Wiener and Kaplansky-Helson.

  7. T'ain't what you say, it's the way that you say it--left insula and inferior frontal cortex work in interaction with superior temporal regions to control the performance of vocal impersonations.

    Science.gov (United States)

    McGettigan, Carolyn; Eisner, Frank; Agnew, Zarinah K; Manly, Tom; Wisbey, Duncan; Scott, Sophie K

    2013-11-01

    Historically, the study of human identity perception has focused on faces, but the voice is also central to our expressions and experiences of identity [Belin, P., Fecteau, S., & Bedard, C. Thinking the voice: Neural correlates of voice perception. Trends in Cognitive Sciences, 8, 129-135, 2004]. Our voices are highly flexible and dynamic; talkers speak differently, depending on their health, emotional state, and the social setting, as well as extrinsic factors such as background noise. However, to date, there have been no studies of the neural correlates of identity modulation in speech production. In the current fMRI experiment, we measured the neural activity supporting controlled voice change in adult participants performing spoken impressions. We reveal that deliberate modulation of vocal identity recruits the left anterior insula and inferior frontal gyrus, supporting the planning of novel articulations. Bilateral sites in posterior superior temporal/inferior parietal cortex and a region in right middle/anterior STS showed greater responses during the emulation of specific vocal identities than for impressions of generic accents. Using functional connectivity analyses, we describe roles for these three sites in their interactions with the brain regions supporting speech planning and production. Our findings mark a significant step toward understanding the neural control of vocal identity, with wider implications for the cognitive control of voluntary motor acts.

  8. Invariant scattering convolution networks.

    Science.gov (United States)

    Bruna, Joan; Mallat, Stéphane

    2013-08-01

    A wavelet scattering network computes a translation invariant image representation which is stable to deformations and preserves high-frequency information for classification. It cascades wavelet transform convolutions with nonlinear modulus and averaging operators. The first network layer outputs SIFT-type descriptors, whereas the next layers provide complementary invariant information that improves classification. The mathematical analysis of wavelet scattering networks explains important properties of deep convolution networks for classification. A scattering representation of stationary processes incorporates higher order moments and can thus discriminate textures having the same Fourier power spectrum. State-of-the-art classification results are obtained for handwritten digits and texture discrimination, with a Gaussian kernel SVM and a generative PCA classifier.

  9. Fast convolutions meet Montgomery

    Science.gov (United States)

    Mihailescu, Preda

    2008-06-01

    Arithmetic in large ring and field extensions is an important problem of symbolic computation, and it consists essentially of the combination of one multiplication and one division in the underlying ring. Methods are known for replacing one division by two short multiplications in the underlying ring, which can be performed essentially by using convolutions. However, while using school-book multiplication, modular multiplication may be grouped into 2 mathsf{M}(mathbf{R}) operations (where mathsf{M}(mathbf{R}) denotes the number of operations of one multiplication in the underlying ring), the short multiplication problem is an important obstruction to convolution. It raises the costs in that case to 3 mathsf{M}(mathbf{R}) . In this paper we give a method for understanding and bypassing this problem, thus reducing the costs of ring arithmetic to roughly 2mathsf{M}(mathbf{R}) when also using fast convolutions. The algorithms have been implemented with results which fit well the theoretical prediction and which shall be presented in a separate paper.

  10. Unilateral spatial neglect due to right frontal lobe haematoma.

    OpenAIRE

    Maeshima, S; Funahashi, K; Ogura, M; Itakura, T; Komai, N

    1994-01-01

    Two patients with unilateral spatial neglect caused by right frontal lobe lesions underwent cerebral blood flow studies. A 54-year-old, right-handed woman developed left hemiplegia and frontal lobe neglect associated with cerebral haemorrhage after surgical excision of a frontal tumour. A 66-year-old, right-handed woman developed a haemorrhage in the right frontal lobe caused by rupture of an aneurysm. This was followed by left hemiplegia and frontal lobe neglect. In both cases, 123I-iodoamph...

  11. The convolution transform

    CERN Document Server

    Hirschman, Isidore Isaac

    2005-01-01

    In studies of general operators of the same nature, general convolution transforms are immediately encountered as the objects of inversion. The relation between differential operators and integral transforms is the basic theme of this work, which is geared toward upper-level undergraduates and graduate students. It may be read easily by anyone with a working knowledge of real and complex variable theory. Topics include the finite and non-finite kernels, variation diminishing transforms, asymptotic behavior of kernels, real inversion theory, representation theory, the Weierstrass transform, and

  12. Impairments in proverb interpretation following focal frontal lobe lesions☆

    Science.gov (United States)

    Murphy, Patrick; Shallice, Tim; Robinson, Gail; MacPherson, Sarah E.; Turner, Martha; Woollett, Katherine; Bozzali, Marco; Cipolotti, Lisa

    2013-01-01

    The proverb interpretation task (PIT) is often used in clinical settings to evaluate frontal “executive” dysfunction. However, only a relatively small number of studies have investigated the relationship between frontal lobe lesions and performance on the PIT. We compared 52 patients with unselected focal frontal lobe lesions with 52 closely matched healthy controls on a proverb interpretation task. Participants also completed a battery of neuropsychological tests, including a fluid intelligence task (Raven’s Advanced Progressive Matrices). Lesions were firstly analysed according to a standard left/right sub-division. Secondly, a finer-grained analysis compared the performance of patients with medial, left lateral and right lateral lesions with healthy controls. Thirdly, a contrast of specific frontal subgroups compared the performance of patients with medial lesions with patients with lateral frontal lesions. The results showed that patients with left frontal lesions were significantly impaired on the PIT, while in patients with right frontal lesions the impairments approached significance. Medial frontal patients were the only frontal subgroup impaired on the PIT, relative to healthy controls and lateral frontal patients. Interestingly, an error analysis indicated that a significantly higher number of concrete responses were found in the left lateral subgroup compared to healthy controls. We found no correlation between scores on the PIT and on the fluid intelligence task. Overall our results suggest that specific regions of the frontal lobes contribute to the performance on the PIT. PMID:23850600

  13. Mind the movement: Frontal asymmetry stands for behavioral motivation, bilateral frontal activation for behavior.

    Science.gov (United States)

    Rodrigues, Johannes; Müller, Mathias; Mühlberger, Andreas; Hewig, Johannes

    2018-01-01

    Frontal asymmetry has been investigated over the past 30 years, and several theories have been developed about its meaning. The original theory of Davidson and its diversification by Harmon-Jones & Allen allocated approach motivation to relative left frontal brain activity and withdrawal motivation to relative right frontal brain activity. Hewig and colleagues extended this theory by adding bilateral frontal activation representing a biological correlate of the behavioral activation system if actual behavior is shown. Wacker and colleagues formulated a theory related to the revised reinforcement sensitivity theory by Gray & McNaughton. Here, relative left frontal brain activation represents the revised behavioral activation system and behavior, while relative right frontal brain activation represents the revised behavioral inhibition system, representing the experience of conflict. These theories were investigated with a newly developed paradigm where participants were able to move around freely in a virtual T maze via joystick while having their EEG recorded. Analyzing the influence of frontal brain activation during this virtual reality task on observable behavior for 30 participants, we found more relative left frontal brain activation during approach behavior and more relative right brain activation for withdrawal behavior of any kind. Additionally, there was more bilateral frontal brain activation when participants were engaged in behavior compared to doing nothing. Hence, this study provides evidence for the idea that frontal asymmetry stands for behavioral approach or avoidance motivation, and bilateral frontal activation stands for behavior. Additionally, observable behavior is not only determined by frontal asymmetry, but also by relevant traits. © 2017 Society for Psychophysiological Research.

  14. Consensus Convolutional Sparse Coding

    KAUST Repository

    Choudhury, Biswarup

    2017-04-11

    Convolutional sparse coding (CSC) is a promising direction for unsupervised learning in computer vision. In contrast to recent supervised methods, CSC allows for convolutional image representations to be learned that are equally useful for high-level vision tasks and low-level image reconstruction and can be applied to a wide range of tasks without problem-specific retraining. Due to their extreme memory requirements, however, existing CSC solvers have so far been limited to low-dimensional problems and datasets using a handful of low-resolution example images at a time. In this paper, we propose a new approach to solving CSC as a consensus optimization problem, which lifts these limitations. By learning CSC features from large-scale image datasets for the first time, we achieve significant quality improvements in a number of imaging tasks. Moreover, the proposed method enables new applications in high dimensional feature learning that has been intractable using existing CSC methods. This is demonstrated for a variety of reconstruction problems across diverse problem domains, including 3D multispectral demosaickingand 4D light field view synthesis.

  15. Consensus Convolutional Sparse Coding

    KAUST Repository

    Choudhury, Biswarup

    2017-12-01

    Convolutional sparse coding (CSC) is a promising direction for unsupervised learning in computer vision. In contrast to recent supervised methods, CSC allows for convolutional image representations to be learned that are equally useful for high-level vision tasks and low-level image reconstruction and can be applied to a wide range of tasks without problem-specific retraining. Due to their extreme memory requirements, however, existing CSC solvers have so far been limited to low-dimensional problems and datasets using a handful of low-resolution example images at a time. In this paper, we propose a new approach to solving CSC as a consensus optimization problem, which lifts these limitations. By learning CSC features from large-scale image datasets for the first time, we achieve significant quality improvements in a number of imaging tasks. Moreover, the proposed method enables new applications in high-dimensional feature learning that has been intractable using existing CSC methods. This is demonstrated for a variety of reconstruction problems across diverse problem domains, including 3D multispectral demosaicing and 4D light field view synthesis.

  16. Separating Underdetermined Convolutive Speech Mixtures

    DEFF Research Database (Denmark)

    Pedersen, Michael Syskind; Wang, DeLiang; Larsen, Jan

    2006-01-01

    a method for underdetermined blind source separation of convolutive mixtures. The proposed framework is applicable for separation of instantaneous as well as convolutive speech mixtures. It is possible to iteratively extract each speech signal from the mixture by combining blind source separation...

  17. Strongly-MDS convolutional codes

    NARCIS (Netherlands)

    Gluesing-Luerssen, H; Rosenthal, J; Smarandache, R

    Maximum-distance separable (MDS) convolutional codes have the property that their free distance is maximal among all codes of the same rate and the same degree. In this paper, a class of MDS convolutional codes is introduced whose column distances reach the generalized Singleton bound at the

  18. The role of frontal EEG asymmetry in post-traumatic stress disorder

    NARCIS (Netherlands)

    Meyer, T.; Smeets, T.J.M.; Giesbrecht, T.; Quaedflieg, C.W.E.M.; Smulders, F.T.Y.; Meijer, E.H.; Merckelbach, H.L.G.J.

    2015-01-01

    Frontal alpha asymmetry, a biomarker derived from electroencephalography (EEG) recordings, has often been associated with psychological adjustment, with more left-sided frontal activity predicting approach motivation and lower levels of depression and anxiety. This suggests high relevance to

  19. Fibrous dysplasia of the frontal sinus: an uncommon cause of frontal lobe abscess.

    Science.gov (United States)

    Aygun, D; Sahin, H

    2004-11-01

    Fibrous dysplasia of the cranial air sinuses is rarely reported in the literature. This is the first report of frontal lobe abscess (FLA) associated with fibrous dysplasia of the frontal sinus (FDFS). A 29-year-old female presented with seizures and acute confusion. Cranial computed tomography (CT) revealed fibrous dysplasia of the left frontal sinus and associated FLA. She was referred to the neurosurgical service and the abscess and dysplastic tissue were removed. Histological examination confirmed fibrous dysplasia. We review the radiological appearance of FDFS with FLA. Clinicians should be aware of the association between these two conditions.

  20. Design of convolutional tornado code

    Science.gov (United States)

    Zhou, Hui; Yang, Yao; Gao, Hongmin; Tan, Lu

    2017-09-01

    As a linear block code, the traditional tornado (tTN) code is inefficient in burst-erasure environment and its multi-level structure may lead to high encoding/decoding complexity. This paper presents a convolutional tornado (cTN) code which is able to improve the burst-erasure protection capability by applying the convolution property to the tTN code, and reduce computational complexity by abrogating the multi-level structure. The simulation results show that cTN code can provide a better packet loss protection performance with lower computation complexity than tTN code.

  1. Regulatory behavior and frontal activity: Considering the role of revised-BIS in relative right frontal asymmetry.

    Science.gov (United States)

    Gable, Philip A; Neal, Lauren B; Threadgill, A Hunter

    2018-01-01

    Essential to human behavior are three core personality systems: approach, avoidance, and a regulatory system governing the two motivational systems. Decades of research has linked approach motivation with greater relative left frontal-cortical asymmetry. Other research has linked avoidance motivation with greater relative right frontal-cortical asymmetry. However, past work linking withdrawal motivation with greater relative right frontal asymmetry has been mixed. The current article reviews evidence suggesting that activation of the regulatory system (revised Behavioral Inhibition System [r-BIS]) may be more strongly related to greater relative right frontal asymmetry than withdrawal motivation. Specifically, research suggests that greater activation of the r-BIS is associated with greater relative right frontal activity, and reduced r-BIS activation is associated with reduced right frontal activity (greater relative left frontal activity). We review evidence examining trait and state frontal activity using EEG, source localization, lesion studies, neuronal stimulation, and fMRI supporting the idea that r-BIS may be the core personality system related to greater relative right frontal activity. In addition, the current review seeks to disentangle avoidance motivation and r-BIS as substrates of relative right frontal asymmetry. © 2017 Society for Psychophysiological Research.

  2. Solutions to Arithmetic Convolution Equations

    Czech Academy of Sciences Publication Activity Database

    Glöckner, H.; Lucht, L.G.; Porubský, Štefan

    2007-01-01

    Roč. 135, č. 6 (2007), s. 1619-1629 ISSN 0002-9939 R&D Projects: GA ČR GA201/04/0381 Institutional research plan: CEZ:AV0Z10300504 Keywords : arithmetic functions * Dirichlet convolution * polynomial equations * analytic equations * topological algebras * holomorphic functional calculus Subject RIV: BA - General Mathematics Impact factor: 0.520, year: 2007

  3. Model structure selection in convolutive mixtures

    DEFF Research Database (Denmark)

    Dyrholm, Mads; Makeig, Scott; Hansen, Lars Kai

    2006-01-01

    The CICAAR algorithm (convolutive independent component analysis with an auto-regressive inverse model) allows separation of white (i.i.d) source signals from convolutive mixtures. We introduce a source color model as a simple extension to the CICAAR which allows for a more parsimoneous...... representation in many practical mixtures. The new filter-CICAAR allows Bayesian model selection and can help answer questions like: 'Are we actually dealing with a convolutive mixture?'. We try to answer this question for EEG data....

  4. Mesial frontal lobe epilepsy.

    Science.gov (United States)

    Unnwongse, Kanjana; Wehner, Tim; Foldvary-Schaefer, Nancy

    2012-10-01

    Mesial frontal lobe epilepsies can be divided into epilepsies arising from the anterior cingulate gyrus and those of the supplementary sensorimotor area. They provide diagnostic challenges because they often lack lateralizing or localizing features on clinical semiology and interictal and ictal scalp electroencephalographic (EEG) recordings. A number of unique semiologic features have been described over the last decade in patients with mesial frontal lobe epilepsy (FLE). There are few reports of applying advanced neurophysiologic techniques such as electrical source imaging, magnetoencephalography, EEG/functional magnetic resonance imaging, or analysis of high-frequency oscillations in patients with mesial FLE. Despite these diagnostic challenges, it seems that patients with mesial FLE benefit from epilepsy surgery to the same extent or even better than patients with FLE do, as a whole.

  5. Convolutions

    Indian Academy of Sciences (India)

    Think of a simple game of chance like throwing a dice. There are six possible outcomes, 1,2,...,6, each with probability 1/6. The probability vector (or the probability distribution) corresponding to this is (1/6,1/6,...,1/6), which for brevity I write as 1. 6(1,1,...,1). Suppose we throw the dice twice and observe the sum of the two.

  6. Convolutions

    Indian Academy of Sciences (India)

    The word 'interactive' is in fashion these days. So I will leave a few things for you to check. Let f1 and f2 be two polynomials, say f1(x) = a0 + a1x + a2x2,. (1) f2(x) = b0 + b1x + b2x2 + b3x3. (2). (Here the coefficients a's and b's could be integers, rational, real, or complex numbers.) Their product f1 f2 is the polynomial f1 f2(x) ...

  7. Convolution-deconvolution in DIGES

    Energy Technology Data Exchange (ETDEWEB)

    Philippacopoulos, A.J.; Simos, N. [Brookhaven National Lab., Upton, NY (United States). Dept. of Advanced Technology

    1995-05-01

    Convolution and deconvolution operations is by all means a very important aspect of SSI analysis since it influences the input to the seismic analysis. This paper documents some of the convolution/deconvolution procedures which have been implemented into the DIGES code. The 1-D propagation of shear and dilatational waves in typical layered configurations involving a stack of layers overlying a rock is treated by DIGES in a similar fashion to that of available codes, e.g. CARES, SHAKE. For certain configurations, however, there is no need to perform such analyses since the corresponding solutions can be obtained in analytic form. Typical cases involve deposits which can be modeled by a uniform halfspace or simple layered halfspaces. For such cases DIGES uses closed-form solutions. These solutions are given for one as well as two dimensional deconvolution. The type of waves considered include P, SV and SH waves. The non-vertical incidence is given special attention since deconvolution can be defined differently depending on the problem of interest. For all wave cases considered, corresponding transfer functions are presented in closed-form. Transient solutions are obtained in the frequency domain. Finally, a variety of forms are considered for representing the free field motion both in terms of deterministic as well as probabilistic representations. These include (a) acceleration time histories, (b) response spectra (c) Fourier spectra and (d) cross-spectral densities.

  8. Convolution-deconvolution in DIGES

    International Nuclear Information System (INIS)

    Philippacopoulos, A.J.; Simos, N.

    1995-01-01

    Convolution and deconvolution operations is by all means a very important aspect of SSI analysis since it influences the input to the seismic analysis. This paper documents some of the convolution/deconvolution procedures which have been implemented into the DIGES code. The 1-D propagation of shear and dilatational waves in typical layered configurations involving a stack of layers overlying a rock is treated by DIGES in a similar fashion to that of available codes, e.g. CARES, SHAKE. For certain configurations, however, there is no need to perform such analyses since the corresponding solutions can be obtained in analytic form. Typical cases involve deposits which can be modeled by a uniform halfspace or simple layered halfspaces. For such cases DIGES uses closed-form solutions. These solutions are given for one as well as two dimensional deconvolution. The type of waves considered include P, SV and SH waves. The non-vertical incidence is given special attention since deconvolution can be defined differently depending on the problem of interest. For all wave cases considered, corresponding transfer functions are presented in closed-form. Transient solutions are obtained in the frequency domain. Finally, a variety of forms are considered for representing the free field motion both in terms of deterministic as well as probabilistic representations. These include (a) acceleration time histories, (b) response spectra (c) Fourier spectra and (d) cross-spectral densities

  9. The general theory of convolutional codes

    Science.gov (United States)

    Mceliece, R. J.; Stanley, R. P.

    1993-01-01

    This article presents a self-contained introduction to the algebraic theory of convolutional codes. This introduction is partly a tutorial, but at the same time contains a number of new results which will prove useful for designers of advanced telecommunication systems. Among the new concepts introduced here are the Hilbert series for a convolutional code and the class of compact codes.

  10. Symbol synchronization in convolutionally coded systems

    Science.gov (United States)

    Baumert, L. D.; Mceliece, R. J.; Van Tilborg, H. C. A.

    1979-01-01

    Alternate symbol inversion is sometimes applied to the output of convolutional encoders to guarantee sufficient richness of symbol transition for the receiver symbol synchronizer. A bound is given for the length of the transition-free symbol stream in such systems, and those convolutional codes are characterized in which arbitrarily long transition free runs occur.

  11. Posttraumatic frontal bone osteomyelitis.

    Science.gov (United States)

    Jung, S Heredero; Aniceto, G Sánchez; Rodríguez, I Zubillaga; Diaz, R Gutiérrez; Recuero, I I García

    2009-05-01

    We present the clinical case of a patient with open bilateral frontal sinus fractures who developed a frontal osteomyelitis. A review of the problem and management ascending to the different alternatives for central anterior skull base defects and fronto-orbital reconstruction is also presented. After extensive radical debridement of the necrotic bone, final reconstruction of the skull base was performed by using a rectus abdominis free flap. A custom-made hard tissue replacement implant was used for the fronto-orbital reconstruction. Extensive debridement is required for the treatment of frontal osteomyelitis. An appropriate isolation of the skull base from the upper aerodigestive system must be obtained to prevent continuous infectious complications. Free flaps are especially useful for skull base reconstruction when traditional methods are not available or have failed because of the lack of available tissue for vascularized reconstruction. Custom-made alloplastic implants are a good reconstructive option for large fronto-orbital defects once the infection is gone and vascularized tissue has been transferred.

  12. Model structure selection in convolutive mixtures

    DEFF Research Database (Denmark)

    Dyrholm, Mads; Makeig, S.; Hansen, Lars Kai

    2006-01-01

    The CICAAR algorithm (convolutive independent component analysis with an auto-regressive inverse model) allows separation of white (i.i.d) source signals from convolutive mixtures. We introduce a source color model as a simple extension to the CICAAR which allows for a more parsimonious represent......The CICAAR algorithm (convolutive independent component analysis with an auto-regressive inverse model) allows separation of white (i.i.d) source signals from convolutive mixtures. We introduce a source color model as a simple extension to the CICAAR which allows for a more parsimonious...... representation in many practical mixtures. The new filter-CICAAR allows Bayesian model selection and can help answer questions like: ’Are we actually dealing with a convolutive mixture?’. We try to answer this question for EEG data....

  13. Dorsolateral frontal lobe epilepsy.

    Science.gov (United States)

    Lee, Ricky W; Worrell, Greg A

    2012-10-01

    Dorsolateral frontal lobe seizures often present as a diagnostic challenge. The diverse semiologies may not produce lateralizing or localizing signs and can appear bizarre and suggest psychogenic events. Unfortunately, scalp electroencephalographic (EEG) and magnetic resonance imaging (MRI) are often unsatisfactory. It is not uncommon that these traditional diagnostic studies are either unhelpful or even misleading. In some cases, SPECT and positron emission tomography imaging can be an effective tool to identify the origin of seizures. However, these techniques and other emerging techniques all have limitations, and new approaches are needed to improve source localization.

  14. Frontal Integration and Coping

    DEFF Research Database (Denmark)

    Larsen, Torben

    2012-01-01

    and risk minimizing Rationalists dominated by dlPFC • R correlates both with your own level of education and that of your parents 3 Conclusion: Empirical verification of the first derivative of NeM uncovers four different coping patterns within the range of normal behaviors with an obvious analogue...... to the classical tempers. In prospect, differentiating the Frontal integration pattern by temper (General risk attitude) opens an evidence-based pathway for individually tailored neural training towards advanced social objectives as multidisciplinary collaboration and healthy living. References 1. Larsen T...... et al. Gender difference in neural response to psychological stress. SCAN 2 2007, 227–233...

  15. Convolutional Neural Network for Image Recognition

    CERN Document Server

    Seifnashri, Sahand

    2015-01-01

    The aim of this project is to use machine learning techniques especially Convolutional Neural Networks for image processing. These techniques can be used for Quark-Gluon discrimination using calorimeters data, but unfortunately I didn’t manage to get the calorimeters data and I just used the Jet data fromminiaodsim(ak4 chs). The Jet data was not good enough for Convolutional Neural Network which is designed for ’image’ recognition. This report is made of twomain part, part one is mainly about implementing Convolutional Neural Network on unphysical data such as MNIST digits and CIFAR-10 dataset and part 2 is about the Jet data.

  16. Athletes in a Slump: Neurophysiological Evidence from Frontal Theta Activity

    Directory of Open Access Journals (Sweden)

    Jingu Kim

    2014-01-01

    Full Text Available The purpose of this study is to investigate the neurophysiological differences in athletes who suffer from a slump and other athletes who do not. Eighteen high school student athletes participated in this experiment. A subjective questionnaire was conducted to identify athletes in a slump (i.e., the slump group and not in a slump (i.e., the no-slump group. EEG data was recorded at 4 regions (left prefrontal, right prefrontal, left frontal, and right frontal. A two-way (2 groups x 4 regions ANOVA was performed on the dependent variable (i.e., frontal theta power. The findings of this study demonstrated that participants in the no-slump group showed higher frontal theta activity than their counterparts in the slump group. From the findings of this study, it is suggested that mental fatigue may cause low frontal theta activity in athletes who experience a slump. The present study makes an important contribution to the current literature by being the first to report that EEG theta power over frontal regions can be used as a marker of athletes suffering from a slump.

  17. Convolution on spaces of locally summable functions

    Directory of Open Access Journals (Sweden)

    Federica Andreano

    2008-01-01

    Full Text Available In this work we prove the existence of convolution on Marcinkiewicz spaces p(ℝ, 1≤p<∞, and, using pointwise approximate identities, we extend the classical definition of Hilbert transform to such spaces.

  18. Rare giant frontal sinus osteoma mimicking fibrous dysplasia.

    Science.gov (United States)

    Exley, R P; Markey, A; Rutherford, S; Bhalla, R K

    2015-03-01

    To present the first report of a giant frontal sinus osteoma treated by excision and single-stage reconstruction with custom-made titanium cranioplasty and left orbital roof prostheses. A 31-year-old man with a history of chronic frontal sinusitis presented with a deforming, painless, midline forehead swelling of 11 years' duration, which had been treated unsuccessfully in Nigeria. Differential diagnosis included both benign and malignant bony tumours. Computerised tomography revealed a giant bony frontal sinus tumour extending beyond the sinus roof and breaching the left orbit, consistent with fibrous dysplasia. Given the extent of the tumour, open craniectomy was performed for surgical extirpation. Histological analysis identified multiple osteomas. This surgical approach achieved excellent cosmesis, with no evidence of recurrence at 12-month follow up. Forehead swelling may pose diagnostic and management dilemmas for the ENT surgeon; however, effective management is facilitated by a multidisciplinary approach.

  19. Frontal alpha asymmetry neurofeedback for the reduction of negative affect and anxiety.

    Science.gov (United States)

    Mennella, Rocco; Patron, Elisabetta; Palomba, Daniela

    2017-05-01

    Frontal alpha asymmetry has been proposed to underlie the balance between approach and withdrawal motivation associated to each individual's affective style. Neurofeedback of EEG frontal alpha asymmetry represents a promising tool to reduce negative affect, although its specific effects on left/right frontal activity and approach/withdrawal motivation are still unclear. The present study employed a neurofeedback training to increase frontal alpha asymmetry (right - left), in order to evaluate discrete changes in alpha power at left and right sites, as well as in positive and negative affect, anxiety and depression. Thirty-two right-handed females were randomly assigned to receive either the neurofeedback on frontal alpha asymmetry, or an active control training (N = 16 in each group). The asymmetry group showed an increase in alpha asymmetry driven by higher alpha at the right site (p neurofeedback for the reduction of negative affect and anxiety in clinical settings. Copyright © 2017 Elsevier Ltd. All rights reserved.

  20. Craniotomy Frontal Bone Defect

    African Journals Online (AJOL)

    2018-03-01

    Mar 1, 2018 ... with cosmetic deformity of fore head (Figure 1), and he claimed that he could not get job because of ... 1: Pre-operative forontal view of patient. Figure 2: Intra operative photography of defect (A) reconstructed defect (B) ... with a cosmetic deformity of forehead on left side. (4nA and B). He was a candidate for.

  1. Frontal mucocele with intracranial extension causing frontal lobe syndrome.

    Science.gov (United States)

    Weidmayer, Sara

    2015-06-01

    Mucoceles are mucus-containing cysts that form in paranasal sinuses; although mucoceles themselves are benign, this case report highlights the extensive damage they can cause as their expansion may lead to bony erosion and extension of the mucocele into the orbit and cranium; it also presents a rarely reported instance of frontal sinus mucocele leading to frontal lobe syndrome. A thorough discussion and review of mucoceles is included. A 68-year-old white man presented with intermittent diplopia and a pressure sensation in the right eye. He had a history of chronic sinusitis and had had endoscopic sinus surgery 5 years prior. A maxillofacial computed tomography scan revealed a large right frontal sinus mucocele, which had caused erosion along the medial wall of the right orbit and the outer and inner tables of the right frontal sinus. The mucocele had protruded both into the right orbit and intracranially, causing mass effect on the frontal lobe, which led to frontal lobe syndrome. The patient was successfully treated with endoscopic right ethmoidectomy, radial frontal sinusotomy, marsupialization of the mucocele, and transcutaneous irrigation. Paranasal sinus mucoceles may expand and lead to bony erosion and can become very invasive in surrounding structures such as the orbit and cranium. This case not only exhibits a very rare presentation of frontal sinus mucocele with intracranial extension and frontal lobe mass effect causing a frontal lobe syndrome but also demonstrates many of the ocular and visual complications commonly associated with paranasal sinus mucoceles. Early identification and surgical intervention is vital for preventing and reducing morbidity associated with invasive mucoceles, and the patient must be followed regularly to monitor for recurrence.

  2. Origami by frontal photopolymerization.

    Science.gov (United States)

    Zhao, Zeang; Wu, Jiangtao; Mu, Xiaoming; Chen, Haosen; Qi, H Jerry; Fang, Daining

    2017-04-01

    Origami structures are of great interest in microelectronics, soft actuators, mechanical metamaterials, and biomedical devices. Current methods of fabricating origami structures still have several limitations, such as complex material systems or tedious processing steps. We present a simple approach for creating three-dimensional (3D) origami structures by the frontal photopolymerization method, which can be easily implemented by using a commercial projector. The concept of our method is based on the volume shrinkage during photopolymerization. By adding photoabsorbers into the polymer resin, an attenuated light field is created and leads to a nonuniform curing along the thickness direction. The layer directly exposed to light cures faster than the next layer; this nonuniform curing degree leads to nonuniform curing-induced volume shrinkage. This further introduces a nonuniform stress field, which drives the film to bend toward the newly formed side. The degree of bending can be controlled by adjusting the gray scale and the irradiation time, an easy approach for creating origami structures. The behavior is examined both experimentally and theoretically. Two methods are also proposed to create different types of 3D origami structures.

  3. The validity of individual frontal alpha asymmetry EEG neurofeedback.

    Science.gov (United States)

    Quaedflieg, C W E M; Smulders, F T Y; Meyer, T; Peeters, F; Merckelbach, H; Smeets, T

    2016-01-01

    Frontal asymmetry in alpha oscillations is assumed to be associated with psychopathology and individual differences in emotional responding. Brain-activity-based feedback is a promising tool for the modulation of cortical activity. Here, we validated a neurofeedback protocol designed to change relative frontal asymmetry based on individual alpha peak frequencies, including real-time average referencing and eye-correction. Participants (N = 60) were randomly assigned to a right, left or placebo neurofeedback group. Results show a difference in trainability between groups, with a linear change in frontal alpha asymmetry over time for the right neurofeedback group during rest. Moreover, the asymmetry changes in the right group were frequency and location specific, even though trainability did not persist at 1 week and 1 month follow-ups. On the behavioral level, subjective stress on the second test day was reduced in the left and placebo neurofeedback groups, but not in the right neurofeedback group. We found individual differences in trainability that were dependent on training group, with participants in the right neurofeedback group being more likely to change their frontal asymmetry in the desired direction. Individual differences in trainability were also reflected in the ability to change frontal asymmetry during the feedback. © The Author (2015). Published by Oxford University Press. For Permissions, please email: journals.permissions@oup.com.

  4. Frontal and striatal alterations associated with psychopathic traits in adolescents

    Science.gov (United States)

    Yang, Yaling; Narr, Katherine L.; Baker, Laura A.; Joshi, Shantanu H.; Jahanshad, Neda; Raine, Adrian; Thompson, Paul M.

    2016-01-01

    Neuroimaging research has demonstrated a range of structural deficits in adults with psychopathy, but little is known about structural correlates of psychopathic tendencies in adolescents. Here we examined structural magnetic resonance imaging (sMRI) data obtained from 14-year-old adolescents (n=108) using tensor-based morphometry (TBM) to isolate global and localized differences in brain tissue volumes associated with psychopathic traits in this otherwise healthy developmental population. We found that greater levels of psychopathic traits were correlated with increased brain tissue volumes in the left putamen, left ansa peduncularis, right superiomedial prefrontal cortex, left inferior frontal cortex, right orbitofrontal cortex, and right medial temporal regions and reduced brain tissues volumes in the right middle frontal cortex, left superior parietal lobule, and left inferior parietal lobule. Post hoc analyses of parcellated regional volumes also showed putamen enlargements to correlate with increased psychopathic traits. Consistent with earlier studies, findings suggest poor decision-making and emotional dysregulation associated with psychopathy may be due, in part, to structural anomalies in frontal and temporal regions whereas striatal structural variations may contribute to sensation-seeking and reward-driven behavior in psychopathic individuals. Future studies will help clarify how disturbances in brain maturational processes might lead to the developmental trajectory from psychopathic tendencies in adolescents to adult psychopathy. PMID:25676553

  5. Paediatric frontal chest radiograph screening with fine-tuned convolutional neural networks

    CSIR Research Space (South Africa)

    Gerrand, Jonathan D

    2017-07-01

    Full Text Available Within developing countries, there is a realistic need for technologies that can assist medical practitioners in meeting the increasing demand for patient screening and monitoring. To this end, computer aided diagnosis (CAD) based approaches...

  6. Temporary Frontal Paralysis Secondary to Blunt Trauma Frontal Sinus Fracture

    Science.gov (United States)

    Hamilton, Stefan; Hearn, Matthew; Kherani, Safeena; Macdonald, Kristian I.

    2017-01-01

    Frontal sinus fractures (FSF) are relatively uncommon and can be challenging for trauma surgeons to manage. Patients with FSF typically present with facial swelling, pain, and nasofrontal ecchymosis. Here we present a rare case of a patient with FSF and anterior table fracture where the main presenting symptom was bilateral frontal paralysis. We outline our management strategy and review the current literature in regard to management of FSF. PMID:28573060

  7. Depression symptom dimensions and asymmetrical frontal cortical activity while anticipating reward.

    Science.gov (United States)

    Nelson, Brady D; Kessel, Ellen M; Klein, Daniel N; Shankman, Stewart A

    2018-01-01

    Unipolar depression has been characterized as involving diminished approach motivation and reward sensitivity. A psychophysiological indicator of approach motivation involves an asymmetry in frontal EEG activity, such that greater left relative to right frontal cortical activity indicates increased approach motivation. Consistent with the perspective of reduced approach motivation tendencies, depression has been associated with decreased relative left frontal cortical activity. To date, supporting research has primarily relied on categorical diagnoses or composite symptom counts. However, given the heterogeneity in depression, it is unclear what specific symptom dimensions relate to decreased relative left frontal cortical activity. The present study examined the association between multiple depression symptom dimensions and asymmetrical frontal cortical activity while anticipating reward in separate undergraduate (n = 75) and clinical samples (current major depressive disorder [n = 68] and never depressed controls [n = 67]). All participants completed the Inventory of Depression and Anxiety Symptoms, a self-report measure of factor-analytically derived symptom dimensions. Frontal cortical activity was assessed during a computerized slot machine task while participants anticipated potential monetary reward or no incentive. In undergraduates with low depression symptoms and never depressed controls, reward trials relative to no-incentive trials elicited greater relative left frontal cortical activity. Furthermore, in both samples across all participants, increased dysphoria and lassitude symptoms were associated with decreased relative left frontal cortical activity while anticipating reward. The present study suggests that depression symptoms consistent with motivational disengagement are associated with decreased relative left frontal cortical activity. © 2017 Society for Psychophysiological Research.

  8. Human Face Recognition Using Convolutional Neural Networks

    Directory of Open Access Journals (Sweden)

    Răzvan-Daniel Albu

    2009-10-01

    Full Text Available In this paper, I present a novel hybrid face recognition approach based on a convolutional neural architecture, designed to robustly detect highly variable face patterns. The convolutional network extracts successively larger features in a hierarchical set of layers. With the weights of the trained neural networks there are created kernel windows used for feature extraction in a 3-stage algorithm. I present experimental results illustrating the efficiency of the proposed approach. I use a database of 796 images of 159 individuals from Reims University which contains quite a high degree of variability in expression, pose, and facial details.

  9. Feedback equivalence of convolutional codes over finite rings

    Directory of Open Access Journals (Sweden)

    DeCastro-García Noemí

    2017-12-01

    Full Text Available The approach to convolutional codes from the linear systems point of view provides us with effective tools in order to construct convolutional codes with adequate properties that let us use them in many applications. In this work, we have generalized feedback equivalence between families of convolutional codes and linear systems over certain rings, and we show that every locally Brunovsky linear system may be considered as a representation of a code under feedback convolutional equivalence.

  10. Frontal brain asymmetry as a marker of depression and effectiveness of TMS therapy

    International Nuclear Information System (INIS)

    Mani, D.; Lithgow, B.

    2010-01-01

    Full text: Resting frontal brain electroencephalography (EEG) asymmetry has been hypothesi sed as a diagnostic marker for depression. A number of studies have shown that depressed individuals are characterised by diminished left sided activation of the prefrontal cortex, which is indicated by greater left than right alpha-band power. Relative left frontal region activity is believed to be associated with positive approach related behaviour and relative right frontal activity is seen to be linked to negative withdrawal related behaviour. In this study, frontal brain EEG was recorded from 17 depressed and 19 control subjects, from which frontal brain asymmetry ratios were calculated. The results confirmed the trend of relative left anterior hypoaclivation for individuals with depression compared to the healthy controls. This study also looked at beta and theta band ratios and found theta for depressed is predominantly negative, while the control group dis played mainly positive values. Beta comparison showed little significant difference between control and depressed groups. In addition, there have been few studies that examined frontal brain asymmetry in depression soon after treatment to gauge its effectiv ness. In a very preliminary study, the effect of Transcranial Magnetic Stimulation (TMS) therapy on the alpha band frontal brain asymmetry ratio for 5 depl'essed subjects before and after treatment found a slight increase in FBA ratio for 4 subjects. Further research and a larger subject group is required to validate these results.

  11. The relation of hedonic hunger and restrained eating to lateralized frontal activation.

    Science.gov (United States)

    Winter, S R; Feig, E H; Kounios, J; Erickson, B; Berkowitz, S; Lowe, M R

    2016-09-01

    Asymmetrical alpha activation in the prefrontal cortex (frontal asymmetry) in electroencephalography (EEG) has been related to eating behavior. Prior studies linked dietary restraint with right frontal asymmetry [1] and disinhibition with left frontal asymmetry [2]. The current study simultaneously assessed restrained eating and hedonic hunger (drive for food reward in the absence of hunger) in relation to frontal asymmetry. Resting-state EEG and measures of restrained eating (Revised Restraint Scale; RRS) and hedonic hunger (Power of Food Scale; PFS) were assessed in 61 non-obese adults. Individually, hedonic hunger predicted left asymmetry. However, PFS and RRS were correlated (r=0.48, phunger exhibited left asymmetry irrespective of RRS scores; among those low in PFS, only those high in RRS showed right asymmetry. Results were consistent with literature linking avoidant behaviors (restraint) with right-frontal asymmetry and approach behaviors (binge eating) with left-frontal asymmetry. It appears that a strong drive toward palatable foods predominates at a neural level even when restraint is high. Findings suggest that lateralized frontal activity is an indicator of motivation both to consume and to avoid consuming highly palatable foods. Copyright © 2016 Elsevier Inc. All rights reserved.

  12. Gradient Flow Convolutive Blind Source Separation

    DEFF Research Database (Denmark)

    Pedersen, Michael Syskind; Nielsen, Chinton Møller

    2004-01-01

    Experiments have shown that the performance of instantaneous gradient flow beamforming by Cauwenberghs et al. is reduced significantly in reverberant conditions. By expanding the gradient flow principle to convolutive mixtures, separation in a reverberant environment is possible. By use of a circ...

  13. Medical Text Classification Using Convolutional Neural Networks.

    Science.gov (United States)

    Hughes, Mark; Li, Irene; Kotoulas, Spyros; Suzumura, Toyotaro

    2017-01-01

    We present an approach to automatically classify clinical text at a sentence level. We are using deep convolutional neural networks to represent complex features. We train the network on a dataset providing a broad categorization of health information. Through a detailed evaluation, we demonstrate that our method outperforms several approaches widely used in natural language processing tasks by about 15%.

  14. Convolutional Neural Networks for SAR Image Segmentation

    DEFF Research Database (Denmark)

    Malmgren-Hansen, David; Nobel-Jørgensen, Morten

    2015-01-01

    Segmentation of Synthetic Aperture Radar (SAR) images has several uses, but it is a difficult task due to a number of properties related to SAR images. In this article we show how Convolutional Neural Networks (CNNs) can easily be trained for SAR image segmentation with good results. Besides...

  15. Differences in the neural correlates of frontal lobe tests.

    Science.gov (United States)

    Matsuoka, Teruyuki; Kato, Yuka; Imai, Ayu; Fujimoto, Hiroshi; Shibata, Keisuke; Nakamura, Kaeko; Yamada, Kei; Narumoto, Jin

    2018-01-01

    The Executive Interview (EXIT25), the executive clock-drawing task (CLOX1), and the Frontal Assessment Battery (FAB) are used to assess executive function at the bedside. These tests assess distinct psychometric properties. The aim of this study was to examine differences in the neural correlates of the EXIT25, CLOX1, and FAB based on magnetic resonance imaging. Fifty-eight subjects (30 with Alzheimer's disease, 10 with mild cognitive impairment, and 18 healthy controls) participated in this study. Multiple regression analyses were performed to examine the brain regions correlated with the EXIT25, CLOX1, and FAB scores. Age, gender, and years of education were included as covariates. Statistical thresholds were set to uncorrected P-values of 0.001 at the voxel level and 0.05 at the cluster level. The EXIT25 score correlated inversely with the regional grey matter volume in the left lateral frontal lobe (Brodmann areas 6, 9, 44, and 45). The CLOX1 score correlated positively with the regional grey matter volume in the right orbitofrontal cortex (Brodmann area 11) and the left supramarginal gyrus (Brodmann area 40). The FAB score correlated positively with the regional grey matter volume in the right precentral gyrus (Brodmann area 6). The left lateral frontal lobe (Brodmann area 9) and the right lateral frontal lobe (Brodmann area 46) were identified as common brain regions that showed association with EXIT25, CLOX1, and FAB based only a voxel-level threshold. The results of this study suggest that the EXIT25, CLOX1, and FAB may be associated with the distinct neural correlates of the frontal cortex. © 2018 Japanese Psychogeriatric Society.

  16. Visuo-spatial construction in patients with frontal and parietal lobe lesions

    Directory of Open Access Journals (Sweden)

    Himani Kashyap

    2011-04-01

    Full Text Available Visuospatial construction, traditionally viewed as a putative parietal function, also requires sustained attention, planning, organization strategies and error correction, and hence frontal lobe mediation. The relative contributions of the frontal and parietal lobes are poorly understood. To examine the contributions of parietal, frontal lobes, as well as right and left cerebral hemispheres to visuospatial construction. The Stick Construction Test for two-dimensional construction and the Block Construction Test for three-dimensional construction were administered pre-surgically to patients with lesions in the parietal lobe (n =9 and the frontal lobe (n=11, along with normal control subjects (n =20 matched to the patients on age (+/- 3 years, gender, education (+/- 3 years and handedness. The patients were significantly slower than the controls on both two-dimensional and three-dimensional tests. Patients with parietal lesions were slower than those with frontal lesions on the test of three-dimensional construction. Within each lobe patients with right and left sided lesions did not differ significantly. It appears that tests of three-dimensional construction might be most sensitive to visuospatial construction deficits. Visuospatial construction involves the mediation of both frontal and parietal lobes. The function does not appear to be lateralized. The networks arising from the parieto-occipital areas and projecting to the frontal cortices (e.g., occipito-frontal fasciculus may be the basis of the mediation of both lobes in visuospatial construction. The present findings need replication from studies with larger sample sizes.

  17. The role of medial frontal gyrus in action anticipation in professional badminton players

    Directory of Open Access Journals (Sweden)

    Huan Xu

    2016-11-01

    Full Text Available Some studies show that the medial frontal cortex is associated with more skilled action anticipation, while similar findings are not observed in some other studies, possibly due to the stimuli employed and the participants used as the control group. In addition, no studies have investigated whether there is any functional connectivity between the medial frontal cortex and other brain regions in more skilled action anticipation. Therefore, the present study aimed to re-investigate how the medial frontal cortex is involved in more skilled action anticipation by circumventing the limitations of previous research and to investigate that the medial frontal cortex functionally connected with other brain regions involved in action processing in more skilled action anticipation. To this end, professional badminton players and novices were asked to anticipate the landing position of the shuttlecock while watching badminton match videos or to judge the gender of the players in the matches. The video clips ended right at the point that the shuttlecock and the racket came into contact to reduce the effect of information about the trajectory of the shuttlecock. Novices who lacked training and watching experience were recruited for the control group to reduce the effect of sport-related experience on the medial frontal cortex. Blood oxygenation level-dependent (BOLD activation was assessed by means of functional magnetic resonance imaging (fMRI. Compared to novices, badminton players exhibited stronger activation in the left medial frontal cortex during action anticipation and greater functional connectivity between left medial frontal cortex and some other brain regions (e.g., right posterior cingulate cortex. Therefore, the present study supports the position that the medial frontal cortex plays a role in more skilled action anticipation and that there is a specific brain network for more skilled action anticipation that involves right posterior cingulate

  18. Human Frontal-Subcortical Circuit and Asymmetric Belief Updating.

    Science.gov (United States)

    Moutsiana, Christina; Charpentier, Caroline J; Garrett, Neil; Cohen, Michael X; Sharot, Tali

    2015-10-21

    How humans integrate information to form beliefs about reality is a question that has engaged scientists for centuries, yet the biological system supporting this process is not well understood. One of the most salient attributes of information is valence. Whether a piece of news is good or bad is critical in determining whether it will alter our beliefs. Here, we reveal a frontal-subcortical circuit in the left hemisphere that is simultaneously associated with enhanced integration of favorable information into beliefs and impaired integration of unfavorable information. Specifically, for favorable information, stronger white matter connectivity within this system, particularly between the left inferior frontal gyrus (IFG) and left subcortical regions (including the amygdala, hippocampus, thalamus, putamen, and pallidum), as well as insular cortex, is associated with greater change in belief. However, for unfavorable information, stronger connectivity within this system, particularly between the left IFG and left pallidum, putamen, and insular cortex, is associated with reduced change in beliefs. These novel results are consistent with models suggesting that partially separable processes govern learning from favorable and unfavorable information. Beliefs of what may happen in the future are important, because they guide decisions and actions. Here, we illuminate how structural brain connectivity is related to the generation of subjective beliefs. We focus on how the valence of information is related to people's tendency to alter their beliefs. By quantifying the extent to which participants update their beliefs in response to desirable and undesirable information and relating those measures to the strength of white matter connectivity using diffusion tensor imaging, we characterize a left frontal-subcortical system that is associated simultaneously with greater belief updating in response to favorable information and reduced belief updating in response to

  19. Gas Classification Using Deep Convolutional Neural Networks.

    Science.gov (United States)

    Peng, Pai; Zhao, Xiaojin; Pan, Xiaofang; Ye, Wenbin

    2018-01-08

    In this work, we propose a novel Deep Convolutional Neural Network (DCNN) tailored for gas classification. Inspired by the great success of DCNN in the field of computer vision, we designed a DCNN with up to 38 layers. In general, the proposed gas neural network, named GasNet, consists of: six convolutional blocks, each block consist of six layers; a pooling layer; and a fully-connected layer. Together, these various layers make up a powerful deep model for gas classification. Experimental results show that the proposed DCNN method is an effective technique for classifying electronic nose data. We also demonstrate that the DCNN method can provide higher classification accuracy than comparable Support Vector Machine (SVM) methods and Multiple Layer Perceptron (MLP).

  20. A convolutional neural network neutrino event classifier

    International Nuclear Information System (INIS)

    Aurisano, A.; Sousa, A.; Radovic, A.; Vahle, P.; Rocco, D.; Pawloski, G.; Himmel, A.; Niner, E.; Messier, M.D.; Psihas, F.

    2016-01-01

    Convolutional neural networks (CNNs) have been widely applied in the computer vision community to solve complex problems in image recognition and analysis. We describe an application of the CNN technology to the problem of identifying particle interactions in sampling calorimeters used commonly in high energy physics and high energy neutrino physics in particular. Following a discussion of the core concepts of CNNs and recent innovations in CNN architectures related to the field of deep learning, we outline a specific application to the NOvA neutrino detector. This algorithm, CVN (Convolutional Visual Network) identifies neutrino interactions based on their topology without the need for detailed reconstruction and outperforms algorithms currently in use by the NOvA collaboration.

  1. Epileptiform spike detection via convolutional neural networks

    DEFF Research Database (Denmark)

    Johansen, Alexander Rosenberg; Jin, Jing; Maszczyk, Tomasz

    2016-01-01

    The EEG of epileptic patients often contains sharp waveforms called "spikes", occurring between seizures. Detecting such spikes is crucial for diagnosing epilepsy. In this paper, we develop a convolutional neural network (CNN) for detecting spikes in EEG of epileptic patients in an automated...... fashion. The CNN has a convolutional architecture with filters of various sizes applied to the input layer, leaky ReLUs as activation functions, and a sigmoid output layer. Balanced mini-batches were applied to handle the imbalance in the data set. Leave-one-patient-out cross-validation was carried out...... to test the CNN and benchmark models on EEG data of five epilepsy patients. We achieved 0.947 AUC for the CNN, while the best performing benchmark model, Support Vector Machines with Gaussian kernel, achieved an AUC of 0.912....

  2. Quantitative electroencephalographic and neuropsychological investigation of an alternative measure of frontal lobe executive functions: the Figure Trail Making Test.

    Science.gov (United States)

    Foster, Paul S; Drago, Valeria; Ferguson, Brad J; Harrison, Patti Kelly; Harrison, David W

    2015-12-01

    The most frequently used measures of executive functioning are either sensitive to left frontal lobe functioning or bilateral frontal functioning. Relatively little is known about right frontal lobe contributions to executive functioning given the paucity of measures sensitive to right frontal functioning. The present investigation reports the development and initial validation of a new measure designed to be sensitive to right frontal lobe functioning, the Figure Trail Making Test (FTMT). The FTMT, the classic Trial Making Test, and the Ruff Figural Fluency Test (RFFT) were administered to 42 right-handed men. The results indicated a significant relationship between the FTMT and both the TMT and the RFFT. Performance on the FTMT was also related to high beta EEG over the right frontal lobe. Thus, the FTMT appears to be an equivalent measure of executive functioning that may be sensitive to right frontal lobe functioning. Applications for use in frontotemporal dementia, Alzheimer's disease, and other patient populations are discussed.

  3. Vehicle Color Recognition using Convolutional Neural Network

    OpenAIRE

    Rachmadi, Reza Fuad; Purnama, I Ketut Eddy

    2015-01-01

    Vehicle color information is one of the important elements in ITS (Intelligent Traffic System). In this paper, we present a vehicle color recognition method using convolutional neural network (CNN). Naturally, CNN is designed to learn classification method based on shape information, but we proved that CNN can also learn classification based on color distribution. In our method, we convert the input image to two different color spaces, HSV and CIE Lab, and run it to some CNN architecture. The...

  4. Satellite image classification using convolutional learning

    Science.gov (United States)

    Nguyen, Thao; Han, Jiho; Park, Dong-Chul

    2013-10-01

    A satellite image classification method using Convolutional Neural Network (CNN) architecture is proposed in this paper. As a special case of deep learning, CNN classifies classes of images without any feature extraction step while other existing classification methods utilize rather complex feature extraction processes. Experiments on a set of satellite image data and the preliminary results show that the proposed classification method can be a promising alternative over existing feature extraction-based schemes in terms of classification accuracy and classification speed.

  5. Auditory aura in nocturnal frontal lobe epilepsy: a red flag to suspect an extra-frontal epileptogenic zone.

    Science.gov (United States)

    Ferri, Lorenzo; Bisulli, Francesca; Nobili, Lino; Tassi, Laura; Licchetta, Laura; Mostacci, Barbara; Stipa, Carlotta; Mainieri, Greta; Bernabè, Giorgia; Provini, Federica; Tinuper, Paolo

    2014-11-01

    To describe the anatomo-electro-clinical findings of patients with nocturnal hypermotor seizures (NHS) preceded by auditory symptoms, to evaluate the localizing value of auditory aura. Our database of 165 patients with nocturnal frontal lobe epilepsy (NFLE) diagnosis confirmed by videopolysomnography (VPSG) was reviewed, selecting those who reported an auditory aura as the initial ictal symptom in at least two NHS during their lifetime. Eleven patients were selected (seven males, four females). According to the anatomo-electro-clinical data, three groups were identified. Group 1 [defined epileptogenic zone (EZ)]: three subjects were studied with stereo-EEG. The EZ lay in the left superior temporal gyrus in two cases, whereas in the third case seizures arose from a dysplastic lesion located in the left temporal lobe. One of these three patients underwent left Heschl's gyrus resection, and is currently seizure-free. Group 2 (presumed EZ): three cases in which a presumed EZ was identified; in the left temporal lobe in two cases and in the left temporal lobe extending to the insula in one subject. Group 3 (uncertain EZ): five cases had anatomo-electro-clinical correlations discordant. This work suggests that auditory aura may be a helpful anamnestic feature suggesting an extra-frontal seizure origin. This finding could guide secondary investigations to improve diagnostic definition and selection of candidates for surgical treatment. Copyright © 2014 The Authors. Published by Elsevier B.V. All rights reserved.

  6. Frontal assessment battery and frontal atrophy in amyotrophic lateral sclerosis

    OpenAIRE

    Terada, Tatsuhiro; Miyata, Jun; Obi, Tomokazu; Kubota, Manabu; Yoshizumi, Miho; Yamazaki, Kinya; Mizoguchi, Kouichi; Murai, Toshiya

    2017-01-01

    Abstract Objectives To determine the potential utility of the frontal assessment battery (FAB) in assessing cognitive impairments in amyotrophic lateral sclerosis (ALS), we investigated the association between the FAB score and regional gray matter volume, and ascertained whether the regional brain alterations related to cognitive impairments occur in relatively mild stage of ALS. Materials and Methods Twenty?four ALS patients with a Mini?Mental State Examination score of >23, a normal score ...

  7. Distinct frontal lobe morphology in girls and boys with ADHD.

    Science.gov (United States)

    Dirlikov, Benjamin; Shiels Rosch, Keri; Crocetti, Deana; Denckla, Martha B; Mahone, E Mark; Mostofsky, Stewart H

    2015-01-01

    This study investigated whether frontal lobe cortical morphology differs for boys and girls with ADHD (ages 8-12 years) in comparison to typically developing (TD) peers. Participants included 226 children between the ages of 8-12 including 93 children with ADHD (29 girls) and 133 TD children (42 girls) for which 3T MPRAGE MRI scans were obtained. A fully automated frontal lobe atlas was used to generate functionally distinct frontal subdivisions, with surface area (SA) and cortical thickness (CT) assessed in each region. Analyses focused on overall diagnostic differences as well as examinations of the effect of diagnosis within boys and girls. Girls, but not boys, with ADHD showed overall reductions in total prefrontal cortex (PFC) SA. Localization revealed that girls showed widely distributed reductions in the bilateral dorsolateral PFC, left inferior lateral PFC, right medial PFC, right orbitofrontal cortex, and left anterior cingulate; and boys showed reduced SA only in the right anterior cingulate and left medial PFC. In contrast, boys, but not girls, with ADHD showed overall reductions in total premotor cortex (PMC) SA. Further localization revealed that in boys, premotor reductions were observed in bilateral lateral PMC regions; and in girls reductions were observed in bilateral supplementary motor complex. In line with diagnostic group differences, PMC and PFC SAs were inversely correlated with symptom severity in both girls and boys with ADHD. These results elucidate sex-based differences in cortical morphology of functional subdivisions of the frontal lobe and provide additional evidence of associations among SA and symptom severity in children with ADHD.

  8. The Urbanik generalized convolutions in the non-commutative ...

    Indian Academy of Sciences (India)

    −sν(dx) < ∞. Now we apply this construction to the Kendall convolution case, starting with the weakly stable measure δ1. Example 1. Let △ be the Kendall convolution, i.e. the generalized convolution with the probability kernel: δ1△δa = (1 − a)δ1 + aπ2 for a ∈ [0, 1] and π2 be the Pareto distribution with the density π2(dx) =.

  9. An Algorithm for the Convolution of Legendre Series

    KAUST Repository

    Hale, Nicholas

    2014-01-01

    An O(N2) algorithm for the convolution of compactly supported Legendre series is described. The algorithm is derived from the convolution theorem for Legendre polynomials and the recurrence relation satisfied by spherical Bessel functions. Combining with previous work yields an O(N 2) algorithm for the convolution of Chebyshev series. Numerical results are presented to demonstrate the improved efficiency over the existing algorithm. © 2014 Society for Industrial and Applied Mathematics.

  10. The IMM Frontal Face Database

    DEFF Research Database (Denmark)

    Fagertun, Jens; Stegmann, Mikkel Bille

    2005-01-01

    This note describes a data set consisting of 120 annotated monocular images of 12 different frontal human faces. Points of correspondence are placed on each image so the data set can be readily used for building statistical models of shape. Format specifications and terms of use are also given in...... in this note. The data set is available in two versions: i) low resolution, given in the zip-file electronic version, ii) high, given in the publication link....

  11. Historical Evolution of the Frontal Lobe Syndrome

    NARCIS (Netherlands)

    Krudop, W.A.; Pijnenburg, Y.A.L.

    2015-01-01

    The function of the frontal lobes and the related frontal lobe syndrome have not been described in detail until relatively late in history. Slowly, the combination of knowledge from animal models, the detailed examination of symptoms after traumatic frontal lobe injuries, and the rise and fall of

  12. Fourier transforms and convolutions for the experimentalist

    CERN Document Server

    Jennison, RC

    1961-01-01

    Fourier Transforms and Convolutions for the Experimentalist provides the experimentalist with a guide to the principles and practical uses of the Fourier transformation. It aims to bridge the gap between the more abstract account of a purely mathematical approach and the rule of thumb calculation and intuition of the practical worker. The monograph springs from a lecture course which the author has given in recent years and for which he has drawn upon a number of sources, including a set of notes compiled by the late Dr. I. C. Browne from a series of lectures given by Mr. J . A. Ratcliffe of t

  13. Convolutional neural networks and face recognition task

    Science.gov (United States)

    Sochenkova, A.; Sochenkov, I.; Makovetskii, A.; Vokhmintsev, A.; Melnikov, A.

    2017-09-01

    Computer vision tasks are remaining very important for the last couple of years. One of the most complicated problems in computer vision is face recognition that could be used in security systems to provide safety and to identify person among the others. There is a variety of different approaches to solve this task, but there is still no universal solution that would give adequate results in some cases. Current paper presents following approach. Firstly, we extract an area containing face, then we use Canny edge detector. On the next stage we use convolutional neural networks (CNN) to finally solve face recognition and person identification task.

  14. Insulator recognition based on convolution neural network

    Directory of Open Access Journals (Sweden)

    Yang Yanli

    2017-01-01

    Full Text Available Insulator fault detection plays an important role in maintaining the safety of transmission lines. Insulator recognition is a prerequisite for its fault detection. An insulator recognition algorithm based on convolution neural network (CNN is proposed. A dataset is established to train the constructed CNN. The correct rate is 98.52% for 1220 training samples and the accuracy rate of testing is 89.04% on 1305 samples. The classification result of the CNN is further used to segment the insulator image. The test results show that the proposed method can realize the effective segmentation of insulators.

  15. Bacterial colony counting by Convolutional Neural Networks.

    Science.gov (United States)

    Ferrari, Alessandro; Lombardi, Stefano; Signoroni, Alberto

    2015-01-01

    Counting bacterial colonies on microbiological culture plates is a time-consuming, error-prone, nevertheless fundamental task in microbiology. Computer vision based approaches can increase the efficiency and the reliability of the process, but accurate counting is challenging, due to the high degree of variability of agglomerated colonies. In this paper, we propose a solution which adopts Convolutional Neural Networks (CNN) for counting the number of colonies contained in confluent agglomerates, that scored an overall accuracy of the 92.8% on a large challenging dataset. The proposed CNN-based technique for estimating the cardinality of colony aggregates outperforms traditional image processing approaches, becoming a promising approach to many related applications.

  16. Target recognition based on convolutional neural network

    Science.gov (United States)

    Wang, Liqiang; Wang, Xin; Xi, Fubiao; Dong, Jian

    2017-11-01

    One of the important part of object target recognition is the feature extraction, which can be classified into feature extraction and automatic feature extraction. The traditional neural network is one of the automatic feature extraction methods, while it causes high possibility of over-fitting due to the global connection. The deep learning algorithm used in this paper is a hierarchical automatic feature extraction method, trained with the layer-by-layer convolutional neural network (CNN), which can extract the features from lower layers to higher layers. The features are more discriminative and it is beneficial to the object target recognition.

  17. Influence of convolution filtering on coronary plaque attenuation values: Observations in an ex vivo model of multislice computed tomography coronary angiography

    NARCIS (Netherlands)

    F. Cademartiri (Filippo); L. la Grutta (Ludovico); G. Runza (Giuseppe); A. Palumbo (Alessandro); E. Maffei (Erica); N.R.A. Mollet (Nico); T.V. Bartolotta (Tommaso); P. Somers (Pamela); M.W. Knaapen (Michiel); S. Verheye (Stefan); M. Midiri (Massimo); R. Hamers (Ronald); N. Bruining (Nico)

    2007-01-01

    textabstractAttenuation variability (measured in Hounsfield Units, HU) of human coronary plaques using multislice computed tomography (MSCT) was evaluated in an ex vivo model with increasing convolution kernels. MSCT was performed in seven ex vivo left coronary arteries sunk into oil followingthe

  18. Innervation of the renal proximal convoluted tubule of the rat

    International Nuclear Information System (INIS)

    Barajas, L.; Powers, K.

    1989-01-01

    Experimental data suggest the proximal tubule as a major site of neurogenic influence on tubular function. The functional and anatomical axial heterogeneity of the proximal tubule prompted this study of the distribution of innervation sites along the early, mid, and late proximal convoluted tubule (PCT) of the rat. Serial section autoradiograms, with tritiated norepinephrine serving as a marker for monoaminergic nerves, were used in this study. Freehand clay models and graphic reconstructions of proximal tubules permitted a rough estimation of the location of the innervation sites along the PCT. In the subcapsular nephrons, the early PCT (first third) was devoid of innervation sites with most of the innervation occurring in the mid (middle third) and in the late (last third) PCT. Innervation sites were found in the early PCT in nephrons located deeper in the cortex. In juxtamedullary nephrons, innervation sites could be observed on the PCT as it left the glomerulus. This gradient of PCT innervation can be explained by the different tubulovascular relationships of nephrons at different levels of the cortex. The absence of innervation sites in the early PCT of subcapsular nephrons suggests that any influence of the renal nerves on the early PCT might be due to an effect of neurotransmitter released from renal nerves reaching the early PCT via the interstitium and/or capillaries

  19. High-Throughput Classification of Radiographs Using Deep Convolutional Neural Networks.

    Science.gov (United States)

    Rajkomar, Alvin; Lingam, Sneha; Taylor, Andrew G; Blum, Michael; Mongan, John

    2017-02-01

    The study aimed to determine if computer vision techniques rooted in deep learning can use a small set of radiographs to perform clinically relevant image classification with high fidelity. One thousand eight hundred eighty-five chest radiographs on 909 patients obtained between January 2013 and July 2015 at our institution were retrieved and anonymized. The source images were manually annotated as frontal or lateral and randomly divided into training, validation, and test sets. Training and validation sets were augmented to over 150,000 images using standard image manipulations. We then pre-trained a series of deep convolutional networks based on the open-source GoogLeNet with various transformations of the open-source ImageNet (non-radiology) images. These trained networks were then fine-tuned using the original and augmented radiology images. The model with highest validation accuracy was applied to our institutional test set and a publicly available set. Accuracy was assessed by using the Youden Index to set a binary cutoff for frontal or lateral classification. This retrospective study was IRB approved prior to initiation. A network pre-trained on 1.2 million greyscale ImageNet images and fine-tuned on augmented radiographs was chosen. The binary classification method correctly classified 100 % (95 % CI 99.73-100 %) of both our test set and the publicly available images. Classification was rapid, at 38 images per second. A deep convolutional neural network created using non-radiological images, and an augmented set of radiographs is effective in highly accurate classification of chest radiograph view type and is a feasible, rapid method for high-throughput annotation.

  20. Convolutional networks for vehicle track segmentation

    Science.gov (United States)

    Quach, Tu-Thach

    2017-10-01

    Existing methods to detect vehicle tracks in coherent change detection images, a product of combining two synthetic aperture radar images taken at different times of the same scene, rely on simple and fast models to label track pixels. These models, however, are unable to capture natural track features, such as continuity and parallelism. More powerful but computationally expensive models can be used in offline settings. We present an approach that uses dilated convolutional networks consisting of a series of 3×3 convolutions to segment vehicle tracks. The design of our networks considers the fact that remote sensing applications tend to operate in low power and have limited training data. As a result, we aim for small and efficient networks that can be trained end-to-end to learn natural track features entirely from limited training data. We demonstrate that our six-layer network, trained on just 90 images, is computationally efficient and improves the F-score on a standard dataset to 0.992, up from 0.959 obtained by the current state-of-the-art method.

  1. Modified Stieltjes Transform and Generalized Convolutions of Probability Distributions

    Directory of Open Access Journals (Sweden)

    Lev B. Klebanov

    2018-01-01

    Full Text Available The classical Stieltjes transform is modified in such a way as to generalize both Stieltjes and Fourier transforms. This transform allows the introduction of new classes of commutative and non-commutative generalized convolutions. A particular case of such a convolution for degenerate distributions appears to be the Wigner semicircle distribution.

  2. Secondary adult encephalocele with abscess formation of calcified frontal sinus mucocele.

    Science.gov (United States)

    Oh, Byeong Ho; Lee, Ok-Jun; Park, Young Seok

    2016-07-01

    Although encephalocele is a rare congenital abnormality, secondary encephalocele is extremely rare and can cause fatal complications. Here, we report a case of secondary encephalocele caused by frontal sinus wall defect due to chronic sinusitis, which was completely removed by cranialization with autologous bone graft. A 50-year-old man with a 10-year history of chronic sinusitis visited our hospital due to suddenly altered mentality characterized by stupor. Computerized tomography scanning and magnetic resonance imaging revealed an enlarged left frontal sinus with sinusitis. The frontal sinus cavity was calcified, and the left frontal lobe had herniated into the cavity accompanied by yellow pus. A large dural defect was also found around the frontal sinus area. After removal of the abscess and some of the frontal lobe, frontal skull base repair by cranialization was performed using autologous bone graft. Streptococcus pneumoniae was cultured from the cerebrospinal fluid (CSF), necessitating treatment with antibiotics. After the operation, the mental status of the patient improved and no CSF leakage was observed. In addition to correct diagnosis and early treatment including antibiotics, the surgical repair of defects is needed in patients with secondary encephalocele to prevent further episodes of meningitis. Surgical correction of frontal sinus encephalocele can be achieved through bifrontal craniotomy or endoscopic transnasal repair. If a patient has CSF leakage, open craniotomy may facilitate repair of the dural defect and allow for cranialization of the sinus. Removal of dysplastic herniated brain tissue and cranialization of the frontal sinus may be a good option for treating secondary encephalocele and its associated complications, including meningitis, abscess formation, and infarction of the herniated brain parenchyma.

  3. Frontal networks in adults with autism spectrum disorder.

    Science.gov (United States)

    Catani, Marco; Dell'Acqua, Flavio; Budisavljevic, Sanja; Howells, Henrietta; Thiebaut de Schotten, Michel; Froudist-Walsh, Seán; D'Anna, Lucio; Thompson, Abigail; Sandrone, Stefano; Bullmore, Edward T; Suckling, John; Baron-Cohen, Simon; Lombardo, Michael V; Wheelwright, Sally J; Chakrabarti, Bhismadev; Lai, Meng-Chuan; Ruigrok, Amber N V; Leemans, Alexander; Ecker, Christine; Consortium, Mrc Aims; Craig, Michael C; Murphy, Declan G M

    2016-02-01

    It has been postulated that autism spectrum disorder is underpinned by an 'atypical connectivity' involving higher-order association brain regions. To test this hypothesis in a large cohort of adults with autism spectrum disorder we compared the white matter networks of 61 adult males with autism spectrum disorder and 61 neurotypical controls, using two complementary approaches to diffusion tensor magnetic resonance imaging. First, we applied tract-based spatial statistics, a 'whole brain' non-hypothesis driven method, to identify differences in white matter networks in adults with autism spectrum disorder. Following this we used a tract-specific analysis, based on tractography, to carry out a more detailed analysis of individual tracts identified by tract-based spatial statistics. Finally, within the autism spectrum disorder group, we studied the relationship between diffusion measures and autistic symptom severity. Tract-based spatial statistics revealed that autism spectrum disorder was associated with significantly reduced fractional anisotropy in regions that included frontal lobe pathways. Tractography analysis of these specific pathways showed increased mean and perpendicular diffusivity, and reduced number of streamlines in the anterior and long segments of the arcuate fasciculus, cingulum and uncinate--predominantly in the left hemisphere. Abnormalities were also evident in the anterior portions of the corpus callosum connecting left and right frontal lobes. The degree of microstructural alteration of the arcuate and uncinate fasciculi was associated with severity of symptoms in language and social reciprocity in childhood. Our results indicated that autism spectrum disorder is a developmental condition associated with abnormal connectivity of the frontal lobes. Furthermore our findings showed that male adults with autism spectrum disorder have regional differences in brain anatomy, which correlate with specific aspects of autistic symptoms. Overall these

  4. Frontal alpha asymmetry in OCD patients and unaffected first-degree relatives.

    Science.gov (United States)

    Grützmann, Rosa; Riesel, Anja; Klawohn, Julia; Heinzel, Stephan; Kaufmann, Christian; Bey, Katharina; Lennertz, Leonard; Wagner, Michael; Kathmann, Norbert

    2017-08-01

    Frontal electroencephalographic alpha asymmetry as an indicator of trait approach and trait inhibition systems has previously been studied in individuals with obsessive-compulsive disorder (OCD) with mixed results. We explored frontal alpha asymmetry as a possible risk factor in OCD by investigating a large sample of OCD patients (n = 113), healthy control participants (n = 113), and unaffected 1st-degree relatives of OCD patients (n = 37). Additionally, the relationship between OCD symptom dimensions and frontal alpha asymmetry was explored. OCD patients and healthy control participants did not differ in alpha asymmetry scores. Hence, the current results do not support the notion that OCD as a diagnostic entity is associated with a shift in frontal cortical activity. Furthermore, alpha asymmetry scores were not statistically related to specific OCD symptom dimensions. Reasons for inconsistent results in OCD are discussed and should be explored in future studies. Compared to OCD patients and healthy control participants, unaffected 1st-degree relatives of OCD patients showed increased left frontal activity. Such asymmetry has previously been found to be associated with positive affect and adaptive emotion regulation under stress. Because stressful life events play an important role in the onset and exacerbation of OCD, increased left frontal activity might serve as a resilience factor in unaffected 1st-degree relatives. Future studies should follow up on these results with longitudinal risk studies and pre- and posttherapy assessments to further explore causality of this putative factor. (PsycINFO Database Record (c) 2017 APA, all rights reserved).

  5. Relationships among transforms, convolutions, and first variations

    Directory of Open Access Journals (Sweden)

    Jeong Gyoo Kim

    1999-01-01

    Full Text Available In this paper, we establish several interesting relationships involving the Fourier-Feynman transform, the convolution product, and the first variation for functionals F on Wiener space of the form F(x=f(〈α1,x〉,…,〈αn,x〉,                                                      (* where 〈αj,x〉 denotes the Paley-Wiener-Zygmund stochastic integral ∫0Tαj(tdx(t.

  6. Robust smile detection using convolutional neural networks

    Science.gov (United States)

    Bianco, Simone; Celona, Luigi; Schettini, Raimondo

    2016-11-01

    We present a fully automated approach for smile detection. Faces are detected using a multiview face detector and aligned and scaled using automatically detected eye locations. Then, we use a convolutional neural network (CNN) to determine whether it is a smiling face or not. To this end, we investigate different shallow CNN architectures that can be trained even when the amount of learning data is limited. We evaluate our complete processing pipeline on the largest publicly available image database for smile detection in an uncontrolled scenario. We investigate the robustness of the method to different kinds of geometric transformations (rotation, translation, and scaling) due to imprecise face localization, and to several kinds of distortions (compression, noise, and blur). To the best of our knowledge, this is the first time that this type of investigation has been performed for smile detection. Experimental results show that our proposal outperforms state-of-the-art methods on both high- and low-quality images.

  7. Body frontal area in passerine birds

    OpenAIRE

    Hedenström, Anders; Rosén, Mikael

    2003-01-01

    Projected body frontal area is used when estimating the parasite drag of bird flight. We investigated the relationship between projected frontal area and body mass among passerine birds, and compared it with an equation based on waterfowl and raptors, which is used as default procedure in a widespread software package for flight performance calculations. The allometric equation based on waterfowl/raptors underestimates the frontal area compared to the passerine equation presented here. Conseq...

  8. An Unusual Presentation of Frontal Bony Defect with Pneumocephalus and its Management in an Elderly Patient

    Directory of Open Access Journals (Sweden)

    Shih-Tong Chen

    2015-03-01

    Full Text Available Pneumocephalus can be caused by neurosurgical procedures, endoscopic sinus surgery, craniofacial trauma, tumors of the skull base, frontal sinus cranialization, and can rarely occur spontaneously. The treatment options are conservative treatment, craniotomy, osteoplastic flap surgery of the frontal sinus, and endoscopic endonasal surgery. We herein present the case of a 61-year-old man with a frontal sinus bony defect with pneumocephalus caused by craniotomy who presented atypically with left facial cellulitis, followed by meningitis and seizures. This bony defect was successfully repaired with endoscopic modified Lothrop procedure (EMLP. At 18 months' follow-up after the surgery, neither obvious postoperative complications nor signs of pneumocephalus were noted. EMLP offered a less invasive, safer, and effective way to repair the frontal bony defect in our elderly patient.

  9. Event-related potential study of frontal activity during imagination of rhythm.

    Science.gov (United States)

    Jomori, Izumi; Uemura, Jun-ichi; Nakagawa, Yoshiro; Hoshiyama, Minoru

    2011-12-01

    In 11 healthy volunteers, we used event-related potentials (ERP) to investigate the frontal activity associated with imagining a beat. In imagery sessions, subjects were asked to imagine a rhythm during a silent recording period following a series of guide sounds played at 1 Hz. In control sessions, subjects were asked to imagine a vowel sound ("a") continuously during the silent recording period. In eight subjects, relative negative potentials were recorded during imagery sessions (compared with potentials in control sessions), with timing that was similar to that of the guide sounds. Activity in the left frontal region was more significant than that in other areas during beat imagination. These data indicate that a semantic strategy for simple rhythm imagery might involve temporary phasic activation in the left frontal area, although rhythm production and perception might be generated in the right side, as reported in previous studies. Copyright © 2011 Elsevier Ltd. All rights reserved.

  10. An Improved Convolutional Neural Network on Crowd Density Estimation

    Directory of Open Access Journals (Sweden)

    Pan Shao-Yun

    2016-01-01

    Full Text Available In this paper, a new method is proposed for crowd density estimation. An improved convolutional neural network is combined with traditional texture feature. The data calculated by the convolutional layer can be treated as a new kind of features.So more useful information of images can be extracted by different features.In the meantime, the size of image has little effect on the result of convolutional neural network. Experimental results indicate that our scheme has adequate performance to allow for its use in real world applications.

  11. Music increases frontal EEG coherence during verbal learning.

    Science.gov (United States)

    Peterson, David A; Thaut, Michael H

    2007-02-02

    Anecdotal and some empirical evidence suggests that music can enhance learning and memory. However, the mechanisms by which music modulates the neural activity associated with learning and memory remain largely unexplored. We evaluated coherent frontal oscillations in the electroencephalogram (EEG) while subjects were engaged in a modified version of Rey's Auditory Verbal Learning Test (AVLT). Subjects heard either a spoken version of the AVLT or the conventional AVLT word list sung. Learning-related changes in coherence (LRCC) were measured by comparing the EEG during word encoding on correctly recalled trials to the immediately preceding trial on which the same word was not recalled. There were no significant changes in coherence associated with conventional verbal learning. However, musical verbal learning was associated with increased coherence within and between left and right frontal areas in theta, alpha, and gamma frequency bands. It is unlikely that the different patterns of LRCC reflect general performance differences; the groups exhibited similar learning performance. The results suggest that verbal learning with a musical template strengthens coherent oscillations in frontal cortical networks involved in verbal encoding.

  12. Dissociating Parieto-Frontal Networks for Phonological and Semantic Word Decisions

    DEFF Research Database (Denmark)

    Hartwigsen, Gesa; Weigel, Anni; Schuschan, Paul

    2016-01-01

    Left posterior inferior frontal gyrus (pIFG) and supramarginal gyrus (SMG) are key regions for phonological decisions, whereas angular gyrus (ANG) and anterior IFG (aIFG) are associated with semantics. However, it is less clear whether the functional contribution of one area changes in the presen...

  13. Synchronous retinotopic frontal-temporal activity during long-term memory for spatial location.

    Science.gov (United States)

    Slotnick, Scott D

    2010-05-12

    Early visual areas in occipital cortex are known to be retinotopic. Recently, retinotopic maps have been reported in frontal and parietal cortex during spatial attention and working memory. The present event-related potential (ERP) and functional magnetic resonance imaging (fMRI) study determined whether spatial long-term memory was associated with retinotopic activity in frontal and parietal regions, and assessed whether retinotopic activity in these higher level control regions was synchronous with retinotopic activity in lower level visual sensory regions. During encoding, abstract shapes were presented to the left or right of fixation. During retrieval, old and new shapes were presented at fixation and participants classified each shape as old and previously on the "left", old and previously on the "right", or "new". Retinotopic effects were manifested by accurate memory for items previously presented on the left producing activity in the right hemisphere and accurate memory for items previously presented on the right producing activity in the left hemisphere. Retinotopic ERP activity was observed in frontal regions and visual sensory (occipital and temporal) regions. In frontal cortex, retinotopic fMRI activity was localized to the frontal eye fields. There were no significant ERP or fMRI retinotopic memory effects in parietal regions. The present long-term memory retinotopic effects complement previous spatial attention and working memory findings (and suggest retinotopic activity in parietal cortex may require an external peripheral stimulus). Furthermore, ERP cross-correlogram analysis revealed that retinotopic activations in frontal and temporal regions were synchronous, indicating that these regions interact during retrieval of spatial information. (c) 2010 Elsevier B.V. All rights reserved.

  14. Frontal anatomy and reaction time in Autism

    NARCIS (Netherlands)

    Schmitz, Nicole; Daly, Eileen; Murphy, Declan

    2007-01-01

    Widespread frontal lobe abnormalities, encompassing anatomy and function, are known to be implicated in Autistic Spectrum Disorders (ASD). The correlation between neurobiology and behaviour, however, is poorly understood in ASD. The aim of this study was to investigate frontal lobe anatomy and

  15. Mucocele formation after frontal sinus obliteration

    NARCIS (Netherlands)

    Hansen, F. S.; van der Poel, N. A.; Freling, N. J. M.; Fokkens, W. J.

    2018-01-01

    A possible complication of frontal sinus obliteration with fat is the formation of mucoceles. We studied the prevalence of mucoceles as well as and the need for revision surgery. Retrospective case review of forty consecutive patients undergoing frontal sinus obliteration from September 1995 to

  16. Asymmetric Frontal Brain Activity and Parental Rejection

    NARCIS (Netherlands)

    Huffmeijer, R.; Alink, L.R.A.; Tops, M.; Bakermans-Kranenburg, M.J.; van IJzendoorn, M.H.

    2013-01-01

    Asymmetric frontal brain activity has been widely implicated in reactions to emotional stimuli and is thought to reflect individual differences in approach-withdrawal motivation. Here, we investigate whether asymmetric frontal activity, as a measure of approach-withdrawal motivation, also predicts

  17. FPGA-based digital convolution for wireless applications

    CERN Document Server

    Guan, Lei

    2017-01-01

    This book presents essential perspectives on digital convolutions in wireless communications systems and illustrates their corresponding efficient real-time field-programmable gate array (FPGA) implementations. Covering these digital convolutions from basic concept to vivid simulation/illustration, the book is also supplemented with MS PowerPoint presentations to aid in comprehension. FPGAs or generic all programmable devices will soon become widespread, serving as the “brains” of all types of real-time smart signal processing systems, like smart networks, smart homes and smart cities. The book examines digital convolution by bringing together the following main elements: the fundamental theory behind the mathematical formulae together with corresponding physical phenomena; virtualized algorithm simulation together with benchmark real-time FPGA implementations; and detailed, state-of-the-art case studies on wireless applications, including popular linear convolution in digital front ends (DFEs); nonlinear...

  18. Classification of urine sediment based on convolution neural network

    Science.gov (United States)

    Pan, Jingjing; Jiang, Cunbo; Zhu, Tiantian

    2018-04-01

    By designing a new convolution neural network framework, this paper breaks the constraints of the original convolution neural network framework requiring large training samples and samples of the same size. Move and cropping the input images, generate the same size of the sub-graph. And then, the generated sub-graph uses the method of dropout, increasing the diversity of samples and preventing the fitting generation. Randomly select some proper subset in the sub-graphic set and ensure that the number of elements in the proper subset is same and the proper subset is not the same. The proper subsets are used as input layers for the convolution neural network. Through the convolution layer, the pooling, the full connection layer and output layer, we can obtained the classification loss rate of test set and training set. In the red blood cells, white blood cells, calcium oxalate crystallization classification experiment, the classification accuracy rate of 97% or more.

  19. Convolutive ICA for Spatio-Temporal Analysis of EEG

    DEFF Research Database (Denmark)

    Dyrholm, Mads; Makeig, Scott; Hansen, Lars Kai

    2007-01-01

    We present a new algorithm for maximum likelihood convolutive ICA (cICA) in which sources are unmixed using stable IIR filters determined implicitly by estimating an FIR filter model of the mixing process. By intro- ducing a FIR model for the sources we show how the order of the filters in the co......We present a new algorithm for maximum likelihood convolutive ICA (cICA) in which sources are unmixed using stable IIR filters determined implicitly by estimating an FIR filter model of the mixing process. By intro- ducing a FIR model for the sources we show how the order of the filters...... in the convolutive model can be correctly detected using Bayesian model selection. We demonstrate a framework for deconvolving an EEG ICA subspace. Initial results suggest that in some cases convolutive mixing may be a more realistic model for EEG signals than the instantaneous ICA model....

  20. Convolutional Codes with Maximum Column Sum Rank for Network Streaming

    OpenAIRE

    Mahmood, Rafid; Badr, Ahmed; Khisti, Ashish

    2015-01-01

    The column Hamming distance of a convolutional code determines the error correction capability when streaming over a class of packet erasure channels. We introduce a metric known as the column sum rank, that parallels column Hamming distance when streaming over a network with link failures. We prove rank analogues of several known column Hamming distance properties and introduce a new family of convolutional codes that maximize the column sum rank up to the code memory. Our construction invol...

  1. Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks

    OpenAIRE

    Radford, Alec; Metz, Luke; Chintala, Soumith

    2015-01-01

    In recent years, supervised learning with convolutional networks (CNNs) has seen huge adoption in computer vision applications. Comparatively, unsupervised learning with CNNs has received less attention. In this work we hope to help bridge the gap between the success of CNNs for supervised learning and unsupervised learning. We introduce a class of CNNs called deep convolutional generative adversarial networks (DCGANs), that have certain architectural constraints, and demonstrate that they ar...

  2. Convolutional Neural Networks for Handwritten Javanese Character Recognition

    OpenAIRE

    Dewa, Chandra Kusuma; Fadhilah, Amanda Lailatul; Afiahayati, A

    2018-01-01

    Convolutional neural network (CNN) is state-of-the-art method in object recognition task. Specialized for spatial input data type, CNN has special convolutional and pooling layers which enable hierarchical feature learning from the input space. For offline handwritten character recognition problem such as classifying character in MNIST database, CNN shows better classification result than any other methods. By leveraging the advantages of CNN over character recognition task, in this paper we ...

  3. On the Fresnel sine integral and the convolution

    Directory of Open Access Journals (Sweden)

    Adem Kılıçman

    2003-01-01

    Full Text Available The Fresnel sine integral S(x, the Fresnel cosine integral C(x, and the associated functions S+(x, S−(x, C+(x, and C−(x are defined as locally summable functions on the real line. Some convolutions and neutrix convolutions of the Fresnel sine integral and its associated functions with x+r, xr are evaluated.

  4. Preoperative neuropsychological presentation of patients with refractory frontal lobe epilepsy.

    Science.gov (United States)

    Patrikelis, Panayiotis; Gatzonis, Stylianos; Siatouni, Anna; Angelopoulos, Elias; Konstantakopoulos, George; Takousi, Maria; Sakas, Damianos E; Zalonis, Ioannis

    2016-06-01

    This study investigated whether certain cognitive deficits are associated with frontal lobe epilepsy (FLE) aiming to contribute with localization data to the preoperative assessment of epilepsy surgery candidates. We evaluated 34 patients with refractory FLE, 37 patients with refractory medial temporal lobe epilepsy (MTLE), and 22 healthy individuals in attention, psychomotor speed, motor function, verbal memory span, verbal fluency, response inhibition/interference, concept formation and set shifting, anticipation and planning, global memory. Neuropsychological performances of FLE and MTLE were similar, with the only exception the WCST-number of categories index, measuring mental flexibility, in which MTLE patients performed significantly worse than FLE patients. Left-FLE patients presented more perseverative responding compared to both other patient groups and healthy controls (HCs), while left-MTLE patients showed worse sorting abilities than the other epilepsy groups. Our findings suggest a weak cognitive differentiation between FLE and MTLE, probably attributed to the intricate nature of fronto-temporal connectivity frequently resulting in overlapping deficits as well as the confounding effects of seizure-related variables. In clinical practice, a highly individualized (idiographic) neuropsychological approach along with the inclusion of concurrent EEG recordings (e.g., interictal coupling) may be of help for neuropsychologists in identifying FLE patients from those with medial temporal pathology presenting frontal dysfunction as a secondary cognitive symptom.

  5. Ventrolateral and dorsomedial frontal cortex lesions impair mnemonic context retrieval.

    Science.gov (United States)

    Chapados, Catherine; Petrides, Michael

    2015-02-22

    The prefrontal cortex appears to contribute to the mnemonic retrieval of the context within which stimuli are experienced, but only under certain conditions that remain to be clarified. Patients with lesions to the frontal cortex, the temporal lobe and neurologically intact individuals were tested for context memory retrieval when verbal stimuli (words) had been experienced across multiple (unstable context condition) or unique (stable context condition) contexts; basic recognition memory of these words-in-contexts was also tested. Patients with lesions to the right ventrolateral prefrontal cortex (VLPFC) were impaired on context retrieval only when the words had been seen in multiple contexts, demonstrating that this prefrontal region is critical for active retrieval processing necessary to disambiguate memory items embedded across multiple contexts. Patients with lesions to the left dorsomedial prefrontal region were impaired on both context retrieval conditions, regardless of the stability of the stimulus-to-context associations. Conversely, prefrontal lesions sparing the ventrolateral and dorsomedial regions did not impair context retrieval. Only patients with temporal lobe excisions were impaired on basic recognition memory. The results demonstrate a basic contribution of the left dorsomedial frontal region to mnemonic context retrieval, with the VLPFC engaged, selectively, when contextual relations are unstable and require disambiguation. © 2015 The Author(s) Published by the Royal Society. All rights reserved.

  6. Resting frontal EEG asymmetry and shyness and sociability in schizophrenia: a pilot study of community-based outpatients.

    Science.gov (United States)

    Jetha, Michelle K; Schmidt, Louis A; Goldberg, Joel O

    2009-01-01

    We conducted a pilot study to examine the relations among the patterns of resting regional electroencephalogram (EEG) alpha activity, trait shyness and sociability, and positive and negative symptoms scores in 20 adults with schizophrenia, attending a community-based treatment and rehabilitation center. As predicted, patients' positive symptoms were related to greater relative resting left frontal EEG activity, replicating earlier work. When only adults with low to no positive symptoms were considered, trait shyness was related to greater relative resting right frontal EEG activity, whereas trait sociability was related to greater relative resting left frontal EEG activity. This finding is similar to what is consistently noted in healthy adults. These pilot data suggest that positive symptoms in patients with schizophrenia may obscure the relations between personality and frontal EEG asymmetry measures observed in healthy adults.

  7. Radial Structure Scaffolds Convolution Patterns of Developing Cerebral Cortex

    Directory of Open Access Journals (Sweden)

    Mir Jalil Razavi

    2017-08-01

    Full Text Available Commonly-preserved radial convolution is a prominent characteristic of the mammalian cerebral cortex. Endeavors from multiple disciplines have been devoted for decades to explore the causes for this enigmatic structure. However, the underlying mechanisms that lead to consistent cortical convolution patterns still remain poorly understood. In this work, inspired by prior studies, we propose and evaluate a plausible theory that radial convolution during the early development of the brain is sculptured by radial structures consisting of radial glial cells (RGCs and maturing axons. Specifically, the regionally heterogeneous development and distribution of RGCs controlled by Trnp1 regulate the convex and concave convolution patterns (gyri and sulci in the radial direction, while the interplay of RGCs' effects on convolution and axons regulates the convex (gyral convolution patterns. This theory is assessed by observations and measurements in literature from multiple disciplines such as neurobiology, genetics, biomechanics, etc., at multiple scales to date. Particularly, this theory is further validated by multimodal imaging data analysis and computational simulations in this study. We offer a versatile and descriptive study model that can provide reasonable explanations of observations, experiments, and simulations of the characteristic mammalian cortical folding.

  8. Metaheuristic Algorithms for Convolution Neural Network.

    Science.gov (United States)

    Rere, L M Rasdi; Fanany, Mohamad Ivan; Arymurthy, Aniati Murni

    2016-01-01

    A typical modern optimization technique is usually either heuristic or metaheuristic. This technique has managed to solve some optimization problems in the research area of science, engineering, and industry. However, implementation strategy of metaheuristic for accuracy improvement on convolution neural networks (CNN), a famous deep learning method, is still rarely investigated. Deep learning relates to a type of machine learning technique, where its aim is to move closer to the goal of artificial intelligence of creating a machine that could successfully perform any intellectual tasks that can be carried out by a human. In this paper, we propose the implementation strategy of three popular metaheuristic approaches, that is, simulated annealing, differential evolution, and harmony search, to optimize CNN. The performances of these metaheuristic methods in optimizing CNN on classifying MNIST and CIFAR dataset were evaluated and compared. Furthermore, the proposed methods are also compared with the original CNN. Although the proposed methods show an increase in the computation time, their accuracy has also been improved (up to 7.14 percent).

  9. Metaheuristic Algorithms for Convolution Neural Network

    Directory of Open Access Journals (Sweden)

    L. M. Rasdi Rere

    2016-01-01

    Full Text Available A typical modern optimization technique is usually either heuristic or metaheuristic. This technique has managed to solve some optimization problems in the research area of science, engineering, and industry. However, implementation strategy of metaheuristic for accuracy improvement on convolution neural networks (CNN, a famous deep learning method, is still rarely investigated. Deep learning relates to a type of machine learning technique, where its aim is to move closer to the goal of artificial intelligence of creating a machine that could successfully perform any intellectual tasks that can be carried out by a human. In this paper, we propose the implementation strategy of three popular metaheuristic approaches, that is, simulated annealing, differential evolution, and harmony search, to optimize CNN. The performances of these metaheuristic methods in optimizing CNN on classifying MNIST and CIFAR dataset were evaluated and compared. Furthermore, the proposed methods are also compared with the original CNN. Although the proposed methods show an increase in the computation time, their accuracy has also been improved (up to 7.14 percent.

  10. Event Discrimination using Convolutional Neural Networks

    Science.gov (United States)

    Menon, Hareesh; Hughes, Richard; Daling, Alec; Winer, Brian

    2017-01-01

    Convolutional Neural Networks (CNNs) are computational models that have been shown to be effective at classifying different types of images. We present a method to use CNNs to distinguish events involving the production of a top quark pair and a Higgs boson from events involving the production of a top quark pair and several quark and gluon jets. To do this, we generate and simulate data using MADGRAPH and DELPHES for a general purpose LHC detector at 13 TeV. We produce images using a particle flow algorithm by binning the particles geometrically based on their position in the detector and weighting the bins by the energy of each particle within each bin, and by defining channels based on particle types (charged track, neutral hadronic, neutral EM, lepton, heavy flavor). Our classification results are competitive with standard machine learning techniques. We have also looked into the classification of the substructure of the events, in a process known as scene labeling. In this context, we look for the presence of boosted objects (such as top quarks) with substructure encompassed within single jets. Preliminary results on substructure classification will be presented.

  11. Multiscale Convolutional Neural Networks for Hand Detection

    Directory of Open Access Journals (Sweden)

    Shiyang Yan

    2017-01-01

    Full Text Available Unconstrained hand detection in still images plays an important role in many hand-related vision problems, for example, hand tracking, gesture analysis, human action recognition and human-machine interaction, and sign language recognition. Although hand detection has been extensively studied for decades, it is still a challenging task with many problems to be tackled. The contributing factors for this complexity include heavy occlusion, low resolution, varying illumination conditions, different hand gestures, and the complex interactions between hands and objects or other hands. In this paper, we propose a multiscale deep learning model for unconstrained hand detection in still images. Deep learning models, and deep convolutional neural networks (CNNs in particular, have achieved state-of-the-art performances in many vision benchmarks. Developed from the region-based CNN (R-CNN model, we propose a hand detection scheme based on candidate regions generated by a generic region proposal algorithm, followed by multiscale information fusion from the popular VGG16 model. Two benchmark datasets were applied to validate the proposed method, namely, the Oxford Hand Detection Dataset and the VIVA Hand Detection Challenge. We achieved state-of-the-art results on the Oxford Hand Detection Dataset and had satisfactory performance in the VIVA Hand Detection Challenge.

  12. ARE LEFT HANDED SURGEONS LEFT OUT?

    OpenAIRE

    SriKamkshi Kothandaraman; Balasubramanian Thiagarajan

    2012-01-01

    Being a left-handed surgeon, more specifically a left-handed ENT surgeon, presents a unique pattern of difficulties.This article is an overview of left-handedness and a personal account of the specific difficulties a left-handed ENT surgeon faces.

  13. Intracranial EEG in predicting surgical outcome in frontal lobe epilepsy.

    Science.gov (United States)

    Holtkamp, Martin; Sharan, Ashwini; Sperling, Michael R

    2012-10-01

    Surgery in frontal lobe epilepsy (FLE) has a worse prognosis regarding seizure freedom than anterior lobectomy in temporal lobe epilepsy. The current study aimed to assess whether intracranial interictal and ictal EEG findings in addition to clinical and scalp EEG data help to predict outcome in a series of patients who needed invasive recording for FLE surgery. Patients with FLE who had resective surgery after chronic intracranial EEG recording were included. Outcome predictors were compared in patients with seizure freedom (group 1) and those with recurrent seizures (group 2) at 19-24 months after surgery. Twenty-five patients (16 female) were included in this study. Mean age of patients at epilepsy surgery was 32.3 ± 15.6 years (range 12-70); mean duration of epilepsy was 16.9 ± 13.4 years (range 1-48). In each outcome group, magnetic resonance imaging revealed frontal lobe lesions in three patients. Fifteen patients (60%) were seizure-free (Engel class 1), 10 patients (40%) continued to have seizures (two were class II, three were class III, and five were class IV). Lack of seizure freedom was seen more often in patients with epilepsy surgery on the left frontal lobe (group 1, 13%; group 2, 70%; p = 0.009) and on the dominant (27%; 70%; p = 0.049) hemisphere as well as in patients without aura (29%; 80%; p = 0.036), whereas sex, age at surgery, duration of epilepsy, and presence of an MRI lesion in the frontal lobe or extrafrontal structures were not different between groups. Electroencephalographic characteristics associated with lack of seizure freedom included presence of interictal epileptiform discharges in scalp recordings (31%; 90%; p = 0.01). Detailed analysis of intracranial EEG revealed widespread (>2 cm) (13%; 70%; p = 0.01) in contrast to focal seizure onset as well as shorter latency to onset of seizure spread (5.8 ± 6.1 s; 1.5 ± 2.3 s; p = 0.016) and to ictal involvement of brain structures beyond the frontal lobe (23.5 ± 22.4 s; 5.8 ± 5.4 s

  14. The prevalence of frontal sinus aplasia in Mashhad, Northeast of Iran

    Directory of Open Access Journals (Sweden)

    Masoud Pezeshki Rad

    2009-04-01

    Full Text Available Introduction: There are various reports of the prevalence of frontal sinus aplasia in different geographical areas and ethnic origins. The size and shape of frontal sinus is different among various populations. This study used CT scan images to investigate the frequency of absence of frontal sinuses in adults of northeastern Iran. Materials and Methods: The present study was performed retrospectively on the axial and coronal CT scans of the paranasal sinuses from a series of 588 patients who had referred to CT scan ward of Mashhad Imam Reza hospital without any other sinus pathology. Results: The mean age of patients was 44.39± 19.44 years. Unilateral and bilateral aplasia of frontal sinuses was seen in 36 and 51 patients, respectively. The dominant sinus was in the left side in 68.24% of cases. Conclusion: The lower incidence of frontal sinus aplasia in this particular ethnic and geographical area relative to other populations emphasizes the effect of environmental and genetic factors on the development of frontal sinuses.  

  15. Vulnerability of the frontal and parietal regions in hypertensive patients during working memory task.

    Science.gov (United States)

    Li, Xin; Wang, Wenxiao; Wang, Ailin; Li, Peng; Zhang, Junying; Tao, Wuhai; Zhang, Zhanjun

    2017-05-01

    Hypertension is related with cognitive decline in the elderly. The frontal-parietal executive system plays an important role in cognitive aging and is also vulnerable to damage in elderly patients with hypertension. Examination of the brain's functional characteristics in frontal-parietal regions of hypertension is likely to be important for understanding the neural mechanisms of hypertension's effect on cognitive aging. We address this issue by comparing hypertension and control-performers in a functional MRI study. Twenty-eight hypertensive patients and 32 elderly controls were tested with n-back task with two load levels. The hypertensive patients exhibited worse executive and memory abilities than control subjects. The patterns of brain activation changed under different working memory loads in the hypertensive patients, who exhibited reduced activation only in the precentral gyrus under low loads and reduced activation in the middle frontal gyrus, left medial superior frontal gyrus and right precuneus under high loads. Thus, more regions of diminished activation were observed in the frontal and parietal regions with increasing task difficulty. More importantly, we found that lower activation in changed frontal and parietal regions was associated with worse cognitive function in high loads. The results demonstrate the relationship between cognitive function and frontoparietal functional activation in hypertension and their relevance to cognitive aging risk. Our findings provide a better understanding of the mechanism of cognitive decline in hypertension and highlight the importance of brain protection in hypertension.

  16. Frontal alpha asymmetry predicts inhibitory processing in youth with attention deficit/hyperactivity disorder.

    Science.gov (United States)

    Ellis, Alissa J; Kinzel, Chantelle; Salgari, Giulia C; Loo, Sandra K

    2017-07-28

    Atypical asymmetry in brain activity has been implicated in the behavioral and attentional dysregulation observed in ADHD. Specifically, asymmetry in neural activity in the right versus left frontal regions has been linked to ADHD, as well as to symptoms often associated with ADHD such as heightened approach behaviors, impulsivity and difficulties with inhibition. Clarifying the role of frontal asymmetry in ADHD-like traits, such as disinhibition, may provide information on the neurophysiological processes underlying these behaviors. ADHD youth (ADHD: n = 25) and healthy, typically developing controls (TD: n = 25) underwent an electroencephalography (EEG) recording while completing a go/no-go task-a commonly used test measuring behavioral inhibition. In addition, advanced signal processing for source localization estimated the location of signal generators underlying frontal alpha asymmetry (FA) during correct and incorrect trials. This is the first study in ADHD to demonstrate that the dorsal-lateral prefrontal cortex (DLPFC) may be responsible for generating frontal alpha. During failed inhibition trials, ADHD youth displayed greater FA than TD youth. In addition, within the ADHD group, frontal asymmetry during later processing stages (i.e., 400-800ms after stimulus) predicted a higher number of commission errors throughout the task. These results suggest that frontal alpha asymmetry may be a specific biomarker of cognitive disinhibition among youth with ADHD. Copyright © 2017 Elsevier Ltd. All rights reserved.

  17. Chronic frontal sinusitis presenting with complications

    Directory of Open Access Journals (Sweden)

    Zainab Sunu Ali

    2013-01-01

    Full Text Available A frontocutaneous fistula secondary to chronic frontal sinusitis can present with a fistulous opening in the forehead or in the eyelid. It may or may not be associated with Pott′s puffy tumor. In this article, we present 2 cases. A case of frontocutaenous fistula with opening in the upper eyelid at the lateral portion of floor of frontal sinus and another case of chronic frontal sinusitis with sub-periosteal abscess in the forehead on the right side near the eyebrow. Relevant literature has also been reviewed.

  18. A study on effective of increasing right frontal alpha and decreasing left frontal alpha on treatment of major depressive disorder

    Directory of Open Access Journals (Sweden)

    Zakaria Eskandari

    2013-07-01

    Full Text Available Various studies have shown some relationship between brain wave abnormalies and depression. The current study aimed to examine the effectiveness of the real neurofeedback treatment compared with mock neurofeedback in decreasing major depression severity of symptoms and change on ? waves into a desirable pattern among some patients who suffer from major depression disorder. The study chooses six patients who were suffering from major depression sufferers and they were randomly placed in two groups called real neurofeedback and mock neurofeedback group (placebo. The two groups were treated for a twenty sessions twice a week. The two groups were examined before, during and after the treatment by Beck Depression Inventory II, Hamilton Depression Scale. The research data were examined through the analysis of the size effect, improvement percentage and charts. The data resulting from the size effect and the improvement percentage suggested that the real neurofeedback was more effective in regulating brain waves and in decreasing major depression disorder symptoms in comparison with the mock neuro-feedback and the groups were significantly different from the clinical point of view. The effectiveness of the real neurofeedback was not from the changes in placebo and it can be used as a complementary treatment in treating major depression disorder. The findings of the current research were congruent with those of the related studies.

  19. Cranialization of the frontal sinus for secondary mucocele prevention following open surgery for benign frontal lesions.

    Directory of Open Access Journals (Sweden)

    Gilad Horowitz

    Full Text Available OBJECTIVE: To compare frontal sinus cranialization to obliteration for future prevention of secondary mucocele formation following open surgery for benign lesions of the frontal sinus. STUDY DESIGN: Retrospective case series. SETTING: Tertiary academic medical center. PATIENTS: Sixty-nine patients operated for benign frontal sinus pathology between 1994 and 2011. INTERVENTIONS: Open excision of benign frontal sinus pathology followed by either frontal obliteration (n = 41, 59% or frontal cranialization (n = 28, 41%. MAIN OUTCOME MEASURES: The prevalence of post-surgical complications and secondary mucocele formation were compiled. RESULTS: Pathologies included osteoma (n = 34, 49%, mucocele (n = 27, 39%, fibrous dysplasia (n = 6, 9%, and encephalocele (n = 2, 3%. Complications included skin infections (n = 6, postoperative cutaneous fistula (n = 1, telecanthus (n = 4, diplopia (n = 3, nasal deformity (n = 2 and epiphora (n = 1. None of the patients suffered from postoperative CSF leak, meningitis or pneumocephalus. Six patients, all of whom had previously undergone frontal sinus obliteration, required revision surgery due to secondary mucocele formation. Statistical analysis using non-inferiority test reveal that cranialization of the frontal sinus is non-inferior to obliteration for preventing secondary mucocele formation (P<0.0001. CONCLUSION: Cranialization of the frontal sinus appears to be a good option for prevention of secondary mucocele development after open excision of benign frontal sinus lesions.

  20. Convolutional Dictionary Learning: Acceleration and Convergence

    Science.gov (United States)

    Chun, Il Yong; Fessler, Jeffrey A.

    2018-04-01

    Convolutional dictionary learning (CDL or sparsifying CDL) has many applications in image processing and computer vision. There has been growing interest in developing efficient algorithms for CDL, mostly relying on the augmented Lagrangian (AL) method or the variant alternating direction method of multipliers (ADMM). When their parameters are properly tuned, AL methods have shown fast convergence in CDL. However, the parameter tuning process is not trivial due to its data dependence and, in practice, the convergence of AL methods depends on the AL parameters for nonconvex CDL problems. To moderate these problems, this paper proposes a new practically feasible and convergent Block Proximal Gradient method using a Majorizer (BPG-M) for CDL. The BPG-M-based CDL is investigated with different block updating schemes and majorization matrix designs, and further accelerated by incorporating some momentum coefficient formulas and restarting techniques. All of the methods investigated incorporate a boundary artifacts removal (or, more generally, sampling) operator in the learning model. Numerical experiments show that, without needing any parameter tuning process, the proposed BPG-M approach converges more stably to desirable solutions of lower objective values than the existing state-of-the-art ADMM algorithm and its memory-efficient variant do. Compared to the ADMM approaches, the BPG-M method using a multi-block updating scheme is particularly useful in single-threaded CDL algorithm handling large datasets, due to its lower memory requirement and no polynomial computational complexity. Image denoising experiments show that, for relatively strong additive white Gaussian noise, the filters learned by BPG-M-based CDL outperform those trained by the ADMM approach.

  1. Lidar Cloud Detection with Fully Convolutional Networks

    Science.gov (United States)

    Cromwell, E.; Flynn, D.

    2017-12-01

    The vertical distribution of clouds from active remote sensing instrumentation is a widely used data product from global atmospheric measuring sites. The presence of clouds can be expressed as a binary cloud mask and is a primary input for climate modeling efforts and cloud formation studies. Current cloud detection algorithms producing these masks do not accurately identify the cloud boundaries and tend to oversample or over-represent the cloud. This translates as uncertainty for assessing the radiative impact of clouds and tracking changes in cloud climatologies. The Atmospheric Radiation Measurement (ARM) program has over 20 years of micro-pulse lidar (MPL) and High Spectral Resolution Lidar (HSRL) instrument data and companion automated cloud mask product at the mid-latitude Southern Great Plains (SGP) and the polar North Slope of Alaska (NSA) atmospheric observatory. Using this data, we train a fully convolutional network (FCN) with semi-supervised learning to segment lidar imagery into geometric time-height cloud locations for the SGP site and MPL instrument. We then use transfer learning to train a FCN for (1) the MPL instrument at the NSA site and (2) for the HSRL. In our semi-supervised approach, we pre-train the classification layers of the FCN with weakly labeled lidar data. Then, we facilitate end-to-end unsupervised pre-training and transition to fully supervised learning with ground truth labeled data. Our goal is to improve the cloud mask accuracy and precision for the MPL instrument to 95% and 80%, respectively, compared to the current cloud mask algorithms of 89% and 50%. For the transfer learning based FCN for the HSRL instrument, our goal is to achieve a cloud mask accuracy of 90% and a precision of 80%.

  2. Necessary Contributions of Human Frontal Lobe Subregions to Reward Learning in a Dynamic, Multidimensional Environment.

    Science.gov (United States)

    Vaidya, Avinash R; Fellows, Lesley K

    2016-09-21

    Real-world decisions are typically made between options that vary along multiple dimensions, requiring prioritization of the important dimensions to support optimal choice. Learning in this setting depends on attributing decision outcomes to the dimensions with predictive relevance rather than to dimensions that are irrelevant and nonpredictive. This attribution problem is computationally challenging, and likely requires an interplay between selective attention and reward learning. Both these processes have been separately linked to the prefrontal cortex, but little is known about how they combine to support learning the reward value of multidimensional stimuli. Here, we examined the necessary contributions of frontal lobe subregions in attributing feedback to relevant and irrelevant dimensions on a trial-by-trial basis in humans. Patients with focal frontal lobe damage completed a demanding reward learning task where options varied on three dimensions, only one of which predicted reward. Participants with left lateral frontal lobe damage attributed rewards to irrelevant dimensions, rather than the relevant dimension. Damage to the ventromedial frontal lobe also impaired learning about the relevant dimension, but did not increase reward attribution to irrelevant dimensions. The results argue for distinct roles for these two regions in learning the value of multidimensional decision options under dynamic conditions, with the lateral frontal lobe required for selecting the relevant dimension to associate with reward, and the ventromedial frontal lobe required to learn the reward association itself. The real world is complex and multidimensional; how do we attribute rewards to predictive features when surrounded by competing cues? Here, we tested the critical involvement of human frontal lobe subregions in a probabilistic, multidimensional learning environment, asking whether focal lesions affected trial-by-trial attribution of feedback to relevant and irrelevant

  3. Is Kinesio Taping to Generate Skin Convolutions Effective for Increasing Local Blood Circulation?

    Science.gov (United States)

    Yang, Jae-Man; Lee, Jung-Hoon

    2018-01-01

    Background It is unclear whether traditional application of Kinesio taping, which produces wrinkles in the skin, is effective for improving blood circulation. This study investigated local skin temperature changes after the application of an elastic therapeutic tape using convolution and non-convolution taping methods (CTM/NCTM). Material/Methods Twenty-eight pain-free men underwent CTM and NCTM randomly applied to the right and left sides of the lower back. Using infrared thermography, skin temperature was measured before, immediately after application, 5 min later, 15 min later, and after the removal of the tape. Results Both CTM and NCTM showed a slight, but significant, decrease in skin temperature for up to 5 min. The skin temperature at 15 min and after the removal of the tape was not significantly different from the initial temperature for CTM and NCTM. There were also no significant differences in the skin temperatures between CTM and NCTM. Conclusions Our findings do not support a therapeutic effect of wrinkling the skin with elastic tape application as a technique to increase local blood flow. PMID:29332101

  4. Is Kinesio Taping to Generate Skin Convolutions Effective for Increasing Local Blood Circulation?

    Science.gov (United States)

    Yang, Jae-Man; Lee, Jung-Hoon

    2018-01-14

    BACKGROUND It is unclear whether traditional application of Kinesio taping, which produces wrinkles in the skin, is effective for improving blood circulation. This study investigated local skin temperature changes after the application of an elastic therapeutic tape using convolution and non-convolution taping methods (CTM/NCTM). MATERIAL AND METHODS Twenty-eight pain-free men underwent CTM and NCTM randomly applied to the right and left sides of the lower back. Using infrared thermography, skin temperature was measured before, immediately after application, 5 min later, 15 min later, and after the removal of the tape. RESULTS Both CTM and NCTM showed a slight, but significant, decrease in skin temperature for up to 5 min. The skin temperature at 15 min and after the removal of the tape was not significantly different from the initial temperature for CTM and NCTM. There were also no significant differences in the skin temperatures between CTM and NCTM. CONCLUSIONS Our findings do not support a therapeutic effect of wrinkling the skin with elastic tape application as a technique to increase local blood flow.

  5. synthesis of microporous polymers by frontal polymerization

    Indian Academy of Sciences (India)

    Unknown

    EGDM) copolymers of varying compositions were synthesized by free-radically triggered thermal frontal polymerization (FP) as well as by suspension polymerization (SP) using azobisisobutyronitrile [AIBN] as initiator. The two sets of copolymers.

  6. Human Frontal Lobes and AI Planning Systems

    Science.gov (United States)

    Levinson, Richard; Lum, Henry Jr. (Technical Monitor)

    1994-01-01

    Human frontal lobes are essential for maintaining a self-regulating balance between predictive and reactive behavior. This paper describes a system that integrates prediction and reaction based on neuropsychological theories of frontal lobe function. In addition to enhancing our understanding of deliberate action in humans' the model is being used to develop and evaluate the same properties in machines. First, the paper presents some background neuropsychology in order to set a general context. The role of frontal lobes is then presented by summarizing three theories which formed the basis for this work. The components of an artificial frontal lobe are then discussed from both neuropsychological and AI perspectives. The paper concludes by discussing issues and methods for evaluating systems that integrate planning and reaction.

  7. Frontal lobe alterations in schizophrenia: a review.

    Science.gov (United States)

    Mubarik, Ateeq; Tohid, Hassaan

    2016-01-01

    To highlight the changes in the frontal lobe of the human brain in people with schizophrenia. This was a qualitative review of the literature. Many schizophrenic patients exhibit functional, structural, and metabolic abnormalities in the frontal lobe. Some patients have few or no alterations, while some have more functional and structural changes than others. Magnetic resonance imaging (MRI) shows structural and functional changes in volume, gray matter, white matter, and functional activity in the frontal lobe, but the mechanisms underlying these changes are not yet fully understood. When schizophrenia is studied as an essential topic in the field of neuropsychiatry, neuroscientists find that the frontal lobe is the most commonly involved area of the human brain. A clear picture of how this lobe is affected in schizophrenia is still lacking. We therefore recommend that further research be conducted to improve understanding of the pathophysiology of this psychiatric dilemma.

  8. Corpus callosum lipoma with frontal encephalocele

    International Nuclear Information System (INIS)

    Srinivasa Rao, A.; Rao, V.R.K.; Ravi Mandalam, K.; Gupta, A.K.; Kumar, S.; Joseph, S.; Unni, M.

    1990-01-01

    Computed tomographic and plain X-ray observations in a patient with corpus callosum lipoma associated with frontal encephalocele are reported. The rarity of the lesion and the specific diagnostic criteria on CT are emphasised. (orig.)

  9. Errors on the Trail Making Test Are Associated with Right Hemispheric Frontal Lobe Damage in Stroke Patients

    Directory of Open Access Journals (Sweden)

    Bruno Kopp

    2015-01-01

    Full Text Available Measures of performance on the Trail Making Test (TMT are among the most popular neuropsychological assessment techniques. Completion time on TMT-A is considered to provide a measure of processing speed, whereas completion time on TMT-B is considered to constitute a behavioral measure of the ability to shift between cognitive sets (cognitive flexibility, commonly attributed to the frontal lobes. However, empirical evidence linking performance on the TMT-B to localized frontal lesions is mostly lacking. Here, we examined the association of frontal lesions following stroke with TMT-B performance measures (i.e., completion time and completion accuracy measures using voxel-based lesion-behavior mapping, with a focus on right hemispheric frontal lobe lesions. Our results suggest that the number of errors, but not completion time on the TMT-B, is associated with right hemispheric frontal lesions. This finding contradicts common clinical practice—the use of completion time on the TMT-B to measure cognitive flexibility, and it underscores the need for additional research on the association between cognitive flexibility and the frontal lobes. Further work in a larger sample, including left frontal lobe damage and with more power to detect effects of right posterior brain injury, is necessary to determine whether our observation is specific for right frontal lesions.

  10. Errors on the Trail Making Test Are Associated with Right Hemispheric Frontal Lobe Damage in Stroke Patients.

    Science.gov (United States)

    Kopp, Bruno; Rösser, Nina; Tabeling, Sandra; Stürenburg, Hans Jörg; de Haan, Bianca; Karnath, Hans-Otto; Wessel, Karl

    2015-01-01

    Measures of performance on the Trail Making Test (TMT) are among the most popular neuropsychological assessment techniques. Completion time on TMT-A is considered to provide a measure of processing speed, whereas completion time on TMT-B is considered to constitute a behavioral measure of the ability to shift between cognitive sets (cognitive flexibility), commonly attributed to the frontal lobes. However, empirical evidence linking performance on the TMT-B to localized frontal lesions is mostly lacking. Here, we examined the association of frontal lesions following stroke with TMT-B performance measures (i.e., completion time and completion accuracy measures) using voxel-based lesion-behavior mapping, with a focus on right hemispheric frontal lobe lesions. Our results suggest that the number of errors, but not completion time on the TMT-B, is associated with right hemispheric frontal lesions. This finding contradicts common clinical practice-the use of completion time on the TMT-B to measure cognitive flexibility, and it underscores the need for additional research on the association between cognitive flexibility and the frontal lobes. Further work in a larger sample, including left frontal lobe damage and with more power to detect effects of right posterior brain injury, is necessary to determine whether our observation is specific for right frontal lesions.

  11. Detecting atrial fibrillation by deep convolutional neural networks.

    Science.gov (United States)

    Xia, Yong; Wulan, Naren; Wang, Kuanquan; Zhang, Henggui

    2018-02-01

    Atrial fibrillation (AF) is the most common cardiac arrhythmia. The incidence of AF increases with age, causing high risks of stroke and increased morbidity and mortality. Efficient and accurate diagnosis of AF based on the ECG is valuable in clinical settings and remains challenging. In this paper, we proposed a novel method with high reliability and accuracy for AF detection via deep learning. The short-term Fourier transform (STFT) and stationary wavelet transform (SWT) were used to analyze ECG segments to obtain two-dimensional (2-D) matrix input suitable for deep convolutional neural networks. Then, two different deep convolutional neural network models corresponding to STFT output and SWT output were developed. Our new method did not require detection of P or R peaks, nor feature designs for classification, in contrast to existing algorithms. Finally, the performances of the two models were evaluated and compared with those of existing algorithms. Our proposed method demonstrated favorable performances on ECG segments as short as 5 s. The deep convolutional neural network using input generated by STFT, presented a sensitivity of 98.34%, specificity of 98.24% and accuracy of 98.29%. For the deep convolutional neural network using input generated by SWT, a sensitivity of 98.79%, specificity of 97.87% and accuracy of 98.63% was achieved. The proposed method using deep convolutional neural networks shows high sensitivity, specificity and accuracy, and, therefore, is a valuable tool for AF detection. Copyright © 2017 Elsevier Ltd. All rights reserved.

  12. New Perspectives on Southern Ocean Frontal Variability

    Science.gov (United States)

    Chapman, Christopher

    2017-04-01

    The frontal structure of the Southern Ocean is investigated using a the Wavelet/Higher Order Statistics Enhancement (WHOSE) frontal detection method, introduced in Chapman (2014). This methodology is applied to 21 years of daily gridded sea-surface height (SSH) data to obtain daily maps of the locations of the fronts. By forming frontal occurrence frequency maps and then approximating these occurrence-maps by a superposition of simple functions, the time-mean locations of the fronts, as well as a measure of their capacity to meander, are obtained and related to the frontal locations found by previous studies. The spatial and temporal variability of the frontal structure is then considered. The number of fronts is found to be highly variable throughout the Southern Ocean, increasing (`splitting') downstream of large bathymetric features and decreasing (`merging') in regions where the fronts are tightly controlled by the underlying topography. In contrast, frontal meandering remains relatively constant. Contrary to many previous studies, little no southward migration of the fronts over the 1993-2014 time period is found, and there is only weak sensitivity to atmospheric forcing related to SAM or ENSO. Finally, the implications of splitting and merging for the flux of tracers will be discussed.

  13. Frontal lobe regulation of blood glucose levels: support for the limited capacity model in hostile violence-prone men.

    Science.gov (United States)

    Walters, Robert P; Harrison, Patti Kelly; Campbell, Ransom W; Harrison, David W

    2016-12-01

    Hostile men have reliably displayed an exaggerated sympathetic stress response across multiple experimental settings, with cardiovascular reactivity for blood pressure and heart rate concurrent with lateralized right frontal lobe stress (Trajanoski et al., in Diabetes Care 19(12):1412-1415, 1996; see Heilman et al., in J Neurol Neurosurg Psychiatry 38(1):69-72, 1975). The current experiment examined frontal lobe regulatory control of glucose in high and low hostile men with concurrent left frontal lobe (Control Oral Word Association Test [verbal]) or right frontal lobe (Ruff Figural Fluency Test [nonverbal]) stress. A significant interaction was found for Group × Condition, F (1,22) = 4.16, p ≤ .05 with glucose levels (mg/dl) of high hostile men significantly elevated as a function of the right frontal stressor (M = 101.37, SD = 13.75) when compared to the verbal stressor (M = 95.79, SD = 11.20). Glucose levels in the low hostile group remained stable for both types of stress. High hostile men made significantly more errors on the right frontal but not the left frontal stressor (M = 17.18, SD = 19.88) when compared to the low hostile men (M = 5.81, SD = 4.33). These findings support our existing frontal capacity model of hostility (Iribarren et al., in J Am Med Assoc 17(19):2546-2551, 2000; McCrimmon et al., in Physiol Behav 67(1):35-39, 1999; Brunner et al., in Diabetes Care 21(4):585-590, 1998), extending the role of the right frontal lobe to regulatory control over glucose mobilization.

  14. Two dimensional convolute integers for machine vision and image recognition

    Science.gov (United States)

    Edwards, Thomas R.

    1988-01-01

    Machine vision and image recognition require sophisticated image processing prior to the application of Artificial Intelligence. Two Dimensional Convolute Integer Technology is an innovative mathematical approach for addressing machine vision and image recognition. This new technology generates a family of digital operators for addressing optical images and related two dimensional data sets. The operators are regression generated, integer valued, zero phase shifting, convoluting, frequency sensitive, two dimensional low pass, high pass and band pass filters that are mathematically equivalent to surface fitted partial derivatives. These operators are applied non-recursively either as classical convolutions (replacement point values), interstitial point generators (bandwidth broadening or resolution enhancement), or as missing value calculators (compensation for dead array element values). These operators show frequency sensitive feature selection scale invariant properties. Such tasks as boundary/edge enhancement and noise or small size pixel disturbance removal can readily be accomplished. For feature selection tight band pass operators are essential. Results from test cases are given.

  15. Developmental dyslexia: dysfunction of a left hemisphere reading network

    Directory of Open Access Journals (Sweden)

    Fabio eRichlan

    2012-05-01

    Full Text Available This mini-review summarizes and integrates findings from recent meta-analyses and original neuroimaging studies on functional brain abnormalities in dyslexic readers. Surprisingly, there is little empirical support for the standard neuroanatomical model of developmental dyslexia, which localizes the primary phonological decoding deficit in left temporo-parietal regions. Rather, recent evidence points to a dysfunction of a left hemisphere reading network, which includes occipito-temporal, inferior frontal, and inferior parietal regions.

  16. Feasible stability region in the frontal plane during human gait.

    Science.gov (United States)

    Yang, Feng; Espy, Debbie; Pai, Yi-Chung

    2009-12-01

    The inability to adequately control the motion of the center of mass (COM) in the frontal plane may result in a loss of balance causing a sideways fall during human gait. The primary purposes of this study were (1) to derive the feasible stability region (FSR) in the mediolateral direction, and (2) to compare the FSR with the COM motion state taken from 193 trials among 39 young subjects at liftoff during walking at different speeds. The lower boundary of the FSR was derived, at a given initial COM location, as the minimum rightward COM velocity, at liftoff of the left foot, required to bring the COM into the base of support (BOS), i.e., the right (stance) foot, as the COM velocity diminishes. The upper boundary was derived as the maximum rightward COM velocity, beyond which the left foot must land to the right of the right foot (BOS) in order to prevent a fall. We established a 2-link human model and employed dynamic optimization to estimate these threshold values for velocity. For a range of initial COM positions, simulated annealing algorithm was used to search for the threshold velocity values. Our study quantified the extent to which mediolateral balance can still be maintained without resorting to a crossover step (the left foot lands to the right of the BOS) for balance recovery. The derived FSR is in good agreement with our gait experimental results.

  17. Automated MRI parcellation of the frontal lobe.

    Science.gov (United States)

    Ranta, Marin E; Chen, Min; Crocetti, Deana; Prince, Jerry L; Subramaniam, Krish; Fischl, Bruce; Kaufmann, Walter E; Mostofsky, Stewart H

    2014-05-01

    Examination of associations between specific disorders and physical properties of functionally relevant frontal lobe sub-regions is a fundamental goal in neuropsychiatry. Here, we present and evaluate automated methods of frontal lobe parcellation with the programs FreeSurfer(FS) and TOADS-CRUISE(T-C), based on the manual method described in Ranta et al. [2009]: Psychiatry Res 172:147-154 in which sulcal-gyral landmarks were used to manually delimit functionally relevant regions within the frontal lobe: i.e., primary motor cortex, anterior cingulate, deep white matter, premotor cortex regions (supplementary motor complex, frontal eye field, and lateral premotor cortex) and prefrontal cortex (PFC) regions (medial PFC, dorsolateral PFC, inferior PFC, lateral orbitofrontal cortex [OFC] and medial OFC). Dice's coefficient, a measure of overlap, and percent volume difference were used to measure the reliability between manual and automated delineations for each frontal lobe region. For FS, mean Dice's coefficient for all regions was 0.75 and percent volume difference was 21.2%. For T-C the mean Dice's coefficient was 0.77 and the mean percent volume difference for all regions was 20.2%. These results, along with a high degree of agreement between the two automated methods (mean Dice's coefficient = 0.81, percent volume difference = 12.4%) and a proof-of-principle group difference analysis that highlights the consistency and sensitivity of the automated methods, indicate that the automated methods are valid techniques for parcellation of the frontal lobe into functionally relevant sub-regions. Thus, the methodology has the potential to increase efficiency, statistical power and reproducibility for population analyses of neuropsychiatric disorders with hypothesized frontal lobe contributions. Copyright © 2013 Wiley Periodicals, Inc.

  18. Hand gesture recognition based on convolutional neural networks

    Science.gov (United States)

    Hu, Yu-lu; Wang, Lian-ming

    2017-11-01

    Hand gesture has been considered a natural, intuitive and less intrusive way for Human-Computer Interaction (HCI). Although many algorithms for hand gesture recognition have been proposed in literature, robust algorithms have been pursued. A recognize algorithm based on the convolutional neural networks is proposed to recognize ten kinds of hand gestures, which include rotation and turnover samples acquired from different persons. When 6000 hand gesture images were used as training samples, and 1100 as testing samples, a 98% recognition rate was achieved with the convolutional neural networks, which is higher than that with some other frequently-used recognition algorithms.

  19. Very deep recurrent convolutional neural network for object recognition

    Science.gov (United States)

    Brahimi, Sourour; Ben Aoun, Najib; Ben Amar, Chokri

    2017-03-01

    In recent years, Computer vision has become a very active field. This field includes methods for processing, analyzing, and understanding images. The most challenging problems in computer vision are image classification and object recognition. This paper presents a new approach for object recognition task. This approach exploits the success of the Very Deep Convolutional Neural Network for object recognition. In fact, it improves the convolutional layers by adding recurrent connections. This proposed approach was evaluated on two object recognition benchmarks: Pascal VOC 2007 and CIFAR-10. The experimental results prove the efficiency of our method in comparison with the state of the art methods.

  20. Convolutional LSTM Networks for Subcellular Localization of Proteins

    DEFF Research Database (Denmark)

    Nielsen, Henrik; Sønderby, Søren Kaae; Sønderby, Casper Kaae

    2015-01-01

    Machine learning is widely used to analyze biological sequence data. Non-sequential models such as SVMs or feed-forward neural networks are often used although they have no natural way of handling sequences of varying length. Recurrent neural networks such as the long short term memory (LSTM) model...... convolutional filters and experiment with an attention mechanism which lets the LSTM focus on specific parts of the protein. Lastly we introduce new visualizations of both the convolutional filters and the attention mechanisms and show how they can be used to extract biologically relevant knowledge from...

  1. Deep Convolutional Neural Networks: Structure, Feature Extraction and Training

    Directory of Open Access Journals (Sweden)

    Namatēvs Ivars

    2017-12-01

    Full Text Available Deep convolutional neural networks (CNNs are aimed at processing data that have a known network like topology. They are widely used to recognise objects in images and diagnose patterns in time series data as well as in sensor data classification. The aim of the paper is to present theoretical and practical aspects of deep CNNs in terms of convolution operation, typical layers and basic methods to be used for training and learning. Some practical applications are included for signal and image classification. Finally, the present paper describes the proposed block structure of CNN for classifying crucial features from 3D sensor data.

  2. [Normal aging of frontal lobe functions].

    Science.gov (United States)

    Calso, Cristina; Besnard, Jérémy; Allain, Philippe

    2016-03-01

    Normal aging in individuals is often associated with morphological, metabolic and cognitive changes, which particularly concern the cerebral frontal regions. Starting from the "frontal lobe hypothesis of cognitive aging" (West, 1996), the present review is based on the neuroanatomical model developed by Stuss (2008), introducing four categories of frontal lobe functions: executive control, behavioural and emotional self-regulation and decision-making, energization and meta-cognitive functions. The selected studies only address the changes of one at least of these functions. The results suggest a deterioration of several cognitive frontal abilities in normal aging: flexibility, inhibition, planning, verbal fluency, implicit decision-making, second-order and affective theory of mind. Normal aging seems also to be characterised by a general reduction in processing speed observed during neuropsychological assessment (Salthouse, 1996). Nevertheless many cognitive functions remain preserved such as automatic or non-conscious inhibition, specific capacities of flexibility and first-order theory of mind. Therefore normal aging doesn't seem to be associated with a global cognitive decline but rather with a selective change in some frontal systems, conclusion which should be taken into account for designing caring programs in normal aging.

  3. Frontal cephalometrics: practical applications, part 2.

    Science.gov (United States)

    Grummons, Duane; Ricketts, Robert M

    2004-01-01

    To (1) demonstrate the needs and benefits of three-dimensional diagnostic and treatment applications; (2) illustrate practical clinical applications of anteroposterior images and frontal analysis; and (3) enhance utilization of the Ricketts and Grummons frontal analyses. Frontal analysis methods and applications are specified and integrated into facial, smile, jaw, and occlusal therapies. Asymmetry conditions must be differentially diagnosed and effectively treated. Frontal and related image analysis and tracing steps are detailed. Asymmetry of facial parts is the rule, rather than the exception. Dental and facial midlines, occlusal plane, chin location, and smile esthetics are primarily emphasized. Beautiful facial proportions and smile harmony can be developed despite initial facial dysmorphosis and disproportions. Patients view themselves from the frontal perspective, so this carries priority when assessing problems. It is important to know the etiology of asymmetry to assist others with genetic counseling. Facial harmony and smile beauty are optimal when facial and maxillary dental midlines are aligned. The maxillary dentition width should be sufficiently wide to be in harmony with the individual patient facial morphology. The occlusal plane should be level and the chin centered as much as possible. Best facial development and proportionality exist when the skeletal and dental components are optimized transversely and are symmetric.

  4. Tratamiento y complicaciones de las fracturas de seno frontal Frontal sinus fracture treatment and complications

    Directory of Open Access Journals (Sweden)

    S. Heredero Jung

    2007-06-01

    Full Text Available Introducción. Las fracturas de seno frontal se producen como resultado de impactos de alta energía. Un tratamiento inadecuado puede conducir a complicaciones serias incluso muchos años después del traumatismo. Objetivos. Evaluar los datos epidemiológicos y revisar las complicaciones asociadas. Estandarizar el protocolo de tratamiento. Materiales y métodos. Se revisaron 95 pacientes diagnosticados de fracturas de seno frontal pertenecientes al servicio de Cirugía Oral y Maxilofacial del Hospital Universitario 12 de Octubre de Madrid, entre enero de 1990 y diciembre de 2004. Resultados. La edad media de los pacientes revisados es de 34 años. La mayoría son hombres (78% y la causa más frecuente del traumatismo, los accidentes de tráfico. El patrón de fractura más común es el que afecta únicamente a la pared anterior del seno frontal. Las complicaciones descritas son: deformidad estética frontal, sinusitis frontal, mucocele frontal, celulitis fronto-orbitaria, intolerancia al material de osteosíntesis, complicaciones infecciosas del SNC y persistencia de fístula de líquido cefalorraquídeo. Conclusiones. El objetivo ha de estar encaminado a prevenir las complicaciones asociadas a los pacientes con fracturas de seno frontal. Hay que individualizar el protocolo de tratamiento en cada caso. Es recomendable un seguimiento a largo plazo para identificar precozmente las posibles complicaciones.Introduction. Frontal sinus fractures are caused by high velocity impacts. Inappropriate treatment can lead to serious complications, even many years after the trauma. Objectives. To evaluate epidemiological data and associated complications. To standardize the treatment protocol. Materials and methods. the clinical records of 95 patients with frontal sinus fractures treated between January 1990 and December 2004 at the Oral and Maxillofacial Surgery Department, "12 de Octubre" Hospital (Madrid, Spain, were reviewed. Results. The average age of

  5. Complex Frontal Pneumosinus Dilatans Associated with Meningioma: A Report of Two Cases and Associated Literature Review

    Science.gov (United States)

    Timms, Sara; Lakhani, Raj; Connor, Steve; Hopkins, Claire

    2017-01-01

    Introduction  Pneumosinus dilatans (PSD) is a rare phenomenon involving the expansion of the paranasal sinuses, without bony destruction or a mass. Previously documented cases have demonstrated simple expansion of a solitary air cell. We present two unique cases of PSD in the presence of meningioma, in which complex new cells developed within the frontal sinus. One of the two patients developed associated sinus disease. Case 1  A 28-year-old man presented with facial pain. A computed tomography scan showed an abnormally enlarged, septated right frontal sinus, not present on childhood scans. He underwent a modified endoscopic Lothrop approach to divide the septations, and his symptoms resolved. Case 2  A 72-year-old woman presented with a 3-month history of headaches. Scans revealed a left frontal meningioma and multiple enlarged, dilated left frontal air cells. She had no clinical sinusitis and therefore was managed conservatively. Conclusions  PSD has been widely documented in association with fibrous dysplasia and meningioma. The most prevalent theory of the mechanism of PSD is of obstruction of the sinus ostium causing sinus expansion through a “ball-valve” effect. Our cases, which demonstrate septated PSD, suggest a more complex process involving local mediators and highlight the need to consider underlying meningioma in pneumosinus dilatans. PMID:28752019

  6. General Purpose Convolution Algorithm in S4 Classes by Means of FFT

    Directory of Open Access Journals (Sweden)

    Peter Ruckdeschel

    2014-08-01

    By means of object orientation this default algorithm is overloaded by more specific algorithms where possible, in particular where explicit convolution formulae are available. Our focus is on R package distr which implements this approach, overloading operator + for convolution; based on this convolution, we define a whole arithmetics of mathematical operations acting on distribution objects, comprising operators +, -, *, /, and ^.

  7. Efficient forward propagation of time-sequences in convolutional neural networks using Deep Shifting

    NARCIS (Netherlands)

    K.L. Groenland (Koen); S.M. Bohte (Sander)

    2016-01-01

    textabstractWhen a Convolutional Neural Network is used for on-the-fly evaluation of continuously updating time-sequences, many redundant convolution operations are performed. We propose the method of Deep Shifting, which remembers previously calculated results of convolution operations in order

  8. Frontal activation and connectivity using near-infrared spectroscopy: verbal fluency language study.

    Science.gov (United States)

    Chaudhary, Ujwal; Hall, Michael; DeCerce, Joe; Rey, Gustavo; Godavarty, Anuradha

    2011-02-28

    Near infrared spectroscopy (NIRS) is an optical technique with high temporal resolution and reasonably good spatial resolution, which allows non invasive measurement of the blood oxygenation of tissue. The current work is focused in assessing and correlating brain activation, connectivity and cortical lateralization of the frontal cortex in response to language-based stimuli, using NIRS. Experimental studies were performed on 15 normal right-handed adults, wherein the participants were presented with a verbal fluency task. The hemodynamic responses in the pre- and anterior frontal cortex were assessed in response to a Word generation task in comparison to the baseline random Jaw movement and Rest conditions. The functional connectivity analysis was performed using zero-order correlations and the cortical lateralization was evaluated as well. An increase in oxy- and a decrease in deoxy-hemoglobin were observed during verbal fluency task in the frontal cortex. Unlike in the pre-frontal cortex, the hemodynamic response in the anterior frontal during verbal fluency task was not significantly different from that during random Jaw movement. Bilateral activation and symmetrical connectivity were observed in the pre-frontal cortex, independent of the stimuli presented. A left cortical dominance and asymmetry connectivity was observed in the anterior frontal during the verbal fluency task. The work is focused to target the pediatric epileptic populations in the future, where understanding the brain functionality (activation, connectivity, and dominance) in response to language is essential as a part of the pre-surgical evaluation in a clinical environment. Copyright © 2011 Elsevier Inc. All rights reserved.

  9. A beamformer analysis of MEG data reveals frontal generators of the musically elicited mismatch negativity.

    Directory of Open Access Journals (Sweden)

    Claudia Lappe

    Full Text Available To localize the neural generators of the musically elicited mismatch negativity with high temporal resolution we conducted a beamformer analysis (Synthetic Aperture Magnetometry, SAM on magnetoencephalography (MEG data from a previous musical mismatch study. The stimuli consisted of a six-tone melodic sequence comprising broken chords in C- and G-major. The musical sequence was presented within an oddball paradigm in which the last tone was lowered occasionally (20% by a minor third. The beamforming analysis revealed significant right hemispheric neural activation in the superior temporal (STC, inferior frontal (IFC, superior frontal (SFC and orbitofrontal (OFC cortices within a time window of 100-200 ms after the occurrence of a deviant tone. IFC and SFC activation was also observed in the left hemisphere. The pronounced early right inferior frontal activation of the auditory mismatch negativity has not been shown in MEG studies so far. The activation in STC and IFC is consistent with earlier electroencephalography (EEG, optical imaging and functional magnetic resonance imaging (fMRI studies that reveal the auditory and inferior frontal cortices as main generators of the auditory MMN. The observed right hemispheric IFC is also in line with some previous music studies showing similar activation patterns after harmonic syntactic violations. The results demonstrate that a deviant tone within a musical sequence recruits immediately a distributed neural network in frontal and prefrontal areas suggesting that top-down processes are involved when expectation violation occurs within well-known stimuli.

  10. Cephalic aura after frontal lobe resection.

    Science.gov (United States)

    Kakisaka, Yosuke; Jehi, Lara; Alkawadri, Rafeed; Wang, Zhong I; Enatsu, Rei; Mosher, John C; Dubarry, Anne-Sophie; Alexopoulos, Andreas V; Burgess, Richard C

    2014-08-01

    A cephalic aura is a common sensory aura typically seen in frontal lobe epilepsy. The generation mechanism of cephalic aura is not fully understood. It is hypothesized that to generate a cephalic aura extensive cortical areas need to be excited. We report a patient who started to have cephalic aura after right frontal lobe resection. Magnetoencephalography (MEG) showed interictal spike and ictal change during cephalic aura, both of which were distributed in the right frontal region, and the latter involved much more widespread areas than the former on MEG sensors. The peculiar seizure onset pattern may indicate that surgical modification of the epileptic network was related to the appearance of cephalic aura. We hypothesize that generation of cephalic aura may be associated with more extensive cortical involvement of epileptic activity than that of interictal activity, in at least a subset of cases. Copyright © 2014 Elsevier Ltd. All rights reserved.

  11. Reduced frontal and occipital lobe asymmetry on the CT-scans of schizophrenic patients. Its specificity and clinical significance

    International Nuclear Information System (INIS)

    Falkai, P.; Schneider, T.; Greve, B.; Klieser, E.; Bogerts, B.

    1995-01-01

    Frontal and occipital lobe widths were determined in the computed tomographic (CT) scans of 135 schizophrenic patients, 158 neuro psychiatrically healthy and 102 psychiatric control subjects, including patients with affective psychosis, neurosis and schizoaffective psychosis. Most healthy right-handed subjects demonstrate a relative enlargement of the right frontal as well as left occipital lobe compared to the opposite hemisphere. These normal frontal and occipital lobe asymmetries were selectively reduced in schizophrenics (f.: 5%, p < .0005; o.: 3%, p < .05), irrespective of the pathophysiological subgroup. Schizophrenic neuroleptic non-responders revealed a significant reduction of frontal lobe asymmetry (3%, p < .05), while no correlation between BPRS-sub scores and disturbed cerebral laterality could be detected. In sum the present study demonstrates the disturbed cerebral lateralisation in schizophrenic patients supporting the hypothesis of interrupted early brain development in schizophrenia. (author)

  12. The effects of gender and age on forensic personal identification from frontal sinus in a Turkish population.

    Science.gov (United States)

    Tatlisumak, Ertugrul; Asirdizer, Mahmut; Bora, Aydin; Hekimoglu, Yavuz; Etli, Yasin; Gumus, Orhan; Keskin, Siddik

    2017-01-01

    To define the dimensions of the frontal sinus in groups standardized for age and gender and to discuss the reasons and the effects of the variations. Methods: Frontal sinus measurements were obtained from paranasal CTscans of 180 males and 180 females in the Radiology Department of Dursun Odabas Medical Center of Yuzuncu Yil University, Van, which is located in Eastern Turkey, between February and March 2016. The width and height of sinuses were measured on a coronal plane, and the anteroposterior length was measured on an axial plane. Volumes were calculated using the Hospital Information Management Systems and Image Archiving and Management Systemprogram. The Statistical Package of the Social Science version 13 was used for statistical analyses.  Results: We determined differences in the frontal sinus measurements of different age groups in a Turkish adult population. Frontal sinus dimensions were usually higher in females and lower in males after 40-49 years of age than their younger counterparts, but the measurements were lower in females and higher in males in 70≤ years of age group than 60-69 years of age. Left frontal sinus was dominant in young age groups but right frontal sinus was dominant in groups 40-49 years of age or older.  Conclusion: We observed crossing of the measurements between the different age groups, which we could not find clear explanations. The results of such studies may affect forensic identification from frontal sinus measurements.

  13. The Urbanik generalized convolutions in the non-commutative ...

    Indian Academy of Sciences (India)

    preserves probability measures on the real line R. An alternative definition associates it to the sum of .... Barbara Jasiulis-Gołdyn and Anna Kula. DEFINITION 1. An associative and commutative binary operation ⊛ on the set P+ is called a generalized convolution on P. + ...... which are translation-invariant and weakly ...

  14. Robust Fusion of Irregularly Sampled Data Using Adaptive Normalized Convolution

    NARCIS (Netherlands)

    Pham, T.Q.; Van Vliet, L.J.; Schutte, K.

    2006-01-01

    We present a novel algorithm for image fusion from irregularly sampled data. The method is based on the framework of normalized convolution (NC), in which the local signal is approximated through a projection onto a subspace. The use of polynomial basis functions in this paper makes NC equivalent to

  15. Spherical convolutions and their application in molecular modelling

    DEFF Research Database (Denmark)

    Boomsma, Wouter; Frellsen, Jes

    2017-01-01

    Convolutional neural networks are increasingly used outside the domain of image analysis, in particular in various areas of the natural sciences concerned with spatial data. Such networks often work out-of-the box, and in some cases entire model architectures from image analysis can be carried ov...

  16. Diffraction and Dirchlet problem for parameter-elliptic convolution ...

    African Journals Online (AJOL)

    In this paper we evaluate the difference between the inverse operators of a Dirichlet problem and of a diffraction problem for parameter-elliptic convolution operators with constant symbols. We prove that the inverse operator of a Dirichlet problem can be obtained as a limit case of such a diffraction problem. Quaestiones ...

  17. Weak Poincar\\'e Inequality for Convolution Probability Measures

    OpenAIRE

    Cheng, Li-Juan; Zhang, Shao-Qin

    2014-01-01

    By using Lyapunov conditions, weak Poincar\\'e inequalities are established for some probability measures on a manifold $(M,g)$. These results are further applied to the convolution of two probability measures on $\\R^d$. Along with explicit results we study concrete examples.

  18. Review of the convolution algorithm for evaluating service integrated systems

    DEFF Research Database (Denmark)

    Iversen, Villy Bæk

    1997-01-01

    In this paper we give a review of the applicability of the convolution algorithm. By this we are able to evaluate communication networks end--to--end with e.g. BPP multi-ratetraffic models insensitive to the holding time distribution. Rearrangement, minimum allocation, and maximum allocation...

  19. Multipliers of Ap((0 ,((0 ,((0,∞)) with order convolution

    Indian Academy of Sciences (India)

    R. Narasimhan (Krishtel eMaging) 1461 1996 Oct 15 13:05:22

    I under the order convolution product denoted by ∗ and total variation norm. Then the. Banach space L1(I) of all measures in M(I) which are absolutely continuous with respect to the Lebesgue measure on I becomes a commutative semisimple Banach algebra in the inherited product ∗. More specifically, for f, g ∈ L1(I),.

  20. Infimal convolution in Efimov-Stečkin Banach spaces

    Czech Academy of Sciences Publication Activity Database

    Fabian, Marián

    2008-01-01

    Roč. 339, č. 1 (2008), s. 735-739 ISSN 0022-247X R&D Projects: GA AV ČR(CZ) IAA100190610 Institutional research plan: CEZ:AV0Z10190503 Keywords : reflexive Banach space * Kadec-Klee norm * infimal convolution Subject RIV: BA - General Mathematics Impact factor: 1.046, year: 2008

  1. CICAAR - Convolutive ICA with an Auto-Regressive Inverse Model

    DEFF Research Database (Denmark)

    Dyrholm, Mads; Hansen, Lars Kai

    2004-01-01

    We invoke an auto-regressive IIR inverse model for convolutive ICA and derive expressions for the likelihood and its gradient. We argue that optimization will give a stable inverse. When there are more sensors than sources the mixing model parameters are estimated in a second step by least squares...

  2. Symbol Stream Combining in a Convolutionally Coded System

    Science.gov (United States)

    Mceliece, R. J.; Pollara, F.; Swanson, L.

    1985-01-01

    Symbol stream combining has been proposed as a method for arraying signals received at different antennas. If convolutional coding and Viterbi decoding are used, it is shown that a Viterbi decoder based on the proposed weighted sum of symbol streams yields maximum likelihood decisions.

  3. Discrete singular convolution for the generalized variable-coefficient ...

    African Journals Online (AJOL)

    Numerical solutions of the generalized variable-coefficient Korteweg-de Vries equation are obtained using a discrete singular convolution and a fourth order singly diagonally implicit Runge-Kutta method for space and time discretisation, respectively. The theoretical convergence of the proposed method is rigorously ...

  4. A combination of differential equations and convolution in ...

    Indian Academy of Sciences (India)

    Keywords. Dynamical model; likelihood; convolution; HIV. Abstract. Nonlinear dynamical method of projecting the transmission of an epidemic is accurate if the input parameters and initial value variables are reliable. Here, such a model is proposed for predicting an epidemic. A method to supplement two variables and two ...

  5. Unsupervised pre-training for fully convolutional neural networks

    NARCIS (Netherlands)

    Wiehman, Stiaan; Kroon, Steve; Villiers, De Hendrik

    2017-01-01

    Unsupervised pre-Training of neural networks has been shown to act as a regularization technique, improving performance and reducing model variance. Recently, fully convolutional networks (FCNs) have shown state-of-The-Art results on various semantic segmentation tasks. Unfortunately, there is no

  6. Deep convolutional neural networks for detection of rail surface defects

    NARCIS (Netherlands)

    Faghih Roohi, S.; Hajizadeh, S.; Nunez Vicencio, Alfredo; Babuska, R.; De Schutter, B.H.K.; Estevez, Pablo A.; Angelov, Plamen P.; Del Moral Hernandez, Emilio

    2016-01-01

    In this paper, we propose a deep convolutional neural network solution to the analysis of image data for the detection of rail surface defects. The images are obtained from many hours of automated video recordings. This huge amount of data makes it impossible to manually inspect the images and

  7. A convolutional neural network to filter artifacts in spectroscopic MRI.

    Science.gov (United States)

    Gurbani, Saumya S; Schreibmann, Eduard; Maudsley, Andrew A; Cordova, James Scott; Soher, Brian J; Poptani, Harish; Verma, Gaurav; Barker, Peter B; Shim, Hyunsuk; Cooper, Lee A D

    2018-03-09

    Proton MRSI is a noninvasive modality capable of generating volumetric maps of in vivo tissue metabolism without the need for ionizing radiation or injected contrast agent. Magnetic resonance spectroscopic imaging has been shown to be a viable imaging modality for studying several neuropathologies. However, a key hurdle in the routine clinical adoption of MRSI is the presence of spectral artifacts that can arise from a number of sources, possibly leading to false information. A deep learning model was developed that was capable of identifying and filtering out poor quality spectra. The core of the model used a tiled convolutional neural network that analyzed frequency-domain spectra to detect artifacts. When compared with a panel of MRS experts, our convolutional neural network achieved high sensitivity and specificity with an area under the curve of 0.95. A visualization scheme was implemented to better understand how the convolutional neural network made its judgement on single-voxel or multivoxel MRSI, and the convolutional neural network was embedded into a pipeline capable of producing whole-brain spectroscopic MRI volumes in real time. The fully automated method for assessment of spectral quality provides a valuable tool to support clinical MRSI or spectroscopic MRI studies for use in fields such as adaptive radiation therapy planning. © 2018 International Society for Magnetic Resonance in Medicine.

  8. The frontal method in hydrodynamics simulations

    Science.gov (United States)

    Walters, R.A.

    1980-01-01

    The frontal solution method has proven to be an effective means of solving the matrix equations resulting from the application of the finite element method to a variety of problems. In this study, several versions of the frontal method were compared in efficiency for several hydrodynamics problems. Three basic modifications were shown to be of value: 1. Elimination of equations with boundary conditions beforehand, 2. Modification of the pivoting procedures to allow dynamic management of the equation size, and 3. Storage of the eliminated equations in a vector. These modifications are sufficiently general to be applied to other classes of problems. ?? 1980.

  9. Behavioral Disorders in Association with Posterior Callosal and Frontal Cerebral Infarction

    Directory of Open Access Journals (Sweden)

    J. P. Lejeune

    1993-01-01

    Full Text Available Behavioral disorders were a prominent clinical feature after the surgical treatment of an anterior communicating artery aneurysm rupture in a 44-year-old man. Callosal apraxia was associated with an alien hand. The latter remained 1 year after surgery while diagonistic apraxia disappeared after 3 months. Other callosal signs included left agraphia, tactile anomia and auditory suppression. MRI revealed posterior callosal infarction and a right frontal infarct. The association of diagonistic apraxia and alien hand is rarely reported.

  10. Migration of Sparganum of the Frontal Lobe to the Ipsilateral Cerebellar Hemisphere: A Case Report

    Energy Technology Data Exchange (ETDEWEB)

    Heo, Eun A; Choi, See Sung; Jeon, Se Jeong; Kim, Hey Won; Lee, Young Hwan [Wonkwang University Hopital, Iksan (Korea, Republic of)

    2009-05-15

    Most cerebral sparganosis lesions are located in the white matter of the cerebral hemisphere. A few cases of cerebral sparganosis where the sparganum have migrated into the contralateral cerebral hemisphere have been reported. We report a case of cerebral sparganosis where the sparganum migrated from the white matter of the left frontal lobe to the ipsilateral cerebellar hemisphere after failure of surgical removal of the worm

  11. Performance on the Frontal Assessment Battery is sensitive to frontal lobe damage in stroke patients.

    Science.gov (United States)

    Kopp, Bruno; Rösser, Nina; Tabeling, Sandra; Stürenburg, Hans Jörg; de Haan, Bianca; Karnath, Hans-Otto; Wessel, Karl

    2013-11-16

    The Frontal Assessment Battery (FAB) is a brief battery of six neuropsychological tasks designed to assess frontal lobe function at bedside [Neurology 55:1621-1626, 2000]. The six FAB tasks explore cognitive and behavioral domains that are thought to be under the control of the frontal lobes, most notably conceptualization and abstract reasoning, lexical verbal fluency and mental flexibility, motor programming and executive control of action, self-regulation and resistance to interference, inhibitory control, and environmental autonomy. We examined the sensitivity of performance on the FAB to frontal lobe damage in right-hemisphere-damaged first-ever stroke patients based on voxel-based lesion-behavior mapping. Voxel-based lesion-behavior mapping of FAB performance revealed that the integrity of the right anterior insula (BA13) is crucial for the FAB global composite score, for the FAB conceptualization score, as well as for the FAB inhibitory control score. Furthermore, the FAB conceptualization and mental flexibility scores were sensitive to damage of the right middle frontal gyrus (MFG; BA9). Finally, the FAB inhibitory control score was sensitive to damage of the right inferior frontal gyrus (IFG; BA44/45). These findings indicate that several FAB scores (including composite and item scores) provide valid measures of right hemispheric lateral frontal lobe dysfunction, specifically of focal lesions near the anterior insula, in the MFG and in the IFG.

  12. Frontal lobe connectivity and cognitive impairment in pediatric frontal lobe epilepsy.

    Science.gov (United States)

    Braakman, Hilde M H; Vaessen, Maarten J; Jansen, Jacobus F A; Debeij-van Hall, Mariette H J A; de Louw, Anton; Hofman, Paul A M; Vles, Johan S H; Aldenkamp, Albert P; Backes, Walter H

    2013-03-01

    Cognitive impairment is frequent in children with frontal lobe epilepsy (FLE), but its etiology is unknown. With functional magnetic resonance imaging (fMRI), we have explored the relationship between brain activation, functional connectivity, and cognitive functioning in a cohort of pediatric patients with FLE and healthy controls. Thirty-two children aged 8-13 years with FLE of unknown cause and 41 healthy age-matched controls underwent neuropsychological assessment and structural and functional brain MRI. We investigated to which extent brain regions activated in response to a working memory task and assessed functional connectivity between distant brain regions. Data of patients were compared to controls, and patients were grouped as cognitively impaired or unimpaired. Children with FLE showed a global decrease in functional brain connectivity compared to healthy controls, whereas brain activation patterns in children with FLE remained relatively intact. Children with FLE complicated by cognitive impairment typically showed a decrease in frontal lobe connectivity. This decreased frontal lobe connectivity comprised both connections within the frontal lobe as well as connections from the frontal lobe to the parietal lobe, temporal lobe, cerebellum, and basal ganglia. Decreased functional frontal lobe connectivity is associated with cognitive impairment in pediatric FLE. The importance of impairment of functional integrity within the frontal lobe network, as well as its connections to distant areas, provides new insights in the etiology of the broad-range cognitive impairments in children with FLE. Wiley Periodicals, Inc. © 2012 International League Against Epilepsy.

  13. [Vectorcardiographic manifestations of left intraventricular conduction disorders].

    Science.gov (United States)

    de Micheli, A; Medrano, G A

    1979-01-01

    Both, the vectorcardiographic changes produced by the various degrees of left bundle branch block and these observed with the different types of left distal block, are described. When a "wave jumping" phenomenon exists, the vectorcardiographic changes are more characteristic in the horizontal plane than in the frontal plane and can be interpreted satisfactorily in basis of the ventricular activation sequence. The normal counterclockwise rotation of the horizontal vectorcardiogram persists in the presence of left bundle branch block of slight and moderate degrees, since the electromotive forces of the free left ventricular wall are still predominant. In the majority of intermediate degree blocks, the middle portion of the RH loop develops with a clockwise rotation and general aspect with a clockwise rotation and the general aspect of the ventricular loop resembles an eight figure. This is due to the electromotive forces originated by the delayed depolarization of the left septal mass that starts to predominate. With advanced degrees of block, the largest portion of the RH loop shows a clockwise rotation, as well as marked notchings and slurrings. The initial anterior portion of the horizontal vectorcardiogram does not disappear, but is situated to the left of the anterior-posterior axis with a counterclockwise rotation (first right septal vector). Otherwise, the direct electrical sign of left distal block emphasized: evidence of delayed activation in a limited zone of the homolateral ventricle. This local delay gives rise to an asynchronism of the activation phenomenon between the upper and lower regions of the ventricle. The diagnosis of left bifascicular block is based essentially on the evidence of unequal delay of the activation sequence in the basal regions and in the inferior ones of the homolateral ventricle and also on the frequent persistence of the first left septal vector.

  14. Prospective memory and frontal lobe function.

    Science.gov (United States)

    Neulinger, Kerryn; Oram, Joanne; Tinson, Helen; O'Gorman, John; Shum, David H K

    2016-01-01

    The study sought to examine the role of frontal lobe functioning in focal prospective memory (PM) performance and its relation to PM deficit in older adults. PM and working memory (WM) differences were studied in younger aged (n = 21), older aged (n = 20), and frontal injury (n = 14) groups. An event-based focal PM task was employed and three measures of WM were administered. The younger aged group differed from the other two groups in showing significantly higher scores on PM and on one of the WM measures, but there were no differences at a statistically significant level between the older aged group and the frontal injury groups on any of the memory measures. There were, however, some differences in correlations with a WM measure between groups. It is concluded that there are similarities and differences in the deficits in PM between older adults and patients with frontal lobe injury on focal as well as nonfocal PM tasks.

  15. Right Frontal White Matter and Tourette Syndrome

    Directory of Open Access Journals (Sweden)

    J Gordon Millichap

    2002-01-01

    Full Text Available An MRI volumetric analysis of frontal and nonfrontal gray and white matter was performed in 11 boys with Tourette syndrome (TS only, 14 with TS + ADHD, 12 with ADHD only, and 26 healthy boys, at the Kennedy Krieger Institute, Baltimore, MD.

  16. Infant Frontal Asymmetry Predicts Child Emotional Availability

    Science.gov (United States)

    Licata, Maria; Paulus, Markus; Kühn-Popp, Nina; Meinhardt, Jorg; Sodian, Beate

    2015-01-01

    While factors influencing maternal emotional availability (EA) have been well investigated, little is known about the development of child EA. The present longitudinal study investigated the role of frontal brain asymmetry in young children with regard to child EA (child responsiveness and involvement) in mother-child interaction in a sample of 28…

  17. The right posterior inferior frontal gyrus contributes to phonological word decisions in the healthy brain

    DEFF Research Database (Denmark)

    Hartwigsen, Gesa; Price, Cathy J; Baumgaertner, Annette

    2010-01-01

    There is consensus that the left hemisphere plays a dominant role in language processing, but functional imaging studies have shown that the right as well as the left posterior inferior frontal gyri (pIFG) are activated when healthy right-handed individuals make phonological word decisions. Here we...... used online transcranial magnetic stimulation (TMS) to examine the functional relevance of the right pIFG for auditory and visual phonological decisions. Healthy right-handed individuals made phonological or semantic word judgements on the same set of auditorily and visually presented words while...... they received stereotactically guided TMS over the left, right or bilateral pIFG (n=14) or the anterior left, right or bilateral IFG (n=14). TMS started 100ms after word onset and consisted of four stimuli given at a rate of 10Hz and intensity of 90% of active motor threshold. Compared to TMS of aIFG, TMS of p...

  18. An Implementation of Error Minimization Data Transmission in OFDM using Modified Convolutional Code

    Directory of Open Access Journals (Sweden)

    Hendy Briantoro

    2016-04-01

    Full Text Available This paper presents about error minimization in OFDM system. In conventional system, usually using channel coding such as BCH Code or Convolutional Code. But, performance BCH Code or Convolutional Code is not good in implementation of OFDM System. Error bits of OFDM system without channel coding is 5.77%. Then, we used convolutional code with code rate 1/2, it can reduce error bitsonly up to 3.85%. So, we proposed OFDM system with Modified Convolutional Code. In this implementation, we used Software Define Radio (SDR, namely Universal Software Radio Peripheral (USRP NI 2920 as the transmitter and receiver. The result of OFDM system using Modified Convolutional Code with code rate is able recover all character received so can decrease until 0% error bit. Increasing performance of Modified Convolutional Code is about 1 dB in BER of 10-4 from BCH Code and Convolutional Code. So, performance of Modified Convolutional better than BCH Code or Convolutional Code. Keywords: OFDM, BCH Code, Convolutional Code, Modified Convolutional Code, SDR, USRP

  19. Biomechanic study of the human liver during a frontal deceleration.

    Science.gov (United States)

    Cheynel, Nicolas; Serre, Thierry; Arnoux, Pierre-Jean; Baque, Patrick; Benoit, Laurent; Berdah, Stephane-Victor; Brunet, Christian

    2006-10-01

    Mechanisms of hepatic injury remain poorly understood. Surgical literature reports some speculative theories that have never been proved. The aim of this study was to examine the behavior of the liver during brutal frontal deceleration. Six trunks, removed from human cadavers, underwent free falls at 4, 6, and 8 meters per second (mps). Accelerometers were positioned in the two lobes of the liver, in front of the vertebra L2, and in the retro hepatic inferior vena cava. Relative motions of the lobes of the liver and of the two other anatomic marks were observed. In parallel, numerical simulations of this experiment have been performed using a finite element model. In the direction of impact, the vertebra L2 had no considerable displacement with the inferior vena cava. There was a noteworthy displacement between the two hepatic lobes. The left hepatic lobe had a large relative displacement with the vertebra L2 and the inferior vena cava. The right hepatic lobe was more stable with the vertebra L2 and the inferior vena cava. Numerical simulation of the same protocol underlined a rotation effect of the liver to the left around the axis of the inferior vena cava. These results support the surgical data. They highlight a crucial zone and explain how dramatic lacerations between the two lobes of the liver can occur.

  20. Hemichorea and dystonia due to frontal lobe meningioma

    Directory of Open Access Journals (Sweden)

    Abdul Qayyum Rana

    2014-01-01

    Full Text Available Tumors originating from the meninges, also known as meningiomas, have rarely been known to cause parkinsonian symptoms and other movement disorders. Although some cases of AV malformations causing movement disorders have been described in the literature, not much has been reported about meningiomas in this regard. The aim of this case report is to further highlight the importance of brain imaging in patients with movement disorders for even a benign tumor; and also emphasize the need for a careful movement disorder examination because more than one phenomenology of movement disorders may result from the mechanical pressure caused by a tumor. We present a case report of a patient with a heavily calcified right frontal lobe meningioma. Our patient had irregular, involuntary, brief, fleeting and unpredictable movements of her left upper and lower extremities, consistent with chorea. The patient also had abnormal dystonic posturing of her left arm while walking. This case report highlights the importance of brain imaging as well as careful neurological examinations of patients with benign meningiomas. Moreover, it illustrates the remarkable specificity yet clinical diversity of meningiomas in presentation through movement disorders.

  1. Ocean Ekman Response to Wind Forcing in Frontal Regions and Implications for Vertical Velocity

    Science.gov (United States)

    Cronin, M. F.; Tozuka, T.

    2016-12-01

    Wind forcing is fundamental to the ocean circulation. According to the classic "Ekman" theory developed in the early twentieth century, wind-induced steady flow spirals to the right of the wind stress in the Northern Hemisphere and to the left in the Southern Hemisphere, resulting in a net wind-forced "Ekman" transport that is 90 degrees to the right of the wind stress in the Northern Hemisphere and to the left in the Southern Hemisphere. This theory, however, assumes that the near-surface ocean is uniform in density (i.e., has no fronts). In frontal regions the surface "geostrophic" currents have a vertical shear aligned with the density front and this oceanic "thermal wind" shear can balance a portion of the surface wind stress. In this study we show that in frontal regions, the classic Ekman response is altered. Surface ocean currents respond to the effective wind stress—the portion of the wind stress that is out of balance with the ocean's surface geostrophic shear. Consequently, the vertical velocity at the base of the mixed layer is better approximated by the curl of the effective wind stress, rather than the full wind stress. Wind blowing along a front can give rise to a local minimum in the effective wind stress and result in a secondary circulation with downwelling on the cold side of the front and upwelling on the warm side. Using data from the high-resolution Japanese Ocean general circulation model For the Earth Simulator (OFES), we show that these frontal effects cannot be ignored in the Tropics or in strong frontal regions in the extratropics, such as found in coastal regions and in western boundary currents of all basins. Furthermore, these frontal effects dominate the classic Ekman response in regions of both hemispheres where trade winds change to westerlies.

  2. Medial frontal cortex and response conflict: Evidence from human intracranial EEG and medial frontal cortex lesion

    NARCIS (Netherlands)

    Cohen, M.X.; Ridderinkhof, K.R.; Haupt, S.; Elger, C.E.; Fell, J.

    2008-01-01

    The medial frontal cortex (MFC) has been implicated in the monitoring and selection of actions in the face of competing alternatives, but much remains unknown about its functional properties, including electrophysiological oscillations, during response conflict tasks. Here, we recorded intracranial

  3. Substance abuse risk in emerging adults associated with smaller frontal gray matter volumes and higher externalizing behaviors.

    Science.gov (United States)

    Weiland, Barbara J; Korycinski, Steven T; Soules, Mary; Zubieta, Jon-Kar; Zucker, Robert A; Heitzeg, Mary M

    2014-04-01

    During emerging adulthood, alcohol and substance use peak. Previous research has suggested that prefrontal and subcortical brain volumes may relate to risk for development of substance abuse. Epidemiological studies indicate that early initiation of alcohol or drug use significantly increases the likelihood of later substance use disorder diagnoses. We hypothesized that frontal regions would be smaller in young adults with early substance use and related problems (early-risk, ER), compared with a control group without early use/problems (C). We further hypothesized that these volumes would be associated with more externalizing behaviors, an additional robust predictor of substance abuse. One hundred and six subjects, ages 18-23, underwent high-resolution anatomical magnetic resonance image scanning. Individuals were categorized as C (n=64) or ER (n=42) using a composite-score of early alcohol/drug use and problems based on prospectively collected assessments; externalizing behaviors were also previously assessed during adolescence. Neuroanatomical volumes were compared between groups and correlated with behavioral measures. ER subjects exhibited more externalizing behaviors than their control counterparts. Total left frontal cortex and left superior frontal cortex volumes were significantly smaller in the ER group, controlling for family history of alcoholism and current substance use. Total gray matter volumes were negatively associated with substance risk score. Further, externalizing behavior score was negatively correlated with both left superior cortical and left total cortical volumes. These findings suggest that smaller frontal cortical volumes, specifically the left superior frontal cortex, represent an underlying risk factor for substance abuse in emerging adults. Copyright © 2014 Elsevier Ireland Ltd. All rights reserved.

  4. Infimal Convolution Regularisation Functionals of BV and Lp Spaces

    KAUST Repository

    Burger, Martin

    2016-02-03

    We study a general class of infimal convolution type regularisation functionals suitable for applications in image processing. These functionals incorporate a combination of the total variation seminorm and Lp norms. A unified well-posedness analysis is presented and a detailed study of the one-dimensional model is performed, by computing exact solutions for the corresponding denoising problem and the case p=2. Furthermore, the dependency of the regularisation properties of this infimal convolution approach to the choice of p is studied. It turns out that in the case p=2 this regulariser is equivalent to the Huber-type variant of total variation regularisation. We provide numerical examples for image decomposition as well as for image denoising. We show that our model is capable of eliminating the staircasing effect, a well-known disadvantage of total variation regularisation. Moreover as p increases we obtain almost piecewise affine reconstructions, leading also to a better preservation of hat-like structures.

  5. Hamiltonian Cycle Enumeration via Fermion-Zeon Convolution

    Science.gov (United States)

    Staples, G. Stacey

    2017-12-01

    Beginning with a simple graph having finite vertex set V, operators are induced on fermion and zeon algebras by the action of the graph's adjacency matrix and combinatorial Laplacian on the vector space spanned by the graph's vertices. When the graph is simple (undirected with no loops or multiple edges), the matrices are symmetric and the induced operators are self-adjoint. The goal of the current paper is to recover a number of known graph-theoretic results from quantum observables constructed as linear operators on fermion and zeon Fock spaces. By considering an "indeterminate" fermion/zeon Fock space, a fermion-zeon convolution operator is defined whose trace recovers the number of Hamiltonian cycles in the graph. This convolution operator is a quantum observable whose expectation reveals the number of Hamiltonian cycles in the graph.

  6. Image Super-Resolution Using Deep Convolutional Networks.

    Science.gov (United States)

    Dong, Chao; Loy, Chen Change; He, Kaiming; Tang, Xiaoou

    2016-02-01

    We propose a deep learning method for single image super-resolution (SR). Our method directly learns an end-to-end mapping between the low/high-resolution images. The mapping is represented as a deep convolutional neural network (CNN) that takes the low-resolution image as the input and outputs the high-resolution one. We further show that traditional sparse-coding-based SR methods can also be viewed as a deep convolutional network. But unlike traditional methods that handle each component separately, our method jointly optimizes all layers. Our deep CNN has a lightweight structure, yet demonstrates state-of-the-art restoration quality, and achieves fast speed for practical on-line usage. We explore different network structures and parameter settings to achieve trade-offs between performance and speed. Moreover, we extend our network to cope with three color channels simultaneously, and show better overall reconstruction quality.

  7. Combining morphometric features and convolutional networks fusion for glaucoma diagnosis

    Science.gov (United States)

    Perdomo, Oscar; Arevalo, John; González, Fabio A.

    2017-11-01

    Glaucoma is an eye condition that leads to loss of vision and blindness. Ophthalmoscopy exam evaluates the shape, color and proportion between the optic disc and physiologic cup, but the lack of agreement among experts is still the main diagnosis problem. The application of deep convolutional neural networks combined with automatic extraction of features such as: the cup-to-disc distance in the four quadrants, the perimeter, area, eccentricity, the major radio, the minor radio in optic disc and cup, in addition to all the ratios among the previous parameters may help with a better automatic grading of glaucoma. This paper presents a strategy to merge morphological features and deep convolutional neural networks as a novel methodology to support the glaucoma diagnosis in eye fundus images.

  8. Deep learning for steganalysis via convolutional neural networks

    Science.gov (United States)

    Qian, Yinlong; Dong, Jing; Wang, Wei; Tan, Tieniu

    2015-03-01

    Current work on steganalysis for digital images is focused on the construction of complex handcrafted features. This paper proposes a new paradigm for steganalysis to learn features automatically via deep learning models. We novelly propose a customized Convolutional Neural Network for steganalysis. The proposed model can capture the complex dependencies that are useful for steganalysis. Compared with existing schemes, this model can automatically learn feature representations with several convolutional layers. The feature extraction and classification steps are unified under a single architecture, which means the guidance of classification can be used during the feature extraction step. We demonstrate the effectiveness of the proposed model on three state-of-theart spatial domain steganographic algorithms - HUGO, WOW, and S-UNIWARD. Compared to the Spatial Rich Model (SRM), our model achieves comparable performance on BOSSbase and the realistic and large ImageNet database.

  9. Convolutional over Recurrent Encoder for Neural Machine Translation

    Directory of Open Access Journals (Sweden)

    Dakwale Praveen

    2017-06-01

    Full Text Available Neural machine translation is a recently proposed approach which has shown competitive results to traditional MT approaches. Standard neural MT is an end-to-end neural network where the source sentence is encoded by a recurrent neural network (RNN called encoder and the target words are predicted using another RNN known as decoder. Recently, various models have been proposed which replace the RNN encoder with a convolutional neural network (CNN. In this paper, we propose to augment the standard RNN encoder in NMT with additional convolutional layers in order to capture wider context in the encoder output. Experiments on English to German translation demonstrate that our approach can achieve significant improvements over a standard RNN-based baseline.

  10. Self-Taught convolutional neural networks for short text clustering.

    Science.gov (United States)

    Xu, Jiaming; Xu, Bo; Wang, Peng; Zheng, Suncong; Tian, Guanhua; Zhao, Jun; Xu, Bo

    2017-04-01

    Short text clustering is a challenging problem due to its sparseness of text representation. Here we propose a flexible Self-Taught Convolutional neural network framework for Short Text Clustering (dubbed STC 2 ), which can flexibly and successfully incorporate more useful semantic features and learn non-biased deep text representation in an unsupervised manner. In our framework, the original raw text features are firstly embedded into compact binary codes by using one existing unsupervised dimensionality reduction method. Then, word embeddings are explored and fed into convolutional neural networks to learn deep feature representations, meanwhile the output units are used to fit the pre-trained binary codes in the training process. Finally, we get the optimal clusters by employing K-means to cluster the learned representations. Extensive experimental results demonstrate that the proposed framework is effective, flexible and outperform several popular clustering methods when tested on three public short text datasets. Copyright © 2017 Elsevier Ltd. All rights reserved.

  11. Improving deep convolutional neural networks with mixed maxout units.

    Directory of Open Access Journals (Sweden)

    Hui-Zhen Zhao

    Full Text Available Motivated by insights from the maxout-units-based deep Convolutional Neural Network (CNN that "non-maximal features are unable to deliver" and "feature mapping subspace pooling is insufficient," we present a novel mixed variant of the recently introduced maxout unit called a mixout unit. Specifically, we do so by calculating the exponential probabilities of feature mappings gained by applying different convolutional transformations over the same input and then calculating the expected values according to their exponential probabilities. Moreover, we introduce the Bernoulli distribution to balance the maximum values with the expected values of the feature mappings subspace. Finally, we design a simple model to verify the pooling ability of mixout units and a Mixout-units-based Network-in-Network (NiN model to analyze the feature learning ability of the mixout models. We argue that our proposed units improve the pooling ability and that mixout models can achieve better feature learning and classification performance.

  12. Genetics Home Reference: autosomal dominant nocturnal frontal lobe epilepsy

    Science.gov (United States)

    ... Health Conditions ADNFLE Autosomal dominant nocturnal frontal lobe epilepsy Printable PDF Open All Close All Enable Javascript ... collapse boxes. Description Autosomal dominant nocturnal frontal lobe epilepsy ( ADNFLE ) is an uncommon form of epilepsy that ...

  13. Optimization of Convolutional Neural Network using Microcanonical Annealing Algorithm

    OpenAIRE

    Ayumi, Vina; Rere, L. M. Rasdi; Fanany, Mohamad Ivan; Arymurthy, Aniati Murni

    2016-01-01

    Convolutional neural network (CNN) is one of the most prominent architectures and algorithm in Deep Learning. It shows a remarkable improvement in the recognition and classification of objects. This method has also been proven to be very effective in a variety of computer vision and machine learning problems. As in other deep learning, however, training the CNN is interesting yet challenging. Recently, some metaheuristic algorithms have been used to optimize CNN using Genetic Algorithm, Parti...

  14. On a generalized Hankel type convolution of generalized functions

    Indian Academy of Sciences (India)

    Springer Verlag Heidelberg #4 2048 1996 Dec 15 10:16:45

    with x ◦ y denoting the hµ, ν-translation on the positive real line. (The analogue of the translation consider for the definition of the usual convolution *.) The function g(x ◦ y) will be called the hµ, ν translate of g(x); provided g(x) is locally bounded on 0

  15. A quantum algorithm for Viterbi decoding of classical convolutional codes

    OpenAIRE

    Grice, Jon R.; Meyer, David A.

    2014-01-01

    We present a quantum Viterbi algorithm (QVA) with better than classical performance under certain conditions. In this paper the proposed algorithm is applied to decoding classical convolutional codes, for instance; large constraint length $Q$ and short decode frames $N$. Other applications of the classical Viterbi algorithm where $Q$ is large (e.g. speech processing) could experience significant speedup with the QVA. The QVA exploits the fact that the decoding trellis is similar to the butter...

  16. Exponential estimates for stochastic convolutions in 2-smooth Banach spaces

    Czech Academy of Sciences Publication Activity Database

    Seidler, Jan

    2010-01-01

    Roč. 15, č. 50 (2010), s. 1556-1573 ISSN 1083-6489 R&D Projects: GA ČR GA201/07/0237 Institutional research plan: CEZ:AV0Z10750506 Keywords : stochastic convolutions in 2-smooth spaces * Burkholder-Davis-Gundy inequality * exponential tail estimates Subject RIV: BA - General Mathematics Impact factor: 0.946, year: 2010 http://library.utia.cas.cz/separaty/2010/SI/seidler-0348352.pdf

  17. General Dirichlet Series, Arithmetic Convolution Equations and Laplace Transforms

    Czech Academy of Sciences Publication Activity Database

    Glöckner, H.; Lucht, L.G.; Porubský, Štefan

    2009-01-01

    Roč. 193, č. 2 (2009), s. 109-129 ISSN 0039-3223 R&D Projects: GA ČR GA201/07/0191 Institutional research plan: CEZ:AV0Z10300504 Keywords : arithmetic function * Dirichlet convolution * polynomial equation * analytic equation * topological algebra * holomorphic functional calculus * implicit function theorem * Laplace transform * semigroup * complex measure Subject RIV: BA - General Mathematics Impact factor: 0.645, year: 2009 http://arxiv.org/abs/0712.3172

  18. Fully convolutional neural networks for polyp segmentation in colonoscopy

    OpenAIRE

    Rosa Brandao, P.; Mazomenos, E.; Ciuti, G.; Bianchi, F.; Menciassi, A.; Dario, P.; Koulaouzidis, A.; Arezzo, A.; Stoyanov, D.

    2017-01-01

    Colorectal cancer (CRC) is one of the most common and deadliest forms of cancer, accounting for nearly 10% of all forms of cancer in the world. Even though colonoscopy is considered the most effective method for screening and diagnosis, the success of the procedure is highly dependent on the operator skills and level of hand-eye coordination. In this work, we propose to adapt fully convolution neural networks (FCN), to identify and segment polyps in colonoscopy images. We converted three esta...

  19. Semi-Supervised Deep Learning for Fully Convolutional Networks

    OpenAIRE

    Baur, Christoph; Albarqouni, Shadi; Navab, Nassir

    2017-01-01

    Deep learning usually requires large amounts of labeled training data, but annotating data is costly and tedious. The framework of semi-supervised learning provides the means to use both labeled data and arbitrary amounts of unlabeled data for training. Recently, semi-supervised deep learning has been intensively studied for standard CNN architectures. However, Fully Convolutional Networks (FCNs) set the state-of-the-art for many image segmentation tasks. To the best of our knowledge, there i...

  20. Recursive Autoconvolution for Unsupervised Learning of Convolutional Neural Networks

    OpenAIRE

    Knyazev, Boris; Barth, Erhardt; Martinetz, Thomas

    2016-01-01

    In visual recognition tasks, such as image classification, unsupervised learning exploits cheap unlabeled data and can help to solve these tasks more efficiently. We show that the recursive autoconvolution operator, adopted from physics, boosts existing unsupervised methods by learning more discriminative filters. We take well established convolutional neural networks and train their filters layer-wise. In addition, based on previous works we design a network which extracts more than 600k fea...

  1. Fast convolutional sparse coding using matrix inversion lemma

    Czech Academy of Sciences Publication Activity Database

    Šorel, Michal; Šroubek, Filip

    2016-01-01

    Roč. 55, č. 1 (2016), s. 44-51 ISSN 1051-2004 R&D Projects: GA ČR GA13-29225S Institutional support: RVO:67985556 Keywords : Convolutional sparse coding * Feature learning * Deconvolution networks * Shift-invariant sparse coding Subject RIV: JD - Computer Applications, Robotics Impact factor: 2.337, year: 2016 http://library.utia.cas.cz/separaty/2016/ZOI/sorel-0459332.pdf

  2. Taxonomic Classification for Living Organisms Using Convolutional Neural Networks

    OpenAIRE

    Khawaldeh, Saed; Pervaiz, Usama; Elsharnoby, Mohammed; Alchalabi, Alaa Eddin; Al-Zubi, Nayel

    2017-01-01

    Taxonomic classification has a wide-range of applications such as finding out more about evolutionary history. Compared to the estimated number of organisms that nature harbors, humanity does not have a thorough comprehension of to which specific classes they belong. The classification of living organisms can be done in many machine learning techniques. However, in this study, this is performed using convolutional neural networks. Moreover, a DNA encoding technique is incorporated in the algo...

  3. Production and reception of meaningful sound in Foville's 'encompassing convolution'.

    Science.gov (United States)

    Schiller, F

    1999-04-01

    In the history of neurology. Achille Louis Foville (1799-1879) is a name deserving to be remembered. In the course of time, his circonvolution d'enceinte of 1844 (surrounding the Sylvian fissure) became the 'convolution encompassing' every aspect of aphasiology, including amusia, ie., the localization in a coherent semicircle of semicircle of cerebral cortext serving the production and perception of language, song and instrumental music in health and disease.

  4. Efficient Convolutional Neural Network with Binary Quantization Layer

    OpenAIRE

    Ravanbakhsh, Mahdyar; Mousavi, Hossein; Nabi, Moin; Marcenaro, Lucio; Regazzoni, Carlo

    2016-01-01

    In this paper we introduce a novel method for segmentation that can benefit from general semantics of Convolutional Neural Network (CNN). Our segmentation proposes visually and semantically coherent image segments. We use binary encoding of CNN features to overcome the difficulty of the clustering on the high-dimensional CNN feature space. These binary encoding can be embedded into the CNN as an extra layer at the end of the network. This results in real-time segmentation. To the best of our ...

  5. Convolutional Neural Networks for Page Segmentation of Historical Document Images

    OpenAIRE

    Chen, Kai; Seuret, Mathias

    2017-01-01

    This paper presents a Convolutional Neural Network (CNN) based page segmentation method for handwritten historical document images. We consider page segmentation as a pixel labeling problem, i.e., each pixel is classified as one of the predefined classes. Traditional methods in this area rely on carefully hand-crafted features or large amounts of prior knowledge. In contrast, we propose to learn features from raw image pixels using a CNN. While many researchers focus on developing deep CNN ar...

  6. Solving singular convolution equations using the inverse fast Fourier transform

    Czech Academy of Sciences Publication Activity Database

    Krajník, E.; Montesinos, V.; Zizler, P.; Zizler, Václav

    2012-01-01

    Roč. 57, č. 5 (2012), s. 543-550 ISSN 0862-7940 R&D Projects: GA AV ČR IAA100190901 Institutional research plan: CEZ:AV0Z10190503 Keywords : singular convolution equations * fast Fourier transform * tempered distribution Subject RIV: BA - General Mathematics Impact factor: 0.222, year: 2012 http://www.springerlink.com/content/m8437t3563214048/

  7. CORRELATION ANALYSIS OF VEHICLE FRONTAL IMPACT PARAMETERS

    Directory of Open Access Journals (Sweden)

    Josef Mík

    2017-12-01

    Full Text Available The article considers a possible improvement of road vehicle safety by using eCall – a system which initiates an emergency call in case of traffic accident. A possible way of better description of a frontal impact accident of a vehicle is examined and enriched by the information from the onboard e-call unit. In this article, we analyze results of frontal crash tests with different types of barriers and overlapping area and look for the correlation between the individual vehicle and collision parameters in order to provide a better description of the severity of the accident by the eCall system. The relation among the selected parameters is described using the correlation analysis.

  8. Nocturnal frontal lobe epilepsy in mucopolysaccharidosis.

    Science.gov (United States)

    Bonanni, Paolo; Volzone, Anna; Randazzo, Giovanna; Antoniazzi, Lisa; Rampazzo, Angelica; Scarpa, Maurizio; Nobili, Lino

    2014-10-01

    Nocturnal frontal lobe epilepsy (NFLE) is an epileptic syndrome that is primarily characterized by seizures with motor signs occurring almost exclusively during sleep. We describe 2 children with mucopolysaccharidosis (MPS) who were referred for significant sleep disturbance. Long term video-EEG monitoring (LT-VEEGM) demonstrated sleep-related hypermotor seizures consistent with NFLE. No case of sleep-related hypermotor seizures has ever been reported to date in MPS. However, differential diagnosis with parasomnias has been previously discussed. The high frequency of frontal lobe seizures causes sleep fragmentation, which may result in sleep disturbances observed in at least a small percentage of MPS patients. We suggest monitoring individuals with MPS using periodic LT-VEEGM, particularly when sleep disorder is present. Moreover, our cases confirm that NFLE in lysosomal storage diseases may occur, and this finding extends the etiologic spectrum of NFLE. Copyright © 2014 The Japanese Society of Child Neurology. Published by Elsevier B.V. All rights reserved.

  9. Frontal lobe epilepsy and EEG: Neurophysiological approach

    OpenAIRE

    García López, Beatriz

    2015-01-01

    La epilepsia del lóbulo frontal es la segunda más frecuente en la mayoría de las series publicadas, después de la epilepsia temporal. Sus características clínicas y electroencefalográficas son muy variadas, lo que hace de su diagnóstico y tratamiento un reto en la práctica clínica. Las crisis frontales suelen aparecen en "clusters", con frecuencia generalizan y el aspecto electroencefalográfico de la actividad intercrítica y crítica suele ser difícil de interpretar por la gran difusión que su...

  10. AFM tip-sample convolution effects for cylinder protrusions

    Science.gov (United States)

    Shen, Jian; Zhang, Dan; Zhang, Fei-Hu; Gan, Yang

    2017-11-01

    A thorough understanding about the AFM tip geometry dependent artifacts and tip-sample convolution effect is essential for reliable AFM topographic characterization and dimensional metrology. Using rigid sapphire cylinder protrusions (diameter: 2.25 μm, height: 575 nm) as the model system, a systematic and quantitative study about the imaging artifacts of four types of tips-two different pyramidal tips, one tetrahedral tip and one super sharp whisker tip-is carried out through comparing tip geometry dependent variations in AFM topography of cylinders and constructing the rigid tip-cylinder convolution models. We found that the imaging artifacts and the tip-sample convolution effect are critically related to the actual inclination of the working cantilever, the tip geometry, and the obstructive contacts between the working tip's planes/edges and the cylinder. Artifact-free images can only be obtained provided that all planes and edges of the working tip are steeper than the cylinder sidewalls. The findings reported here will contribute to reliable AFM characterization of surface features of micron or hundreds of nanometers in height that are frequently met in semiconductor, biology and materials fields.

  11. On the growth and form of cortical convolutions

    Science.gov (United States)

    Tallinen, Tuomas; Chung, Jun Young; Rousseau, François; Girard, Nadine; Lefèvre, Julien; Mahadevan, L.

    2016-06-01

    The rapid growth of the human cortex during development is accompanied by the folding of the brain into a highly convoluted structure. Recent studies have focused on the genetic and cellular regulation of cortical growth, but understanding the formation of the gyral and sulcal convolutions also requires consideration of the geometry and physical shaping of the growing brain. To study this, we use magnetic resonance images to build a 3D-printed layered gel mimic of the developing smooth fetal brain; when immersed in a solvent, the outer layer swells relative to the core, mimicking cortical growth. This relative growth puts the outer layer into mechanical compression and leads to sulci and gyri similar to those in fetal brains. Starting with the same initial geometry, we also build numerical simulations of the brain modelled as a soft tissue with a growing cortex, and show that this also produces the characteristic patterns of convolutions over a realistic developmental course. All together, our results show that although many molecular determinants control the tangential expansion of the cortex, the size, shape, placement and orientation of the folds arise through iterations and variations of an elementary mechanical instability modulated by early fetal brain geometry.

  12. Edgeworth Expansion Based Model for the Convolutional Noise pdf

    Directory of Open Access Journals (Sweden)

    Yonatan Rivlin

    2014-01-01

    Full Text Available Recently, the Edgeworth expansion up to order 4 was used to represent the convolutional noise probability density function (pdf in the conditional expectation calculations where the source pdf was modeled with the maximum entropy density approximation technique. However, the applied Lagrange multipliers were not the appropriate ones for the chosen model for the convolutional noise pdf. In this paper we use the Edgeworth expansion up to order 4 and up to order 6 to model the convolutional noise pdf. We derive the appropriate Lagrange multipliers, thus obtaining new closed-form approximated expressions for the conditional expectation and mean square error (MSE as a byproduct. Simulation results indicate hardly any equalization improvement with Edgeworth expansion up to order 4 when using optimal Lagrange multipliers over a nonoptimal set. In addition, there is no justification for using the Edgeworth expansion up to order 6 over the Edgeworth expansion up to order 4 for the 16QAM and easy channel case. However, Edgeworth expansion up to order 6 leads to improved equalization performance compared to the Edgeworth expansion up to order 4 for the 16QAM and hard channel case as well as for the case where the 64QAM is sent via an easy channel.

  13. Traffic sign recognition based on deep convolutional neural network

    Science.gov (United States)

    Yin, Shi-hao; Deng, Ji-cai; Zhang, Da-wei; Du, Jing-yuan

    2017-11-01

    Traffic sign recognition (TSR) is an important component of automated driving systems. It is a rather challenging task to design a high-performance classifier for the TSR system. In this paper, we propose a new method for TSR system based on deep convolutional neural network. In order to enhance the expression of the network, a novel structure (dubbed block-layer below) which combines network-in-network and residual connection is designed. Our network has 10 layers with parameters (block-layer seen as a single layer): the first seven are alternate convolutional layers and block-layers, and the remaining three are fully-connected layers. We train our TSR network on the German traffic sign recognition benchmark (GTSRB) dataset. To reduce overfitting, we perform data augmentation on the training images and employ a regularization method named "dropout". The activation function we employ in our network adopts scaled exponential linear units (SELUs), which can induce self-normalizing properties. To speed up the training, we use an efficient GPU to accelerate the convolutional operation. On the test dataset of GTSRB, we achieve the accuracy rate of 99.67%, exceeding the state-of-the-art results.

  14. Nuclear norm regularized convolutional Max Pos@Top machine

    KAUST Repository

    Li, Qinfeng

    2016-11-18

    In this paper, we propose a novel classification model for the multiple instance data, which aims to maximize the number of positive instances ranked before the top-ranked negative instances. This method belongs to a recently emerged performance, named as Pos@Top. Our proposed classification model has a convolutional structure that is composed by four layers, i.e., the convolutional layer, the activation layer, the max-pooling layer and the full connection layer. In this paper, we propose an algorithm to learn the convolutional filters and the full connection weights to maximize the Pos@Top measure over the training set. Also, we try to minimize the rank of the filter matrix to explore the low-dimensional space of the instances in conjunction with the classification results. The rank minimization is conducted by the nuclear norm minimization of the filter matrix. In addition, we develop an iterative algorithm to solve the corresponding problem. We test our method on several benchmark datasets. The experimental results show the superiority of our method compared with other state-of-the-art Pos@Top maximization methods.

  15. A model of traffic signs recognition with convolutional neural network

    Science.gov (United States)

    Hu, Haihe; Li, Yujian; Zhang, Ting; Huo, Yi; Kuang, Wenqing

    2016-10-01

    In real traffic scenes, the quality of captured images are generally low due to some factors such as lighting conditions, and occlusion on. All of these factors are challengeable for automated recognition algorithms of traffic signs. Deep learning has provided a new way to solve this kind of problems recently. The deep network can automatically learn features from a large number of data samples and obtain an excellent recognition performance. We therefore approach this task of recognition of traffic signs as a general vision problem, with few assumptions related to road signs. We propose a model of Convolutional Neural Network (CNN) and apply the model to the task of traffic signs recognition. The proposed model adopts deep CNN as the supervised learning model, directly takes the collected traffic signs image as the input, alternates the convolutional layer and subsampling layer, and automatically extracts the features for the recognition of the traffic signs images. The proposed model includes an input layer, three convolutional layers, three subsampling layers, a fully-connected layer, and an output layer. To validate the proposed model, the experiments are implemented using the public dataset of China competition of fuzzy image processing. Experimental results show that the proposed model produces a recognition accuracy of 99.01 % on the training dataset, and yield a record of 92% on the preliminary contest within the fourth best.

  16. Intraparenchymal schwannoma of the frontal lobe.

    Directory of Open Access Journals (Sweden)

    Deogaonkar M

    1994-10-01

    Full Text Available A 45 year old woman with bifrontal headaches and progressive diminution in vision over 6 months was found to have bilateral papilloedema. CT scan showed large right frontal lesion with surrounding oedema. Right basal frontotemporal craniotomy was performed to excise the multinodular, intraparenchymatous tumor. Hispathology confirmed the diagnosis of schwannoma. Post-operative course was uneventful with disappearance of pre-operative signs and symptoms.

  17. Frontal lobe function in temporal lobe epilepsy

    Science.gov (United States)

    Stretton, J.; Thompson, P.J.

    2012-01-01

    Summary Temporal lobe epilepsy (TLE) is typically associated with long-term memory dysfunction. The frontal lobes support high-level cognition comprising executive skills and working memory that is vital for daily life functioning. Deficits in these functions have been increasingly reported in TLE. Evidence from both the neuropsychological and neuroimaging literature suggests both executive function and working memory are compromised in the presence of TLE. In relation to executive impairment, particular focus has been paid to set shifting as measured by the Wisconsin Card Sorting Task. Other discrete executive functions such as decision-making and theory of mind also appear vulnerable but have received little attention. With regard to working memory, the medial temporal lobe structures appear have a more critical role, but with emerging evidence of hippocampal dependent and independent processes. The relative role of underlying pathology and seizure spread is likely to have considerable bearing upon the cognitive phenotype and trajectory in TLE. The identification of the nature of frontal lobe dysfunction in TLE thus has important clinical implications for prognosis and surgical management. Longitudinal neuropsychological and neuroimaging studies assessing frontal lobe function in TLE patients pre- and postoperatively will improve our understanding further. PMID:22100147

  18. Role of Frontal Alpha Oscillations in Creativity

    Science.gov (United States)

    Lustenberger, Caroline; Boyle, Michael R.; Foulser, A. Alban; Mellin, Juliann M.; Fröhlich, Flavio

    2015-01-01

    Creativity, the ability to produce innovative ideas, is a key higher-order cognitive function that is poorly understood. At the level of macroscopic cortical network dynamics, recent EEG data suggests that cortical oscillations in the alpha frequency band (8 – 12 Hz) are correlated with creative thinking. However, whether alpha oscillations play a fundamental role in creativity has remained unknown. Here we show that creativity is increased by enhancing alpha power using 10 Hz transcranial alternating current stimulation (10Hz-tACS) of the frontal cortex. In a study of 20 healthy participants with a randomized, balanced cross-over design, we found a significant improvement of 7.4% in the Creativity Index measured by the Torrance Test of Creative Thinking, a comprehensive and most frequently used assay of creative potential and strengths. In a second similar study with 20 subjects, 40Hz-tACS was used in instead of 10Hz-tACS to rule out a general “electrical stimulation” effect. No significant change in the Creativity Index was found for such frontal gamma stimulation. Our results suggest that alpha activity in frontal brain areas is selectively involved in creativity; this enhancement represents the first demonstration of specific neuronal dynamics that drive creativity and can be modulated by non-invasive brain stimulation. Our findings agree with the model that alpha recruitment increases with internal processing demands and is involved in inhibitory top-down control, which is an important requirement for creative ideation. PMID:25913062

  19. Subperiostal Orbital Abscess and Frontal Epidural Abscess Due to Sinusitis: A Case Report

    Directory of Open Access Journals (Sweden)

    Burak Ulaş

    2013-12-01

    Full Text Available A seventeen-year-old girl was admitted to our clinic with complaint of rubor, swelling, and pain on the left upper eyelid. Her medical history revealed that she had received high-dose oral steroid treatment for one week for the diagnosis of acute angioedema in another clinic. On ophthalmologic examination, her left upper eyelid had edema, swelling, and hyperemia. Additionally, she had restriction in up-gaze in the left eye. Her best-corrected visual acuity was 0.7. The patient’s computerized tomography revealed ethmoidal, maxillary and frontal sinusitis, as well as subperiostal orbital abscess, and frontal epidural abscess. Intravenous antibiotic treatment has been arranged. Due to persistence of the clinical signs, surgical drainage of the abscesses has been performed. Following, she has been discharged from the hospital on oral antibiotic treatment. Postoperatively, at the first-month visit, the left eye’s up-gaze restriction was recovered, and visual acuity was improved to 1.0. If a patient presents with eyelid swelling, differential diagnosis should be performed carefully before making the decision to start steroid treatment. Sinusitis, which is seen frequently in clinical practice, should be kept in mind due to its potential to cause orbital abscess, epidural abscess, and intracranial complications. (Turk J Ophthalmol 2013; 43: 464-7

  20. Frontal cortical asymmetry may partially mediate the influence of social power on anger expression

    Directory of Open Access Journals (Sweden)

    Dongdong eLi

    2016-02-01

    Full Text Available When irritated by other people, powerful people usually tend to express their anger explicitly and directly, whereas people in less powerful positions are more likely not to show their feelings freely. The neural mechanism behind power and its influence on expression tendency has been scarcely explored. This study recorded frontal EEG activity at rest and frontal EEG activation while participants were engaged in a writing task describing an anger-eliciting event, in which they were irritated by people with higher or lower social power. Participants’ anger levels and expression inclination levels were self-reported on nine-point visual analog Likert scales, and also rated by independent raters based on the essays they had written. The results showed that high social power was indeed associated with greater anger expression tendency and greater left frontal activation than low social power. This is in line with the approach-inhibition theory of power. The mid-frontal asymmetric activation served as a partial mediator between social power and expression inclination. This effect may relate to the functions of the prefrontal cortex, which is in charge of information integration and evaluation and the control of motivation direction, as reported by previous studies.

  1. Virtual endoscopy and 3D volume rendering in the management of frontal sinus fractures.

    Science.gov (United States)

    Belina, Stanko; Cuk, Viseslav; Klapan, Ivica

    2009-12-01

    Frontal sinus fractures (FSF) are commonly caused by traffic accidents, assaults, industrial accidents and gunshot wounds. Classical roentgenography has high proportion of false negative findings in cases of FSF and is not particularly useful in examining the severity of damage to the frontal sinus posterior table and the nasofrontal duct region. High resolution computed tomography was inavoidable during the management of such patients but it may produce large quantity of 2D images. Postprocessing of datasets acquired by high resolution computer tomography from patients with severe head trauma may offer a valuable additional help in diagnostics and surgery planning. We performed virtual endoscopy (VE) and 3D volume rendering (3DVR) on high resolution CT data acquired from a 54-year-old man with with both anterior and posterior frontal sinus wall fracture in order to demonstrate advantages and disadvantages of these methods. Data acquisition was done by Siemens Somatom Emotion scanner and postprocessing was performed with Syngo 2006G software. VE and 3DVR were performed in a man who suffered blunt trauma to his forehead and nose in an traffic accident. Left frontal sinus anterior wall fracture without dislocation and fracture of tabula interna with dislocation were found. 3D position and orientation of fracture lines were shown in by 3D rendering software. We concluded that VE and 3DVR can clearly display the anatomic structure of the paranasal sinuses and nasopharyngeal cavity, revealing damage to the sinus wall caused by a fracture and its relationship to surrounding anatomical structures.

  2. Frontal brain activation in young children during picture book reading with their mothers.

    Science.gov (United States)

    Ohgi, S; Loo, K K; Mizuike, C

    2010-02-01

    This study was to measure changes in frontal brain activation in young children during picture book reading with their mothers. The cross-sectional sample consisted of 15 young Japanese children (eight girls and seven boys, mean age 23.1 +/- 3.4). Two experimental tasks were presented as follows: Task 1 (picture book reading with their mothers); Task 2 (viewing of book-on-video). Duration of task stimulus was 180-sec and the 60-sec interval was filled. Brain activation was measured using an optical topography system. Significant increases in oxy-Hb were observed in both right and left frontal areas in response to Task 1 compared with Task 2. There were significant correlations between child's brain activity and mothers' and children's verbal-nonverbal behaviours. There was greater frontal lobe activation in children when they were engaged in a picture book reading task with their mothers, as opposed to passive viewing of a videotape in which the story was read to them. Social and verbal engagement of the mother in reading picture books with her young child may mediate frontal brain activity in the child.

  3. Measurement of Different Dimension of Maxillary and Frontal Sinus Through Computed Tomography

    Directory of Open Access Journals (Sweden)

    Winniecia Dkhar

    2017-05-01

    Full Text Available The objective of the study was to estimate the normal upper and lower limit cut-off value of the different dimensions of maxillary and frontal sinuses, and also to determine the influence of gender on the obtained variables and to observe the effect of nasal septal deviation on maxillary sinus. The observational study was carried out and a total of 60 samples were collected from both males and females with the age group range from 20-50 years. Different dimensions of maxillary and frontal sinuses were measured and the volume was calculated. The mean of all the measured parameters in right and left maxillary sinus and frontal sinus shows the higher value in males as compared to females. The volumes of maxillary and frontal sinuses of both sides were significantly greater in males than females. All the measured dimensions were larger in males and also the volume of maxillary sinus was found to be larger in males. This study also showed that there was no effect of nasal septal deviation on the volume of maxillary sinus.

  4. Less efficient and costly processes of frontal cortex in childhood chronic fatigue syndrome

    Directory of Open Access Journals (Sweden)

    Kei Mizuno

    2015-01-01

    Full Text Available The ability to divide one's attention deteriorates in patients with childhood chronic fatigue syndrome (CCFS. We conducted a study using a dual verbal task to assess allocation of attentional resources to two simultaneous activities (picking out vowels and reading for story comprehension and functional magnetic resonance imaging. Patients exhibited a much larger area of activation, recruiting additional frontal areas. The right middle frontal gyrus (MFG, which is included in the dorsolateral prefrontal cortex, of CCFS patients was specifically activated in both the single and dual tasks; this activation level was positively correlated with motivation scores for the tasks and accuracy of story comprehension. In addition, in patients, the dorsal anterior cingulate gyrus (dACC and left MFG were activated only in the dual task, and activation levels of the dACC and left MFG were positively associated with the motivation and fatigue scores, respectively. Patients with CCFS exhibited a wider area of activated frontal regions related to attentional resources in order to increase their poorer task performance with massive mental effort. This is likely to be less efficient and costly in terms of energy requirements. It seems to be related to the pathophysiology of patients with CCFS and to cause a vicious cycle of further increases in fatigue.

  5. Anodal Transcranial Direct Current Stimulation Promotes Frontal Compensatory Mechanisms in Healthy Elderly Subjects

    Directory of Open Access Journals (Sweden)

    Jesús Cespón

    2017-12-01

    Full Text Available Recent studies have demonstrated that transcranial direct current stimulation (tDCS is potentially useful to improve working memory. In the present study, young and elderly subjects performed a working memory task (n-back task during an electroencephalogram recording before and after receiving anodal, cathodal, and sham tDCS over the left dorsolateral prefrontal cortex (DLPFC. We investigated modulations of behavioral performance and electrophysiological correlates of working memory processes (frontal and parietal P300 event-related potentials. A strong tendency to modulated working memory performance was observed after the application of tDCS. In detail, young, but not elderly, subjects benefited from additional practice in the absence of real tDCS, as indicated by their more accurate responses after sham tDCS. The cathodal tDCS had no effect in any group of participants. Importantly, anodal tDCS improved accuracy in elderly. Moreover, increased accuracy after anodal tDCS was correlated with a larger frontal P300 amplitude. These findings suggest that, in elderly subjects, improved working memory after anodal tDCS applied over the left DLPFC may be related to the promotion of frontal compensatory mechanisms, which are related to attentional processes.

  6. Attachment dismissal predicts frontal slow-wave ERPs during rejection by unfamiliar peers.

    Science.gov (United States)

    White, Lars O; Wu, Jia; Borelli, Jessica L; Rutherford, Helena J V; David, Daryn H; Kim-Cohen, Julia; Mayes, Linda C; Crowley, Michael J

    2012-08-01

    Attachment representations are thought to provide a cognitive-affective template, guiding the way individuals interact with unfamiliar social partners. To examine the neural correlates of this process, we sampled event-related potentials (ERPs) during exclusion by unfamiliar peers to differentiate insecure-dismissing from securely attached youth, as indexed by the child attachment interview. Thirteen secure and 10 dismissing 11- to 15-year-olds were ostensibly connected with two peers via the Internet to play a computerized ball-toss game. Actually, peers were computer generated, first distributing the ball evenly, but eventually excluding participants. Afterward children rated their distress. As in previous studies, distress was related to a negative left frontal slow wave (500-900 ms) during rejection, a waveform implicated in negative appraisals and less approach motivation. Though attachment classifications were comparable in frontal ERPs and distress, an attachment-related dismissal dimension predicted a negative left frontal slow wave during rejection, suggesting that high dismissal potentially involves elevated anticipation of rejection. As expected, dismissal and self-reported distress were uncorrelated. Yet, a new approach to quantifying the dissociation between self-reports and rejection-related ERPs revealed that dismissal predicted underreporting of distress relative to ERPs. Our findings imply that evaluations and regulatory strategies linked to attachment generalize to distressing social contexts in early adolescence.

  7. Alopecia frontal fibrosante: relato de seis casos Frontal fibrosing alopecia: report of six cases

    Directory of Open Access Journals (Sweden)

    Fabiane Mulinari-Brenner

    2007-10-01

    Full Text Available Alopecia frontal fibrosante é forma progressiva de alopecia cicatricial. Os casos iniciais foram relatados a partir 1994, na Austrália, em pacientes do sexo feminino pós-menopausa. Desde então inúmeros casos foram descritos na literatura sugerindo que ela é mais prevalente do que inicialmente se supunha. Seu curso progressivo se assemelha ao da alopecia androgenética; histologicamente, entretanto, o infiltrado liquenóide é evidente. O artigo relata seis casos brasileiros e discute a alopecia frontal fibrosante dentro do grupo das alopecias cicatriciais, como variante do líquen plano pilar.Frontal fibrosing alopecia is a progressive cicatricial alopecia. The first cases were described in Australia in postmenopausal women, in 1994. Since then, numerous cases were reported, suggesting that frontal fibrosing alopecia is more prevalent than initially thought. Its progressive course in postmenopausal women, clinically resembles androgenetic alopecia; however, histologically, lichenoid infiltrate is evident. This article report six brazilian cases of frontal fibrosing alopecia and discusses them in the context of cicatricial alopecias, as a variant of lichen planopilaris.

  8. Resting frontal EEG asymmetry in children: meta-analyses of the effects of psychosocial risk factors and associations with internalizing and externalizing behavior.

    Science.gov (United States)

    Peltola, Mikko J; Bakermans-Kranenburg, Marian J; Alink, Lenneke R A; Huffmeijer, Renske; Biro, Szilvia; van IJzendoorn, Marinus H

    2014-09-01

    Asymmetry of frontal cortical electroencephalogram (EEG) activity in children is influenced by the social environment and considered a marker of vulnerability to emotional and behavioral problems. To determine the reliability of these associations, we used meta-analysis to test whether variation in resting frontal EEG asymmetry is consistently associated with (a) having experienced psychosocial risk (e.g., parental depression or maltreatment) and (b) internalizing and externalizing behavior outcomes in children ranging from newborns to adolescents. Three meta-analyses including 38 studies (N = 2,523) and 50 pertinent effect sizes were carried out. The studies included in the analyses reported associations between frontal EEG asymmetry and psychosocial risk (k = 20; predominantly studies with maternal depression as the risk factor) as well as internalizing (k = 20) and externalizing (k = 10) behavior outcomes. Psychosocial risk was significantly associated with greater relative right frontal asymmetry, with an effect size of d = .36 (p < .01), the effects being stronger in girls. A non-significant relation was observed between right frontal asymmetry and internalizing symptoms (d = .19, p = .08), whereas no association between left frontal asymmetry and externalizing symptoms was observed (d = .04, p = .79). Greater relative right frontal asymmetry appears to be a fairly consistent marker of the presence of familial stressors in children but the power of frontal asymmetry to directly predict emotional and behavioral problems is modest. © 2014 Wiley Periodicals, Inc.

  9. Behavioral Approach System Sensitivity and Risk Taking Interact to Predict Left-Frontal EEG Asymmetry

    OpenAIRE

    Black, Chelsea L.; Goldstein, Kim E.; LaBelle, Denise R.; Brown, Christopher W.; Harmon-Jones, Eddie; Abramson, Lyn Y.; Alloy, Lauren B.

    2014-01-01

    The Behavioral Approach System (BAS) hypersensitivity theory of bipolar disorder (BD; Alloy & Abramson, 2010; Depue & Iacono, 1989) suggests that hyperreactivity in the BAS results in the extreme fluctuations of mood characteristic of BD. In addition to risk conferred by BAS hypersensitivity, cognitive and personality variables may play a role in determining risk. We evaluated relationships among BAS sensitivity, risk taking, and an electrophysiological correlate of approach motivation, relat...

  10. How Left Inferior Frontal Cortex Participates in Syntactic Processing: Evidence from Aphasia

    Science.gov (United States)

    Love, Tracy; Swinney, David; Walenski, Matthew; Zurif, Edgar

    2008-01-01

    We report on three experiments that provide a real-time processing perspective on the poor comprehension of Broca's aphasic patients for non-canonically structured sentences. In the first experiment we presented sentences (via a Cross Modal Lexical Priming (CMLP) paradigm) to Broca's patients at a normal rate of speech. Unlike the pattern found…

  11. Model selection for convolutive ICA with an application to spatiotemporal analysis of EEG

    DEFF Research Database (Denmark)

    Dyrholm, Mads; Makeig, S.; Hansen, Lars Kai

    2007-01-01

    We present a new algorithm for maximum likelihood convolutive independent component analysis (ICA) in which components are unmixed using stable autoregressive filters determined implicitly by estimating a convolutive model of the mixing process. By introducing a convolutive mixing model...... for the components, we show how the order of the filters in the model can be correctly detected using Bayesian model selection. We demonstrate a framework for deconvolving a subspace of independent components in electroencephalography (EEG). Initial results suggest that in some cases, convolutive mixing may...

  12. Sign changes in linear combinations of derivatives and convolutions of Polya frequency functions

    Directory of Open Access Journals (Sweden)

    Steven Nahmias

    1979-01-01

    combinations of derivatives and convolutions of Polya frequency functions using the variation diminishing properties of totally positive functions. These constitute extensions of earlier results of Karlin and Proschan.

  13. Golden Proportion in Frontal Social Smile from Orthodontic Viewpoint

    Directory of Open Access Journals (Sweden)

    z Tabatabaei

    2011-09-01

    Full Text Available Introduction: Physical attraction has a significant effect on all aspects of personal life, and in this category facial appearance is the most important part of the body in prediction of attractiveness. In the face, mouth and specially shape and size of anterior teeth is important to gain dental and facial esthetic. The aim of this study is evaluation of golden proportion from orthodontic view in maxillary anterior teeth in both sexes. Methods: Considering inclusion and exclusion criteria, 100 students of Rafsanjan University of Medical Sciences were selected, and photographs of their frontal social smile were taken by a standard method from 30cm distance. Then visible part of central, lateral and canine teeth was measured by Photoshop software (Adobe Photoshop ver8 with 0.1mm precision. Data was evaluated by descriptive statistical analysis and sample T-test using SPSS. Results: According to descriptive statistical analysis and sample T- test, mean ratio of central to lateral teeth in the left side in men and women was 1.209±0.199 and 1.157±0.156 and in the right side in men and women was 1.179± 0.27 and 1.158± 0.145, respectively. The ratio of lateral to canine teeth in the left side in men and women was 1.522±0.146 and 1.494±0.127 and in the right side in men and women was 1.55±0.164 and 1.51±0.114, respectively. Golden proportion was seen between central and lateral teeth in 16% in the right side and 3.4% in the left side only in men. Conclusion: Golden proportion was seen between central and lateral in the left side and right side in men, but due to large canine in men, this proportion was not seen between lateral and canine teeth and so due to small lateral in women, it was not seen between anterior teeth.

  14. Dyslexic children lack word selectivity gradients in occipito-temporal and inferior frontal cortex

    Directory of Open Access Journals (Sweden)

    O.A. Olulade

    2015-01-01

    Full Text Available fMRI studies using a region-of-interest approach have revealed that the ventral portion of the left occipito-temporal cortex, which is specialized for orthographic processing of visually presented words (and includes the so-called “visual word form area”, VWFA, is characterized by a posterior-to-anterior gradient of increasing selectivity for words in typically reading adults, adolescents, and children (e.g. Brem et al., 2006, 2009. Similarly, the left inferior frontal cortex (IFC has been shown to exhibit a medial-to-lateral gradient of print selectivity in typically reading adults (Vinckier et al., 2007. Functional brain imaging studies of dyslexia have reported relative underactivity in left hemisphere occipito-temporal and inferior frontal regions using whole-brain analyses during word processing tasks. Hence, the question arises whether gradient sensitivities in these regions are altered in dyslexia. Indeed, a region-of-interest analysis revealed the gradient-specific functional specialization in the occipito-temporal cortex to be disrupted in dyslexic children (van der Mark et al., 2009. Building on these studies, we here (1 investigate if a word-selective gradient exists in the inferior frontal cortex in addition to the occipito-temporal cortex in normally reading children, (2 compare typically reading with dyslexic children, and (3 examine functional connections between these regions in both groups. We replicated the previously reported anterior-to-posterior gradient of increasing selectivity for words in the left occipito-temporal cortex in typically reading children, and its absence in the dyslexic children. Our novel finding is the detection of a pattern of increasing selectivity for words along the medial-to-lateral axis of the left inferior frontal cortex in typically reading children and evidence of functional connectivity between the most lateral aspect of this area and the anterior aspects of the occipito-temporal cortex. We

  15. Temporal Lobe and Frontal-Subcortical Dissociations in Non-Demented Parkinson's Disease with Verbal Memory Impairment.

    Science.gov (United States)

    Tanner, Jared J; Mareci, Thomas H; Okun, Michael S; Bowers, Dawn; Libon, David J; Price, Catherine C

    2015-01-01

    The current investigation examined verbal memory in idiopathic non-dementia Parkinson's disease and the significance of the left entorhinal cortex and left entorhinal-retrosplenial region connections (via temporal cingulum) on memory impairment in Parkinson's disease. Forty non-demented Parkinson's disease patients and forty non-Parkinson's disease controls completed two verbal memory tests--a wordlist measure (Philadelphia repeatable Verbal Memory Test) and a story measure (Logical Memory). All participants received T1-weighted and diffusion magnetic resonance imaging (3T; Siemens) sequences. Left entorhinal volume and left entorhinal-retrosplenial connectivity (temporal cingulum edge weight) were the primary imaging variables of interest with frontal lobe thickness and subcortical structure volumes as dissociating variables. Individuals with Parkinson's disease showed worse verbal memory, smaller entorhinal volumes, but did not differ in entorhinal-retrosplenial connectivity. For Parkinson's disease entorhinal-retrosplenial edge weight had the strongest associations with verbal memory. A subset of Parkinson's disease patients (23%) had deficits (z-scores frontal-subcortical gray or frontal white matter regions. These findings argue for additional investigation into medial temporal lobe gray and white matter connectivity for understanding memory in Parkinson's disease.

  16. Brain Abscess Associated with Isolated Left Superior Vena Cava Draining into the Left Atrium in the Absence of Coronary Sinus and Atrial Septal Defect

    International Nuclear Information System (INIS)

    Erol, Ilknur; Cetin, I. Ilker; Alehan, Fuesun; Varan, Birguel; Ozkan, Sueleyman; Agildere, A. Muhtesem; Tokel, Kursad

    2006-01-01

    A previously healthy 12-year-old girl presented with severe headache for 2 weeks. On physical examination, there was finger clubbing without apparent cyanosis. Neurological examination revealed only papiledema without focal neurologic signs. Cerebral magnetic resonance imaging showed the characteristic features of brain abscess in the left frontal lobe. Cardiologic workup to exclude a right-to-left shunt showed an abnormality of the systemic venous drainage: presence of isolated left superior vena cava draining into the left atrium in the absence of coronary sinus and atrial septal defect. This anomaly is rare, because only a few other cases have been reported

  17. Posterior paralimbic and frontal metabolite impairments in asymptomatic hypertension with different treatment outcomes

    International Nuclear Information System (INIS)

    Garcia Santos, J.M.; Fuentes, L.J.; Vidal, J.B.

    2010-01-01

    Hypertension is associated with cognitive decline in elderly persons. We studied asymptomatic hypertensive subjects using brain magnetic resonance (MR) spectroscopy to evaluate metabolite impairments before the appearance of symptoms in patients with different treatment outcomes. In all, 14 healthy controls and 37 asymptomatic hypertensive patients (17 controlled and 20 resistant) underwent brain structural MR and MR spectroscopy of the posterior paralimbic (PPL) area and left frontal white matter. Ischemic burden (IB), global cortical atrophy and microbleeds were analyzed with visual scales. Metabolite ratios involving N-acetyl-aspartate (NAA), creatine (Cr), choline (Cho) and myoinositol (ml) were computed. Ultrasound measurements, including intima-media thickness, plaques and hemodynamic ratios, were obtained. Intergroup differences in IB, atrophy and metabolite ratios, and the atrophy and IB relationship were assessed with parametric and nonparametric statistical tests. In addition, the impacts of demographic, analytic and clinical factors, ischemia and atrophy, and ultrasound measurements on metabolite ratios were assessed. The significance level was set at P≤0.05. Higher atrophy scores presented with higher total or frontal IB (P<0.05). However, there was no intergroup difference in atrophy and IB. PPL ml/Cr was increased in resistant hypertension (P<0.021), whereas frontal NAA/Cr (P<0.007) showed opposite trends between controlled (increased ratios) and resistant (decreased ratios) hypertension. Unlike PPL ml/Cr, frontal NAA/Cr showed significant correlations with the lipid profile and ultrasound measurements. PPL ml/Cr increases in resistant hypertension, and frontal NAA/Cr diverges between controlled and resistant hypertension before physical and neuropsychological symptoms appear. (author)

  18. Structural connectivity of right frontal hyperactive areas scales with stuttering severity.

    Science.gov (United States)

    Neef, Nicole E; Anwander, Alfred; Bütfering, Christoph; Schmidt-Samoa, Carsten; Friederici, Angela D; Paulus, Walter; Sommer, Martin

    2018-01-01

    A neuronal sign of persistent developmental stuttering is the magnified coactivation of right frontal brain regions during speech production. Whether and how stuttering severity relates to the connection strength of these hyperactive right frontal areas to other brain areas is an open question. Scrutinizing such brain-behaviour and structure-function relationships aims at disentangling suspected underlying neuronal mechanisms of stuttering. Here, we acquired diffusion-weighted and functional images from 31 adults who stutter and 34 matched control participants. Using a newly developed structural connectivity measure, we calculated voxel-wise correlations between connection strength and stuttering severity within tract volumes that originated from functionally hyperactive right frontal regions. Correlation analyses revealed that with increasing speech motor deficits the connection strength increased in the right frontal aslant tract, the right anterior thalamic radiation, and in U-shaped projections underneath the right precentral sulcus. In contrast, with decreasing speech motor deficits connection strength increased in the right uncinate fasciculus. Additional group comparisons of whole-brain white matter skeletons replicated the previously reported reduction of fractional anisotropy in the left and right superior longitudinal fasciculus as well as at the junction of right frontal aslant tract and right superior longitudinal fasciculus in adults who stutter compared to control participants. Overall, our investigation suggests that right fronto-temporal networks play a compensatory role as a fluency enhancing mechanism. In contrast, the increased connection strength within subcortical-cortical pathways may be implied in an overly active global response suppression mechanism in stuttering. Altogether, this combined functional MRI-diffusion tensor imaging study disentangles different networks involved in the neuronal underpinnings of the speech motor deficit in

  19. Acute Aldosterone-mediated Signaling Networks in Distal Convoluted Tubules

    DEFF Research Database (Denmark)

    Cheng, Lei; Wu, Qi; Olesen, Emma T. B.

    2017-01-01

    The kidney distal convoluted tubule (DCT) plays an important role in modulating body sodium balance and blood pressure. Long-term effects of aldosterone to increase sodium reabsorption in the DCT are well described. However, potential effects of aldosterone to acutely modulate DCT function via non...... in abundance following aldosterone treatment. The EGFR, ERK1/2, AKT, GSK3B and P70S6K were predicted to be important pathway nodes based on the quantitative proteomics data using network analysis. Ex vivo studies in isolated mouse cortical tubules demonstrated an increase in phosphorylated (active) NCC...

  20. Phase transitions in glassy systems via convolutional neural networks

    Science.gov (United States)

    Fang, Chao

    Machine learning is a powerful approach commonplace in industry to tackle large data sets. Most recently, it has found its way into condensed matter physics, allowing for the first time the study of, e.g., topological phase transitions and strongly-correlated electron systems. The study of spin glasses is plagued by finite-size effects due to the long thermalization times needed. Here we use convolutional neural networks in an attempt to detect a phase transition in three-dimensional Ising spin glasses. Our results are compared to traditional approaches.

  1. Convolutional neural networks for synthetic aperture radar classification

    Science.gov (United States)

    Profeta, Andrew; Rodriguez, Andres; Clouse, H. Scott

    2016-05-01

    For electro-optical object recognition, convolutional neural networks (CNNs) are the state-of-the-art. For large datasets, CNNs are able to learn meaningful features used for classification. However, their application to synthetic aperture radar (SAR) has been limited. In this work we experimented with various CNN architectures on the MSTAR SAR dataset. As the input to the CNN we used the magnitude and phase (2 channels) of the SAR imagery. We used the deep learning toolboxes CAFFE and Torch7. Our results show that we can achieve 93% accuracy on the MSTAR dataset using CNNs.

  2. Blind separation of more sources than sensors in convolutive mixtures

    DEFF Research Database (Denmark)

    Olsson, Rasmus Kongsgaard; Hansen, Lars Kai

    2006-01-01

    We demonstrate that blind separation of more sources than sensors can be performed based solely on the second order statistics of the observed mixtures. This a generalization of well-known robust algorithms that are suited for equal number of sources and sensors. It is assumed that the sources...... are non-stationary and sparsely distributed in the time-frequency plane. The mixture model is convolutive, i.e. acoustic setups such as the cocktail party problem are contained. The limits of identifiability are determined in the framework of the PARAFAC model. In the experimental section...

  3. Salient regions detection using convolutional neural networks and color volume

    Science.gov (United States)

    Liu, Guang-Hai; Hou, Yingkun

    2018-03-01

    Convolutional neural network is an important technique in machine learning, pattern recognition and image processing. In order to reduce the computational burden and extend the classical LeNet-5 model to the field of saliency detection, we propose a simple and novel computing model based on LeNet-5 network. In the proposed model, hue, saturation and intensity are utilized to extract depth cues, and then we integrate depth cues and color volume to saliency detection following the basic structure of the feature integration theory. Experimental results show that the proposed computing model outperforms some existing state-of-the-art methods on MSRA1000 and ECSSD datasets.

  4. Taxonomic Classification for Living Organisms Using Convolutional Neural Networks.

    Science.gov (United States)

    Khawaldeh, Saed; Pervaiz, Usama; Elsharnoby, Mohammed; Alchalabi, Alaa Eddin; Al-Zubi, Nayel

    2017-11-17

    Taxonomic classification has a wide-range of applications such as finding out more about evolutionary history. Compared to the estimated number of organisms that nature harbors, humanity does not have a thorough comprehension of to which specific classes they belong. The classification of living organisms can be done in many machine learning techniques. However, in this study, this is performed using convolutional neural networks. Moreover, a DNA encoding technique is incorporated in the algorithm to increase performance and avoid misclassifications. The algorithm proposed outperformed the state of the art algorithms in terms of accuracy and sensitivity, which illustrates a high potential for using it in many other applications in genome analysis.

  5. Stochastic convolutions driven by martingales: maximal inequalities and exponential integrability

    Czech Academy of Sciences Publication Activity Database

    Hausenblas, E.; Seidler, Jan

    2008-01-01

    Roč. 26, č. 1 (2008), s. 98-119 ISSN 0736-2994 Grant - others:Austrian Academy of Sciences(AT) APART 700; GA ČR(CZ) GA201/04/0750; GA ČR(CZ) GA201/01/1197; GA MSM(CZ) Kontakt 2001-05 Program:GA; GA Institutional research plan: CEZ:AV0Z10750506 Source of funding: V - iné verejné zdroje ; V - iné verejné zdroje Keywords : maximal inequality * exponential tail estimates * stochastic convolution Subject RIV: BA - General Mathematics Impact factor: 0.528, year: 2008

  6. Fast Convolutional Sparse Coding in the Dual Domain

    KAUST Repository

    Affara, Lama Ahmed

    2017-09-27

    Convolutional sparse coding (CSC) is an important building block of many computer vision applications ranging from image and video compression to deep learning. We present two contributions to the state of the art in CSC. First, we significantly speed up the computation by proposing a new optimization framework that tackles the problem in the dual domain. Second, we extend the original formulation to higher dimensions in order to process a wider range of inputs, such as color inputs, or HOG features. Our results show a significant speedup compared to the current state of the art in CSC.

  7. Accelerated Time-Domain Modeling of Electromagnetic Pulse Excitation of Finite-Length Dissipative Conductors over a Ground Plane via Function Fitting and Recursive Convolution

    Energy Technology Data Exchange (ETDEWEB)

    Campione, Salvatore [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States); Warne, Larry K. [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States); Sainath, Kamalesh [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States); Basilio, Lorena I. [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)

    2017-10-01

    In this report we overview the fundamental concepts for a pair of techniques which together greatly hasten computational predictions of electromagnetic pulse (EMP) excitation of finite-length dissipative conductors over a ground plane. In a time- domain, transmission line (TL) model implementation, predictions are computationally bottlenecked time-wise, either for late-time predictions (about 100ns-10000ns range) or predictions concerning EMP excitation of long TLs (order of kilometers or more ). This is because the method requires a temporal convolution to account for the losses in the ground. Addressing this to facilitate practical simulation of EMP excitation of TLs, we first apply a technique to extract an (approximate) complex exponential function basis-fit to the ground/Earth's impedance function, followed by incorporating this into a recursion-based convolution acceleration technique. Because the recursion-based method only requires the evaluation of the most recent voltage history data (versus the entire history in a "brute-force" convolution evaluation), we achieve necessary time speed- ups across a variety of TL/Earth geometry/material scenarios. Intentionally Left Blank

  8. Fast space-varying convolution using matrix source coding with applications to camera stray light reduction.

    Science.gov (United States)

    Wei, Jianing; Bouman, Charles A; Allebach, Jan P

    2014-05-01

    Many imaging applications require the implementation of space-varying convolution for accurate restoration and reconstruction of images. Here, we use the term space-varying convolution to refer to linear operators whose impulse response has slow spatial variation. In addition, these space-varying convolution operators are often dense, so direct implementation of the convolution operator is typically computationally impractical. One such example is the problem of stray light reduction in digital cameras, which requires the implementation of a dense space-varying deconvolution operator. However, other inverse problems, such as iterative tomographic reconstruction, can also depend on the implementation of dense space-varying convolution. While space-invariant convolution can be efficiently implemented with the fast Fourier transform, this approach does not work for space-varying operators. So direct convolution is often the only option for implementing space-varying convolution. In this paper, we develop a general approach to the efficient implementation of space-varying convolution, and demonstrate its use in the application of stray light reduction. Our approach, which we call matrix source coding, is based on lossy source coding of the dense space-varying convolution matrix. Importantly, by coding the transformation matrix, we not only reduce the memory required to store it; we also dramatically reduce the computation required to implement matrix-vector products. Our algorithm is able to reduce computation by approximately factoring the dense space-varying convolution operator into a product of sparse transforms. Experimental results show that our method can dramatically reduce the computation required for stray light reduction while maintaining high accuracy.

  9. The frontal aslant tract underlies speech fluency in persistent developmental stuttering.

    Science.gov (United States)

    Kronfeld-Duenias, Vered; Amir, Ofer; Ezrati-Vinacour, Ruth; Civier, Oren; Ben-Shachar, Michal

    2016-01-01

    The frontal aslant tract (FAT) is a pathway that connects the inferior frontal gyrus with the supplementary motor area (SMA) and pre-SMA. The FAT was recently identified and introduced as part of a "motor stream" that plays an important role in speech production. In this study, we use diffusion imaging to examine the hypothesis that the FAT underlies speech fluency, by studying its properties in individuals with persistent developmental stuttering, a speech disorder that disrupts the production of fluent speech. We use tractography to quantify the volume and diffusion properties of the FAT in a group of adults who stutter (AWS) and fluent controls. Additionally, we use tractography to extract these measures from the corticospinal tract (CST), a well-known component of the motor system. We compute diffusion measures in multiple points along the tracts, and examine the correlation between these diffusion measures and behavioral measures of speech fluency. Our data show increased mean diffusivity in bilateral FAT of AWS compared with controls. In addition, the results show regions within the left FAT and the left CST where diffusivity values are increased in AWS compared with controls. Last, we report that in AWS, diffusivity values measured within sub-regions of the left FAT negatively correlate with speech fluency. Our findings are the first to relate the FAT with fluent speech production in stuttering, thus adding to the current knowledge of the functional role that this tract plays in speech production and to the literature of the etiology of persistent developmental stuttering.

  10. Lateral frontal cortex volume reduction in Tourette syndrome revealed by VBM

    Directory of Open Access Journals (Sweden)

    Wittfoth Matthias

    2012-02-01

    Full Text Available Abstract Background Structural changes have been found predominantly in the frontal cortex and in the striatum in children and adolescents with Gilles de la Tourette syndrome (GTS. The influence of comorbid symptomatology is unclear. Here we sought to address the question of gray matter abnormalities in GTS patients with co-morbid obsessive-compulsive disorder (OCD and/or attention deficit hyperactivity disorder (ADHD using voxel-based morphometry (VBM in twenty-nine adult actually unmedicated GTS patients and twenty-five healthy control subjects. Results In GTS we detected a cluster of decreased gray matter volume in the left inferior frontal gyrus (IFG, but no regions demonstrating volume increases. By comparing subgroups of GTS with comorbid ADHD to the subgroup with comorbid OCD, we found a left-sided amygdalar volume increase. Conclusions From our results it is suggested that the left IFG may constitute a common underlying structural correlate of GTS with co-morbid OCD/ADHD. A volume reduction in this brain region that has been previously identified as a key region in OCD and was associated with the active inhibition of attentional processes may reflect the failure to control behavior. Amygdala volume increase is discussed on the background of a linkage of this structure with ADHD symptomatology. Correlations with clinical data revealed gray matter volume changes in specific brain areas that have been described in these conditions each.

  11. Cranialization of the frontal sinus-the final remedy for refractory chronic frontal sinusitis

    NARCIS (Netherlands)

    van Dijk, J. Marc C.; Wagemakers, Michiel; Korsten-Meijer, Astrid G. W.; Buiter, C. T. Kees; van der Laan, Bernard F. A. M.; Mooij, Jan Jakob A.

    Object. Chronic sinusitis can be a debilitating disease with significant impact on quality of life. Frontal sinusitis has a relatively low prevalence, but complications can be severe due to its anatomical location. After failure of conservative measures, typically endoscopic procedures are performed

  12. Left atrial volume index

    DEFF Research Database (Denmark)

    Poulsen, Mikael K; Dahl, Jordi S; Henriksen, Jan Erik

    2013-01-01

    To determine the prognostic importance of left atrial (LA) dilatation in patients with type 2 diabetes (T2DM) and no history of cardiovascular disease.......To determine the prognostic importance of left atrial (LA) dilatation in patients with type 2 diabetes (T2DM) and no history of cardiovascular disease....

  13. Convolution and non convolution Perfectly Matched Layer techniques optimized at grazing incidence for high-order wave propagation modelling

    Science.gov (United States)

    Martin, Roland; Komatitsch, Dimitri; Bruthiaux, Emilien; Gedney, Stephen D.

    2010-05-01

    We present and discuss here two different unsplit formulations of the frequency shift PML based on convolution or non convolution integrations of auxiliary memory variables. Indeed, the Perfectly Matched Layer absorbing boundary condition has proven to be very efficient from a numerical point of view for the elastic wave equation to absorb both body waves with non-grazing incidence and surface waves. However, at grazing incidence the classical discrete Perfectly Matched Layer method suffers from large spurious reflections that make it less efficient for instance in the case of very thin mesh slices, in the case of sources located very close to the edge of the mesh, and/or in the case of receivers located at very large offset. In [1] we improve the Perfectly Matched Layer at grazing incidence for the seismic wave equation based on an unsplit convolution technique. This improved PML has a cost that is similar in terms of memory storage to that of the classical PML. We illustrate the efficiency of this improved Convolutional Perfectly Matched Layer based on numerical benchmarks using a staggered finite-difference method on a very thin mesh slice for an isotropic material and show that results are significantly improved compared with the classical Perfectly Matched Layer technique. We also show that, as the classical model, the technique is intrinsically unstable in the case of some anisotropic materials. In this case, retaining an idea of [2], this has been stabilized by adding correction terms adequately along any coordinate axis [3]. More specifically this has been applied to the spectral-element method based on a hybrid first/second order time integration scheme in which the Newmark time marching scheme allows us to match perfectly at the base of the absorbing layer a velocity-stress formulation in the PML and a second order displacement formulation in the inner computational domain.Our CPML unsplit formulation has the advantage to reduce the memory storage of CPML

  14. Differential activation of frontal and parietal regions during visual word recognition: an optical topography study.

    Science.gov (United States)

    Hofmann, Markus J; Herrmann, Martin J; Dan, Ippeita; Obrig, Hellmuth; Conrad, Markus; Kuchinke, Lars; Jacobs, Arthur M; Fallgatter, Andreas J

    2008-04-15

    The present study examined cortical oxygenation changes during lexical decision on words and pseudowords using functional Near-Infrared Spectroscopy (fNIRS). Focal hyperoxygenation as an indicator of functional activation was compared over three target areas over the left hemisphere. A 52-channel Hitachi ETG-4000 was used covering the superior frontal gyrus (SFG), the left inferior parietal gyrus (IPG) and the left inferior frontal gyrus (IFG). To allow for anatomical inference a recently developed probabilistic mapping method was used to determine the most likely anatomic locations of the changes in cortical activation [Tsuzuki, D., Jurcak, V., Singh, A.K., Okamoto, M., Watanabe, E., Dan, I., 2007. Virtual spatial registration of stand-alone fNIRS data to MNI space. NeuroImage 43 (4), 1506-1518. Subjects made lexical decisions on 50 low and 50 high frequency words and 100 pseudowords. With respect to the lexicality effect, words elicited a larger focal hyperoxygenation in comparison to pseudowords in two regions identified as the SFG and left IPG. The SFG activation difference was interpreted to reflect decision-related mechanisms according to the Multiple Read-Out Model [Grainger, J., Jacobs, A.M., 1996. Orthographic processing in visual word recognition: A multiple read-out model. Psychological Review 103, 518-565]. The greater oxygenation response to words in the left IPG suggests that this region connects orthographic, phonological and semantic representations. A decrease of deoxygenated hemoglobin was observed to low frequency in comparison to high frequency words in a region identified as IFG. This region's sensitivity to word frequency suggests its involvement in grapheme-phoneme conversion, or its role during the selection of pre-activated semantic candidates.

  15. Frontal sinus asymmetry: Is it an effect of cranial asymmetry? X-ray analysis of 469 normal adult human frontal sinus

    Directory of Open Access Journals (Sweden)

    Ayhan Kanat

    2015-01-01

    Full Text Available Background and Aims: There is no study in the literature that investigates an asymmetric morphological feature of the frontal sinus (FS. Materials and Methods: Four hundred and sixty-nine consecutive direct X-rays of FSs were analyzed for the asymmetry between the right and left sides. When an asymmetry in the height and contour of the FS existed, this difference was quantified. Results: Of the 469 patients, X-rays of 402 patients (85.7%, there was an asymmetry between right and left sides of the FS. Of these 235 (50.1% were dominant on the left side, whereas 167 (35.6% were dominant on the right, the sinuses of remaining 67 patients (14.3% was symmetric. Statistical Analysis: The comparisons between parameters were performed using Wilkinson signed rank test. The relationship between handedness and sinus asymmetry was also examined by two proportions test. There is statistically significant difference between the dominance of left and right FS. Conclusions: Hemispheric dominance may have some effect (s of on sinus asymmetry of the human cranium. Surgeons sometimes enter the cranium through the FS and knowledge of asymmetric FS is important to minimize surgical complications.

  16. Aphasia induced by gliomas growing in the ventrolateral frontal region: assessment with diffusion MR tractography, functional MR imaging and neuropsychology.

    Science.gov (United States)

    Bizzi, Alberto; Nava, Simone; Ferrè, Francesca; Castelli, Gianmarco; Aquino, Domenico; Ciaraffa, Francesca; Broggi, Giovanni; DiMeco, Francesco; Piacentini, Sylvie

    2012-02-01

    Lesions in the ventrolateral region of the dominant frontal lobe have been historically associated with aphasia. Recent imaging results suggest that frontal language regions extend beyond classically defined Broca's area to include the ventral precentral gyrus (VPCG) and the arcuate fasciculus (AF). Frontal gliomas offer a unique opportunity to identify structures that are essential for speech production. The aim of this prospective study was to investigate the correlation between language deficits and lesion location in patients with gliomas. Nineteen patients with glioma and 10 healthy subjects were evaluated with diffusion tensor imaging magnetic resonance (MR) tractography, functional MR (verb generation task) and the Aachener Aphasie Test. Patients were divided into two groups according to lesion location with respect to the ventral precentral sulcus: (i) anterior (n=8) with glioma growing in the inferior frontal gyrus (IFG) and underlying white matter; (ii) posterior (n=11) with glioma growing in the VPCG and underlying white matter. Virtual dissection of the AF, frontal intralobar tract, uncinate fasciculus (UF) and inferior frontal occipital fasciculus (IFOF) was performed with a deterministic approach. Seven posterior patients showed aphasia classified as conduction (4), Broca (1), transcortical motor (1) and an isolated deficit of semantic fluency; one anterior patient had transcortical mixed aphasia. All posterior patients had invasion of the VPCG, however only patients with aphasia had also lesion extension to the AF as demonstrated by tractography dissections. All patients with language deficits had high grade glioma. Groups did not differ regarding tumour volume. A functional pars opercularis was identified with functional MR imaging (fMRI) in 17 patients. Gliomas growing in the left VPCG are much more likely to cause speech deficits than gliomas infiltrating the IFG, including Broca's area. Lesion extension to the AF connecting frontal to parietal

  17. Auditory aura in frontal opercular epilepsy: sounds from afar.

    Science.gov (United States)

    Thompson, Stephen A; Alexopoulos, Andreas; Bingaman, William; Gonzalez-Martinez, Jorge; Bulacio, Juan; Nair, Dileep; So, Norman K

    2015-06-01

    Auditory auras are typically considered to localize to the temporal neocortex. Herein, we present two cases of frontal operculum/perisylvian epilepsy with auditory auras. Following a non-invasive evaluation, including ictal SPECT and magnetoencephalography, implicating the frontal operculum, these cases were evaluated with invasive monitoring, using stereoelectroencephalography and subdural (plus depth) electrodes, respectively. Spontaneous and electrically-induced seizures showed an ictal onset involving the frontal operculum in both cases. A typical auditory aura was triggered by stimulation of the frontal operculum in one. Resection of the frontal operculum and subjacent insula rendered one case seizure- (and aura-) free. From a hodological (network) perspective, we discuss these findings with consideration of the perisylvian and insular network(s) interconnecting the frontal and temporal lobes, and revisit the non-invasive data, specifically that of ictal SPECT.

  18. Siamese convolutional networks for tracking the spine motion

    Science.gov (United States)

    Liu, Yuan; Sui, Xiubao; Sun, Yicheng; Liu, Chengwei; Hu, Yong

    2017-09-01

    Deep learning models have demonstrated great success in various computer vision tasks such as image classification and object tracking. However, tracking the lumbar spine by digitalized video fluoroscopic imaging (DVFI), which can quantitatively analyze the motion mode of spine to diagnose lumbar instability, has not yet been well developed due to the lack of steady and robust tracking method. In this paper, we propose a novel visual tracking algorithm of the lumbar vertebra motion based on a Siamese convolutional neural network (CNN) model. We train a full-convolutional neural network offline to learn generic image features. The network is trained to learn a similarity function that compares the labeled target in the first frame with the candidate patches in the current frame. The similarity function returns a high score if the two images depict the same object. Once learned, the similarity function is used to track a previously unseen object without any adapting online. In the current frame, our tracker is performed by evaluating the candidate rotated patches sampled around the previous frame target position and presents a rotated bounding box to locate the predicted target precisely. Results indicate that the proposed tracking method can detect the lumbar vertebra steadily and robustly. Especially for images with low contrast and cluttered background, the presented tracker can still achieve good tracking performance. Further, the proposed algorithm operates at high speed for real time tracking.

  19. Digital image correlation based on a fast convolution strategy

    Science.gov (United States)

    Yuan, Yuan; Zhan, Qin; Xiong, Chunyang; Huang, Jianyong

    2017-10-01

    In recent years, the efficiency of digital image correlation (DIC) methods has attracted increasing attention because of its increasing importance for many engineering applications. Based on the classical affine optical flow (AOF) algorithm and the well-established inverse compositional Gauss-Newton algorithm, which is essentially a natural extension of the AOF algorithm under a nonlinear iterative framework, this paper develops a set of fast convolution-based DIC algorithms for high-efficiency subpixel image registration. Using a well-developed fast convolution technique, the set of algorithms establishes a series of global data tables (GDTs) over the digital images, which allows the reduction of the computational complexity of DIC significantly. Using the pre-calculated GDTs, the subpixel registration calculations can be implemented efficiently in a look-up-table fashion. Both numerical simulation and experimental verification indicate that the set of algorithms significantly enhances the computational efficiency of DIC, especially in the case of a dense data sampling for the digital images. Because the GDTs need to be computed only once, the algorithms are also suitable for efficiently coping with image sequences that record the time-varying dynamics of specimen deformations.

  20. Classifying images using restricted Boltzmann machines and convolutional neural networks

    Science.gov (United States)

    Zhao, Zhijun; Xu, Tongde; Dai, Chenyu

    2017-07-01

    To improve the feature recognition ability of deep model transfer learning, we propose a hybrid deep transfer learning method for image classification based on restricted Boltzmann machines (RBM) and convolutional neural networks (CNNs). It integrates learning abilities of two models, which conducts subject classification by exacting structural higher-order statistics features of images. While the method transfers the trained convolutional neural networks to the target datasets, fully-connected layers can be replaced by restricted Boltzmann machine layers; then the restricted Boltzmann machine layers and Softmax classifier are retrained, and BP neural network can be used to fine-tuned the hybrid model. The restricted Boltzmann machine layers has not only fully integrated the whole feature maps, but also learns the statistical features of target datasets in the view of the biggest logarithmic likelihood, thus removing the effects caused by the content differences between datasets. The experimental results show that the proposed method has improved the accuracy of image classification, outperforming other methods on Pascal VOC2007 and Caltech101 datasets.

  1. Development of a morphological convolution operator for bearing fault detection

    Science.gov (United States)

    Li, Yifan; Liang, Xihui; Liu, Weiwei; Wang, Yan

    2018-05-01

    This paper presents a novel signal processing scheme, namely morphological convolution operator (MCO) lifted morphological undecimated wavelet (MUDW), for rolling element bearing fault detection. In this scheme, a MCO is first designed to fully utilize the advantage of the closing & opening gradient operator and the closing-opening & opening-closing gradient operator for feature extraction as well as the merit of excellent denoising characteristics of the convolution operator. The MCO is then introduced into MUDW for the purpose of improving the fault detection ability of the reported MUDWs. Experimental vibration signals collected from a train wheelset test rig and the bearing data center of Case Western Reserve University are employed to evaluate the effectiveness of the proposed MCO lifted MUDW on fault detection of rolling element bearings. The results show that the proposed approach has a superior performance in extracting fault features of defective rolling element bearings. In addition, comparisons are performed between two reported MUDWs and the proposed MCO lifted MUDW. The MCO lifted MUDW outperforms both of them in detection of outer race faults and inner race faults of rolling element bearings.

  2. Histopathological image classification with bilinear convolutional neural networks.

    Science.gov (United States)

    Chaofeng Wang; Jun Shi; Qi Zhang; Shihui Ying

    2017-07-01

    The computer-aided quantitative analysis for histopathological images has attracted considerable attention. The stain decomposition on histopathological images is usually recommended to address the issue of co-localization or aliasing of tissue substances. Although the convolutional neural networks (CNN) is a popular deep learning algorithm for various tasks on histopathological image analysis, it is only directly performed on histopathological images without considering stain decomposition. The bilinear CNN (BCNN) is a new CNN model for fine-grained classification. BCNN consists of two CNNs, whose convolutional-layer outputs are multiplied with outer product at each spatial location. In this work, we propose a novel BCNN-based method for classification of histopathological images, which first decomposes histopathological images into hematoxylin and eosin stain components, and then perform BCNN on the decomposed images to fuse and improve the feature representation performance. The experimental results on the colorectal cancer histopathological image dataset with eight classes indicate that the proposed BCNN-based algorithm is superior to the traditional CNN.

  3. Multi-Input Convolutional Neural Network for Flower Grading

    Directory of Open Access Journals (Sweden)

    Yu Sun

    2017-01-01

    Full Text Available Flower grading is a significant task because it is extremely convenient for managing the flowers in greenhouse and market. With the development of computer vision, flower grading has become an interdisciplinary focus in both botany and computer vision. A new dataset named BjfuGloxinia contains three quality grades; each grade consists of 107 samples and 321 images. A multi-input convolutional neural network is designed for large scale flower grading. Multi-input CNN achieves a satisfactory accuracy of 89.6% on the BjfuGloxinia after data augmentation. Compared with a single-input CNN, the accuracy of multi-input CNN is increased by 5% on average, demonstrating that multi-input convolutional neural network is a promising model for flower grading. Although data augmentation contributes to the model, the accuracy is still limited by lack of samples diversity. Majority of misclassification is derived from the medium class. The image processing based bud detection is useful for reducing the misclassification, increasing the accuracy of flower grading to approximately 93.9%.

  4. Multi-Branch Fully Convolutional Network for Face Detection

    KAUST Repository

    Bai, Yancheng

    2017-07-20

    Face detection is a fundamental problem in computer vision. It is still a challenging task in unconstrained conditions due to significant variations in scale, pose, expressions, and occlusion. In this paper, we propose a multi-branch fully convolutional network (MB-FCN) for face detection, which considers both efficiency and effectiveness in the design process. Our MB-FCN detector can deal with faces at all scale ranges with only a single pass through the backbone network. As such, our MB-FCN model saves computation and thus is more efficient, compared to previous methods that make multiple passes. For each branch, the specific skip connections of the convolutional feature maps at different layers are exploited to represent faces in specific scale ranges. Specifically, small faces can be represented with both shallow fine-grained and deep powerful coarse features. With this representation, superior improvement in performance is registered for the task of detecting small faces. We test our MB-FCN detector on two public face detection benchmarks, including FDDB and WIDER FACE. Extensive experiments show that our detector outperforms state-of-the-art methods on all these datasets in general and by a substantial margin on the most challenging among them (e.g. WIDER FACE Hard subset). Also, MB-FCN runs at 15 FPS on a GPU for images of size 640 x 480 with no assumption on the minimum detectable face size.

  5. Transforming Musical Signals through a Genre Classifying Convolutional Neural Network

    Science.gov (United States)

    Geng, S.; Ren, G.; Ogihara, M.

    2017-05-01

    Convolutional neural networks (CNNs) have been successfully applied on both discriminative and generative modeling for music-related tasks. For a particular task, the trained CNN contains information representing the decision making or the abstracting process. One can hope to manipulate existing music based on this 'informed' network and create music with new features corresponding to the knowledge obtained by the network. In this paper, we propose a method to utilize the stored information from a CNN trained on musical genre classification task. The network was composed of three convolutional layers, and was trained to classify five-second song clips into five different genres. After training, randomly selected clips were modified by maximizing the sum of outputs from the network layers. In addition to the potential of such CNNs to produce interesting audio transformation, more information about the network and the original music could be obtained from the analysis of the generated features since these features indicate how the network 'understands' the music.

  6. Real-Time Video Convolutional Face Finder on Embedded Platforms

    Directory of Open Access Journals (Sweden)

    Franck Mamalet

    2007-03-01

    Full Text Available A high-level optimization methodology is applied for implementing the well-known convolutional face finder (CFF algorithm for real-time applications on mobile phones, such as teleconferencing, advanced user interfaces, image indexing, and security access control. CFF is based on a feature extraction and classification technique which consists of a pipeline of convolutions and subsampling operations. The design of embedded systems requires a good trade-off between performance and code size due to the limited amount of available resources. The followed methodology copes with the main drawbacks of the original implementation of CFF such as floating-point computation and memory allocation, in order to allow parallelism exploitation and perform algorithm optimizations. Experimental results show that our embedded face detection system can accurately locate faces with less computational load and memory cost. It runs on a 275 MHz Starcore DSP at 35 QCIF images/s with state-of-the-art detection rates and very low false alarm rates.

  7. Video-based face recognition via convolutional neural networks

    Science.gov (United States)

    Bao, Tianlong; Ding, Chunhui; Karmoshi, Saleem; Zhu, Ming

    2017-06-01

    Face recognition has been widely studied recently while video-based face recognition still remains a challenging task because of the low quality and large intra-class variation of video captured face images. In this paper, we focus on two scenarios of video-based face recognition: 1)Still-to-Video(S2V) face recognition, i.e., querying a still face image against a gallery of video sequences; 2)Video-to-Still(V2S) face recognition, in contrast to S2V scenario. A novel method was proposed in this paper to transfer still and video face images to an Euclidean space by a carefully designed convolutional neural network, then Euclidean metrics are used to measure the distance between still and video images. Identities of still and video images that group as pairs are used as supervision. In the training stage, a joint loss function that measures the Euclidean distance between the predicted features of training pairs and expanding vectors of still images is optimized to minimize the intra-class variation while the inter-class variation is guaranteed due to the large margin of still images. Transferred features are finally learned via the designed convolutional neural network. Experiments are performed on COX face dataset. Experimental results show that our method achieves reliable performance compared with other state-of-the-art methods.

  8. Image Classification Based on Convolutional Denoising Sparse Autoencoder

    Directory of Open Access Journals (Sweden)

    Shuangshuang Chen

    2017-01-01

    Full Text Available Image classification aims to group images into corresponding semantic categories. Due to the difficulties of interclass similarity and intraclass variability, it is a challenging issue in computer vision. In this paper, an unsupervised feature learning approach called convolutional denoising sparse autoencoder (CDSAE is proposed based on the theory of visual attention mechanism and deep learning methods. Firstly, saliency detection method is utilized to get training samples for unsupervised feature learning. Next, these samples are sent to the denoising sparse autoencoder (DSAE, followed by convolutional layer and local contrast normalization layer. Generally, prior in a specific task is helpful for the task solution. Therefore, a new pooling strategy—spatial pyramid pooling (SPP fused with center-bias prior—is introduced into our approach. Experimental results on the common two image datasets (STL-10 and CIFAR-10 demonstrate that our approach is effective in image classification. They also demonstrate that none of these three components: local contrast normalization, SPP fused with center-prior, and l2 vector normalization can be excluded from our proposed approach. They jointly improve image representation and classification performance.

  9. Discriminative Unsupervised Feature Learning with Exemplar Convolutional Neural Networks.

    Science.gov (United States)

    Dosovitskiy, Alexey; Fischer, Philipp; Springenberg, Jost Tobias; Riedmiller, Martin; Brox, Thomas

    2016-09-01

    Deep convolutional networks have proven to be very successful in learning task specific features that allow for unprecedented performance on various computer vision tasks. Training of such networks follows mostly the supervised learning paradigm, where sufficiently many input-output pairs are required for training. Acquisition of large training sets is one of the key challenges, when approaching a new task. In this paper, we aim for generic feature learning and present an approach for training a convolutional network using only unlabeled data. To this end, we train the network to discriminate between a set of surrogate classes. Each surrogate class is formed by applying a variety of transformations to a randomly sampled 'seed' image patch. In contrast to supervised network training, the resulting feature representation is not class specific. It rather provides robustness to the transformations that have been applied during training. This generic feature representation allows for classification results that outperform the state of the art for unsupervised learning on several popular datasets (STL-10, CIFAR-10, Caltech-101, Caltech-256). While features learned with our approach cannot compete with class specific features from supervised training on a classification task, we show that they are advantageous on geometric matching problems, where they also outperform the SIFT descriptor.

  10. Real-Time Video Convolutional Face Finder on Embedded Platforms

    Directory of Open Access Journals (Sweden)

    Mamalet Franck

    2007-01-01

    Full Text Available A high-level optimization methodology is applied for implementing the well-known convolutional face finder (CFF algorithm for real-time applications on mobile phones, such as teleconferencing, advanced user interfaces, image indexing, and security access control. CFF is based on a feature extraction and classification technique which consists of a pipeline of convolutions and subsampling operations. The design of embedded systems requires a good trade-off between performance and code size due to the limited amount of available resources. The followed methodology copes with the main drawbacks of the original implementation of CFF such as floating-point computation and memory allocation, in order to allow parallelism exploitation and perform algorithm optimizations. Experimental results show that our embedded face detection system can accurately locate faces with less computational load and memory cost. It runs on a 275 MHz Starcore DSP at 35 QCIF images/s with state-of-the-art detection rates and very low false alarm rates.

  11. Single-Cell Phenotype Classification Using Deep Convolutional Neural Networks.

    Science.gov (United States)

    Dürr, Oliver; Sick, Beate

    2016-10-01

    Deep learning methods are currently outperforming traditional state-of-the-art computer vision algorithms in diverse applications and recently even surpassed human performance in object recognition. Here we demonstrate the potential of deep learning methods to high-content screening-based phenotype classification. We trained a deep learning classifier in the form of convolutional neural networks with approximately 40,000 publicly available single-cell images from samples treated with compounds from four classes known to lead to different phenotypes. The input data consisted of multichannel images. The construction of appropriate feature definitions was part of the training and carried out by the convolutional network, without the need for expert knowledge or handcrafted features. We compare our results against the recent state-of-the-art pipeline in which predefined features are extracted from each cell using specialized software and then fed into various machine learning algorithms (support vector machine, Fisher linear discriminant, random forest) for classification. The performance of all classification approaches is evaluated on an untouched test image set with known phenotype classes. Compared to the best reference machine learning algorithm, the misclassification rate is reduced from 8.9% to 6.6%. © 2016 Society for Laboratory Automation and Screening.

  12. Classification of stroke disease using convolutional neural network

    Science.gov (United States)

    Marbun, J. T.; Seniman; Andayani, U.

    2018-03-01

    Stroke is a condition that occurs when the blood supply stop flowing to the brain because of a blockage or a broken blood vessel. A symptoms that happen when experiencing stroke, some of them is a dropped consciousness, disrupted vision and paralyzed body. The general examination is being done to get a picture of the brain part that have stroke using Computerized Tomography (CT) Scan. The image produced from CT will be manually checked and need a proper lighting by doctor to get a type of stroke. That is why it needs a method to classify stroke from CT image automatically. A method proposed in this research is Convolutional Neural Network. CT image of the brain is used as the input for image processing. The stage before classification are image processing (Grayscaling, Scaling, Contrast Limited Adaptive Histogram Equalization, then the image being classified with Convolutional Neural Network. The result then showed that the method significantly conducted was able to be used as a tool to classify stroke disease in order to distinguish the type of stroke from CT image.

  13. Convolutional Neural Network for Histopathological Analysis of Osteosarcoma.

    Science.gov (United States)

    Mishra, Rashika; Daescu, Ovidiu; Leavey, Patrick; Rakheja, Dinesh; Sengupta, Anita

    2018-03-01

    Pathologists often deal with high complexity and sometimes disagreement over osteosarcoma tumor classification due to cellular heterogeneity in the dataset. Segmentation and classification of histology tissue in H&E stained tumor image datasets is a challenging task because of intra-class variations, inter-class similarity, crowded context, and noisy data. In recent years, deep learning approaches have led to encouraging results in breast cancer and prostate cancer analysis. In this article, we propose convolutional neural network (CNN) as a tool to improve efficiency and accuracy of osteosarcoma tumor classification into tumor classes (viable tumor, necrosis) versus nontumor. The proposed CNN architecture contains eight learned layers: three sets of stacked two convolutional layers interspersed with max pooling layers for feature extraction and two fully connected layers with data augmentation strategies to boost performance. The use of a neural network results in higher accuracy of average 92% for the classification. We compare the proposed architecture with three existing and proven CNN architectures for image classification: AlexNet, LeNet, and VGGNet. We also provide a pipeline to calculate percentage necrosis in a given whole slide image. We conclude that the use of neural networks can assure both high accuracy and efficiency in osteosarcoma classification.

  14. Classifications of Multispectral Colorectal Cancer Tissues Using Convolution Neural Network.

    Science.gov (United States)

    Haj-Hassan, Hawraa; Chaddad, Ahmad; Harkouss, Youssef; Desrosiers, Christian; Toews, Matthew; Tanougast, Camel

    2017-01-01

    Colorectal cancer (CRC) is the third most common cancer among men and women. Its diagnosis in early stages, typically done through the analysis of colon biopsy images, can greatly improve the chances of a successful treatment. This paper proposes to use convolution neural networks (CNNs) to predict three tissue types related to the progression of CRC: benign hyperplasia (BH), intraepithelial neoplasia (IN), and carcinoma (Ca). Multispectral biopsy images of thirty CRC patients were retrospectively analyzed. Images of tissue samples were divided into three groups, based on their type (10 BH, 10 IN, and 10 Ca). An active contour model was used to segment image regions containing pathological tissues. Tissue samples were classified using a CNN containing convolution, max-pooling, and fully-connected layers. Available tissue samples were split into a training set, for learning the CNN parameters, and test set, for evaluating its performance. An accuracy of 99.17% was obtained from segmented image regions, outperforming existing approaches based on traditional feature extraction, and classification techniques. Experimental results demonstrate the effectiveness of CNN for the classification of CRC tissue types, in particular when using presegmented regions of interest.

  15. Classifications of multispectral colorectal cancer tissues using convolution neural network

    Directory of Open Access Journals (Sweden)

    Hawraa Haj-Hassan

    2017-01-01

    Full Text Available Background: Colorectal cancer (CRC is the third most common cancer among men and women. Its diagnosis in early stages, typically done through the analysis of colon biopsy images, can greatly improve the chances of a successful treatment. This paper proposes to use convolution neural networks (CNNs to predict three tissue types related to the progression of CRC: benign hyperplasia (BH, intraepithelial neoplasia (IN, and carcinoma (Ca. Methods: Multispectral biopsy images of thirty CRC patients were retrospectively analyzed. Images of tissue samples were divided into three groups, based on their type (10 BH, 10 IN, and 10 Ca. An active contour model was used to segment image regions containing pathological tissues. Tissue samples were classified using a CNN containing convolution, max-pooling, and fully-connected layers. Available tissue samples were split into a training set, for learning the CNN parameters, and test set, for evaluating its performance. Results: An accuracy of 99.17% was obtained from segmented image regions, outperforming existing approaches based on traditional feature extraction, and classification techniques. Conclusions: Experimental results demonstrate the effectiveness of CNN for the classification of CRC tissue types, in particular when using presegmented regions of interest.

  16. Thermalnet: a Deep Convolutional Network for Synthetic Thermal Image Generation

    Science.gov (United States)

    Kniaz, V. V.; Gorbatsevich, V. S.; Mizginov, V. A.

    2017-05-01

    Deep convolutional neural networks have dramatically changed the landscape of the modern computer vision. Nowadays methods based on deep neural networks show the best performance among image recognition and object detection algorithms. While polishing of network architectures received a lot of scholar attention, from the practical point of view the preparation of a large image dataset for a successful training of a neural network became one of major challenges. This challenge is particularly profound for image recognition in wavelengths lying outside the visible spectrum. For example no infrared or radar image datasets large enough for successful training of a deep neural network are available to date in public domain. Recent advances of deep neural networks prove that they are also capable to do arbitrary image transformations such as super-resolution image generation, grayscale image colorisation and imitation of style of a given artist. Thus a natural question arise: how could be deep neural networks used for augmentation of existing large image datasets? This paper is focused on the development of the Thermalnet deep convolutional neural network for augmentation of existing large visible image datasets with synthetic thermal images. The Thermalnet network architecture is inspired by colorisation deep neural networks.

  17. Coronary artery calcification (CAC) classification with deep convolutional neural networks

    Science.gov (United States)

    Liu, Xiuming; Wang, Shice; Deng, Yufeng; Chen, Kuan

    2017-03-01

    Coronary artery calcification (CAC) is a typical marker of the coronary artery disease, which is one of the biggest causes of mortality in the U.S. This study evaluates the feasibility of using a deep convolutional neural network (DCNN) to automatically detect CAC in X-ray images. 1768 posteroanterior (PA) view chest X-Ray images from Sichuan Province Peoples Hospital, China were collected retrospectively. Each image is associated with a corresponding diagnostic report written by a trained radiologist (907 normal, 861 diagnosed with CAC). Onequarter of the images were randomly selected as test samples; the rest were used as training samples. DCNN models consisting of 2,4,6 and 8 convolutional layers were designed using blocks of pre-designed CNN layers. Each block was implemented in Theano with Graphics Processing Units (GPU). Human-in-the-loop learning was also performed on a subset of 165 images with framed arteries by trained physicians. The results from the DCNN models were compared to the diagnostic reports. The average diagnostic accuracies for models with 2,4,6,8 layers were 0.85, 0.87, 0.88, and 0.89 respectively. The areas under the curve (AUC) were 0.92, 0.95, 0.95, and 0.96. As the model grows deeper, the AUC or diagnostic accuracies did not have statistically significant changes. The results of this study indicate that DCNN models have promising potential in the field of intelligent medical image diagnosis practice.

  18. Disorganized behavior on Link's cube test is sensitive to right hemispheric frontal lobe damage in stroke patients

    Science.gov (United States)

    Kopp, Bruno; Rösser, Nina; Tabeling, Sandra; Stürenburg, Hans Jörg; de Haan, Bianca; Karnath, Hans-Otto; Wessel, Karl

    2014-01-01

    One of Luria's favorite neuropsychological tasks for challenging frontal lobe functions was Link's cube test (LCT). The LCT is a cube construction task in which the subject must assemble 27 small cubes into one large cube in such a manner that only the painted surfaces of the small cubes are visible. We computed two new LCT composite scores, the constructive plan composite score, reflecting the capability to envisage a cubical-shaped volume, and the behavioral (dis-) organization composite score, reflecting the goal-directedness of cube construction. Voxel-based lesion-behavior mapping (VLBM) was used to test the relationship between performance on the LCT and brain injury in a sample of stroke patients with right hemisphere damage (N = 32), concentrated in the frontal lobe. We observed a relationship between the measure of behavioral (dis-) organization on the LCT and right frontal lesions. Further work in a larger sample, including left frontal lobe damage and with more power to detect effects of right posterior brain injury, is necessary to determine whether this observation is specific for right frontal lesions. PMID:24596552

  19. Using convolutional decoding to improve time delay and phase estimation in digital communications

    Science.gov (United States)

    Ormesher, Richard C [Albuquerque, NM; Mason, John J [Albuquerque, NM

    2010-01-26

    The time delay and/or phase of a communication signal received by a digital communication receiver can be estimated based on a convolutional decoding operation that the communication receiver performs on the received communication signal. If the original transmitted communication signal has been spread according to a spreading operation, a corresponding despreading operation can be integrated into the convolutional decoding operation.

  20. An upper bound on the number of errors corrected by a convolutional code

    DEFF Research Database (Denmark)

    Justesen, Jørn

    2000-01-01

    The number of errors that a convolutional codes can correct in a segment of the encoded sequence is upper bounded by the number of distinct syndrome sequences of the relevant length.......The number of errors that a convolutional codes can correct in a segment of the encoded sequence is upper bounded by the number of distinct syndrome sequences of the relevant length....

  1. Linear diffusion-wave channel routing using a discrete Hayami convolution method

    Science.gov (United States)

    Li Wang; Joan Q. Wu; William J. Elliot; Fritz R. Feidler; Sergey. Lapin

    2014-01-01

    The convolution of an input with a response function has been widely used in hydrology as a means to solve various problems analytically. Due to the high computation demand in solving the functions using numerical integration, it is often advantageous to use the discrete convolution instead of the integration of the continuous functions. This approach greatly reduces...

  2. Frontal bone metastasis from an occult follicular thyroid carcinoma: Diagnosed by FNAC

    Directory of Open Access Journals (Sweden)

    Rajnish Kalra

    2017-01-01

    Full Text Available Metastatic deposits in skull bones from follicular thyroid carcinoma is rare, and metastatic disease in skull being the presenting symptom without obvious thyroid lesion (occult primary is even rarer. A 60-year-old female patient presented with a mass in the frontal region of the skull. Fine needle aspiration cytology was done which revealed an adenocarcinoma with repeated follicular pattern, reminiscent of follicular neoplasm of thyroid, which on immunocytochemistry revealed positivity for thyroglobulin. Patient was investigated further for primary thyroid malignancy, and imaging revealed a nodule in the left lobe of thyroid. Neuroimaging showed osteolytic lesion involving the cranium.

  3. Frontal bone metastasis from an occult follicular thyroid carcinoma: Diagnosed by FNAC.

    Science.gov (United States)

    Kalra, Rajnish; Pawar, Richa; Hasija, Sonia; Chandna, Abha; Sankla, Manoj; Malhotra, Chanchal

    2017-01-01

    Metastatic deposits in skull bones from follicular thyroid carcinoma is rare, and metastatic disease in skull being the presenting symptom without obvious thyroid lesion (occult primary) is even rarer. A 60-year-old female patient presented with a mass in the frontal region of the skull. Fine needle aspiration cytology was done which revealed an adenocarcinoma with repeated follicular pattern, reminiscent of follicular neoplasm of thyroid, which on immunocytochemistry revealed positivity for thyroglobulin. Patient was investigated further for primary thyroid malignancy, and imaging revealed a nodule in the left lobe of thyroid. Neuroimaging showed osteolytic lesion involving the cranium.

  4. Pott's Puffy Tumor Arising from Frontal Sinusitis

    Energy Technology Data Exchange (ETDEWEB)

    Lim, Ji Yeon; Kang, Hyun Koo [Seoul Veterans Hospital, Seoul (Korea, Republic of)

    2010-02-15

    Pott's puffy tumor is an extremely rare and potentially life-threatening complication of frontal sinusitis. We report a case of a 64-year-old man who presented at our emergency department with mild tenderness on the glabellar area and diplopia. Computed Tomography (CT) revealed frontal sinusitis and osteomyelitis of the frontal bone. Following sinus trephination and long-term antibiotic therapy, the patient achieved a complete recovery.

  5. Study on the development of frontal sinuses by morphometric analysis of the skull - doi: 10.4025/actascibiolsci.v35i2.13334

    Directory of Open Access Journals (Sweden)

    Carolina Peixoto Magalhães

    2013-05-01

    Full Text Available The frontal sinuses are cranial areas of clinical, forensic and pathology importance whose development mechanisms are still poorly defined. Nasal airflow and brain development are two of the main theories. Current analysis debates whether they are the real determinants of frontal sinuses growth, which may be proved by the skull’s morphometric analysis. Four groups of measures related to the external cranial architecture, the pyriform aperture, orbital cavities and frontal sinuses were defined. Thirty-three skulls of individuals, mean age 68 years, from the Laboratory of Anatomy of the Academic Centre of Victoria – UFPE – Brazil, were used. Statistical analysis showed total agenesis of the frontal sinus in 18.2% of the skulls. There was significant correlation between the development of the right frontal sinus and the pyriform aperture, and between the left frontal sinus and two cranial measurements (p ≤ 0.05. Significant differences between mean of pyriform aperture areas of the skulls with or without sinuses were also reported (p ≤ 0.01. Results supported the fact that there was a modulation activity by nasal aeration and brain formation in the development of frontal sinuses.

  6. Frontal Lobe Function in Chess Players

    Directory of Open Access Journals (Sweden)

    Vahid Nejati

    2012-05-01

    Full Text Available Chess is considered as a cognitive game because of severe engagement of the mental resources during playing. The purpose of this study is evaluation of frontal lobe function of chess players with matched non-players. Wisconsin Card Sorting Test (WCST data showed no difference between the player and non-player groups in preservation error and completed categories but surprisingly showed significantly lower grade of the player group in correct response. Our data reveal that chess players dont have any preference in any stage of Stroop test. Chess players dont have any preference in selective attention, inhibition and executive cognitive function. Chess players' have lower shifting abilities than non-players.

  7. Use of Frontal Sinus and Nasal Septum Pattern as an Aid in Personal Identification and Determination of Gender: A Radiographic Study.

    Science.gov (United States)

    Verma, Kavita; Nahar, Prashant; Singh, Mohit Pal; Mathur, Hemant; Bhuvaneshwari, S

    2017-01-01

    Personal identification and gender determination of unknown person has a vital importance in forensic investigation. Human skull radiography is a useful tool in human identification in natural disaster, in any accidents such as fire accident and road traffic accident where body remains become degraded or severely destroyed. Present study was performed to evaluate the measurement of frontal sinus, uniqueness of various pattern of nasal septum when combined with frontal sinus observed on posterio anterior cephalogram for sex determination as well as personal identification. A total of 80 individuals, 40 males and 40 females, between the age ranges of 18-30 years were selected. The selected individuals had their Posterio Anterior (PA) cephalogram performed after taking their informed consent. Right and left areas and the maximum height and width of the frontal sinus were determined and septum patterns were evaluated and both patterns were also combined and compared. The radiographs were taken on Xtropan 2000 OPG X-ray machine with cephalography attachment and KODAK CR 7400 digital radiography system. Mean and SD values of the greatest height and width of frontal sinus in male and female patients were thus evaluated. The mean values of the frontal sinus were greater in males and the left area was larger than the right area, based on student's t-test at the 5% level of significance. The combination of Frontal Sinus Patterns and Nasal Septum Patterns (FP+NSP) were assessed and found that there were nine classifiable patterns in 26 (32.5%) individuals (12 males and 14 females), each of which had common representations in more than one individual. Besides these patterns, there were unique unclassifiable patterns in 54 (67.5%) individuals. The present study supports the use of radiographic evaluation of frontal sinus dimensions, frontal sinus patterns, nasal septum deviations and the combination FP+NSP patterns for personal identification and gender determination in

  8. Motivated malleability: Frontal cortical asymmetry predicts the susceptibility to social influence.

    Science.gov (United States)

    Schnuerch, Robert; Pfattheicher, Stefan

    2017-07-16

    Humans, just as many other animals, regulate their behavior in terms of approaching stimuli associated with pleasure and avoiding stimuli linked to harm. A person's current and chronic motivational direction - that is, approach versus avoidance orientation - is reliably reflected in the asymmetry of frontal cortical low-frequency oscillations. Using resting electroencephalography (EEG), we show that frontal asymmetry is predictive of the tendency to yield to social influence: Stronger right- than left-side frontolateral activation during a resting-state session prior to the experiment was robustly associated with a stronger inclination to adopt a peer group's judgments during perceptual decision-making (Study 1). We posit that this reflects the role of a person's chronic avoidance orientation in socially adjusted behavior. This claim was strongly supported by additional survey investigations (Studies 2a, 2b, 2c), all of which consistently revealed that trait avoidance was positively linked to the susceptibility to social influence. The present contribution thus stresses the relevance of chronic avoidance orientation in social conformity, refining (yet not contradicting) the longstanding view that socially influenced behavior is motivated by approach-related goals. Moreover, our findings valuably underscore and extend our knowledge on the association between frontal cortical asymmetry and a variety of psychological variables.

  9. Frontal lobe epileptic seizures are accompanied by elevated pitch during verbal communication.

    Science.gov (United States)

    Speck, Iva; Echternach, Matthias; Sammler, Daniela; Schulze-Bonhage, Andreas

    2018-01-31

    The objective of our study was to assess alterations in speech as a possible localizing sign in frontal lobe epilepsy. Ictal speech was analyzed in 18 patients with frontal lobe epilepsy (FLE) during seizures and in the interictal period. Matched identical words were analyzed regarding alterations in fundamental frequency (ƒo) as an approximation of pitch. In patients with FLE, ƒo of ictal utterances was significantly higher than ƒo in interictal recordings (p = 0.016). Ictal ƒo increases occurred in both FLE of right and left seizure origin. In contrast, a matched temporal lobe epilepsy (TLE) group showed less pronounced increases in ƒo, and only in patients with right-sided seizure foci. This study for the first time shows significant voice alterations in ictal speech in a cohort of patients with FLE. This may contribute to the localization of the epileptic focus. Increases in ƒo were interestingly found in frontal lobe seizures with origin in either hemisphere, suggesting a bilateral involvement to the planning of speech production, in contrast to a more right-sided lateralization of pitch perception in prosodic processing. Wiley Periodicals, Inc. © 2018 International League Against Epilepsy.

  10. Are personality traits of juvenile myoclonic epilepsy related to frontal lobe dysfunctions? A proton MRS study.

    Science.gov (United States)

    de Araújo Filho, Gerardo Maria; Lin, Katia; Lin, Jaime; Peruchi, Mirella M; Caboclo, Luís Otávio S F; Guaranha, Mirian S B; Guilhoto, Laura M F F; Carrete, Henrique; Yacubian, Elza Márcia T

    2009-05-01

    Personality traits characterized by emotional instability and immaturity, unsteadiness, lack of discipline, hedonism, frequent and rapid mood changes, and indifference toward one's disease have been associated with patients who have juvenile myoclonic epilepsy (JME). Literature data demonstrate worse seizure control and more psychosocial dysfunctions among patients with JME who have those traits. In this controlled study we performed a correlation analysis of psychiatric scores with magnetic resonance spectroscopy (MRS) values across JME patients, aiming to verify the existence of a possible relation between frontal lobe dysfunction and the prevalence of personality disorders (PDs) in JME. Sixteen JME patients with cluster B PDs, 41 JME patients without any psychiatric disorder, and 30 healthy controls were submitted to a psychiatric evaluation and to a quantitative multivoxel MRS of thalamus; insula; cingulate gyrus; striatum; and frontal, parietal, and occipital lobes. Groups were homogeneous according to age, gender, and manual dominance. Psychiatric evaluation was performed through the Scheduled Clinical Interview for DSM-IV, Axis I and II (SCID I and II, respectively). A significant reduction of N-acetyl-aspartate over creatinine (NAA/Cr) ratio was observed mainly in the left frontal lobe in the JME and PD group. In addition, a significant increase in the glutamate-glutamine over creatinine GLX/Cr ratio was also observed in this referred region in the same group. These data support the hypothesis that PDs in JME could represent neuronal dysfunction and possibly a more severe form of this epileptic syndrome.

  11. A Postmortem Study of Frontal and Temporal Gyri Thickness and Cell Number in Human Obesity.

    Science.gov (United States)

    Gómez-Apo, Erick; García-Sierra, Adrián; Silva-Pereyra, Juan; Soto-Abraham, Virgilia; Mondragón-Maya, Alejandra; Velasco-Vales, Verónica; Pescatello, Linda S

    2018-01-01

    This study aimed to compare cortex thickness and neuronal cell density in postmortem brain tissue from people with overweight or obesity and normal weight. The cortex thickness and neuron density of eight donors with overweight or obesity (mean = 31.6 kg/m 2 ; SD = 4.35; n = 8; 6 male) and eight donors with normal weight (mean = 21.8 kg/m 2 ; SD = 1.5; n = 8; 5 male) were compared. All participants were Mexican and lived in Mexico City. Randomly selected thickness measures of different cortex areas from the frontal and temporal lobes were analyzed based on high-resolution real-size photographs. A histological analysis of systematic-random fields was used to quantify the number of neurons in postmortem left and right of the first, second, and third gyri of frontal and temporal lobe brain samples. No statistical difference was found in cortical thickness between donors with overweight or obesity and individuals with normal weight. A smaller number of neurons was found among the donors with overweight or obesity than the donors with normal weight at different frontal and temporal areas. A lower density of neurons is associated with overweight or obesity. The morphological basis for structural brain changes in obesity requires further investigation. © 2017 The Obesity Society.

  12. Occipital cortex of blind individuals is functionally coupled with executive control areas of frontal cortex.

    Science.gov (United States)

    Deen, Ben; Saxe, Rebecca; Bedny, Marina

    2015-08-01

    In congenital blindness, the occipital cortex responds to a range of nonvisual inputs, including tactile, auditory, and linguistic stimuli. Are these changes in functional responses to stimuli accompanied by altered interactions with nonvisual functional networks? To answer this question, we introduce a data-driven method that searches across cortex for functional connectivity differences across groups. Replicating prior work, we find increased fronto-occipital functional connectivity in congenitally blind relative to blindfolded sighted participants. We demonstrate that this heightened connectivity extends over most of occipital cortex but is specific to a subset of regions in the inferior, dorsal, and medial frontal lobe. To assess the functional profile of these frontal areas, we used an n-back working memory task and a sentence comprehension task. We find that, among prefrontal areas with overconnectivity to occipital cortex, one left inferior frontal region responds to language over music. By contrast, the majority of these regions responded to working memory load but not language. These results suggest that in blindness occipital cortex interacts more with working memory systems and raise new questions about the function and mechanism of occipital plasticity.

  13. ISOMETRIC GLUTEUS MEDIUS MUSCLE TORQUE AND FRONTAL PLANE PELVIC MOTION DURING RUNNING

    Directory of Open Access Journals (Sweden)

    Evie N. Burnet

    2009-06-01

    Full Text Available The objective of this study was to investigate the relationship between isometric GM torque and the degree of frontal plane pelvic drop during running. Twenty-one healthy, recreational runners (9 males, 12 females who ran 8.05 km or more per week were obtained from a sample of convenience. GM maximal isometric torque was collected prior to the run. Subjects then ran on a treadmill for 30 minutes while bilateral three-dimensional pelvic kinematic data were collected for 10 seconds at each 2 minute increment. Left side pelvic drop showed a slight increase (effect size = 0.61; while, the right side pelvic drop remained stable (effect size = 0.18. Pearson's Correlations showed no relationship between GM isometric torque and frontal plane pelvic drop for any of the data collection periods during the 30-minute run. These results suggest that isometric GM torque was a poor predictor of frontal plane pelvic drop. One should question whether a dynamic rather than static measure of GM strength would be more appropriate. Future research is needed to identify dynamic strength measures that would better predict biomechanical components of running gait

  14. Effective Connectivity Hierarchically Links Temporoparietal and Frontal Areas of the Auditory Dorsal Stream with the Motor Cortex Lip Area during Speech Perception

    Science.gov (United States)

    Murakami, Takenobu; Restle, Julia; Ziemann, Ulf

    2012-01-01

    A left-hemispheric cortico-cortical network involving areas of the temporoparietal junction (Tpj) and the posterior inferior frontal gyrus (pIFG) is thought to support sensorimotor integration of speech perception into articulatory motor activation, but how this network links with the lip area of the primary motor cortex (M1) during speech…

  15. SPM analysis of brain perfusion SPECT and F-18 FDG PET in the Korean autosomal dominant nocturnal frontal lobe epilepsy family

    International Nuclear Information System (INIS)

    Won, Kyoung Sook; Zeon, Seok Kil

    2004-01-01

    This study attempted to investigate the specific pattern of brain perfusion and glucose metabolism in the Korean autosomal dominant nocturnal frontal lobe epilepsy (ADNFLE) family. Using Tc-99m ECD brain perfusion SPECT. we assessed brain perfusion in 6 patients at interictal period and 5 patients at ictal period. Interictal F-18 FDG PET was performed on 6 affected family members. The scans were statistically analyzed by using statistical parametric mapping (SPM99). The data of the affected family members were compared to those of the control subjects. Interictal F-18 FDG PET SPM group analysis showed decreased glucose metabolism over the left middle and superior frontal gyri and the left central regions including the anterior parietal lobe. There was a less pronounced decrease in glucose uptake in the right anterior superior frontal gyrus. Interictal brain perfusion SPECT SPM group analysis showed similar pattern of decreased perfusion compared to those of interictal F-18 FDG PET. Ictal brain perfusion SPECT SPM group analysis revealed increased perfusion over the left pre-and postcentral gyri and less pronounced increased perfusion in the right postcentral gyrus. lnterictal F -18 PET and brain perfusion SPECT SPM group analysis suggest that major abnormalities of ADNFLE family are in the left frontal lobe. These findings may be helpful to elucidate the pathophysiological mechanism of this rare disease entity

  16. Left heart catheterization

    Science.gov (United States)

    Catheterization - left heart ... to help guide the catheters up into your heart and arteries. Dye (sometimes called "contrast") will be ... in the blood vessels that lead to your heart. The catheter is then moved through the aortic ...

  17. The Human Frontal Lobes and Frontal Network Systems: An Evolutionary, Clinical, and Treatment Perspective

    Science.gov (United States)

    Hoffmann, Michael

    2013-01-01

    Frontal lobe syndromes, better termed as frontal network systems, are relatively unique in that they may manifest from almost any brain region, due to their widespread connectivity. The understandings of the manifold expressions seen clinically are helped by considering evolutionary origins, the contribution of the state-dependent ascending monoaminergic neurotransmitter systems, and cerebral connectivity. Hence, the so-called networktopathies may be a better term for the syndromes encountered clinically. An increasing array of metric tests are becoming available that complement that long standing history of qualitative bedside assessments pioneered by Alexander Luria, for example. An understanding of the vast panoply of frontal systems' syndromes has been pivotal in understanding and diagnosing the most common dementia syndrome under the age of 60, for example, frontotemporal lobe degeneration. New treatment options are also progressively becoming available, with recent evidence of dopaminergic augmentation, for example, being helpful in traumatic brain injury. The latter include not only psychopharmacological options but also device-based therapies including mirror visual feedback therapy. PMID:23577266

  18. Activation of extrastriate and frontal cortical areas by visual words and word-like stimuli

    International Nuclear Information System (INIS)

    Petersen, S.E.; Fox, P.T.; Snyder, A.Z.; Raichle, M.E.

    1990-01-01

    Visual presentation of words activates extrastriate regions of the occipital lobes of the brain. When analyzed by positron emission tomography (PET), certain areas in the left, medial extrastriate visual cortex were activated by visually presented pseudowords that obey English spelling rules, as well as by actual words. These areas were not activated by nonsense strings of letters or letter-like forms. Thus visual word form computations are based on learned distinctions between words and nonwords. In addition, during passive presentation of words, but not pseudowords, activation occurred in a left frontal area that is related to semantic processing. These findings support distinctions made in cognitive psychology and computational modeling between high-level visual and semantic computations on single words and describe the anatomy that may underlie these distinctions

  19. An orbital fistula complicating anaerobic frontal sinusitis and osteomyelitis

    NARCIS (Netherlands)

    H.J. Simonsz (Huib); H.J.F. Peeters; G.M. Bleeker

    1982-01-01

    textabstractA patient is described with an orbital fistula complicating frontal sinusitis and osteomyelitis of the frontal bone. The fistula was excised, but a fortnight later an acute exacerbation occurred. From the discharging pus a Staphylococcus aureus was cultured and from mucosa obtained

  20. Obsessive-compulsive disorder and ventromedial frontal lesions

    DEFF Research Database (Denmark)

    Irle, E; Exner, C; Thielen, K

    1998-01-01

    on the Wisconsin Card Sorting Test. Subjects with lesions of the dorsolateral frontal convexity also showed memory problems, attentional slowing, and lower performance IQ. CONCLUSIONS: Restricted ventromedial frontal leukotomy should be discussed as a last-resort treatment for severe and refractory OCD...

  1. Case Report: Frontal lobe tuberculoma: A clinical and imaging ...

    African Journals Online (AJOL)

    Background: Pediatric nervous system tuberculomas are usually infra-tentorial and multiple. A frontal lobe location is rare. Case Details: We report a 10 year-old boy who presented with a chronic headache and episodes of loss of consciousness. He had no signs of primary pulmonary tuberculosis and a diagnosis of frontal ...

  2. Post-Traumatic Pneumocele of the Frontal Sinus

    Energy Technology Data Exchange (ETDEWEB)

    Karadag, Demet; Calisir, Cuneyt; Adapinar, Baki [Eskisehir Osmangazi University, Eskisehir (Turkmenistan)

    2008-08-15

    A pneumocele is an abnormal dilatation of a paranasal sinus, most commonly affecting the frontal sinus. Although the etiology of pneumocele is not entirely known, several causative factors have been suggested including trauma, surgery, tumor and infection. We report here a case of post-traumatic pneumocele of the frontal sinus following a head trauma.

  3. Minimally invasive approach for lesions involving the frontal sinus ...

    African Journals Online (AJOL)

    Background: Traditional open surgery for frontal sinus pathology and cerebrospinal fluid (CSF) leaks is complex and involves a craniotomy. Minimally invasive options offer an alternate solution. We describe and assess the outcome of a minimally invasive approach for lesions and defects involving the frontal sinus.

  4. Non-frontal Model Based Approach to Forensic Face Recognition

    NARCIS (Netherlands)

    Dutta, A.; Veldhuis, Raymond N.J.; Spreeuwers, Lieuwe Jan

    2012-01-01

    In this paper, we propose a non-frontal model based approach which ensures that a face recognition system always gets to compare images having similar view (or pose). This requires a virtual suspect reference set that consists of non-frontal suspect images having pose similar to the surveillance

  5. An effective convolutional neural network model for Chinese sentiment analysis

    Science.gov (United States)

    Zhang, Yu; Chen, Mengdong; Liu, Lianzhong; Wang, Yadong

    2017-06-01

    Nowadays microblog is getting more and more popular. People are increasingly accustomed to expressing their opinions on Twitter, Facebook and Sina Weibo. Sentiment analysis of microblog has received significant attention, both in academia and in industry. So far, Chinese microblog exploration still needs lots of further work. In recent years CNN has also been used to deal with NLP tasks, and already achieved good results. However, these methods ignore the effective use of a large number of existing sentimental resources. For this purpose, we propose a Lexicon-based Sentiment Convolutional Neural Networks (LSCNN) model focus on Weibo's sentiment analysis, which combines two CNNs, trained individually base on sentiment features and word embedding, at the fully connected hidden layer. The experimental results show that our model outperforms the CNN model only with word embedding features on microblog sentiment analysis task.

  6. Deep Convolutional Neural Networks for Image Classification: A Comprehensive Review.

    Science.gov (United States)

    Rawat, Waseem; Wang, Zenghui

    2017-09-01

    Convolutional neural networks (CNNs) have been applied to visual tasks since the late 1980s. However, despite a few scattered applications, they were dormant until the mid-2000s when developments in computing power and the advent of large amounts of labeled data, supplemented by improved algorithms, contributed to their advancement and brought them to the forefront of a neural network renaissance that has seen rapid progression since 2012. In this review, which focuses on the application of CNNs to image classification tasks, we cover their development, from their predecessors up to recent state-of-the-art deep learning systems. Along the way, we analyze (1) their early successes, (2) their role in the deep learning renaissance, (3) selected symbolic works that have contributed to their recent popularity, and (4) several improvement attempts by reviewing contributions and challenges of over 300 publications. We also introduce some of their current trends and remaining challenges.

  7. Convolutional Neural Networks for patient-specific ECG classification.

    Science.gov (United States)

    Kiranyaz, Serkan; Ince, Turker; Hamila, Ridha; Gabbouj, Moncef

    2015-01-01

    We propose a fast and accurate patient-specific electrocardiogram (ECG) classification and monitoring system using an adaptive implementation of 1D Convolutional Neural Networks (CNNs) that can fuse feature extraction and classification into a unified learner. In this way, a dedicated CNN will be trained for each patient by using relatively small common and patient-specific training data and thus it can also be used to classify long ECG records such as Holter registers in a fast and accurate manner. Alternatively, such a solution can conveniently be used for real-time ECG monitoring and early alert system on a light-weight wearable device. The experimental results demonstrate that the proposed system achieves a superior classification performance for the detection of ventricular ectopic beats (VEB) and supraventricular ectopic beats (SVEB).

  8. Towards Better Analysis of Deep Convolutional Neural Networks.

    Science.gov (United States)

    Liu, Mengchen; Shi, Jiaxin; Li, Zhen; Li, Chongxuan; Zhu, Jun; Liu, Shixia

    2017-01-01

    Deep convolutional neural networks (CNNs) have achieved breakthrough performance in many pattern recognition tasks such as image classification. However, the development of high-quality deep models typically relies on a substantial amount of trial-and-error, as there is still no clear understanding of when and why a deep model works. In this paper, we present a visual analytics approach for better understanding, diagnosing, and refining deep CNNs. We formulate a deep CNN as a directed acyclic graph. Based on this formulation, a hybrid visualization is developed to disclose the multiple facets of each neuron and the interactions between them. In particular, we introduce a hierarchical rectangle packing algorithm and a matrix reordering algorithm to show the derived features of a neuron cluster. We also propose a biclustering-based edge bundling method to reduce visual clutter caused by a large number of connections between neurons. We evaluated our method on a set of CNNs and the results are generally favorable.

  9. Forecasting Flare Activity Using Deep Convolutional Neural Networks

    Science.gov (United States)

    Hernandez, T.

    2017-12-01

    Current operational flare forecasting relies on human morphological analysis of active regions and the persistence of solar flare activity through time (i.e. that the Sun will continue to do what it is doing right now: flaring or remaining calm). In this talk we present the results of applying deep Convolutional Neural Networks (CNNs) to the problem of solar flare forecasting. CNNs operate by training a set of tunable spatial filters that, in combination with neural layer interconnectivity, allow CNNs to automatically identify significant spatial structures predictive for classification and regression problems. We will start by discussing the applicability and success rate of the approach, the advantages it has over non-automated forecasts, and how mining our trained neural network provides a fresh look into the mechanisms behind magnetic energy storage and release.

  10. Convolutional neural networks with balanced batches for facial expressions recognition

    Science.gov (United States)

    Battini Sönmez, Elena; Cangelosi, Angelo

    2017-03-01

    This paper considers the issue of fully automatic emotion classification on 2D faces. In spite of the great effort done in recent years, traditional machine learning approaches based on hand-crafted feature extraction followed by the classification stage failed to develop a real-time automatic facial expression recognition system. The proposed architecture uses Convolutional Neural Networks (CNN), which are built as a collection of interconnected processing elements to simulate the brain of human beings. The basic idea of CNNs is to learn a hierarchical representation of the input data, which results in a better classification performance. In this work we present a block-based CNN algorithm, which uses noise, as data augmentation technique, and builds batches with a balanced number of samples per class. The proposed architecture is a very simple yet powerful CNN, which can yield state-of-the-art accuracy on the very competitive benchmark algorithm of the Extended Cohn Kanade database.

  11. Multiview Convolutional Neural Networks for Multidocument Extractive Summarization.

    Science.gov (United States)

    Zhang, Yong; Er, Meng Joo; Zhao, Rui; Pratama, Mahardhika

    2017-10-01

    Multidocument summarization has gained popularity in many real world applications because vital information can be extracted within a short time. Extractive summarization aims to generate a summary of a document or a set of documents by ranking sentences and the ranking results rely heavily on the quality of sentence features. However, almost all previous algorithms require hand-crafted features for sentence representation. In this paper, we leverage on word embedding to represent sentences so as to avoid the intensive labor in feature engineering. An enhanced convolutional neural networks (CNNs) termed multiview CNNs is successfully developed to obtain the features of sentences and rank sentences jointly. Multiview learning is incorporated into the model to greatly enhance the learning capability of original CNN. We evaluate the generic summarization performance of our proposed method on five Document Understanding Conference datasets. The proposed system outperforms the state-of-the-art approaches and the improvement is statistically significant shown by paired t -test.

  12. Fully convolutional network with cluster for semantic segmentation

    Science.gov (United States)

    Ma, Xiao; Chen, Zhongbi; Zhang, Jianlin

    2018-04-01

    At present, image semantic segmentation technology has been an active research topic for scientists in the field of computer vision and artificial intelligence. Especially, the extensive research of deep neural network in image recognition greatly promotes the development of semantic segmentation. This paper puts forward a method based on fully convolutional network, by cluster algorithm k-means. The cluster algorithm using the image's low-level features and initializing the cluster centers by the super-pixel segmentation is proposed to correct the set of points with low reliability, which are mistakenly classified in great probability, by the set of points with high reliability in each clustering regions. This method refines the segmentation of the target contour and improves the accuracy of the image segmentation.

  13. Taxonomic Classification for Living Organisms Using Convolutional Neural Networks

    Directory of Open Access Journals (Sweden)

    Saed Khawaldeh

    2017-11-01

    Full Text Available Taxonomic classification has a wide-range of applications such as finding out more about evolutionary history. Compared to the estimated number of organisms that nature harbors, humanity does not have a thorough comprehension of to which specific classes they belong. The classification of living organisms can be done in many machine learning techniques. However, in this study, this is performed using convolutional neural networks. Moreover, a DNA encoding technique is incorporated in the algorithm to increase performance and avoid misclassifications. The algorithm proposed outperformed the state of the art algorithms in terms of accuracy and sensitivity, which illustrates a high potential for using it in many other applications in genome analysis.

  14. Plant species classification using deep convolutional neural network

    DEFF Research Database (Denmark)

    Dyrmann, Mads; Karstoft, Henrik; Midtiby, Henrik Skov

    2016-01-01

    Information on which weed species are present within agricultural fields is important for site specific weed management. This paper presents a method that is capable of recognising plant species in colour images by using a convolutional neural network. The network is built from scratch trained...... and tested on a total of 10,413 images containing 22 weed and crop species at early growth stages. These images originate from six different data sets, which have variations with respect to lighting, resolution, and soil type. This includes images taken under controlled conditions with regard to camera...... stabilisation and illumination, and images shot with hand-held mobile phones in fields with changing lighting conditions and different soil types. For these 22 species, the network is able to achieve a classification accuracy of 86.2%....

  15. Feature Fusion Based on Convolutional Neural Network for SAR ATR

    Directory of Open Access Journals (Sweden)

    Chen Shi-Qi

    2017-01-01

    Full Text Available Recent breakthroughs in algorithms related to deep convolutional neural networks (DCNN have stimulated the development of various of signal processing approaches, where the specific application of Automatic Target Recognition (ATR using Synthetic Aperture Radar (SAR data has spurred widely attention. Inspired by the more efficient distributed training such as inception architecture and residual network, a new feature fusion structure which jointly exploits all the merits of each version is proposed to reduce the data dimensions and the complexity of computation. The detailed procedure presented in this paper consists of the fused features, which make the representation of SAR images more distinguishable after the extraction of a set of features from DCNN, followed by a trainable classifier. In particular, the obtained results on the 10-class benchmark data set demonstrate that the presented architecture can achieve remarkable classification performance to the current state-of-the-art methods.

  16. A New Method for Face Recognition Using Convolutional Neural Network

    Directory of Open Access Journals (Sweden)

    Patrik Kamencay

    2017-01-01

    Full Text Available In this paper, the performance of the proposed Convolutional Neural Network (CNN with three well-known image recognition methods such as Principal Component Analysis (PCA, Local Binary Patterns Histograms (LBPH and K–Nearest Neighbour (KNN is tested. In our experiments, the overall recognition accuracy of the PCA, LBPH, KNN and proposed CNN is demonstrated. All the experiments were implemented on the ORL database and the obtained experimental results were shown and evaluated. This face database consists of 400 different subjects (40 classes/ 10 images for each class. The experimental result shows that the LBPH provide better results than PCA and KNN. These experimental results on the ORL database demonstrated the effectiveness of the proposed method for face recognition. For proposed CNN we have obtained a best recognition accuracy of 98.3 %. The proposed method based on CNN outperforms the state of the art methods.

  17. Convolution backprojection image reconstruction for spotlight mode synthetic aperture radar.

    Science.gov (United States)

    Desai, M D; Jenkins, W K

    1992-01-01

    Convolution backprojection (CBP) image reconstruction has been proposed as a means of producing high-resolution synthetic-aperture radar (SAR) images by processing data directly in the polar recording format which is the conventional recording format for spotlight mode SAR. The CBP algorithm filters each projection as it is recorded and then backprojects the ensemble of filtered projections to create the final image in a pixel-by-pixel format. CBP reconstruction produces high-quality images by handling the recorded data directly in polar format. The CBP algorithm requires only 1-D interpolation along the filtered projections to determine the precise values that must be contributed to the backprojection summation from each projection. The algorithm is thus able to produce higher quality images by eliminating the inaccuracies of 2-D interpolation, as well as using all the data recorded in the spectral domain annular sector more effectively. The computational complexity of the CBP algorithm is O(N (3)).

  18. Training strategy for convolutional neural networks in pedestrian gender classification

    Science.gov (United States)

    Ng, Choon-Boon; Tay, Yong-Haur; Goi, Bok-Min

    2017-06-01

    In this work, we studied a strategy for training a convolutional neural network in pedestrian gender classification with limited amount of labeled training data. Unsupervised learning by k-means clustering on pedestrian images was used to learn the filters to initialize the first layer of the network. As a form of pre-training, supervised learning for the related task of pedestrian classification was performed. Finally, the network was fine-tuned for gender classification. We found that this strategy improved the network's generalization ability in gender classification, achieving better test results when compared to random weights initialization and slightly more beneficial than merely initializing the first layer filters by unsupervised learning. This shows that unsupervised learning followed by pre-training with pedestrian images is an effective strategy to learn useful features for pedestrian gender classification.

  19. High Order Tensor Formulation for Convolutional Sparse Coding

    KAUST Repository

    Bibi, Adel Aamer

    2017-12-25

    Convolutional sparse coding (CSC) has gained attention for its successful role as a reconstruction and a classification tool in the computer vision and machine learning community. Current CSC methods can only reconstruct singlefeature 2D images independently. However, learning multidimensional dictionaries and sparse codes for the reconstruction of multi-dimensional data is very important, as it examines correlations among all the data jointly. This provides more capacity for the learned dictionaries to better reconstruct data. In this paper, we propose a generic and novel formulation for the CSC problem that can handle an arbitrary order tensor of data. Backed with experimental results, our proposed formulation can not only tackle applications that are not possible with standard CSC solvers, including colored video reconstruction (5D- tensors), but it also performs favorably in reconstruction with much fewer parameters as compared to naive extensions of standard CSC to multiple features/channels.

  20. XOR-FREE Implementation of Convolutional Encoder for Reconfigurable Hardware

    Directory of Open Access Journals (Sweden)

    Gaurav Purohit

    2016-01-01

    Full Text Available This paper presents a novel XOR-FREE algorithm to implement the convolutional encoder using reconfigurable hardware. The approach completely removes the XOR processing of a chosen nonsystematic, feedforward generator polynomial of larger constraint length. The hardware (HW implementation of new architecture uses Lookup Table (LUT for storing the parity bits. The design implements architectural reconfigurability by modifying the generator polynomial of the same constraint length and code rate to reduce the design complexity. The proposed architecture reduces the dynamic power up to 30% and improves the hardware cost and propagation delay up to 20% and 32%, respectively. The performance of the proposed architecture is validated in MATLAB Simulink and tested on Zynq-7 series FPGA.

  1. Plane-wave decomposition by spherical-convolution microphone array

    Science.gov (United States)

    Rafaely, Boaz; Park, Munhum

    2004-05-01

    Reverberant sound fields are widely studied, as they have a significant influence on the acoustic performance of enclosures in a variety of applications. For example, the intelligibility of speech in lecture rooms, the quality of music in auditoria, the noise level in offices, and the production of 3D sound in living rooms are all affected by the enclosed sound field. These sound fields are typically studied through frequency response measurements or statistical measures such as reverberation time, which do not provide detailed spatial information. The aim of the work presented in this seminar is the detailed analysis of reverberant sound fields. A measurement and analysis system based on acoustic theory and signal processing, designed around a spherical microphone array, is presented. Detailed analysis is achieved by decomposition of the sound field into waves, using spherical Fourier transform and spherical convolution. The presentation will include theoretical review, simulation studies, and initial experimental results.

  2. Deep convolutional neural network for prostate MR segmentation

    Science.gov (United States)

    Tian, Zhiqiang; Liu, Lizhi; Fei, Baowei

    2017-03-01

    Automatic segmentation of the prostate in magnetic resonance imaging (MRI) has many applications in prostate cancer diagnosis and therapy. We propose a deep fully convolutional neural network (CNN) to segment the prostate automatically. Our deep CNN model is trained end-to-end in a single learning stage based on prostate MR images and the corresponding ground truths, and learns to make inference for pixel-wise segmentation. Experiments were performed on our in-house data set, which contains prostate MR images of 20 patients. The proposed CNN model obtained a mean Dice similarity coefficient of 85.3%+/-3.2% as compared to the manual segmentation. Experimental results show that our deep CNN model could yield satisfactory segmentation of the prostate.

  3. AUTOMATIC MUSCLE PERIMYSIUM ANNOTATION USING DEEP CONVOLUTIONAL NEURAL NETWORK.

    Science.gov (United States)

    Sapkota, Manish; Xing, Fuyong; Su, Hai; Yang, Lin

    2015-04-01

    Diseased skeletal muscle expresses mononuclear cell infiltration in the regions of perimysium. Accurate annotation or segmentation of perimysium can help biologists and clinicians to determine individualized patient treatment and allow for reasonable prognostication. However, manual perimysium annotation is time consuming and prone to inter-observer variations. Meanwhile, the presence of ambiguous patterns in muscle images significantly challenge many traditional automatic annotation algorithms. In this paper, we propose an automatic perimysium annotation algorithm based on deep convolutional neural network (CNN). We formulate the automatic annotation of perimysium in muscle images as a pixel-wise classification problem, and the CNN is trained to label each image pixel with raw RGB values of the patch centered at the pixel. The algorithm is applied to 82 diseased skeletal muscle images. We have achieved an average precision of 94% on the test dataset.

  4. Drug-Drug Interaction Extraction via Convolutional Neural Networks

    Directory of Open Access Journals (Sweden)

    Shengyu Liu

    2016-01-01

    Full Text Available Drug-drug interaction (DDI extraction as a typical relation extraction task in natural language processing (NLP has always attracted great attention. Most state-of-the-art DDI extraction systems are based on support vector machines (SVM with a large number of manually defined features. Recently, convolutional neural networks (CNN, a robust machine learning method which almost does not need manually defined features, has exhibited great potential for many NLP tasks. It is worth employing CNN for DDI extraction, which has never been investigated. We proposed a CNN-based method for DDI extraction. Experiments conducted on the 2013 DDIExtraction challenge corpus demonstrate that CNN is a good choice for DDI extraction. The CNN-based DDI extraction method achieves an F-score of 69.75%, which outperforms the existing best performing method by 2.75%.

  5. Finger vein recognition based on convolutional neural network

    Directory of Open Access Journals (Sweden)

    Meng Gesi

    2017-01-01

    Full Text Available Biometric Authentication Technology has been widely used in this information age. As one of the most important technology of authentication, finger vein recognition attracts our attention because of its high security, reliable accuracy and excellent performance. However, the current finger vein recognition system is difficult to be applied widely because its complicated image pre-processing and not representative feature vectors. To solve this problem, a finger vein recognition method based on the convolution neural network (CNN is proposed in the paper. The image samples are directly input into the CNN model to extract its feature vector so that we can make authentication by comparing the Euclidean distance between these vectors. Finally, the Deep Learning Framework Caffe is adopted to verify this method. The result shows that there are great improvements in both speed and accuracy rate compared to the previous research. And the model has nice robustness in illumination and rotation.

  6. Classification of decays involving variable decay chains with convolutional architectures

    CERN Multimedia

    CERN. Geneva

    2018-01-01

    Vidyo contribution We present a technique to perform classification of decays that exhibit decay chains involving a variable number of particles, which include a broad class of $B$ meson decays sensitive to new physics. The utility of such decays as a probe of the Standard Model is dependent upon accurate determination of the decay rate, which is challenged by the combinatorial background arising in high-multiplicity decay modes. In our model, each particle in the decay event is represented as a fixed-dimensional vector of feature attributes, forming an $n \\times k$ representation of the event, where $n$ is the number of particles in the event and $k$ is the dimensionality of the feature vector. A convolutional architecture is used to capture dependencies between the embedded particle representations and perform the final classification. The proposed model performs outperforms standard machine learning approaches based on Monte Carlo studies across a range of variable final-state decays with the Belle II det...

  7. Network Intrusion Detection through Stacking Dilated Convolutional Autoencoders

    Directory of Open Access Journals (Sweden)

    Yang Yu

    2017-01-01

    Full Text Available Network intrusion detection is one of the most important parts for cyber security to protect computer systems against malicious attacks. With the emergence of numerous sophisticated and new attacks, however, network intrusion detection techniques are facing several significant challenges. The overall objective of this study is to learn useful feature representations automatically and efficiently from large amounts of unlabeled raw network traffic data by using deep learning approaches. We propose a novel network intrusion model by stacking dilated convolutional autoencoders and evaluate our method on two new intrusion detection datasets. Several experiments were carried out to check the effectiveness of our approach. The comparative experimental results demonstrate that the proposed model can achieve considerably high performance which meets the demand of high accuracy and adaptability of network intrusion detection systems (NIDSs. It is quite potential and promising to apply our model in the large-scale and real-world network environments.

  8. Deep learning with convolutional neural network in radiology.

    Science.gov (United States)

    Yasaka, Koichiro; Akai, Hiroyuki; Kunimatsu, Akira; Kiryu, Shigeru; Abe, Osamu

    2018-04-01

    Deep learning with a convolutional neural network (CNN) is gaining attention recently for its high performance in image recognition. Images themselves can be utilized in a learning process with this technique, and feature extraction in advance of the learning process is not required. Important features can be automatically learned. Thanks to the development of hardware and software in addition to techniques regarding deep learning, application of this technique to radiological images for predicting clinically useful information, such as the detection and the evaluation of lesions, etc., are beginning to be investigated. This article illustrates basic technical knowledge regarding deep learning with CNNs along the actual course (collecting data, implementing CNNs, and training and testing phases). Pitfalls regarding this technique and how to manage them are also illustrated. We also described some advanced topics of deep learning, results of recent clinical studies, and the future directions of clinical application of deep learning techniques.

  9. Executive functioning and depressed mood before and after unilateral frontal lobe resection for intractable epilepsy.

    Science.gov (United States)

    Dulay, Mario F; Busch, Robyn M; Chapin, Jessica S; Jehi, Lara; Najm, Imad

    2013-06-01

    Executive dysfunction occurs in a variety of patients who have sustained damage to the frontal lobes. In individuals with frontal lobe epilepsy (FLE) or after unilateral frontal lobe resection (FLR), a unique neuropsychological profile linking executive functions (EF) with the frontal lobe has been elusive, with conflicting findings in the literature. Some studies show greater risk of executive impairment with left-sided FLE or FLR, while others report greater risk for right-sided patients. Some studies report no relationship between FLE and EF impairment, while others show EF impairment regardless of side of seizure foci or surgery. In patients with temporal lobe epilepsy, executive dysfunction is associated with depressed mood possibly reflecting disruption of cortical-limbic pathways and/or frontal-striatal circuitry. Although not previously examined, depression level may affect executive functioning in those with FLE or FLR. We hypothesized that FLE patients with poor mood state would show greater executive dysfunction than FLE patients without poor mood state. The relationship among EF, side of surgery and depressed mood before and 8 months after unilateral FLR was evaluated in 64 patients using validated measures of EF and mood state (Beck Depression Inventory-II). Results indicated that individuals with depressed mood before surgery had greater difficulty on a task of mental flexibility compared to patients without preoperative depressed mood. Further, individuals with depressed mood before surgery had significant increases in perseverative responding and completed fewer categories on a card-sorting task after surgery compared to patients without preoperative depressed mood. Regression analyses showed that among side of surgery, seizure freedom status after surgery and depression status, only pre-surgical depression status explained a significant amount of variance in executive functioning performance after surgery. Results suggest that clinically elevated

  10. Spatial Pyramid Pooling in Deep Convolutional Networks for Visual Recognition.

    Science.gov (United States)

    He, Kaiming; Zhang, Xiangyu; Ren, Shaoqing; Sun, Jian

    2015-09-01

    Existing deep convolutional neural networks (CNNs) require a fixed-size (e.g., 224 × 224) input image. This requirement is "artificial" and may reduce the recognition accuracy for the images or sub-images of an arbitrary size/scale. In this work, we equip the networks with another pooling strategy, "spatial pyramid pooling", to eliminate the above requirement. The new network structure, called SPP-net, can generate a fixed-length representation regardless of image size/scale. Pyramid pooling is also robust to object deformations. With these advantages, SPP-net should in general improve all CNN-based image classification methods. On the ImageNet 2012 dataset, we demonstrate that SPP-net boosts the accuracy of a variety of CNN architectures despite their different designs. On the Pascal VOC 2007 and Caltech101 datasets, SPP-net achieves state-of-the-art classification results using a single full-image representation and no fine-tuning. The power of SPP-net is also significant in object detection. Using SPP-net, we compute the feature maps from the entire image only once, and then pool features in arbitrary regions (sub-images) to generate fixed-length representations for training the detectors. This method avoids repeatedly computing the convolutional features. In processing test images, our method is 24-102 × faster than the R-CNN method, while achieving better or comparable accuracy on Pascal VOC 2007. In ImageNet Large Scale Visual Recognition Challenge (ILSVRC) 2014, our methods rank #2 in object detection and #3 in image classification among all 38 teams. This manuscript also introduces the improvement made for this competition.

  11. Fovea detection in optical coherence tomography using convolutional neural networks

    Science.gov (United States)

    Liefers, Bart; Venhuizen, Freerk G.; Theelen, Thomas; Hoyng, Carel; van Ginneken, Bram; Sánchez, Clara I.

    2017-02-01

    The fovea is an important clinical landmark that is used as a reference for assessing various quantitative measures, such as central retinal thickness or drusen count. In this paper we propose a novel method for automatic detection of the foveal center in Optical Coherence Tomography (OCT) scans. Although the clinician will generally aim to center the OCT scan on the fovea, post-acquisition image processing will give a more accurate estimate of the true location of the foveal center. A Convolutional Neural Network (CNN) was trained on a set of 781 OCT scans that classifies each pixel in the OCT B-scan with a probability of belonging to the fovea. Dilated convolutions were used to obtain a large receptive field, while maintaining pixel-level accuracy. In order to train the network more effectively, negative patches were sampled selectively after each epoch. After CNN classification of the entire OCT volume, the predicted foveal center was chosen as the voxel with maximum output probability, after applying an optimized three-dimensional Gaussian blurring. We evaluate the performance of our method on a data set of 99 OCT scans presenting different stages of Age-related Macular Degeneration (AMD). The fovea was correctly detected in 96:9% of the cases, with a mean distance error of 73 μm(+/-112 μm). This result was comparable to the performance of a second human observer who obtained a mean distance error of 69 μm (+/-94 μm). Experiments showed that the proposed method is accurate and robust even in retinas heavily affected by pathology.

  12. Topology reduction in deep convolutional feature extraction networks

    Science.gov (United States)

    Wiatowski, Thomas; Grohs, Philipp; Bölcskei, Helmut

    2017-08-01

    Deep convolutional neural networks (CNNs) used in practice employ potentially hundreds of layers and 10,000s of nodes. Such network sizes entail significant computational complexity due to the large number of convolutions that need to be carried out; in addition, a large number of parameters needs to be learned and stored. Very deep and wide CNNs may therefore not be well suited to applications operating under severe resource constraints as is the case, e.g., in low-power embedded and mobile platforms. This paper aims at understanding the impact of CNN topology, specifically depth and width, on the network's feature extraction capabilities. We address this question for the class of scattering networks that employ either Weyl-Heisenberg filters or wavelets, the modulus non-linearity, and no pooling. The exponential feature map energy decay results in Wiatowski et al., 2017, are generalized to O(a-N), where an arbitrary decay factor a > 1 can be realized through suitable choice of the Weyl-Heisenberg prototype function or the mother wavelet. We then show how networks of fixed (possibly small) depth N can be designed to guarantee that ((1 - ɛ) · 100)% of the input signal's energy are contained in the feature vector. Based on the notion of operationally significant nodes, we characterize, partly rigorously and partly heuristically, the topology-reducing effects of (effectively) band-limited input signals, band-limited filters, and feature map symmetries. Finally, for networks based on Weyl-Heisenberg filters, we determine the prototype function bandwidth that minimizes - for fixed network depth N - the average number of operationally significant nodes per layer.

  13. Selective involvement of superior frontal cortex during working memory for shapes.

    Science.gov (United States)

    Yee, Lydia T S; Roe, Katherine; Courtney, Susan M

    2010-01-01

    A spatial/nonspatial functional dissociation between the dorsal and ventral visual pathways is well established and has formed the basis of domain-specific theories of prefrontal cortex (PFC). Inconsistencies in the literature regarding prefrontal organization, however, have led to questions regarding whether the nature of the dissociations observed in PFC during working memory are equivalent to those observed in the visual pathways for perception. In particular, the dissociation between dorsal and ventral PFC during working memory for locations versus object identities has been clearly present in some studies but not in others, seemingly in part due to the type of objects used. The current study compared functional MRI activation during delayed-recognition tasks for shape or color, two object features considered to be processed by the ventral pathway for perceptual recognition. Activation for the shape-delayed recognition task was greater than that for the color task in the lateral occipital cortex, in agreement with studies of visual perception. Greater memory-delay activity was also observed, however, in the parietal and superior frontal cortices for the shape than for the color task. Activity in superior frontal cortex was associated with better performance on the shape task. Conversely, greater delay activity for color than for shape was observed in the left anterior insula and this activity was associated with better performance on the color task. These results suggest that superior frontal cortex contributes to performance on tasks requiring working memory for object identities, but it represents different information about those objects than does the ventral frontal cortex.

  14. DETERMINATION OF CLINICALLY RELEVANT DIFFERENCES IN FRONTAL PLANE HOP TESTS IN WOMEN'S COLLEGIATE BASKETBALL AND SOCCER PLAYERS

    Science.gov (United States)

    Hardesty, Kelly; Hegedus, Eric J.; Ford, Kevin R.; Nguyen, Anh‐Dung

    2017-01-01

    Background ACL injury prevention programs are less successful in female basketball players than in soccer players. Previous authors have identified anthropometric and biomechanical differences between the athletes and different sport‐specific demands, including a higher frequency of frontal plane activities in basketball. Current injury risk screening and preventive training practices do not place a strong emphasis on frontal plane activities. The medial and lateral triple hop for distance tests may be beneficial for use in the basketball population. Hypothesis/Purpose To 1) establish normative values for the medial and lateral triple hop tests in healthy female collegiate athletes, and 2) analyze differences in test scores between female basketball and soccer players. It was hypothesized that due to the frequent frontal plane demands of their sport, basketball players would exhibit greater performance during these frontal plane performance tests. Study Design Cross‐sectional. Methods Thirty‐two NCAA Division‐1 female athletes (20 soccer, 12 basketball) performed three trials each of a medial and lateral triple hop for distance test. Distances were normalized to height and mass in order to account for anthropometric differences. Repeated measures ANOVAs were performed to identify statistically significant main effects of sport (basketball vs. soccer), and side (right vs. left), and sport x side interactions. Results After accounting for anthropometric differences, soccer players exhibited significantly better performance than basketball players in the medial and lateral triple hop tests (p jumped farther on their left (400.3 ± 41.5 cm) than right (387.9 ± 43.4 cm) limbs, but no side differences were identified in the lateral triple hop. No significant side x sport interactions were identified. Conclusions Women's basketball players exhibit decreased performance of frontal plane hop tests when compared to women's soccer players. Additionally

  15. Interictal epileptic discharge correlates with global and frontal cognitive dysfunction in temporal lobe epilepsy.

    Science.gov (United States)

    Dinkelacker, Vera; Xin, Xu; Baulac, Michel; Samson, Séverine; Dupont, Sophie

    2016-09-01

    Temporal lobe epilepsy (TLE) with hippocampal sclerosis has widespread effects on structural and functional connectivity and often entails cognitive dysfunction. EEG is mandatory to disentangle interactions in epileptic and physiological networks which underlie these cognitive comorbidities. Here, we examined how interictal epileptic discharges (IEDs) affect cognitive performance. Thirty-four patients (right TLE=17, left TLE=17) were examined with 24-hour video-EEG and a battery of neuropsychological tests to measure intelligence quotient and separate frontal and temporal lobe functions. Hippocampal segmentation of high-resolution T1-weighted imaging was performed with FreeSurfer. Partial correlations were used to compare the number and distribution of clinical interictal spikes and sharp waves with data from imagery and psychological tests. The number of IEDs was negatively correlated with executive functions, including verbal fluency and intelligence quotient (IQ). Interictal epileptic discharge affected cognitive function in patients with left and right TLE differentially, with verbal fluency strongly related to temporofrontal spiking. In contrast, IEDs had no clear effects on memory functions after corrections with partial correlations for age, age at disease onset, disease duration, and hippocampal volume. In patients with TLE of long duration, IED occurrence was strongly related to cognitive deficits, most pronounced for frontal lobe function. These data suggest that IEDs reflect dysfunctional brain circuitry and may serve as an independent biomarker for cognitive comorbidity. Copyright © 2016. Published by Elsevier Inc.

  16. Reduced right frontal fractional anisotropy correlated with early elevated plasma LDL levels in obese young adults.

    Directory of Open Access Journals (Sweden)

    Baohui Lou

    Full Text Available OBJECTIVE: To investigate the underlying physiological mechanisms of the structural differences in gray matter (GM and white matter (WM associated with obesity in young Chinese adults. MATERIALS AND METHODS: A total of 49 right-handed obese or overweight (n = 22, mean age 31.72±8.04 years and normal weight (n = 27, mean age 29.04±7.32 years Han Chinese individuals were recruited. All participants underwent voxel-based morphometry analysis of T1-weighted MRI and tract-based spatial statistics analysis of diffusion tensor imaging. Partial correlation analysis was performed between the physiological data obtained and the abnormal structural alterations. RESULTS: In the OO group, GM atrophy occurred in the left prefrontal cortex, bilateral cingulate gyrus, and the right temporal lobe, while enlargement was observed in the bilateral putamen. WM atrophy was observed predominantly in the regions that regulate food intake, such as the bilateral basal ganglia, the right amygdala, and the left insula. The OO group exhibited lower fractional anisotropy (FA in bilateral frontal corticospinal tracts and the right brainstem. Significant negative correlations were observed between FA values of those three clusters and BMI, and waist circumference, while the volume of bilateral putamen positively correlated with both BMI and waist circumference. High plasma LDL levels were correlated with low FA values in the right frontal corticospinal tract. Interestingly, the negative correlation was limited to male participants. CONCLUSIONS: Obesity-related alterations of GM and WM volumes were observed predominantly in food reward circuit, which may motivate abnormal dietary intake. Further, early elevated plasma LDL might contribute to low right frontal FA values of male adults, which requires further demonstration by larger-scale and longitudinal studies.

  17. Reduced frontal-subcortical white matter connectivity in association with suicidal ideation in major depressive disorder

    Science.gov (United States)

    Myung, W; Han, C E; Fava, M; Mischoulon, D; Papakostas, G I; Heo, J-Y; Kim, K W; Kim, S T; Kim, D J H; Kim, D K; Seo, S W; Seong, J-K; Jeon, H J

    2016-01-01

    Major depressive disorder (MDD) and suicidal behavior have been associated with structural and functional changes in the brain. However, little is known regarding alterations of brain networks in MDD patients with suicidal ideation. We investigated whether or not MDD patients with suicidal ideation have different topological organizations of white matter networks compared with MDD patients without suicidal ideation. Participants consisted of 24 patients with MDD and suicidal ideation, 25 age- and gender-matched MDD patients without suicidal ideation and 31 healthy subjects. A network-based statistics (NBS) and a graph theoretical analysis were performed to assess differences in the inter-regional connectivity. Diffusion tensor imaging (DTI) was performed to assess topological changes according to suicidal ideation in MDD patients. The Scale for Suicide Ideation (SSI) and the Korean version of the Barrett Impulsiveness Scale (BIS) were used to assess the severity of suicidal ideation and impulsivity, respectively. Reduced structural connectivity in a characterized subnetwork was found in patients with MDD and suicidal ideation by utilizing NBS analysis. The subnetwork included the regions of the frontosubcortical circuits and the regions involved in executive function in the left hemisphere (rostral middle frontal, pallidum, superior parietal, frontal pole, caudate, putamen and thalamus). The graph theoretical analysis demonstrated that network measures of the left rostral middle frontal had a significant positive correlation with severity of SSI (r=0.59, P=0.02) and BIS (r=0.59, P=0.01). The total edge strength that was significantly associated with suicidal ideation did not differ between MDD patients without suicidal ideation and healthy subjects. Our findings suggest that the reduced frontosubcortical circuit of structural connectivity, which includes regions associated with executive function and impulsivity, appears to have a role in the emergence of suicidal

  18. Emotional Responses to Music: Shifts in Frontal Brain Asymmetry Mark Periods of Musical Change.

    Science.gov (United States)

    Arjmand, Hussain-Abdulah; Hohagen, Jesper; Paton, Bryan; Rickard, Nikki S

    2017-01-01

    Recent studies have demonstrated increased activity in brain regions associated with emotion and reward when listening to pleasurable music. Unexpected change in musical features intensity and tempo - and thereby enhanced tension and anticipation - is proposed to be one of the primary mechanisms by which music induces a strong emotional response in listeners. Whether such musical features coincide with central measures of emotional response has not, however, been extensively examined. In this study, subjective and physiological measures of experienced emotion were obtained continuously from 18 participants (12 females, 6 males; 18-38 years) who listened to four stimuli-pleasant music, unpleasant music (dissonant manipulations of their own music), neutral music, and no music, in a counter-balanced order. Each stimulus was presented twice: electroencephalograph (EEG) data were collected during the first, while participants continuously subjectively rated the stimuli during the second presentation. Frontal asymmetry (FA) indices from frontal and temporal sites were calculated, and peak periods of bias toward the left (indicating a shift toward positive affect) were identified across the sample. The music pieces were also examined to define the temporal onset of key musical features. Subjective reports of emotional experience averaged across the condition confirmed participants rated their music selection as very positive, the scrambled music as negative, and the neutral music and silence as neither positive nor negative. Significant effects in FA were observed in the frontal electrode pair FC3-FC4, and the greatest increase in left bias from baseline was observed in response to pleasurable music. These results are consistent with findings from previous research. Peak FA responses at this site were also found to co-occur with key musical events relating to change, for instance, the introduction of a new motif, or an instrument change, or a change in low level acoustic

  19. Limitations of a convolution method for modeling geometric uncertainties in radiation therapy. I. The effect of shift invariance

    International Nuclear Information System (INIS)

    Craig, Tim; Battista, Jerry; Van Dyk, Jake

    2003-01-01

    Convolution methods have been used to model the effect of geometric uncertainties on dose delivery in radiation therapy. Convolution assumes shift invariance of the dose distribution. Internal inhomogeneities and surface curvature lead to violations of this assumption. The magnitude of the error resulting from violation of shift invariance is not well documented. This issue is addressed by comparing dose distributions calculated using the Convolution method with dose distributions obtained by Direct Simulation. A comparison of conventional Static dose distributions was also made with Direct Simulation. This analysis was performed for phantom geometries and several clinical tumor sites. A modification to the Convolution method to correct for some of the inherent errors is proposed and tested using example phantoms and patients. We refer to this modified method as the Corrected Convolution. The average maximum dose error in the calculated volume (averaged over different beam arrangements in the various phantom examples) was 21% with the Static dose calculation, 9% with Convolution, and reduced to 5% with the Corrected Convolution. The average maximum dose error in the calculated volume (averaged over four clinical examples) was 9% for the Static method, 13% for Convolution, and 3% for Corrected Convolution. While Convolution can provide a superior estimate of the dose delivered when geometric uncertainties are present, the violation of shift invariance can result in substantial errors near the surface of the patient. The proposed Corrected Convolution modification reduces errors near the surface to 3% or less

  20. Trauma of the Frontal Region Is Influenced by the Volume of Frontal Sinuses. A Finite Element Study

    Directory of Open Access Journals (Sweden)

    Srbislav S. Pajic

    2017-07-01

    Full Text Available Anatomy of frontal sinuses varies individually, from differences in volume and shape to a rare case when the sinuses are absent. However, there are scarce data related to influence of these variations on impact generated fracture pattern. Therefore, the aim of this study was to analyse the influence of frontal sinus volume on the stress distribution and fracture pattern in the frontal region. The study included four representative Finite Element models of the skull. Reference model was built on the basis of computed tomography scans of a human head with normally developed frontal sinuses. By modifying the reference model, three additional models were generated: a model without sinuses, with hypoplasic, and with hyperplasic sinuses. A 7.7 kN force was applied perpendicularly to the forehead of each model, in order to simulate a frontal impact. The results demonstrated that the distribution of impact stress in frontal region depends on the frontal sinus volume. The anterior sinus wall showed the highest fragility in case with hyperplasic sinuses, whereas posterior wall/inner plate showed more fragility in cases with hypoplasic and undeveloped sinuses. Well-developed frontal sinuses might, through absorption of the impact energy by anterior wall, protect the posterior wall and intracranial contents.

  1. Frontal Brain Asymmetry and Willingness to Pay

    Directory of Open Access Journals (Sweden)

    Thomas Z. Ramsøy

    2018-03-01

    Full Text Available Consumers frequently make decisions about how much they are willing to pay (WTP for specific products and services, but little is known about the neural mechanisms underlying such calculations. In this study, we were interested in testing whether specific brain activation—the asymmetry in engagement of the prefrontal cortex—would be related to consumer choice. Subjects saw products and subsequently decided how much they were willing to pay for each product, while undergoing neuroimaging using electroencephalography. Our results demonstrate that prefrontal asymmetry in the gamma frequency band, and a trend in the beta frequency band that was recorded during product viewing was significantly related to subsequent WTP responses. Frontal asymmetry in the alpha band was not related to WTP decisions. Besides suggesting separate neuropsychological mechanisms of consumer choice, we find that one specific measure—the prefrontal gamma asymmetry—was most strongly related to WTP responses, and was most coupled to the actual decision phase. These findings are discussed in light of the psychology of WTP calculations, and in relation to the recent emergence of consumer neuroscience and neuromarketing.

  2. Frontal Brain Asymmetry and Willingness to Pay.

    Science.gov (United States)

    Ramsøy, Thomas Z; Skov, Martin; Christensen, Maiken K; Stahlhut, Carsten

    2018-01-01

    Consumers frequently make decisions about how much they are willing to pay (WTP) for specific products and services, but little is known about the neural mechanisms underlying such calculations. In this study, we were interested in testing whether specific brain activation-the asymmetry in engagement of the prefrontal cortex-would be related to consumer choice. Subjects saw products and subsequently decided how much they were willing to pay for each product, while undergoing neuroimaging using electroencephalography. Our results demonstrate that prefrontal asymmetry in the gamma frequency band, and a trend in the beta frequency band that was recorded during product viewing was significantly related to subsequent WTP responses. Frontal asymmetry in the alpha band was not related to WTP decisions. Besides suggesting separate neuropsychological mechanisms of consumer choice, we find that one specific measure-the prefrontal gamma asymmetry-was most strongly related to WTP responses, and was most coupled to the actual decision phase. These findings are discussed in light of the psychology of WTP calculations, and in relation to the recent emergence of consumer neuroscience and neuromarketing.

  3. Wheelchair caster loading during frontal impact.

    Science.gov (United States)

    Bertocci, Gina E; van Roosmalen, Linda

    2003-01-01

    Many wheelchair users are required or choose to use their wheelchairs as a motor vehicle seat during transport. It is therefore key that the wheelchair components be designed to tolerate crash-level loading conditions. Casters are particularly prone to failure under crash loading conditions. Our study evaluated wheelchair caster loading during 20g/48 kph frontal sled impact testing using an 85-kg surrogate wheelchair base (SWCB) with casters positioned on a load-measuring platform. A Hybrid III 50th percentile male test dummy was seated in the SWCB, which simulated a power wheelchair and was secured using four-point tiedowns. Various rear securement point heights and wheelchair seating systems were used to study their effect on caster loading. Caster normal loading was found to vary from 769 to 7,209 N depending on rear securement location and integrity of the seating system. Dynamic sled impact test results showed that normal loading of the front wheelchair casters was influenced by wheelchair seating system integrity and rear wheelchair securement height. Shear loading varied from 781 to 1,589 N and did not appear to be dependent on seat integrity or rear securement height. The load/time histories measured during dynamic impact testing can be used to guide the development of transit-safe caster design.

  4. Development and application of deep convolutional neural network in target detection

    Science.gov (United States)

    Jiang, Xiaowei; Wang, Chunping; Fu, Qiang

    2018-04-01

    With the development of big data and algorithms, deep convolution neural networks with more hidden layers have more powerful feature learning and feature expression ability than traditional machine learning methods, making artificial intelligence surpass human level in many fields. This paper first reviews the development and application of deep convolutional neural networks in the field of object detection in recent years, then briefly summarizes and ponders some existing problems in the current research, and the future development of deep convolutional neural network is prospected.

  5. Frontal EEG delta/alpha ratio and screening for post-stroke cognitive deficits: the power of four electrodes.

    Science.gov (United States)

    Schleiger, Emma; Sheikh, Nabeel; Rowland, Tennille; Wong, Andrew; Read, Stephen; Finnigan, Simon

    2014-10-01

    This study analysed correlations between post-stroke, quantitative electroencephalographic (QEEG) indices, and cognition-specific, functional outcome measures. Results were compared between QEEG indices calculated from the standard 19 versus 4 frontal (or 4 posterior) electrodes to assess the feasibility and efficacy of employing a reduced electrode montage. Resting-state EEG was recorded at the bedside within 62-101 h after onset of symptoms of middle cerebral artery, ischaemic stroke (confirmed radiologically). Relative power for delta, theta, alpha and beta, delta/alpha ratio (DAR) and pairwise-derived brain symmetry index (pdBSI) were averaged; over all electrodes (global), over F3, F4, F7, F8 (frontal) and P3, P4, T5, T6 (posterior). The functional independence measure and functional assessment measure (FIM-FAM) was administered at mean 105 days post-stroke. Total (30 items) and cognition-specific (5 items) FIM-FAM scores were correlated with QEEG indices using Spearman's coefficient, with a Bonferroni correction. Twenty-five patients were recruited, 4 died within 3 months and 1 was lost to follow-up. Hence 20 cases (10 female; 9 left hemisphere; mean age 68 years, range 38-84) were analysed. Two QEEG indices demonstrated highly-significant correlations with cognitive outcomes: frontal DAR (ρ = -0.664, p ≤ 0.001) and global, relative alpha power (ρ = 0.67, p ≤ 0.001). After correction there were no other significant correlations. Alpha activity - particularly frontally - may index post-stroke attentional capacity, which appears to be a key determinant of functional and cognitive outcomes. Likewise frontal delta pathophysiology influences such outcomes. Pending further studies, DAR from 4 frontal electrodes may inform early screening for post-MCA stroke cognitive deficits, and thereby, clinical decisions. Copyright © 2014 Elsevier B.V. All rights reserved.

  6. Application of a Hybrid Model Based on a Convolutional Auto-Encoder and Convolutional Neural Network in Object-Oriented Remote Sensing Classification

    Directory of Open Access Journals (Sweden)

    Wei Cui

    2018-01-01

    Full Text Available Variation in the format and classification requirements for remote sensing data makes establishing a standard remote sensing sample dataset difficult. As a result, few remote sensing deep neural network models have been widely accepted. We propose a hybrid deep neural network model based on a convolutional auto-encoder and a complementary convolutional neural network to solve this problem. The convolutional auto-encoder supports feature extraction and data dimension reduction of remote sensing data. The extracted features are input into the convolutional neural network and subsequently classified. Experimental results show that in the proposed model, the classification accuracy increases from 0.916 to 0.944, compared to a traditional convolutional neural network model; furthermore, the number of training runs is reduced from 40,000 to 22,000, and the number of labelled samples can be reduced by more than half, all while ensuring a classification accuracy of no less than 0.9, which suggests the effectiveness and feasibility of the proposed model.

  7. Othello syndrome in a patient with two left hemispheric tumors

    Directory of Open Access Journals (Sweden)

    Po-Kuan Yeh

    2016-01-01

    Full Text Available We report a case of a patient with Othello syndrome caused by two left hemispheric tumors. This 50-year-old female had experienced seizures for 10 years and developed manic-like symptoms, delusions of jealousy, persecution and being watched, auditory hallucinations, irritable mood, and violent and disorganized behavior for the past 3 years. Brain imaging studies revealed two left frontal tumors, the larger of which was causing a mass effect. The delusions of jealousy in Othello syndrome resolved after removing the larger tumor, and the other psychiatric symptoms improved after treatment with psychotropic medications. This report aims to raise awareness of Othello syndrome related to disruptions in cortico-subcortical connections in the left orbitofrontal region. Timely surgical treatment may prevent associated psychiatric comorbidities and increase the likelihood of a good outcome.

  8. Cirurgia de osteoma de seio frontal Surgery of frontal sinus osteoma

    Directory of Open Access Journals (Sweden)

    Lisete Pessoa de Oliveira Fobe

    2002-03-01

    Full Text Available Os osteomas do seio frontal correspondem a 57% dos osteomas dos seios paranasais, com incidência variando de 0,01% a 3%. A remoção cirúrgica nos osteomas frontais é indicada nos pacientes sintomáticos. Nos pacientes assintomáticos pode-se adotar a conduta conservadora ou cirúrgica em todos os pacientes independente da sua localização ou extensão. Cinco pacientes com diagnóstico de osteoma de seio frontal foram operados entre 1995 e 1999. A idade média foi 38,4 anos (extremos de 12 a 55 anos, sendo 3 homens e 2 mulheres. O período de sintomatologia variou de 6 meses a 3 anos com média de 10,5 meses. Quatro pacientes apresentaram cefaléia. Um paciente apresentou epistaxe. Os exames complementares realizados foram: radiografia simples e tomografia computadorizada de seios paranasais com cortes axiais e coronais. Em dois pacientes o diâmetro do osteoma foi maior que 3 cm, e menor que 3 cm em três. A decisão da técnica cirúrgica entre coronal e supraciliar foi estética, reservando-se a abordagem supraciliar para um paciente com calvície, apesar do tumor ser volumoso com extensão para seio etmoidal. Nenhuma dificuldade técnica intra-operatória foi atribuída à escolha da abordagem. O óstio nasofrontal não foi obstruído no intra-operatório. O seguimento pós-operatório mínimo foi de dois anos. Em todos os casos a remoção foi total sem recidiva ou resíduos tumorais. Os sintomas clínicos, achados radiológicos e abordagens cirúrgicas são discutidos. Não ocorreram complicações pós-operatórias.Frontal sinus osteomas are 57% of all paranasal sinus osteomas, with an incidence of 00.1 to 3%. Surgical removal of the frontal sinus osteomas is done in symptomatic patients. Asymptomatic patients can be managed conservatively or submitted to surgery in spite of its location or extension. Five patients having the diagnosis of frontal sinus osteoma were operated on between 1995 and 1999. Medium age was 38.4 years (from 12

  9. [Recurrent left atrial myxoma].

    Science.gov (United States)

    Moreno Martínez, Francisco L; Lagomasino Hidalgo, Alvaro; Mirabal Rodríguez, Roger; López Bermúdez, Félix H; López Bernal, Omaida J

    2003-01-01

    Primary cardiac tumors are rare. Mixomas are the most common among them; 75% are located in the left atrium, 20% in the right atrium, and the rest in the ventricles. The seldom appear in atrio-ventricular valves. Recidivant mixoma are also rare, appearing in 1-5% of all patients that have undergone surgical treatment of a mixoma. In this paper we present our experience with a female patient, who 8 years after having been operated of a left atrial mixoma, began with symptoms of mild heart failure. Transthoracic echocardiography revealed recurrence of the tumor, and was therefore subjected to a second open-heart surgery from which she recovered without complications.

  10. Analisis Penyerapan Energi Crash Box Pola Origami pada Pengujian Frontal Impact Posisi Angular Frontal

    Directory of Open Access Journals (Sweden)

    Redi Bintarto

    2017-05-01

    Full Text Available In the car, the body structure is designed in such a way so as to transfer and absorb energy. This serves to minimize the result of this accident related to kinetic energy. This needs a system to absorb the kinetic energy maximally, so as a result of a frontal collision events that can be reduced optimally and kinetic energy can be absorbed by a front body structure. Devices used for absorbing kinetic energy in the car is usually called a crash box, which is located between the main structure and bumper. Crash Box generally tubular thin shaped. It has been a lot of research about the crash box. In this study using crash box origami patterns and using methods taguchi orthogonal array L9 (34. AA7003-T7 aluminum material modeled as bilinear isotropic hardening, the loading method is Frontal Impact Frontal Angular Position with impact angles of 5, 15 and 30 degree by using the finite element software simulation methods. The simulation results showed that the crash box in the lowest possible energy absorption were happened at crash box with 5 degree, with 683 153 Joule energy absorbsion. The highest result was happened to crash box number 5 with the results of 3,140.778 Joule. Lowest absorption on impact of 15 degree and 30 degree were happened to crash box number 1 and number 3 with a value of 245 685 Joule and 174 845 Joule, while the highest absorption at mumber 3 with each value 1,708.521 Joule and 1,750.872 Joule.

  11. [Functional asymmetry of the frontal cortex and lateral hypothalamus of cats during food instrumental conditioning].

    Science.gov (United States)

    Vanetsiian, G L; Pavlova, I V

    2003-01-01

    The synchronism and latency of auditory evoked potentials (EP) recorded in symmetric points of the frontal cortex and lateral hypothalamus of cats were measured at different stages of instrumental food conditioning and after the urgent transition to 30% reinforcement. Correlation coefficients between EPs in the cortex and hypothalamus were high (with left-side dominance) at the beginning of the experiments, when food motivation was high, and during the whole experiments in cases of high-probability of conditioned performance. Analysis of early positive P55-80 EP component showed that at all conditioning stages the peak latency of this component was shorter in the left cortical areas than in symmetrical points, whereas in the hypothalamus the shorter latency at the left side was observed at the stage of unstable conditioned reflex, and at the stage of stable reflex the latency of the studied component was shorter at the right side. During transition to 30% reinforcement, the latency was also shorter in the right hypothalamus. It is suggested that the high left-side correlation between the hypothalamus and cortex was associated with motivational and motor component of behavior rather than reflected the emotional stress induced by transition to another stereotype of food reinforcement (30%).

  12. Frontal Dynamics of Powder Snow Avalanches

    Science.gov (United States)

    Louge, M. Y.; Carroll, C. S.; Turnbull, B.

    2012-04-01

    We model the dynamics of the head of dilute powder snow avalanches sustained by a massive frontal blow-out, arising as a weakly cohesive snow cover is fluidized by the very pore pressure gradients that the avalanche induces within the snow pack. Such material eruption just behind the front acts as a source of denser fluid thrust into a uniform ambient air flow at high Reynolds number. In such "eruption current", fluidization depth is inversely proportional to a bulk Richardson number representing avalanche height. By excluding situations in which the snow cover is not fluidized up to its free surface, we derive a criterion combining snow pack friction and density indicating which avalanches can produce a sustainable powder cloud. A mass balance involving snow cover and powder cloud sets avalanche height and mean density. By determining which solution of the mass balance is stable, we find that avalanches reach constant growth and acceleration rates for fixed slope and avalanche width. Under these conditions, we calculate the fraction of the fluidized cover that is actually scoured and blown-out into the cloud, and deduce from a momentum balance on the head that the avalanche accelerates at a rate only 14% of the gravitational component along the flow. We also calculate how far a powder cloud travels until its mean density becomes constant. Finally, we show that the dynamics of powder snow avalanches are crucially affected by the rate of change of their width, for example by reaching an apparent steady speed as their channel widens. If such widening is rapid, or if slope inclination vanishes, we calculate where and how powder clouds collapse. Predictions agree well with observations of powder snow avalanches carried out at the Vallee de la Sionne (Switzerland).

  13. Left atrial appendage occlusion

    Directory of Open Access Journals (Sweden)

    Ahmad Mirdamadi

    2013-01-01

    Full Text Available Left atrial appendage (LAA occlusion is a treatment strategy to prevent blood clot formation in atrial appendage. Although, LAA occlusion usually was done by catheter-based techniques, especially percutaneous trans-luminal mitral commissurotomy (PTMC, it can be done during closed and open mitral valve commissurotomy (CMVC, OMVC and mitral valve replacement (MVR too. Nowadays, PTMC is performed as an optimal management of severe mitral stenosis (MS and many patients currently are treated by PTMC instead of previous surgical methods. One of the most important contraindications of PTMC is presence of clot in LAA. So, each patient who suffers of severe MS is evaluated by Trans-Esophageal Echocardiogram to rule out thrombus in LAA before PTMC. At open heart surgery, replacement of the mitral valve was performed for 49-year-old woman. Also, left atrial appendage occlusion was done during surgery. Immediately after surgery, echocardiography demonstrates an echo imitated the presence of a thrombus in left atrial appendage area, although there was not any evidence of thrombus in pre-pump TEE. We can conclude from this case report that when we suspect of thrombus of left atrial, we should obtain exact history of previous surgery of mitral valve to avoid misdiagnosis clotted LAA, instead of obliterated LAA. Consequently, it can prevent additional evaluations and treatments such as oral anticoagulation and exclusion or postponing surgeries including PTMC.

  14. A new convolution algorithm for loss probablity analysis in multiservice networks

    DEFF Research Database (Denmark)

    Huang, Qian; Ko, King-Tim; Iversen, Villy Bæk

    2011-01-01

    Performance analysis in multiservice loss systems generally focuses on accurate and efficient calculation methods for traffic loss probability. Convolution algorithm is one of the existing efficient numerical methods. Exact loss probabilities are obtainable from the convolution algorithm in systems...... where the bandwidth is fully shared by all traffic classes; but not available for systems with trunk reservation, i.e. part of the bandwidth is reserved for a special class of traffic. A proposal known as asymmetric convolution algorithm (ACA) has been made to overcome the deficiency of the convolution...... algorithm. It obtains an approximation of the channel occupancy distribution in multiservice systems with trunk reservation. However, the ACA approximation is only accurate with two traffic flows; increased approximation errors are observed for systems with three or more traffic flows. In this paper, we...

  15. Single Image Super-Resolution Based on Multi-Scale Competitive Convolutional Neural Network.

    Science.gov (United States)

    Du, Xiaofeng; Qu, Xiaobo; He, Yifan; Guo, Di

    2018-03-06

    Deep convolutional neural networks (CNNs) are successful in single-image super-resolution. Traditional CNNs are limited to exploit multi-scale contextual information for image reconstruction due to the fixed convolutional kernel in their building modules. To restore various scales of image details, we enhance the multi-scale inference capability of CNNs by introducing competition among multi-scale convolutional filters, and build up a shallow network under limited computational resources. The proposed network has the following two advantages: (1) the multi-scale convolutional kernel provides the multi-context for image super-resolution, and (2) the maximum competitive strategy adaptively chooses the optimal scale of information for image reconstruction. Our experimental results on image super-resolution show that the performance of the proposed network outperforms the state-of-the-art methods.

  16. Directional Radiometry and Radiative Transfer: the Convoluted Path From Centuries-old Phenomenology to Physical Optics

    Science.gov (United States)

    Mishchenko, Michael I.

    2014-01-01

    This Essay traces the centuries-long history of the phenomenological disciplines of directional radiometry and radiative transfer in turbid media, discusses their fundamental weaknesses, and outlines the convoluted process of their conversion into legitimate branches of physical optics.

  17. Time-Domain Convolutive Blind Source Separation Employing Selective-Tap Adaptive Algorithms

    Directory of Open Access Journals (Sweden)

    Pan Qiongfeng

    2007-01-01

    Full Text Available We investigate novel algorithms to improve the convergence and reduce the complexity of time-domain convolutive blind source separation (BSS algorithms. First, we propose MMax partial update time-domain convolutive BSS (MMax BSS algorithm. We demonstrate that the partial update scheme applied in the MMax LMS algorithm for single channel can be extended to multichannel time-domain convolutive BSS with little deterioration in performance and possible computational complexity saving. Next, we propose an exclusive maximum selective-tap time-domain convolutive BSS algorithm (XM BSS that reduces the interchannel coherence of the tap-input vectors and improves the conditioning of the autocorrelation matrix resulting in improved convergence rate and reduced misalignment. Moreover, the computational complexity is reduced since only half of the tap inputs are selected for updating. Simulation results have shown a significant improvement in convergence rate compared to existing techniques.

  18. Time-Domain Convolutive Blind Source Separation Employing Selective-Tap Adaptive Algorithms

    Directory of Open Access Journals (Sweden)

    Qiongfeng Pan

    2007-04-01

    Full Text Available We investigate novel algorithms to improve the convergence and reduce the complexity of time-domain convolutive blind source separation (BSS algorithms. First, we propose MMax partial update time-domain convolutive BSS (MMax BSS algorithm. We demonstrate that the partial update scheme applied in the MMax LMS algorithm for single channel can be extended to multichannel time-domain convolutive BSS with little deterioration in performance and possible computational complexity saving. Next, we propose an exclusive maximum selective-tap time-domain convolutive BSS algorithm (XM BSS that reduces the interchannel coherence of the tap-input vectors and improves the conditioning of the autocorrelation matrix resulting in improved convergence rate and reduced misalignment. Moreover, the computational complexity is reduced since only half of the tap inputs are selected for updating. Simulation results have shown a significant improvement in convergence rate compared to existing techniques.

  19. Alteraciones de memoria en daño cerebral frontal

    OpenAIRE

    Vega Rodríguez, Irene de la; Noreña, David de

    2007-01-01

    El córtex frontal está implicado en importantes procesos de memoria, pero tiene un papel diferente al de las estructuras temporales y diencefálicas mediales. Mientras que el daño en estas estructuras produce una grave amnesia anterógrada, en el daño frontal se manifiestan una serie de problemas y distorsiones concretas como las fabulaciones, la amnesia de la fuente, el déficit de memoria prospectiva o las alteraciones en el recuerdo libre. El lóbulo frontal no está implicado en el almacenamie...

  20. Automatic segmentation of the clinical target volume and organs at risk in the planning CT for rectal cancer using deep dilated convolutional neural networks.

    Science.gov (United States)

    Men, Kuo; Dai, Jianrong; Li, Yexiong

    2017-12-01

    Delineation of the clinical target volume (CTV) and organs at risk (OARs) is very important for radiotherapy but is time-consuming and prone to inter-observer variation. Here, we proposed a novel deep dilated convolutional neural network (DDCNN)-based method for fast and consistent auto-segmentation of these structures. Our DDCNN method was an end-to-end architecture enabling fast training and testing. Specifically, it employed a novel multiple-scale convolutional architecture to extract multiple-scale context features in the early layers, which contain the original information on fine texture and boundaries and which are very useful for accurate auto-segmentation. In addition, it enlarged the receptive fields of dilated convolutions at the end of networks to capture complementary context features. Then, it replaced the fully connected layers with fully convolutional layers to achieve pixel-wise segmentation. We used data from 278 patients with rectal cancer for evaluation. The CTV and OARs were delineated and validated by senior radiation oncologists in the planning computed tomography (CT) images. A total of 218 patients chosen randomly were used for training, and the remaining 60 for validation. The Dice similarity coefficient (DSC) was used to measure segmentation accuracy. Performance was evaluated on segmentation of the CTV and OARs. In addition, the performance of DDCNN was compared with that of U-Net. The proposed DDCNN method outperformed the U-Net for all segmentations, and the average DSC value of DDCNN was 3.8% higher than that of U-Net. Mean DSC values of DDCNN were 87.7% for the CTV, 93.4% for the bladder, 92.1% for the left femoral head, 92.3% for the right femoral head, 65.3% for the intestine, and 61.8% for the colon. The test time was 45 s per patient for segmentation of all the CTV, bladder, left and right femoral heads, colon, and intestine. We also assessed our approaches and results with those in the literature: our system showed superior

  1. Frontal Motor Cortex Activity During Reactive Control Is Associated With Past Suicidal Behavior in Recent-Onset Schizophrenia.

    Science.gov (United States)

    Minzenberg, Michael J; Lesh, Tyler; Niendam, Tara; Yoon, Jong H; Cheng, Yaoan; Rhoades, Remy N; Carter, Cameron S

    2015-01-01

    Suicide is prevalent in schizophrenia (SZ), yet the neural system functions that confer suicide risk remain obscure. Circuits operated by the prefrontal cortex (PFC) are altered in SZ, including those that support reactive control, and PFC changes are observed in postmortem studies of heterogeneous suicide victims. We tested whether history of suicide attempt is associated with altered frontal motor cortex activity during reactive control processes. We evaluated 17 patients with recent onset of DSM-IV-TR-defined SZ using the Columbia Suicide Severity Rating Scale and functional magnetic resonance imaging during Stroop task performance. Group-level regression models relating past suicidal behavior to frontal activation controlled for depression, psychosis, and impulsivity. Past suicidal behavior was associated with relatively higher activation in the left-hemisphere supplementary motor area (SMA), pre-SMA, premotor cortex, and dorsolateral PFC, all ipsilateral to the active primary motor cortex. This study provides unique evidence that suicidal behavior in patients with recent-onset SZ directly relates to frontal motor cortex activity during reactive control, in a pattern reciprocal to the relationship with proactive control found previously. Further work should address how frontal-based control functions change with risk over time, and their potential utility as a biomarker for interventions to mitigate suicide risk in SZ.

  2. Test-retest reliability of frontal alpha electroencephalogram (EEG) and electrocardiogram (ECG) measures in adolescents: a pilot study.

    Science.gov (United States)

    Winegust, Adira K; Mathewson, Karen J; Schmidt, Louis A

    2014-12-01

    A number of studies have shown that the pattern of resting frontal EEG alpha power and asymmetry and heart rate are predictive of individual differences in affective style in children and adults. Although test-retest reliability of frontal electrocortical and autonomic measures has been established in adult and child and some clinical populations, few studies have examined test-retest reliability of these measures in adolescents. Here, we conducted a pilot study to examine the test-retest reliability of frontal EEG alpha power and asymmetry and heart period and heart rate in 10 typically developing adolescent participants (M age = 15.9 years) over a 1 month period. We found acceptable test-retest reliability using Pearson and intra-class correlations in left and right mid-frontal alpha power and asymmetry and heart period and heart rate over 1 month. These results provide initial evidence for acceptable levels of test-retest reliability in central and peripheral psychophysiological measures in adolescents used to index affective style in children and adults. Future studies are needed with a larger sample to ensure the reliability of these results.

  3. Hypoplastic left heart syndrome

    Directory of Open Access Journals (Sweden)

    Thiagarajan Ravi

    2007-05-01

    Full Text Available Abstract Hypoplastic left heart syndrome(HLHS refers to the abnormal development of the left-sided cardiac structures, resulting in obstruction to blood flow from the left ventricular outflow tract. In addition, the syndrome includes underdevelopment of the left ventricle, aorta, and aortic arch, as well as mitral atresia or stenosis. HLHS has been reported to occur in approximately 0.016 to 0.036% of all live births. Newborn infants with the condition generally are born at full term and initially appear healthy. As the arterial duct closes, the systemic perfusion becomes decreased, resulting in hypoxemia, acidosis, and shock. Usually, no heart murmur, or a non-specific heart murmur, may be detected. The second heart sound is loud and single because of aortic atresia. Often the liver is enlarged secondary to congestive heart failure. The embryologic cause of the disease, as in the case of most congenital cardiac defects, is not fully known. The most useful diagnostic modality is the echocardiogram. The syndrome can be diagnosed by fetal echocardiography between 18 and 22 weeks of gestation. Differential diagnosis includes other left-sided obstructive lesions where the systemic circulation is dependent on ductal flow (critical aortic stenosis, coarctation of the aorta, interrupted aortic arch. Children with the syndrome require surgery as neonates, as they have duct-dependent systemic circulation. Currently, there are two major modalities, primary cardiac transplantation or a series of staged functionally univentricular palliations. The treatment chosen is dependent on the preference of the institution, its experience, and also preference. Although survival following initial surgical intervention has improved significantly over the last 20 years, significant mortality and morbidity are present for both surgical strategies. As a result pediatric cardiologists continue to be challenged by discussions with families regarding initial decision

  4. Method for assessing the probability of accumulated doses from an intermittent source using the convolution technique

    International Nuclear Information System (INIS)

    Coleman, J.H.

    1980-10-01

    A technique is discussed for computing the probability distribution of the accumulated dose received by an arbitrary receptor resulting from several single releases from an intermittent source. The probability density of the accumulated dose is the convolution of the probability densities of doses from the intermittent releases. Emissions are not assumed to be constant over the brief release period. The fast fourier transform is used in the calculation of the convolution

  5. V-Net: Fully Convolutional Neural Networks for Volumetric Medical Image Segmentation

    OpenAIRE

    Milletari, Fausto; Navab, Nassir; Ahmadi, Seyed-Ahmad

    2016-01-01

    Convolutional Neural Networks (CNNs) have been recently employed to solve problems from both the computer vision and medical image analysis fields. Despite their popularity, most approaches are only able to process 2D images while most medical data used in clinical practice consists of 3D volumes. In this work we propose an approach to 3D image segmentation based on a volumetric, fully convolutional, neural network. Our CNN is trained end-to-end on MRI volumes depicting prostate, and learns t...

  6. Experimental study of current loss and plasma formation in the Z machine post-hole convolute

    Directory of Open Access Journals (Sweden)

    M. R. Gomez

    2017-01-01

    Full Text Available The Z pulsed-power generator at Sandia National Laboratories drives high energy density physics experiments with load currents of up to 26 MA. Z utilizes a double post-hole convolute to combine the current from four parallel magnetically insulated transmission lines into a single transmission line just upstream of the load. Current loss is observed in most experiments and is traditionally attributed to inefficient convolute performance. The apparent loss current varies substantially for z-pinch loads with different inductance histories; however, a similar convolute impedance history is observed for all load types. This paper details direct spectroscopic measurements of plasma density, temperature, and apparent and actual plasma closure velocities within the convolute. Spectral measurements indicate a correlation between impedance collapse and plasma formation in the convolute. Absorption features in the spectra show the convolute plasma consists primarily of hydrogen, which likely forms from desorbed electrode contaminant species such as H_{2}O, H_{2}, and hydrocarbons. Plasma densities increase from 1×10^{16}  cm^{−3} (level of detectability just before peak current to over 1×10^{17}  cm^{−3} at stagnation (tens of ns later. The density seems to be highest near the cathode surface, with an apparent cathode to anode plasma velocity in the range of 35–50  cm/μs. Similar plasma conditions and convolute impedance histories are observed in experiments with high and low losses, suggesting that losses are driven largely by load dynamics, which determine the voltage on the convolute.

  7. Segmentation of Drosophila Heart in Optical Coherence Microscopy Images Using Convolutional Neural Networks

    OpenAIRE

    Duan, Lian; Qin, Xi; He, Yuanhao; Sang, Xialin; Pan, Jinda; Xu, Tao; Men, Jing; Tanzi, Rudolph E.; Li, Airong; Ma, Yutao; Zhou, Chao

    2018-01-01

    Convolutional neural networks are powerful tools for image segmentation and classification. Here, we use this method to identify and mark the heart region of Drosophila at different developmental stages in the cross-sectional images acquired by a custom optical coherence microscopy (OCM) system. With our well-trained convolutional neural network model, the heart regions through multiple heartbeat cycles can be marked with an intersection over union (IOU) of ~86%. Various morphological and dyn...

  8. A Convolution Tree with Deconvolution Branches: Exploiting Geometric Relationships for Single Shot Keypoint Detection

    OpenAIRE

    Kumar, Amit; Chellappa, Rama

    2017-01-01

    Recently, Deep Convolution Networks (DCNNs) have been applied to the task of face alignment and have shown potential for learning improved feature representations. Although deeper layers can capture abstract concepts like pose, it is difficult to capture the geometric relationships among the keypoints in DCNNs. In this paper, we propose a novel convolution-deconvolution network for facial keypoint detection. Our model predicts the 2D locations of the keypoints and their individual visibility ...

  9. ADHD Symptoms in Post-Institutionalized Children Are Partially Mediated by Altered Frontal EEG Asymmetry.

    Science.gov (United States)

    Frenkel, Tahl I; Koss, Kalsea J; Donzella, Bonny; Frenn, Kristin A; Lamm, Connie; Fox, Nathan A; Gunnar, Megan R

    2017-07-01

    Individual differences in the propensity for left versus right frontal electroencephalogram (EEG) asymmetry may underlie differences in approach/withdrawal tendencies and mental health deficits. Growing evidence suggests that early life adversity may shape brain development and contribute to the emergence of mental health problems. The present study examined frontal EEG asymmetry (FEA) following the transition to family care in children adopted internationally from institutional care settings between 15 and 36 months of age (N = 82; 46 female, 36 male). Two comparison groups were included: an international adoption control consisting of children adopted from foster care with little to no institutional deprivation (N = 45; 17 female, 28 male) and a post-adoption condition control consisting of children reared in birth families of the same education and income as the adoptive families (N = 48; 23 female, 25 male). Consistent with evidence of greater approach and impulsivity-related behavior problems in post-institutionalized (PI) children, PI status was associated with greater left FEA than found in the other two groups. In addition, left FEA served as a mediator between institutionalization and age 5 ADHD symptoms for girls. Age at adoption and other preadoption factors were examined with results suggesting that earlier adoption into a supportive family resulted in a more typical pattern of brain functioning. Findings support the idea that the capacity of brain activity to evidence typical functioning following perturbation may differ in relation to the timing of intervention and suggest that the earlier the intervention of adoption, the better.

  10. Altered basal ganglia-cortical functional connections in frontal lobe epilepsy: A resting-state fMRI study.

    Science.gov (United States)

    Dong, Li; Wang, Pu; Peng, Rui; Jiang, Sisi; Klugah-Brown, Benjamin; Luo, Cheng; Yao, Dezhong

    2016-12-01

    The purpose of this study was to investigate alterations of basal ganglia-cortical functional connections in patients with frontal lobe epilepsy (FLE). Resting-state functional magnetic resonance imaging (fMRI) data were gathered from 19 FLE patients and 19 age- and gender-matched healthy controls. Functional connectivity (FC) analysis was used to assess the functional connections between basal ganglia and cerebral cortex. Regions of interest, including the left/right caudate, putamen, pallidum and thalamus, were selected as the seeds. Two sample t-test was used to determine the difference between patients and controls, while controlling the age, gender and head motions. Compared with controls, FLE patients demonstrated increased FCs between basal ganglia and regions including the right fusiform gyrus, the bilateral cingulate gyrus, the precuneus and anterior cingulate gyrus. Reduced FCs were mainly located in a range of brain regions including the bilateral middle occipital gyrus, the ventral frontal lobe, the right putamen, the left fusiform gyrus and right rolandic operculum. In addition, the relationships between basal ganglia-cingulate connections and durations of epilepsy were also found. The alterations of functional integrity within the basal ganglia, as well as its connections to limbic and ventral frontal areas, indicate the important roles of the basal ganglia-cortical functional connections in FLE, and provide new insights in the pathophysiological mechanism of FLE. Copyright © 2016 Elsevier B.V. All rights reserved.

  11. Frontal Lobe Decortication (Frontal Lobectomy with Ventricular Preservation) in Epilepsy-Part 1: Anatomic Landmarks and Surgical Technique.

    Science.gov (United States)

    Wen, Hung Tzu; Da Róz, Leila Maria; Rhoton, Albert L; Castro, Luiz Henrique Martins; Teixeira, Manoel Jacobsen

    2017-02-01

    An extensive frontal resection is a frequently performed neurosurgical procedure, especially for treating brain tumor and refractory epilepsy. However, there is a paucity of reports available regarding its surgical anatomy and technique. We sought to present the anatomic landmarks and surgical technique of the frontal lobe decortication (FLD) in epilepsy. The goals were to maximize the gray matter removal, spare primary and supplementary motor areas, and preserve the frontal horn. The anatomic study was based on dissections performed in 15 formalin-fixed adult cadaveric heads. The clinical experience with 15 patients is summarized. FLD consists of 5 steps: 1) coagulation and section of arterial branches of lateral surface; 2) paramedian subpial resection 3 cm ahead of the precentral sulcus to reach the genu of corpus callosum; 3) resection of gray matter of lateral surface, preserving the frontal horn; 4) removal of gray matter of basal surface preserving olfactory tract; 5) removal of gray matter of the medial surface under the rostrum of corpus callosum. The frontal horn was preserved in all 15 patients; 12 patients (80%) had no complications; 2 patients presented temporary hemiparesis; and 1 Rasmussen syndrome patient developed postoperative fever. The best seizure control was in cases with focal magnetic resonance imaging abnormalities limited to the frontal lobe. FLD is an anatomy-based surgical technique for extensive frontal lobe resection. It presents reliable anatomic landmarks, selective gray matter removal, preservation of frontal horn, and low complication rate in our series. It can be an alternative option to the classical frontal lobectomy. Copyright © 2016 Elsevier Inc. All rights reserved.

  12. [Vectorcardiographic manifestations of left ventricular and biventricular enlargement].

    Science.gov (United States)

    de Micheli, A; Medrano, G A

    1979-01-01

    The basic criteria for the vectorcardiographic diagnosis of left ventricular and biventricular enlargements are discussed on the basis of the myocardial activation sequence. Left ventricular dilatation, secondary to isolated diastolic overloading, increases the manifestation of all the vectors resulting of the activation of this ventricle. These changes reflect the proximity of the left ventricular walls to the exploring electrodes. The vectors above mentioned project themselves as wide ventricular curves with counterclockwise rotation on the three planes. The T loop, of secondary type, is concordant in its orientation with the R loop. Cases with left ventricular hypertrophy, produced by a sustained systolic overloading, are also described. In the presence of global left ventricular hypertrophy without LBBB, the manifestation of all the vectors resulting from the depolarization of this ventricle (I, IIl, IIIl), is increased. This is due to a prolonged duration of the corresponding activation fronts. These vectors are projected on the different segments of the ventricular curves and they show a counterclockwise rotation on the three planes. When LBBB is also present, the first septal vector is not evident. The T loop, of secondary type, opposes the R loop on the frontal and horizontal planes. The presence of left ventricular hypertrophy of the segmentary type, generally increases the manifestation of the vector I, and sometimes, also that of the vector IIIl. When both ventricles are hypertrophied, the electromotive forces of the chamber more severely affected predominate in the vectorcardiographic records.

  13. A convolution method for predicting mean treatment dose including organ motion at imaging

    International Nuclear Information System (INIS)

    Booth, J.T.; Zavgorodni, S.F.; Royal Adelaide Hospital, SA

    2000-01-01

    Full text: The random treatment delivery errors (organ motion and set-up error) can be incorporated into the treatment planning software using a convolution method. Mean treatment dose is computed as the convolution of a static dose distribution with a variation kernel. Typically this variation kernel is Gaussian with variance equal to the sum of the organ motion and set-up error variances. We propose a novel variation kernel for the convolution technique that additionally considers the position of the mobile organ in the planning CT image. The systematic error of organ position in the planning CT image can be considered random for each patient over a population. Thus the variance of the variation kernel will equal the sum of treatment delivery variance and organ motion variance at planning for the population of treatments. The kernel is extended to deal with multiple pre-treatment CT scans to improve tumour localisation for planning. Mean treatment doses calculated with the convolution technique are compared to benchmark Monte Carlo (MC) computations. Calculations of mean treatment dose using the convolution technique agreed with MC results for all cases to better than ± 1 Gy in the planning treatment volume for a prescribed 60 Gy treatment. Convolution provides a quick method of incorporating random organ motion (captured in the planning CT image and during treatment delivery) and random set-up errors directly into the dose distribution. Copyright (2000) Australasian College of Physical Scientists and Engineers in Medicine

  14. Application of structured support vector machine backpropagation to a convolutional neural network for human pose estimation.

    Science.gov (United States)

    Witoonchart, Peerajak; Chongstitvatana, Prabhas

    2017-08-01

    In this study, for the first time, we show how to formulate a structured support vector machine (SSVM) as two layers in a convolutional neural network, where the top layer is a loss augmented inference layer and the bottom layer is the normal convolutional layer. We show that a deformable part model can be learned with the proposed structured SVM neural network by backpropagating the error of the deformable part model to the convolutional neural network. The forward propagation calculates the loss augmented inference and the backpropagation calculates the gradient from the loss augmented inference layer to the convolutional layer. Thus, we obtain a new type of convolutional neural network called an Structured SVM convolutional neural network, which we applied to the human pose estimation problem. This new neural network can be used as the final layers in deep learning. Our method jointly learns the structural model parameters and the appearance model parameters. We implemented our method as a new layer in the existing Caffe library. Copyright © 2017 Elsevier Ltd. All rights reserved.

  15. Frontal dynamic aphasia in progressive supranuclear palsy: Distinguishing between generation and fluent sequencing of novel thoughts.

    Science.gov (United States)

    Robinson, Gail A; Spooner, Donna; Harrison, William J

    2015-10-01

    Frontal dynamic aphasia is characterised by a profound reduction in spontaneous speech despite well-preserved naming, repetition and comprehension. Since Luria (1966, 1970) designated this term, two main forms of dynamic aphasia have been identified: one, a language-specific selection deficit at the level of word/sentence generation, associated with left inferior frontal lesions; and two, a domain-general impairment in generating multiple responses or connected speech, associated with more extensive bilateral frontal and/or frontostriatal damage. Both forms of dynamic aphasia have been interpreted as arising due to disturbances in early prelinguistic conceptual preparation mechanisms that are critical for language production. We investigate language-specific and domain-general accounts of dynamic aphasia and address two issues: one, whether deficits in multiple conceptual preparation mechanisms can co-occur; and two, the contribution of broader cognitive processes such as energization, the ability to initiate and sustain response generation over time, to language generation failure. Thus, we report patient WAL who presented with frontal dynamic aphasia in the context of progressive supranuclear palsy (PSP). WAL was given a series of experimental tests that showed that his dynamic aphasia was not underpinned by a language-specific deficit in selection or in microplanning. By contrast, WAL presented with a domain-general deficit in fluent sequencing of novel thoughts. The latter replicated the pattern documented in a previous PSP patient (Robinson, et al., 2006); however, unique to WAL, generating novel thoughts was impaired but there was no evidence of a sequencing deficit because perseveration was absent. Thus, WAL is the first unequivocal case to show a distinction between novel thought generation and subsequent fluent sequencing. Moreover, WAL's generation deficit encompassed verbal and non-verbal responses, showing a similar (but more profoundly reduced) pattern

  16. Increased frontal electroencephalogram theta amplitude in patients with anorexia nervosa compared to healthy controls

    Directory of Open Access Journals (Sweden)

    Hestad KA

    2016-09-01

    Full Text Available Knut A Hestad,1–3 Siri Weider,3,4 Kristian Bernhard Nilsen,5–7 Marit Sæbø Indredavik,8,9 Trond Sand7,10 1Department of Research, Innlandet Hospital Trust, Brumunddal, Norway; 2Department of Public Health, Hedmark University of Applied Sciences, Elverum, Norway; 3Department of Psychology, Faculty of Social Sciences and Technology Management, Norwegian University of Science and Technology (NTNU, Trondheim, Norway; 4Department of Psychiatry, Specialised Unit for Eating Disorder Patients, Levanger Hospital, Health Trust Nord-Trøndelag, Levanger, Norway; 5Department of Neuroscience, Norwegian University of Science and Technology (NTNU, Trondheim, Norway; 6Department of Work Psychology and Physiology, National Institute of Occupational Health, Oslo, Norway; 7Department of Neurology, Section for Clinical Neurophysiology, Oslo University Hospital, Ullevål, Oslo, Norway; 8Regional Centre for Child and Youth Mental Health and Child Welfare, Faculty of Medicine, Norwegian University of Science and Technology (NTNU, Trondheim, Norway; 9Department of Child and Adolescent Psychiatry, St Olavs Hospital, Trondheim University Hospital, Trondheim, Norway; 10Department of Neurology and Clinical Neurophysiology, St Olavs Hospital, Trondheim University Hospital, Trondheim, Norway Objective: To conduct a blind study of quantitative electroencephalogram-band amplitudes in patients with anorexia nervosa (AN and healthy controls.Methods: Twenty-one patients with AN and 24 controls were examined with eyes-closed 16-channel electroencephalogram. Main variables were absolute alpha, theta, and delta amplitudes in frontal, temporal, and posterior regions.Results: There were no significant differences between the AN patients and controls regarding absolute regional band amplitudes in µV. Borderline significance was found for anterior theta (P=0.051. Significantly increased left and right frontal electrode theta amplitude was found in AN patients (F3, P=0.014; F4, P

  17. Theory of mind and the frontal lobes Teoria da mente e lobos frontais

    Directory of Open Access Journals (Sweden)

    Glauco C. Igliori

    2006-06-01

    Full Text Available BACKGROUND: Theory of mind (ToM is the ability to attribute mental states to other individuals. Its cerebral organization is not enough established, even though the literature suggests the relevant role of the frontal lobes. OBEJECTIVE: To evaluate frontal lobe patients and controls in ToM tests. METHOD:We studied 20 patients with lesions limited to the frontal lobes (as shown by CT or MRI, and 10 normal control subjects by means of ToM tests (recognizing himself in mirrors, false belief, first and second order ToM tasks, as well as tests of other cognitive functions (counter-proofs. RESULTS: Patients and controls performed similarly in ToM tests. There was significant difference between frontal subgroups (left, right, bifrontal in the double-bluff task (second order ToM (p=0.021, without relation to verbal fluency (p=0.302 or delayed recall ability (p=0.159. The only two patients with deficits in ToM tasks had impairment of social behavior. CONCLUSION: Frontal lesions do not necessarily implicate in ToM deficits, which may occur when such lesions are associated to disturbance of social behavior.CONTEXTO: Teoria da mente (TM é a capacidade de atribuir estados mentais aos outros. Sua organização cerebral não está suficientemente esclarecida, embora a literatura indique que os lobos frontais desempenham papel relevante. OBEJETIVO: Avaliar pacientes com lesões frontais e controles em testes de TM. MÉTODO: Foram estudados 20 pacientes com lesões restritas aos lobos frontais (conforme imagens de CT ou RM e 10 controles normais em testes de TM (reconhecimento da própria imagem no espelho, falsa crença, TM de 1ª ordem e TM de 2ª ordem, usando como contra-provas testes de outras funções cognitivas. RESULTADOS: Não houve diferença entre pacientes e controles nos testes de TM. Os subgrupos frontais (direito, esquerdo, bilateral diferiram significativamente no teste de "duplo blefe" (TM 2ª ordem (p=0,021, sem relação com a flu

  18. Determination of frontal offset test conditions based on crash data

    Science.gov (United States)

    1998-01-01

    This paper reports on the test procedure development : phase of the agencys Improved Frontal Protection : research program. It is anticipated that even after all cars : and light trucks have air bags for drivers and front seat : passengers there w...

  19. Right-frontal cortical asymmetry predicts increased proneness to nostalgia.

    Science.gov (United States)

    Tullett, Alexa M; Wildschut, Tim; Sedikides, Constantine; Inzlicht, Michael

    2015-08-01

    Nostalgia is often triggered by feelings-such as sadness, loneliness, or meaninglessness-that are typically associated with withdrawal motivation. Here, we examined whether a trait tendency to experience withdrawal motivation is associated with nostalgia proneness. Past work indicates that baseline right-frontal cortical asymmetry is a neural correlate of withdrawal-related motivation. We therefore hypothesized that higher baseline levels of right-frontal asymmetry would predict increased proneness to nostalgia. We assessed participants' baseline levels of frontal cortical activity using EEG. Results supported the hypothesis and demonstrated that the association between relative right-frontal asymmetry and increased nostalgia remained significant when controlling for the Big Five personality traits. Overall, these findings indicate that individuals with a stronger dispositional tendency to experience withdrawal-related motivation are more prone to nostalgia. © 2015 Society for Psychophysiological Research.

  20. [Morphometric vectorial method of analysis of the frontal sinuses].

    Science.gov (United States)

    Iordan, A; Ulmeanu, D

    2008-03-01

    The frontal sinuses are pneumatic cavities located in the thickness of the squama frontalis, which communicate with the nasal cavity through the frontonasal duct. These cavities develop by the pneumatisation extent of some anterior ethmoidal cells. Morphologically, there is a large variability of the frontal sinus shape, size and extent, the position of the intersinusal septum, the existence and number of intrasinusal septa. There exist morphologically atypical frontal sinuses as: uni- or bilateral frontal sinuses aplasia, supernumerary sinuses, great extent of the cavities. Paranasal sinuses can be explored by different methods, but the most accessible and easy to perform is conventional radiological imaging. The radiographs can be morphometrically assessed in order to prove the individuality of these air cavities.

  1. Frontal parosteal lipoma with thickening of diploic space

    Directory of Open Access Journals (Sweden)

    Tsuyoshi Morishita

    2015-09-01

    Full Text Available Parosteal lipoma is a rare benign tumor that is composed mainly of benign mature lipocytes, and it has an intimate association with the underlying affected bone. Parosteal lipoma involving the head and neck is very rare, and there are only two reported cases of parosteal lipoma of the skull in English literature. This paper reports a rare case of frontal parosteal lipoma in a young child with a hard enlargement of the forehead region after blunt trauma. Computed tomography revealed a large soft tissue mass and an osseous projection of the unilateral frontal bone. The pathology report identified lipoma and thickening of diploic space of the frontal bone. Here, we present a new case of parosteal lipoma in the frontal region.

  2. Distinct frontal lobe morphology in girls and boys with ADHD

    Directory of Open Access Journals (Sweden)

    Benjamin Dirlikov

    2015-01-01

    Conclusions: These results elucidate sex-based differences in cortical morphology of functional subdivisions of the frontal lobe and provide additional evidence of associations among SA and symptom severity in children with ADHD.

  3. Convolutional Sparse Coding for RGB+NIR Imaging.

    Science.gov (United States)

    Hu, Xuemei; Heide, Felix; Dai, Qionghai; Wetzstein, Gordon

    2018-04-01

    Emerging sensor designs increasingly rely on novel color filter arrays (CFAs) to sample the incident spectrum in unconventional ways. In particular, capturing a near-infrared (NIR) channel along with conventional RGB color is an exciting new imaging modality. RGB+NIR sensing has broad applications in computational photography, such as low-light denoising, it has applications in computer vision, such as facial recognition and tracking, and it paves the way toward low-cost single-sensor RGB and depth imaging using structured illumination. However, cost-effective commercial CFAs suffer from severe spectral cross talk. This cross talk represents a major challenge in high-quality RGB+NIR imaging, rendering existing spatially multiplexed sensor designs impractical. In this work, we introduce a new approach to RGB+NIR image reconstruction using learned convolutional sparse priors. We demonstrate high-quality color and NIR imaging for challenging scenes, even including high-frequency structured NIR illumination. The effectiveness of the proposed method is validated on a large data set of experimental captures, and simulated benchmark results which demonstrate that this work achieves unprecedented reconstruction quality.

  4. Forged Signature Distinction Using Convolutional Neural Network for Feature Extraction

    Directory of Open Access Journals (Sweden)

    Seungsoo Nam

    2018-01-01

    Full Text Available This paper proposes a dynamic verification scheme for finger-drawn signatures in smartphones. As a dynamic feature, the movement of a smartphone is recorded with accelerometer sensors in the smartphone, in addition to the moving coordinates of the signature. To extract high-level longitudinal and topological features, the proposed scheme uses a convolution neural network (CNN for feature extraction, and not as a conventional classifier. We assume that a CNN trained with forged signatures can extract effective features (called S-vector, which are common in forging activities such as hesitation and delay before drawing the complicated part. The proposed scheme also exploits an autoencoder (AE as a classifier, and the S-vector is used as the input vector to the AE. An AE has high accuracy for the one-class distinction problem such as signature verification, and is also greatly dependent on the accuracy of input data. S-vector is valuable as the input of AE, and, consequently, could lead to improved verification accuracy especially for distinguishing forged signatures. Compared to the previous work, i.e., the MLP-based finger-drawn signature verification scheme, the proposed scheme decreases the equal error rate by 13.7%, specifically, from 18.1% to 4.4%, for discriminating forged signatures.

  5. Animal Recognition System Based on Convolutional Neural Network

    Directory of Open Access Journals (Sweden)

    Tibor Trnovszky

    2017-01-01

    Full Text Available In this paper, the performances of well-known image recognition methods such as Principal Component Analysis (PCA, Linear Discriminant Analysis (LDA, Local Binary Patterns Histograms (LBPH and Support Vector Machine (SVM are tested and compared with proposed convolutional neural network (CNN for the recognition rate of the input animal images. In our experiments, the overall recognition accuracy of PCA, LDA, LBPH and SVM is demonstrated. Next, the time execution for animal recognition process is evaluated. The all experimental results on created animal database were conducted. This created animal database consist of 500 different subjects (5 classes/ 100 images for each class. The experimental result shows that the PCA features provide better results as LDA and LBPH for large training set. On the other hand, LBPH is better than PCA and LDA for small training data set. For proposed CNN we have obtained a recognition accuracy of 98%. The proposed method based on CNN outperforms the state of the art methods.

  6. Reconstruction of Micropattern Detector Signals using Convolutional Neural Networks

    Science.gov (United States)

    Flekova, L.; Schott, M.

    2017-10-01

    Micropattern gaseous detector (MPGD) technologies, such as GEMs or MicroMegas, are particularly suitable for precision tracking and triggering in high rate environments. Given their relatively low production costs, MPGDs are an exemplary candidate for the next generation of particle detectors. Having acknowledged these advantages, both the ATLAS and CMS collaborations at the LHC are exploiting these new technologies for their detector upgrade programs in the coming years. When MPGDs are utilized for triggering purposes, the measured signals need to be precisely reconstructed within less than 200 ns, which can be achieved by the usage of FPGAs. In this work, we present a novel approach to identify reconstructed signals, their timing and the corresponding spatial position on the detector. In particular, we study the effect of noise and dead readout strips on the reconstruction performance. Our approach leverages the potential of convolutional neural network (CNNs), which have recently manifested an outstanding performance in a range of modeling tasks. The proposed neural network architecture of our CNN is designed simply enough, so that it can be modeled directly by an FPGA and thus provide precise information on reconstructed signals already in trigger level.

  7. Rank-based pooling for deep convolutional neural networks.

    Science.gov (United States)

    Shi, Zenglin; Ye, Yangdong; Wu, Yunpeng

    2016-11-01

    Pooling is a key mechanism in deep convolutional neural networks (CNNs) which helps to achieve translation invariance. Numerous studies, both empirically and theoretically, show that pooling consistently boosts the performance of the CNNs. The conventional pooling methods are operated on activation values. In this work, we alternatively propose rank-based pooling. It is derived from the observations that ranking list is invariant under changes of activation values in a pooling region, and thus rank-based pooling operation may achieve more robust performance. In addition, the reasonable usage of rank can avoid the scale problems encountered by value-based methods. The novel pooling mechanism can be regarded as an instance of weighted pooling where a weighted sum of activations is used to generate the pooling output. This pooling mechanism can also be realized as rank-based average pooling (RAP), rank-based weighted pooling (RWP) and rank-based stochastic pooling (RSP) according to different weighting strategies. As another major contribution, we present a novel criterion to analyze the discriminant ability of various pooling methods, which is heavily under-researched in machine learning and computer vision community. Experimental results on several image benchmarks show that rank-based pooling outperforms the existing pooling methods in classification performance. We further demonstrate better performance on CIFAR datasets by integrating RSP into Network-in-Network. Copyright © 2016 Elsevier Ltd. All rights reserved.

  8. Very Deep Convolutional Neural Networks for Morphologic Classification of Erythrocytes.

    Science.gov (United States)

    Durant, Thomas J S; Olson, Eben M; Schulz, Wade L; Torres, Richard

    2017-12-01

    Morphologic profiling of the erythrocyte population is a widely used and clinically valuable diagnostic modality, but one that relies on a slow manual process associated with significant labor cost and limited reproducibility. Automated profiling of erythrocytes from digital images by capable machine learning approaches would augment the throughput and value of morphologic analysis. To this end, we sought to evaluate the performance of leading implementation strategies for convolutional neural networks (CNNs) when applied to classification of erythrocytes based on morphology. Erythrocytes were manually classified into 1 of 10 classes using a custom-developed Web application. Using recent literature to guide architectural considerations for neural network design, we implemented a "very deep" CNN, consisting of >150 layers, with dense shortcut connections. The final database comprised 3737 labeled cells. Ensemble model predictions on unseen data demonstrated a harmonic mean of recall and precision metrics of 92.70% and 89.39%, respectively. Of the 748 cells in the test set, 23 misclassification errors were made, with a correct classification frequency of 90.60%, represented as a harmonic mean across the 10 morphologic classes. These findings indicate that erythrocyte morphology profiles could be measured with a high degree of accuracy with "very deep" CNNs. Further, these data support future efforts to expand classes and optimize practical performance in a clinical environment as a prelude to full implementation as a clinical tool. © 2017 American Association for Clinical Chemistry.

  9. Landcover Classification Using Deep Fully Convolutional Neural Networks

    Science.gov (United States)

    Wang, J.; Li, X.; Zhou, S.; Tang, J.

    2017-12-01

    Land cover classification has always been an essential application in remote sensing. Certain image features are needed for land cover classification whether it is based on pixel or object-based methods. Different from other machine learning methods, deep learning model not only extracts useful information from multiple bands/attributes, but also learns spatial characteristics. In recent years, deep learning methods have been developed rapidly and widely applied in image recognition, semantic understanding, and other application domains. However, there are limited studies applying deep learning methods in land cover classification. In this research, we used fully convolutional networks (FCN) as the deep learning model to classify land covers. The National Land Cover Database (NLCD) within the state of Kansas was used as training dataset and Landsat images were classified using the trained FCN model. We also applied an image segmentation method to improve the original results from the FCN model. In addition, the pros and cons between deep learning and several machine learning methods were compared and explored. Our research indicates: (1) FCN is an effective classification model with an overall accuracy of 75%; (2) image segmentation improves the classification results with better match of spatial patterns; (3) FCN has an excellent ability of learning which can attains higher accuracy and better spatial patterns compared with several machine learning methods.

  10. Shape Synthesis from Sketches via Procedural Models and Convolutional Networks.

    Science.gov (United States)

    Huang, Haibin; Kalogerakis, Evangelos; Yumer, Ersin; Mech, Radomir

    2017-08-01

    Procedural modeling techniques can produce high quality visual content through complex rule sets. However, controlling the outputs of these techniques for design purposes is often notoriously difficult for users due to the large number of parameters involved in these rule sets and also their non-linear relationship to the resulting content. To circumvent this problem, we present a sketch-based approach to procedural modeling. Given an approximate and abstract hand-drawn 2D sketch provided by a user, our algorithm automatically computes a set of procedural model parameters, which in turn yield multiple, detailed output shapes that resemble the user's input sketch. The user can then select an output shape, or further modify the sketch to explore alternative ones. At the heart of our approach is a deep Convolutional Neural Network (CNN) that is trained to map sketches to procedural model parameters. The network is trained by large amounts of automatically generated synthetic line drawings. By using an intuitive medium, i.e., freehand sketching as input, users are set free from manually adjusting procedural model parameters, yet they are still able to create high quality content. We demonstrate the accuracy and efficacy of our method in a variety of procedural modeling scenarios including design of man-made and organic shapes.

  11. A quantum algorithm for Viterbi decoding of classical convolutional codes

    Science.gov (United States)

    Grice, Jon R.; Meyer, David A.

    2015-07-01

    We present a quantum Viterbi algorithm (QVA) with better than classical performance under certain conditions. In this paper, the proposed algorithm is applied to decoding classical convolutional codes, for instance, large constraint length and short decode frames . Other applications of the classical Viterbi algorithm where is large (e.g., speech processing) could experience significant speedup with the QVA. The QVA exploits the fact that the decoding trellis is similar to the butterfly diagram of the fast Fourier transform, with its corresponding fast quantum algorithm. The tensor-product structure of the butterfly diagram corresponds to a quantum superposition that we show can be efficiently prepared. The quantum speedup is possible because the performance of the QVA depends on the fanout (number of possible transitions from any given state in the hidden Markov model) which is in general much less than . The QVA constructs a superposition of states which correspond to all legal paths through the decoding lattice, with phase as a function of the probability of the path being taken given received data. A specialized amplitude amplification procedure is applied one or more times to recover a superposition where the most probable path has a high probability of being measured.

  12. Automatic detection and classification of leukocytes using convolutional neural networks.

    Science.gov (United States)

    Zhao, Jianwei; Zhang, Minshu; Zhou, Zhenghua; Chu, Jianjun; Cao, Feilong

    2017-08-01

    The detection and classification of white blood cells (WBCs, also known as Leukocytes) is a hot issue because of its important applications in disease diagnosis. Nowadays the morphological analysis of blood cells is operated manually by skilled operators, which results in some drawbacks such as slowness of the analysis, a non-standard accuracy, and the dependence on the operator's skills. Although there have been many papers studying the detection of WBCs or classification of WBCs independently, few papers consider them together. This paper proposes an automatic detection and classification system for WBCs from peripheral blood images. It firstly proposes an algorithm to detect WBCs from the microscope images based on the simple relation of colors R, B and morphological operation. Then a granularity feature (pairwise rotation invariant co-occurrence local binary pattern, PRICoLBP feature) and SVM are applied to classify eosinophil and basophil from other WBCs firstly. Lastly, convolution neural networks are used to extract features in high level from WBCs automatically, and a random forest is applied to these features to recognize the other three kinds of WBCs: neutrophil, monocyte and lymphocyte. Some detection experiments on Cellavison database and ALL-IDB database show that our proposed detection method has better effect almost than iterative threshold method with less cost time, and some classification experiments show that our proposed classification method has better accuracy almost than some other methods.

  13. HEp-2 Cell Image Classification With Deep Convolutional Neural Networks.

    Science.gov (United States)

    Gao, Zhimin; Wang, Lei; Zhou, Luping; Zhang, Jianjia

    2017-03-01

    Efficient Human Epithelial-2 cell image classification can facilitate the diagnosis of many autoimmune diseases. This paper proposes an automatic framework for this classification task, by utilizing the deep convolutional neural networks (CNNs) which have recently attracted intensive attention in visual recognition. In addition to describing the proposed classification framework, this paper elaborates several interesting observations and findings obtained by our investigation. They include the important factors that impact network design and training, the role of rotation-based data augmentation for cell images, the effectiveness of cell image masks for classification, and the adaptability of the CNN-based classification system across different datasets. Extensive experimental study is conducted to verify the above findings and compares the proposed framework with the well-established image classification models in the literature. The results on benchmark datasets demonstrate that 1) the proposed framework can effectively outperform existing models by properly applying data augmentation, 2) our CNN-based framework has excellent adaptability across different datasets, which is highly desirable for cell image classification under varying laboratory settings. Our system is ranked high in the cell image classification competition hosted by ICPR 2014.

  14. Deblurring adaptive optics retinal images using deep convolutional neural networks.

    Science.gov (United States)

    Fei, Xiao; Zhao, Junlei; Zhao, Haoxin; Yun, Dai; Zhang, Yudong

    2017-12-01

    The adaptive optics (AO) can be used to compensate for ocular aberrations to achieve near diffraction limited high-resolution retinal images. However, many factors such as the limited aberration measurement and correction accuracy with AO, intraocular scatter, imaging noise and so on will degrade the quality of retinal images. Image post processing is an indispensable and economical method to make up for the limitation of AO retinal imaging procedure. In this paper, we proposed a deep learning method to restore the degraded retinal images for the first time. The method directly learned an end-to-end mapping between the blurred and restored retinal images. The mapping was represented as a deep convolutional neural network that was trained to output high-quality images directly from blurry inputs without any preprocessing. This network was validated on synthetically generated retinal images as well as real AO retinal images. The assessment of the restored retinal images demonstrated that the image quality had been significantly improved.

  15. On the Relationship between Visual Attributes and Convolutional Networks

    KAUST Repository

    Castillo, Victor

    2015-06-02

    One of the cornerstone principles of deep models is their abstraction capacity, i.e. their ability to learn abstract concepts from ‘simpler’ ones. Through extensive experiments, we characterize the nature of the relationship between abstract concepts (specifically objects in images) learned by popular and high performing convolutional networks (conv-nets) and established mid-level representations used in computer vision (specifically semantic visual attributes). We focus on attributes due to their impact on several applications, such as object description, retrieval and mining, and active (and zero-shot) learning. Among the findings we uncover, we show empirical evidence of the existence of Attribute Centric Nodes (ACNs) within a conv-net, which is trained to recognize objects (not attributes) in images. These special conv-net nodes (1) collectively encode information pertinent to visual attribute representation and discrimination, (2) are unevenly and sparsely distribution across all layers of the conv-net, and (3) play an important role in conv-net based object recognition.

  16. Bone age detection via carpogram analysis using convolutional neural networks

    Science.gov (United States)

    Torres, Felipe; Bravo, María. Alejandra; Salinas, Emmanuel; Triana, Gustavo; Arbeláez, Pablo

    2017-11-01

    Bone age assessment is a critical factor for determining delayed development in children, which can be a sign of pathologies such as endocrine diseases, growth abnormalities, chromosomal, neurological and congenital disorders among others. In this paper we present BoneNet, a methodology to assess automatically the skeletal maturity state in pediatric patients based on Convolutional Neural Networks. We train and evaluate our algorithm on a database of X-Ray images provided by the hospital Fundacion Santa Fe de Bogot ´ a with around 1500 images of patients between the ages 1 to 18. ´ We compare two different architectures to classify the given data in order to explore the generality of our method. To accomplish this, we define multiple binary age assessment problems, dividing the data by bone age and differentiating the patients by their gender. Thus, exploring several parameters, we develop BoneNet. Our approach is holistic, efficient, and modular, since it is possible for the specialists to use all the networks combined to determine how is the skeletal maturity of a patient. BoneNet achieves over 90% accuracy for most of the critical age thresholds, when differentiating the images between over or under a given age.

  17. ANNA: A Convolutional Neural Network Code for Spectroscopic Analysis

    Science.gov (United States)

    Lee-Brown, Donald; Anthony-Twarog, Barbara J.; Twarog, Bruce A.

    2018-01-01

    We present ANNA, a Python-based convolutional neural network code for the automated analysis of stellar spectra. ANNA provides a flexible framework that allows atmospheric parameters such as temperature and metallicity to be determined with accuracies comparable to those of established but less efficient techniques. ANNA performs its parameterization extremely quickly; typically several thousand spectra can be analyzed in less than a second. Additionally, the code incorporates features which greatly speed up the training process necessary for the neural network to measure spectra accurately, resulting in a tool that can easily be run on a single desktop or laptop computer. Thus, ANNA is useful in an era when spectrographs increasingly have the capability to collect dozens to hundreds of spectra each night. This talk will cover the basic features included in ANNA and demonstrate its performance in two use cases: an open cluster abundance analysis involving several hundred spectra, and a metal-rich field star study. Applicability of the code to large survey datasets will also be discussed.

  18. Transfer Learning with Convolutional Neural Networks for SAR Ship Recognition

    Science.gov (United States)

    Zhang, Di; Liu, Jia; Heng, Wang; Ren, Kaijun; Song, Junqiang

    2018-03-01

    Ship recognition is the backbone of marine surveillance systems. Recent deep learning methods, e.g. Convolutional Neural Networks (CNNs), have shown high performance for optical images. Learning CNNs, however, requires a number of annotated samples to estimate numerous model parameters, which prevents its application to Synthetic Aperture Radar (SAR) images due to the limited annotated training samples. Transfer learning has been a promising technique for applications with limited data. To this end, a novel SAR ship recognition method based on CNNs with transfer learning has been developed. In this work, we firstly start with a CNNs model that has been trained in advance on Moving and Stationary Target Acquisition and Recognition (MSTAR) database. Next, based on the knowledge gained from this image recognition task, we fine-tune the CNNs on a new task to recognize three types of ships in the OpenSARShip database. The experimental results show that our proposed approach can obviously increase the recognition rate comparing with the result of merely applying CNNs. In addition, compared to existing methods, the proposed method proves to be very competitive and can learn discriminative features directly from training data instead of requiring pre-specification or pre-selection manually.

  19. Classification of breast cancer histology images using Convolutional Neural Networks.

    Directory of Open Access Journals (Sweden)

    Teresa Araújo

    Full Text Available Breast cancer is one of the main causes of cancer death worldwide. The diagnosis of biopsy tissue with hematoxylin and eosin stained images is non-trivial and specialists often disagree on the final diagnosis. Computer-aided Diagnosis systems contribute to reduce the cost and increase the efficiency of this process. Conventional classification approaches rely on feature extraction methods designed for a specific problem based on field-knowledge. To overcome the many difficulties of the feature-based approaches, deep learning methods are becoming important alternatives. A method for the classification of hematoxylin and eosin stained breast biopsy images using Convolutional Neural Networks (CNNs is proposed. Images are classified in four classes, normal tissue, benign lesion, in situ carcinoma and invasive carcinoma, and in two classes, carcinoma and non-carcinoma. The architecture of the network is designed to retrieve information at different scales, including both nuclei and overall tissue organization. This design allows the extension of the proposed system to whole-slide histology images. The features extracted by the CNN are also used for training a Support Vector Machine classifier. Accuracies of 77.8% for four class and 83.3% for carcinoma/non-carcinoma are achieved. The sensitivity of our method for cancer cases is 95.6%.

  20. Classification of breast cancer cytological specimen using convolutional neural network

    Science.gov (United States)

    Żejmo, Michał; Kowal, Marek; Korbicz, Józef; Monczak, Roman

    2017-01-01

    The paper presents a deep learning approach for automatic classification of breast tumors based on fine needle cytology. The main aim of the system is to distinguish benign from malignant cases based on microscopic images. Experiment was carried out on cytological samples derived from 50 patients (25 benign cases + 25 malignant cases) diagnosed in Regional Hospital in Zielona Góra. To classify microscopic images, we used convolutional neural networks (CNN) of two types: GoogLeNet and AlexNet. Due to the very large size of images of cytological specimen (on average 200000 × 100000 pixels), they were divided into smaller patches of size 256 × 256 pixels. Breast cancer classification usually is based on morphometric features of nuclei. Therefore, training and validation patches were selected using Support Vector Machine (SVM) so that suitable amount of cell material was depicted. Neural classifiers were tuned using GPU accelerated implementation of gradient descent algorithm. Training error was defined as a cross-entropy classification loss. Classification accuracy was defined as the percentage ratio of successfully classified validation patches to the total number of validation patches. The best accuracy rate of 83% was obtained by GoogLeNet model. We observed that more misclassified patches belong to malignant cases.

  1. Three-dimensional fingerprint recognition by using convolution neural network

    Science.gov (United States)

    Tian, Qianyu; Gao, Nan; Zhang, Zonghua

    2018-01-01

    With the development of science and technology and the improvement of social information, fingerprint recognition technology has become a hot research direction and been widely applied in many actual fields because of its feasibility and reliability. The traditional two-dimensional (2D) fingerprint recognition method relies on matching feature points. This method is not only time-consuming, but also lost three-dimensional (3D) information of fingerprint, with the fingerprint rotation, scaling, damage and other issues, a serious decline in robustness. To solve these problems, 3D fingerprint has been used to recognize human being. Because it is a new research field, there are still lots of challenging problems in 3D fingerprint recognition. This paper presents a new 3D fingerprint recognition method by using a convolution neural network (CNN). By combining 2D fingerprint and fingerprint depth map into CNN, and then through another CNN feature fusion, the characteristics of the fusion complete 3D fingerprint recognition after classification. This method not only can preserve 3D information of fingerprints, but also solves the problem of CNN input. Moreover, the recognition process is simpler than traditional feature point matching algorithm. 3D fingerprint recognition rate by using CNN is compared with other fingerprint recognition algorithms. The experimental results show that the proposed 3D fingerprint recognition method has good recognition rate and robustness.

  2. A deep convolutional neural network for recognizing foods

    Science.gov (United States)

    Jahani Heravi, Elnaz; Habibi Aghdam, Hamed; Puig, Domenec

    2015-12-01

    Controlling the food intake is an efficient way that each person can undertake to tackle the obesity problem in countries worldwide. This is achievable by developing a smartphone application that is able to recognize foods and compute their calories. State-of-art methods are chiefly based on hand-crafted feature extraction methods such as HOG and Gabor. Recent advances in large-scale object recognition datasets such as ImageNet have revealed that deep Convolutional Neural Networks (CNN) possess more representation power than the hand-crafted features. The main challenge with CNNs is to find the appropriate architecture for each problem. In this paper, we propose a deep CNN which consists of 769; 988 parameters. Our experiments show that the proposed CNN outperforms the state-of-art methods and improves the best result of traditional methods 17%. Moreover, using an ensemble of two CNNs that have been trained two different times, we are able to improve the classification performance 21:5%.

  3. Chinese Sentence Classification Based on Convolutional Neural Network

    Science.gov (United States)

    Gu, Chengwei; Wu, Ming; Zhang, Chuang

    2017-10-01

    Sentence classification is one of the significant issues in Natural Language Processing (NLP). Feature extraction is often regarded as the key point for natural language processing. Traditional ways based on machine learning can not take high level features into consideration, such as Naive Bayesian Model. The neural network for sentence classification can make use of contextual information to achieve greater results in sentence classification tasks. In this paper, we focus on classifying Chinese sentences. And the most important is that we post a novel architecture of Convolutional Neural Network (CNN) to apply on Chinese sentence classification. In particular, most of the previous methods often use softmax classifier for prediction, we embed a linear support vector machine to substitute softmax in the deep neural network model, minimizing a margin-based loss to get a better result. And we use tanh as an activation function, instead of ReLU. The CNN model improve the result of Chinese sentence classification tasks. Experimental results on the Chinese news title database validate the effectiveness of our model.

  4. Semantic Segmentation of Indoor Point Clouds Using Convolutional Neural Network

    Science.gov (United States)

    Babacan, K.; Chen, L.; Sohn, G.

    2017-11-01

    As Building Information Modelling (BIM) thrives, geometry becomes no longer sufficient; an ever increasing variety of semantic information is needed to express an indoor model adequately. On the other hand, for the existing buildings, automatically generating semantically enriched BIM from point cloud data is in its infancy. The previous research to enhance the semantic content rely on frameworks in which some specific rules and/or features that are hand coded by specialists. These methods immanently lack generalization and easily break in different circumstances. On this account, a generalized framework is urgently needed to automatically and accurately generate semantic information. Therefore we propose to employ deep learning techniques for the semantic segmentation of point clouds into meaningful parts. More specifically, we build a volumetric data representation in order to efficiently generate the high number of training samples needed to initiate a convolutional neural network architecture. The feedforward propagation is used in such a way to perform the classification in voxel level for achieving semantic segmentation. The method is tested both for a mobile laser scanner point cloud, and a larger scale synthetically generated data. We also demonstrate a case study, in which our method can be effectively used to leverage the extraction of planar surfaces in challenging cluttered indoor environments.

  5. Building Extraction from Remote Sensing Data Using Fully Convolutional Networks

    Science.gov (United States)

    Bittner, K.; Cui, S.; Reinartz, P.

    2017-05-01

    Building detection and footprint extraction are highly demanded for many remote sensing applications. Though most previous works have shown promising results, the automatic extraction of building footprints still remains a nontrivial topic, especially in complex urban areas. Recently developed extensions of the CNN framework made it possible to perform dense pixel-wise classification of input images. Based on these abilities we propose a methodology, which automatically generates a full resolution binary building mask out of a Digital Surface Model (DSM) using a Fully Convolution Network (FCN) architecture. The advantage of using the depth information is that it provides geometrical silhouettes and allows a better separation of buildings from background as well as through its invariance to illumination and color variations. The proposed framework has mainly two steps. Firstly, the FCN is trained on a large set of patches consisting of normalized DSM (nDSM) as inputs and available ground truth building mask as target outputs. Secondly, the generated predictions from FCN are viewed as unary terms for a Fully connected Conditional Random Fields (FCRF), which enables us to create a final binary building mask. A series of experiments demonstrate that our methodology is able to extract accurate building footprints which are close to the buildings original shapes to a high degree. The quantitative and qualitative analysis show the significant improvements of the results in contrast to the multy-layer fully connected network from our previous work.

  6. BUILDING EXTRACTION FROM REMOTE SENSING DATA USING FULLY CONVOLUTIONAL NETWORKS

    Directory of Open Access Journals (Sweden)

    K. Bittner

    2017-05-01

    Full Text Available Building detection and footprint extraction are highly demanded for many remote sensing applications. Though most previous works have shown promising results, the automatic extraction of building footprints still remains a nontrivial topic, especially in complex urban areas. Recently developed extensions of the CNN framework made it possible to perform dense pixel-wise classification of input images. Based on these abilities we propose a methodology, which automatically generates a full resolution binary building mask out of a Digital Surface Model (DSM using a Fully Convolution Network (FCN architecture. The advantage of using the depth information is that it provides geometrical silhouettes and allows a better separation of buildings from background as well as through its invariance to illumination and color variations. The proposed framework has mainly two steps. Firstly, the FCN is trained on a large set of patches consisting of normalized DSM (nDSM as inputs and available ground truth building mask as target outputs. Secondly, the generated predictions from FCN are viewed as unary terms for a Fully connected Conditional Random Fields (FCRF, which enables us to create a final binary building mask. A series of experiments demonstrate that our methodology is able to extract accurate building footprints which are close to the buildings original shapes to a high degree. The quantitative and qualitative analysis show the significant improvements of the results in contrast to the multy-layer fully connected network from our previous work.

  7. Multi-resolution Convolution Methodology for ICP Waveform Morphology Analysis.

    Science.gov (United States)

    Shaw, Martin; Piper, Ian; Hawthorne, Christopher

    2016-01-01

    Intracranial pressure (ICP) monitoring is a key clinical tool in the assessment and treatment of patients in neurointensive care. ICP morphology analysis can be useful in the classification of waveform features.A methodology for the decomposition of an ICP signal into clinically relevant dimensions has been devised that allows the identification of important ICP waveform types. It has three main components. First, multi-resolution convolution analysis is used for the main signal decomposition. Then, an impulse function is created, with multiple parameters, that can represent any form in the signal under analysis. Finally, a simple, localised optimisation technique is used to find morphologies of interest in the decomposed data.A pilot application of this methodology using a simple signal has been performed. This has shown that the technique works with performance receiver operator characteristic area under the curve values for each of the waveform types: plateau wave, B wave and high and low compliance states of 0.936, 0.694, 0.676 and 0.698, respectively.This is a novel technique that showed some promise during the pilot analysis. However, it requires further optimisation to become a usable clinical tool for the automated analysis of ICP signals.

  8. Fully automated quantitative cephalometry using convolutional neural networks.

    Science.gov (United States)

    Arık, Sercan Ö; Ibragimov, Bulat; Xing, Lei

    2017-01-01

    Quantitative cephalometry plays an essential role in clinical diagnosis, treatment, and surgery. Development of fully automated techniques for these procedures is important to enable consistently accurate computerized analyses. We study the application of deep convolutional neural networks (CNNs) for fully automated quantitative cephalometry for the first time. The proposed framework utilizes CNNs for detection of landmarks that describe the anatomy of the depicted patient and yield quantitative estimation of pathologies in the jaws and skull base regions. We use a publicly available cephalometric x-ray image dataset to train CNNs for recognition of landmark appearance patterns. CNNs are trained to output probabilistic estimations of different landmark locations, which are combined using a shape-based model. We evaluate the overall framework on the test set and compare with other proposed techniques. We use the estimated landmark locations to assess anatomically relevant measurements and classify them into different anatomical types. Overall, our results demonstrate high anatomical landmark detection accuracy ([Formula: see text] to 2% higher success detection rate for a 2-mm range compared with the top benchmarks in the literature) and high anatomical type classification accuracy ([Formula: see text] average classification accuracy for test set). We demonstrate that CNNs, which merely input raw image patches, are promising for accurate quantitative cephalometry.

  9. Toward Content Based Image Retrieval with Deep Convolutional Neural Networks.

    Science.gov (United States)

    Sklan, Judah E S; Plassard, Andrew J; Fabbri, Daniel; Landman, Bennett A

    2015-03-19

    Content-based image retrieval (CBIR) offers the potential to identify similar case histories, understand rare disorders, and eventually, improve patient care. Recent advances in database capacity, algorithm efficiency, and deep Convolutional Neural Networks (dCNN), a machine learning technique, have enabled great CBIR success for general photographic images. Here, we investigate applying the leading ImageNet CBIR technique to clinically acquired medical images captured by the Vanderbilt Medical Center. Briefly, we (1) constructed a dCNN with four hidden layers, reducing dimensionality of an input scaled to 128×128 to an output encoded layer of 4×384, (2) trained the network using back-propagation 1 million random magnetic resonance (MR) and computed tomography (CT) images, (3) labeled an independent set of 2100 images, and (4) evaluated classifiers on the projection of the labeled images into manifold space. Quantitative results were disappointing (averaging a true positive rate of only 20%); however, the data suggest that improvements would be possible with more evenly distributed sampling across labels and potential re-grouping of label structures. This prelimainry effort at automated classification of medical images with ImageNet is promising, but shows that more work is needed beyond direct adaptation of existing techniques.

  10. Image reconstruction in computerized tomography using the convolution method

    International Nuclear Information System (INIS)

    Oliveira Rebelo, A.M. de.

    1984-03-01

    In the present work an algoritin was derived, using the analytical convolution method (filtered back-projection) for two-dimensional or three-dimensional image reconstruction in computerized tomography applied to non-destructive testing and to the medical use. This mathematical model is based on the analytical Fourier transform method for image reconstruction. This model consists of a discontinuous system formed by an NxN array of cells (pixels). The attenuation in the object under study of a colimated gamma ray beam has been determined for various positions and incidence angles (projections) in terms of the interaction of the beam with the intercepted pixels. The contribution of each pixel to beam attenuation was determined using the weight function W ij which was used for simulated tests. Simulated tests using standard objects with attenuation coefficients in the range of 0,2 to 0,7 cm -1 were carried out using cell arrays of up to 25x25. One application was carried out in the medical area simulating image reconstruction of an arm phantom with attenuation coefficients in the range of 0,2 to 0,5 cm -1 using cell arrays of 41x41. The simulated results show that, in objects with a great number of interfaces and great variations of attenuation coefficients at these interfaces, a good reconstruction is obtained with the number of projections equal to the reconstruction matrix dimension. A good reconstruction is otherwise obtained with fewer projections. (author) [pt

  11. Image aesthetic quality evaluation using convolution neural network embedded learning

    Science.gov (United States)

    Li, Yu-xin; Pu, Yuan-yuan; Xu, Dan; Qian, Wen-hua; Wang, Li-peng

    2017-11-01

    A way of embedded learning convolution neural network (ELCNN) based on the image content is proposed to evaluate the image aesthetic quality in this paper. Our approach can not only solve the problem of small-scale data but also score the image aesthetic quality. First, we chose Alexnet and VGG_S to compare for confirming which is more suitable for this image aesthetic quality evaluation task. Second, to further boost the image aesthetic quality classification performance, we employ the image content to train aesthetic quality classification models. But the training samples become smaller and only using once fine-tuning cannot make full use of the small-scale data set. Third, to solve the problem in second step, a way of using twice fine-tuning continually based on the aesthetic quality label and content label respective is proposed, the classification probability of the trained CNN models is used to evaluate the image aesthetic quality. The experiments are carried on the small-scale data set of Photo Quality. The experiment results show that the classification accuracy rates of our approach are higher than the existing image aesthetic quality evaluation approaches.

  12. Classification of Two Comic Books based on Convolutional Neural Networks

    Directory of Open Access Journals (Sweden)

    Miki UENO

    2017-03-01

    Full Text Available Unphotographic images are the powerful representations described various situations. Thus, understanding intellectual products such as comics and picture books is one of the important topics in the field of artificial intelligence. Hence, stepwise analysis of a comic story, i.e., features of a part of the image, information features, features relating to continuous scene etc., was pursued. Especially, the length and each scene of four-scene comics are limited so as to ensure a clear interpretation of the contents.In this study, as the first step in this direction, the problem to classify two four-scene comics by the same artists were focused as the example. Several classifiers were constructed by utilizing a Convolutional Neural Network(CNN, and the results of classification by a human annotator and by a computational method were compared.From these experiments, we have clearly shown that CNN is efficient way to classify unphotographic gray scaled images and found that characteristic features of images to classify incorrectly.

  13. Classification and Segmentation of Satellite Orthoimagery Using Convolutional Neural Networks

    Directory of Open Access Journals (Sweden)

    Martin Längkvist

    2016-04-01

    Full Text Available The availability of high-resolution remote sensing (HRRS data has opened up the possibility for new interesting applications, such as per-pixel classification of individual objects in greater detail. This paper shows how a convolutional neural network (CNN can be applied to multispectral orthoimagery and a digital surface model (DSM of a small city for a full, fast and accurate per-pixel classification. The predicted low-level pixel classes are then used to improve the high-level segmentation. Various design choices of the CNN architecture are evaluated and analyzed. The investigated land area is fully manually labeled into five categories (vegetation, ground, roads, buildings and water, and the classification accuracy is compared to other per-pixel classification works on other land areas that have a similar choice of categories. The results of the full classification and segmentation on selected segments of the map show that CNNs are a viable tool for solving both the segmentation and object recognition task for remote sensing data.

  14. Classification of breast cancer histology images using Convolutional Neural Networks.

    Science.gov (United States)

    Araújo, Teresa; Aresta, Guilherme; Castro, Eduardo; Rouco, José; Aguiar, Paulo; Eloy, Catarina; Polónia, António; Campilho, Aurélio

    2017-01-01

    Breast cancer is one of the main causes of cancer death worldwide. The diagnosis of biopsy tissue with hematoxylin and eosin stained images is non-trivial and specialists often disagree on the final diagnosis. Computer-aided Diagnosis systems contribute to reduce the cost and increase the efficiency of this process. Conventional classification approaches rely on feature extraction methods designed for a specific problem based on field-knowledge. To overcome the many difficulties of the feature-based approaches, deep learning methods are becoming important alternatives. A method for the classification of hematoxylin and eosin stained breast biopsy images using Convolutional Neural Networks (CNNs) is proposed. Images are classified in four classes, normal tissue, benign lesion, in situ carcinoma and invasive carcinoma, and in two classes, carcinoma and non-carcinoma. The architecture of the network is designed to retrieve information at different scales, including both nuclei and overall tissue organization. This design allows the extension of the proposed system to whole-slide histology images. The features extracted by the CNN are also used for training a Support Vector Machine classifier. Accuracies of 77.8% for four class and 83.3% for carcinoma/non-carcinoma are achieved. The sensitivity of our method for cancer cases is 95.6%.

  15. Noisy Ocular Recognition Based on Three Convolutional Neural Networks

    Directory of Open Access Journals (Sweden)

    Min Beom Lee

    2017-12-01

    Full Text Available In recent years, the iris recognition system has been gaining increasing acceptance for applications such as access control and smartphone security. When the images of the iris are obtained under unconstrained conditions, an issue of undermined quality is caused by optical and motion blur, off-angle view (the user’s eyes looking somewhere else, not into the front of the camera, specular reflection (SR and other factors. Such noisy iris images increase intra-individual variations and, as a result, reduce the accuracy of iris recognition. A typical iris recognition system requires a near-infrared (NIR illuminator along with an NIR camera, which are larger and more expensive than fingerprint recognition equipment. Hence, many studies have proposed methods of using iris images captured by a visible light camera without the need for an additional illuminator. In this research, we propose a new recognition method for noisy iris and ocular images by using one iris and two periocular regions, based on three convolutional neural networks (CNNs. Experiments were conducted by using the noisy iris challenge evaluation-part II (NICE.II training dataset (selected from the university of Beira iris (UBIRIS.v2 database, mobile iris challenge evaluation (MICHE database, and institute of automation of Chinese academy of sciences (CASIA-Iris-Distance database. As a result, the method proposed by this study outperformed previous methods.

  16. Giant cell tumor of the frontal sinus: case report

    Energy Technology Data Exchange (ETDEWEB)

    Matushita, Joao Paulo, E-mail: jpauloejulieta@gmail.com [Universidade Federal de Minas Gerais (UFMG), Belo Horizonte, MG (Brazil). Hospital das Clinicas; Matushita, Julieta S.; Matushita Junior, Joao Paulo Kawaoka [Centro de Diagnostico por Imagem Dr. Matsushita, Belo Horizonte, MG (Brazil); Matushita, Cristina S. [Universidade Federal do Rio de Janeiro (UFRJ), RJ (Brazil). Hospital Universitario Clementino Fraga Filho; Simoes, Luiz Antonio Monteiro; Carvalho Neto, Lizando Franco de

    2013-06-15

    The authors report the case of a giant cell tumor of the frontal sinus in a 54-year-old male patient. This tumor location is rare, and this is the third case reported in the literature with radiographic documentation and histopathological confirmation. The patient underwent surgery, with curettage of frontal sinus and placement of a prosthesis. He died because a voluntary abrupt discontinuation of corticosteroids. (author)

  17. Frontal Lobe Tuberculoma: A Clinical and Imaging Challenge

    OpenAIRE

    Alemayehu, Tinsae; Ergete, Wondwossen; Abebe, Workeabeba

    2017-01-01

    Background Pediatric nervous system tuberculomas are usually infra-tentorial and multiple. A frontal lobe location is rare. Case Details We report a 10 year-old boy who presented with a chronic headache and episodes of loss of consciousness. He had no signs of primary pulmonary tuberculosis and a diagnosis of frontal tuberculoma was made upon a post-operative biopsy. He improved following treatment with anti-tubercular drugs. Conclusion Tuberculosis should be considered in children with a chr...

  18. Unfamiliar Face Matching With Frontal and Profile Views.

    Science.gov (United States)

    Kramer, Robin S S; Reynolds, Michael G

    2018-04-01

    Research has systematically examined how laboratory participants and real-world practitioners decide whether two face photographs show the same person or not using frontal images. In contrast, research has not examined face matching using profile images. In Experiment 1, we ask whether matching unfamiliar faces is easier with frontal compared with profile views. Participants completed the original, frontal version of the Glasgow Face Matching Test, and also an adapted version where all face pairs were presented in profile. There was no difference in performance across the two tasks, suggesting that both views were similarly useful for face matching. Experiments 2 and 3 examined whether matching unfamiliar faces is improved when both frontal and profile views are provided. We compared face matching accuracy when both a frontal and a profile image of each face were presented, with accuracy using each view alone. Surprisingly, we found no benefit when both views were presented together in either experiment. Overall, these results suggest that either frontal or profile views provide substantially overlapping information regarding identity or participants are unable to utilise both sources of information when making decisions. Each of these conclusions has important implications for face matching research and real-world identification development.

  19. Left Ventricular Assist Devices

    Directory of Open Access Journals (Sweden)

    Khuansiri Narajeenron

    2017-04-01

    Full Text Available Audience: The audience for this classic team-based learning (cTBL session is emergency medicine residents, faculty, and students; although this topic is applicable to internal medicine and family medicine residents. Introduction: A left ventricular assist device (LVAD is a mechanical circulatory support device that can be placed in critically-ill patients who have poor left ventricular function. After LVAD implantation, patients have improved quality of life.1 The number of LVAD patients worldwide continues to rise. Left-ventricular assist device patients may present to the emergency department (ED with severe, life-threatening conditions. It is essential that emergency physicians have a good understanding of LVADs and their complications. Objectives: Upon completion of this cTBL module, the learner will be able to: 1 Properly assess LVAD patients’ circulatory status; 2 appropriately resuscitate LVAD patients; 3 identify common LVAD complications; 4 evaluate and appropriately manage patients with LVAD malfunctions. Method: The method for this didactic session is cTBL.

  20. Influence of convolution filtering on coronary plaque attenuation values: observations in an ex vivo model of multislice computed tomography coronary angiography

    International Nuclear Information System (INIS)

    Cademartiri, Filippo; Palumbo, Alessandro; La Grutta, Ludovico; Runza, Giuseppe; Maffei, Erica; Mollet, Nico R.; Hamers, Ronald; Bruining, Nico; Bartolotta, Tommaso V.; Midiri, Massimo; Somers, Pamela; Knaapen, Michiel; Verheye, Stefan

    2007-01-01

    Attenuation variability (measured in Hounsfield Units, HU) of human coronary plaques using multislice computed tomography (MSCT) was evaluated in an ex vivo model with increasing convolution kernels. MSCT was performed in seven ex vivo left coronary arteries sunk into oil followingthe instillation of saline (1/∞) and a 1/50 solution of contrast material (400 mgI/ml iomeprol). Scan parameters were: slices/collimation, 16/0.75 mm; rotation time, 375 ms. Four convolution kernels were used: b30f-smooth, b36f-medium smooth, b46f-medium and b60f-sharp. An experienced radiologist scored for the presence of plaques and measured the attenuation in lumen, calcified and noncalcified plaques and the surrounding oil. The results were compared by the ANOVA test and correlated with Pearson's test. The signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) were calculated. The mean attenuation values were significantly different between the four filters (p < 0.0001) in each structure with both solutions. After clustering for the filter, all of the noncalcified plaque values (20.8 ± 39.1, 14.2 ± 35.8, 14.0 ± 32.0, 3.2 ± 32.4 HU with saline; 74.7 ± 66.6, 68.2 ± 63.3, 66.3 ± 66.5, 48.5 ± 60.0 HU in contrast solution) were significantly different, with the exception of the pair b36f-b46f, for which a moderate-high correlation was generally found. Improved SNRs and CNRs were achieved by b30f and b46f. The use of different convolution filters significantly modified the attenuation values, while sharper filtering increased the calcified plaque attenuation and reduced the noncalcified plaque attenuation. (orig.)

  1. Dissociable contribution of the parietal and frontal cortex to coding movement direction and amplitude

    Directory of Open Access Journals (Sweden)

    Marco eDavare

    2015-05-01

    Full Text Available To reach for an object, we must convert its spatial location into an appropriate motor command, merging movement direction and amplitude. In humans, it has been suggested that this visuo-motor transformation occurs in a dorsomedial parieto-frontal pathway, although the causal contribution of the areas constituting the reaching circuit remains unknown. Here we used transcranial magnetic stimulation (TMS in healthy volunteers to disrupt the function of either the medial intraparietal area (mIPS or dorsal premotor cortex (PMd, in each hemisphere. The task consisted in performing step-tracking movements with the right wrist towards targets located in different directions and eccentricities; the targets were either visible for the whole trial (Target-ON or flashed for 200 ms (Target-OFF. Left and right mIPS disruption led to errors in the initial direction of movements performed towards contralateral targets. These errors were corrected online in the Target-ON condition but when the target was flashed for 200 ms, mIPS TMS manifested as a larger endpoint spreading. In contrast, left PMd virtual lesions led to higher acceleration and velocity peaks - two parameters typically used to probe the planned movement amplitude - irrespective of the target position, hemifield and presentation condition; in the Target-OFF condition, left PMd TMS induced overshooting and increased the endpoint dispersion along the axis of the target direction. These results indicate that left PMd intervenes in coding amplitude during movement preparation. The critical TMS timings leading to errors in direction and amplitude were different, namely 160-100 ms before movement onset for mIPS and 100-40 ms for left PMd. TMS applied over right PMd had no significant effect. These results indicate that, during motor preparation, direction and amplitude of goal-directed movements are processed by different cortical areas, at distinct timings, and according to a specific hemispheric

  2. Asymmetry and sexual dimorphism of the medial frontal gyrus visible surface in humans

    Directory of Open Access Journals (Sweden)

    Spasojević Goran

    2010-01-01

    Full Text Available Background/Aim. Studies of visible (extrasulcal surface of the brain hemispheres are not feasible for measurements of the brain size, but are valuable for analysis and quantification of sexual dimorphism and/or asymmetries of the human brain. Morphological and morphometric investigations of the brain may contribute in genetic studies of the human nervous system. The aim of this study was to determine and to quantify sexual dimorphism and the right/left morphological asymmetry of the visible surface of medial frontal gyrus (gyrus frontalis medialis - GFM. Methods. Measurements and analysis of the visible surface of GFM were done on 84 hemispheres (42 brains from the persons of both sexes: 26 males and 16 females, 20-65 years of age. After fixation in 10% formalin and dissection, digital morphometric measurements were performed. We studied these in relation to the side of the hemisphere and the person's sex. Standardized digital AutoCAD planimetry of the visible surface of GFM was enabled by the use of coordinate system of intercommissural line. Results. In the whole sample, the visible surface of the right GFM (21.39 cm2 was statistically significantly greater (p < 0.05 than the left GFM (18.35 cm2 indicating the right/left asymmetry of the visible surface of GFM. Also, the visible surface of the right GFM in the males (22.66 cm2 was significantly greater (p < 0.05 than in the females (19.35 cm2, while the difference in size of the left GFM between the males and the females was not significant (p > 0.05. Conclusion. Morphological analysis of visible surface of GFM performed by digital planimetry showed sexual dimorphism of the visible surface and the presence of right/left asymmetry of GFM.

  3. Quantifying the brain's sheet structure with normalized convolution.

    Science.gov (United States)

    Tax, Chantal M W; Westin, Carl-Fredrik; Dela Haije, Tom; Fuster, Andrea; Viergever, Max A; Calabrese, Evan; Florack, Luc; Leemans, Alexander

    2017-07-01

    The hypothesis that brain pathways form 2D sheet-like structures layered in 3D as "pages of a book" has been a topic of debate in the recent literature. This hypothesis was mainly supported by a qualitative evaluation of "path neighborhoods" reconstructed with diffusion MRI (dMRI) tractography. Notwithstanding the potentially important implications of the sheet structure hypothesis for our understanding of brain structure and development, it is still considered controversial by many for lack of quantitative analysis. A means to quantify sheet structure is therefore necessary to reliably investigate its occurrence in the brain. Previous work has proposed the Lie bracket as a quantitative indicator of sheet structure, which could be computed by reconstructing path neighborhoods from the peak orientations of dMRI orientation density functions. Robust estimation of the Lie bracket, however, is challenging due to high noise levels and missing peak orientations. We propose a novel method to estimate the Lie bracket that does not involve the reconstruction of path neighborhoods with tractography. This method requires the computation of derivatives of the fiber peak orientations, for which we adopt an approach called normalized convolution. With simulations and experimental data we show that the new approach is more robust with respect to missing peaks and noise. We also demonstrate that the method is able to quantify to what extent sheet structure is supported for dMRI data of different species, acquired with different scanners, diffusion weightings, dMRI sampling schemes, and spatial resolutions. The proposed method can also be used with directional data derived from other techniques than dMRI, which will facilitate further validation of the existence of sheet structure. Copyright © 2017 Elsevier B.V. All rights reserved.

  4. HLA class I binding prediction via convolutional neural networks.

    Science.gov (United States)

    Vang, Yeeleng S; Xie, Xiaohui

    2017-09-01

    Many biological processes are governed by protein-ligand interactions. One such example is the recognition of self and non-self cells by the immune system. This immune response process is regulated by the major histocompatibility complex (MHC) protein which is encoded by the human leukocyte antigen (HLA) complex. Understanding the binding potential between MHC and peptides can lead to the design of more potent, peptide-based vaccines and immunotherapies for infectious autoimmune diseases. We apply machine learning techniques from the natural language processing (NLP) domain to address the task of MHC-peptide binding prediction. More specifically, we introduce a new distributed representation of amino acids, name HLA-Vec, that can be used for a variety of downstream proteomic machine learning tasks. We then propose a deep convolutional neural network architecture, name HLA-CNN, for the task of HLA class I-peptide binding prediction. Experimental results show combining the new distributed representation with our HLA-CNN architecture achieves state-of-the-art results in the majority of the latest two Immune Epitope Database (IEDB) weekly automated benchmark datasets. We further apply our model to predict binding on the human genome and identify 15 genes with potential for self binding. Codes to generate the HLA-Vec and HLA-CNN are publicly available at: https://github.com/uci-cbcl/HLA-bind . xhx@ics.uci.edu. Supplementary data are available at Bioinformatics online. © The Author (2017). Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com

  5. Two-stream Convolutional Neural Network for Methane Emissions Quantification

    Science.gov (United States)

    Wang, J.; Ravikumar, A. P.; McGuire, M.; Bell, C.; Tchapmi, L. P.; Brandt, A. R.

    2017-12-01

    Methane, a key component of natural gas, has a 25x higher global warming potential than carbon dioxide on a 100-year basis. Accurately monitoring and mitigating methane emissions require cost-effective detection and quantification technologies. Optical gas imaging, one of the most commonly used leak detection technology, adopted by Environmental Protection Agency, cannot estimate leak-sizes. In this work, we harness advances in computer science to allow for rapid and automatic leak quantification. Particularly, we utilize two-stream deep Convolutional Networks (ConvNets) to estimate leak-size by capturing complementary spatial information from still plume frames, and temporal information from plume motion between frames. We build large leak datasets for training and evaluating purposes by collecting about 20 videos (i.e. 397,400 frames) of leaks. The videos were recorded at six distances from the source, covering 10 -60 ft. Leak sources included natural gas well-heads, separators, and tanks. All frames were labeled with a true leak size, which has eight levels ranging from 0 to 140 MCFH. Preliminary analysis shows that two-stream ConvNets provides significant accuracy advantage over single steam ConvNets. Spatial stream ConvNet can achieve an accuracy of 65.2%, by extracting important features, including texture, plume area, and pattern. Temporal stream, fed by the results of optical flow analysis, results in an accuracy of 58.3%. The integration of the two-stream ConvNets gives a combined accuracy of 77.6%. For future work, we will split the training and testing datasets in distinct ways in order to test the generalization of the algorithm for different leak sources. Several analytic metrics, including confusion matrix and visualization of key features, will be used to understand accuracy rates and occurrences of false positives. The quantification algorithm can help to find and fix super-emitters, and improve the cost-effectiveness of leak detection and repair

  6. Aerial Images and Convolutional Neural Network for Cotton Bloom Detection

    Directory of Open Access Journals (Sweden)

    Rui Xu

    2018-02-01

    Full Text Available Monitoring flower development can provide useful information for production management, estimating yield and selecting specific genotypes of crops. The main goal of this study was to develop a methodology to detect and count cotton flowers, or blooms, using color images acquired by an unmanned aerial system. The aerial images were collected from two test fields in 4 days. A convolutional neural network (CNN was designed and trained to detect cotton blooms in raw images, and their 3D locations were calculated using the dense point cloud constructed from the aerial images with the structure from motion method. The quality of the dense point cloud was analyzed and plots with poor quality were excluded from data analysis. A constrained clustering algorithm was developed to register the same bloom detected from different images based on the 3D location of the bloom. The accuracy and incompleteness of the dense point cloud were analyzed because they affected the accuracy of the 3D location of the blooms and thus the accuracy of the bloom registration result. The constrained clustering algorithm was validated using simulated data, showing good efficiency and accuracy. The bloom count from the proposed method was comparable with the number counted manually with an error of −4 to 3 blooms for the field with a single plant per plot. However, more plots were underestimated in the field with multiple plants per plot due to hidden blooms that were not captured by the aerial images. The proposed methodology provides a high-throughput method to continuously monitor the flowering progress of cotton.

  7. Convolutional neural networks for prostate cancer recurrence prediction

    Science.gov (United States)

    Kumar, Neeraj; Verma, Ruchika; Arora, Ashish; Kumar, Abhay; Gupta, Sanchit; Sethi, Amit; Gann, Peter H.

    2017-03-01

    Accurate prediction of the treatment outcome is important for cancer treatment planning. We present an approach to predict prostate cancer (PCa) recurrence after radical prostatectomy using tissue images. We used a cohort whose case vs. control (recurrent vs. non-recurrent) status had been determined using post-treatment follow up. Further, to aid the development of novel biomarkers of PCa recurrence, cases and controls were paired based on matching of other predictive clinical variables such as Gleason grade, stage, age, and race. For this cohort, tissue resection microarray with up to four cores per patient was available. The proposed approach is based on deep learning, and its novelty lies in the use of two separate convolutional neural networks (CNNs) - one to detect individual nuclei even in the crowded areas, and the other to classify them. To detect nuclear centers in an image, the first CNN predicts distance transform of the underlying (but unknown) multi-nuclear map from the input HE image. The second CNN classifies the patches centered at nuclear centers into those belonging to cases or controls. Voting across patches extracted from image(s) of a patient yields the probability of recurrence for the patient. The proposed approach gave 0.81 AUC for a sample of 30 recurrent cases and 30 non-recurrent controls, after being trained on an independent set of 80 case-controls pairs. If validated further, such an approach might help in choosing between a combination of treatment options such as active surveillance, radical prostatectomy, radiation, and hormone therapy. It can also generalize to the prediction of treatment outcomes in other cancers.

  8. Classifying Radio Galaxies with the Convolutional Neural Network

    Energy Technology Data Exchange (ETDEWEB)

    Aniyan, A. K.; Thorat, K. [Department of Physics and Electronics, Rhodes University, Grahamstown (South Africa)

    2017-06-01

    We present the application of a deep machine learning technique to classify radio images of extended sources on a morphological basis using convolutional neural networks (CNN). In this study, we have taken the case of the Fanaroff–Riley (FR) class of radio galaxies as well as radio galaxies with bent-tailed morphology. We have used archival data from the Very Large Array (VLA)—Faint Images of the Radio Sky at Twenty Centimeters survey and existing visually classified samples available in the literature to train a neural network for morphological classification of these categories of radio sources. Our training sample size for each of these categories is ∼200 sources, which has been augmented by rotated versions of the same. Our study shows that CNNs can classify images of the FRI and FRII and bent-tailed radio galaxies with high accuracy (maximum precision at 95%) using well-defined samples and a “fusion classifier,” which combines the results of binary classifications, while allowing for a mechanism to find sources with unusual morphologies. The individual precision is highest for bent-tailed radio galaxies at 95% and is 91% and 75% for the FRI and FRII classes, respectively, whereas the recall is highest for FRI and FRIIs at 91% each, while the bent-tailed class has a recall of 79%. These results show that our results are comparable to that of manual classification, while being much faster. Finally, we discuss the computational and data-related challenges associated with the morphological classification of radio galaxies with CNNs.

  9. Classifying Radio Galaxies with the Convolutional Neural Network

    Science.gov (United States)

    Aniyan, A. K.; Thorat, K.

    2017-06-01

    We present the application of a deep machine learning technique to classify radio images of extended sources on a morphological basis using convolutional neural networks (CNN). In this study, we have taken the case of the Fanaroff-Riley (FR) class of radio galaxies as well as radio galaxies with bent-tailed morphology. We have used archival data from the Very Large Array (VLA)—Faint Images of the Radio Sky at Twenty Centimeters survey and existing visually classified samples available in the literature to train a neural network for morphological classification of these categories of radio sources. Our training sample size for each of these categories is ˜200 sources, which has been augmented by rotated versions of the same. Our study shows that CNNs can classify images of the FRI and FRII and bent-tailed radio galaxies with high accuracy (maximum precision at 95%) using well-defined samples and a “fusion classifier,” which combines the results of binary classifications, while allowing for a mechanism to find sources with unusual morphologies. The individual precision is highest for bent-tailed radio galaxies at 95% and is 91% and 75% for the FRI and FRII classes, respectively, whereas the recall is highest for FRI and FRIIs at 91% each, while the bent-tailed class has a recall of 79%. These results show that our results are comparable to that of manual classification, while being much faster. Finally, we discuss the computational and data-related challenges associated with the morphological classification of radio galaxies with CNNs.

  10. Classifying Radio Galaxies with the Convolutional Neural Network

    International Nuclear Information System (INIS)

    Aniyan, A. K.; Thorat, K.

    2017-01-01

    We present the application of a deep machine learning technique to classify radio images of extended sources on a morphological basis using convolutional neural networks (CNN). In this study, we have taken the case of the Fanaroff–Riley (FR) class of radio galaxies as well as radio galaxies with bent-tailed morphology. We have used archival data from the Very Large Array (VLA)—Faint Images of the Radio Sky at Twenty Centimeters survey and existing visually classified samples available in the literature to train a neural network for morphological classification of these categories of radio sources. Our training sample size for each of these categories is ∼200 sources, which has been augmented by rotated versions of the same. Our study shows that CNNs can classify images of the FRI and FRII and bent-tailed radio galaxies with high accuracy (maximum precision at 95%) using well-defined samples and a “fusion classifier,” which combines the results of binary classifications, while allowing for a mechanism to find sources with unusual morphologies. The individual precision is highest for bent-tailed radio galaxies at 95% and is 91% and 75% for the FRI and FRII classes, respectively, whereas the recall is highest for FRI and FRIIs at 91% each, while the bent-tailed class has a recall of 79%. These results show that our results are comparable to that of manual classification, while being much faster. Finally, we discuss the computational and data-related challenges associated with the morphological classification of radio galaxies with CNNs.

  11. A convolution-superposition dose calculation engine for GPUs

    Energy Technology Data Exchange (ETDEWEB)

    Hissoiny, Sami; Ozell, Benoit; Despres, Philippe [Departement de genie informatique et genie logiciel, Ecole polytechnique de Montreal, 2500 Chemin de Polytechnique, Montreal, Quebec H3T 1J4 (Canada); Departement de radio-oncologie, CRCHUM-Centre hospitalier de l' Universite de Montreal, 1560 rue Sherbrooke Est, Montreal, Quebec H2L 4M1 (Canada)

    2010-03-15

    Purpose: Graphic processing units (GPUs) are increasingly used for scientific applications, where their parallel architecture and unprecedented computing power density can be exploited to accelerate calculations. In this paper, a new GPU implementation of a convolution/superposition (CS) algorithm is presented. Methods: This new GPU implementation has been designed from the ground-up to use the graphics card's strengths and to avoid its weaknesses. The CS GPU algorithm takes into account beam hardening, off-axis softening, kernel tilting, and relies heavily on raytracing through patient imaging data. Implementation details are reported as well as a multi-GPU solution. Results: An overall single-GPU acceleration factor of 908x was achieved when compared to a nonoptimized version of the CS algorithm implemented in PlanUNC in single threaded central processing unit (CPU) mode, resulting in approximatively 2.8 s per beam for a 3D dose computation on a 0.4 cm grid. A comparison to an established commercial system leads to an acceleration factor of approximately 29x or 0.58 versus 16.6 s per beam in single threaded mode. An acceleration factor of 46x has been obtained for the total energy released per mass (TERMA) calculation and a 943x acceleration factor for the CS calculation compared to PlanUNC. Dose distributions also have been obtained for a simple water-lung phantom to verify that the implementation gives accurate results. Conclusions: These results suggest that GPUs are an attractive solution for radiation therapy applications and that careful design, taking the GPU architecture into account, is critical in obtaining significant acceleration factors. These results potentially can have a significant impact on complex dose delivery techniques requiring intensive dose calculations such as intensity-modulated radiation therapy (IMRT) and arc therapy. They also are relevant for adaptive radiation therapy where dose results must be obtained rapidly.

  12. Brain Tumor Segmentation Using Convolutional Neural Networks in MRI Images.

    Science.gov (United States)

    Pereira, Sergio; Pinto, Adriano; Alves, Victor; Silva, Carlos A

    2016-05-01

    Among brain tumors, gliomas are the most common and aggressive, leading to a very short life expectancy in their highest grade. Thus, treatment planning is a key stage to improve the quality of life of oncological patients. Magnetic resonance imaging (MRI) is a widely used imaging technique to assess these tumors, but the large amount of data produced by MRI prevents manual segmentation in a reasonable time, limiting the use of precise quantitative measurements in the clinical practice. So, automatic and reliable segmentation methods are required; however, the large spatial and structural variability among brain tumors make automatic segmentation a challenging problem. In this paper, we propose an automatic segmentation method based on Convolutional Neural Networks (CNN), exploring small 3 ×3 kernels. The use of small kernels allows designing a deeper architecture, besides having a positive effect against overfitting, given the fewer number of weights in the network. We also investigated the use of intensity normalization as a pre-processing step, which though not common in CNN-based segmentation methods, proved together with data augmentation to be very effective for brain tumor segmentation in MRI images. Our proposal was validated in the Brain Tumor Segmentation Challenge 2013 database (BRATS 2013), obtaining simultaneously the first position for the complete, core, and enhancing regions in Dice Similarity Coefficient metric (0.88, 0.83, 0.77) for the Challenge data set. Also, it obtained the overall first position by the online evaluation platform. We also participated in the on-site BRATS 2015 Challenge using the same model, obtaining the second place, with Dice Similarity Coefficient metric of 0.78, 0.65, and 0.75 for the complete, core, and enhancing regions, respectively.

  13. Automatic Seismic-Event Classification with Convolutional Neural Networks.

    Science.gov (United States)

    Bueno Rodriguez, A.; Titos Luzón, M.; Garcia Martinez, L.; Benitez, C.; Ibáñez, J. M.

    2017-12-01

    Active volcanoes exhibit a wide range of seismic signals, providing vast amounts of unlabelled volcano-seismic data that can be analyzed through the lens of artificial intelligence. However, obtaining high-quality labelled data is time-consuming and expensive. Deep neural networks can process data in their raw form, compute high-level features and provide a better representation of the input data distribution. These systems can be deployed to classify seismic data at scale, enhance current early-warning systems and build extensive seismic catalogs. In this research, we aim to classify spectrograms from seven different seismic events registered at "Volcán de Fuego" (Colima, Mexico), during four eruptive periods. Our approach is based on convolutional neural networks (CNNs), a sub-type of deep neural networks that can exploit grid structure from the data. Volcano-seismic signals can be mapped into a grid-like structure using the spectrogram: a representation of the temporal evolution in terms of time and frequency. Spectrograms were computed from the data using Hamming windows with 4 seconds length, 2.5 seconds overlapping and 128 points FFT resolution. Results are compared to deep neural networks, random forest and SVMs. Experiments show that CNNs can exploit temporal and frequency information, attaining a classification accuracy of 93%, similar to deep networks 91% but outperforming SVM and random forest. These results empirically show that CNNs are powerful models to classify a wide range of volcano-seismic signals, and achieve good generalization. Furthermore, volcano-seismic spectrograms contains useful discriminative information for the CNN, as higher layers of the network combine high-level features computed for each frequency band, helping to detect simultaneous events in time. Being at the intersection of deep learning and geophysics, this research enables future studies of how CNNs can be used in volcano monitoring to accurately determine the detection and

  14. Dermoscopic Image Segmentation via Multistage Fully Convolutional Networks.

    Science.gov (United States)

    Bi, Lei; Kim, Jinman; Ahn, Euijoon; Kumar, Ashnil; Fulham, Michael; Feng, Dagan

    2017-09-01

    Segmentation of skin lesions is an important step in the automated computer aided diagnosis of melanoma. However, existing segmentation methods have a tendency to over- or under-segment the lesions and perform poorly when the lesions have fuzzy boundaries, low contrast with the background, inhomogeneous textures, or contain artifacts. Furthermore, the performance of these methods are heavily reliant on the appropriate tuning of a large number of parameters as well as the use of effective preprocessing techniques, such as illumination correction and hair removal. We propose to leverage fully convolutional networks (FCNs) to automatically segment the skin lesions. FCNs are a neural network architecture that achieves object detection by hierarchically combining low-level appearance information with high-level semantic information. We address the issue of FCN producing coarse segmentation boundaries for challenging skin lesions (e.g., those with fuzzy boundaries and/or low difference in the textures between the foreground and the background) through a multistage segmentation approach in which multiple FCNs learn complementary visual characteristics of different skin lesions; early stage FCNs learn coarse appearance and localization information while late-stage FCNs learn the subtle characteristics of the lesion boundaries. We also introduce a new parallel integration method to combine the complementary information derived from individual segmentation stages to achieve a final segmentation result that has accurate localization and well-defined lesion boundaries, even for the most challenging skin lesions. We achieved an average Dice coefficient of 91.18% on the ISBI 2016 Skin Lesion Challenge dataset and 90.66% on the PH2 dataset. Our extensive experimental results on two well-established public benchmark datasets demonstrate that our method is more effective than other state-of-the-art methods for skin lesion segmentation.

  15. Left Ventricular Pseudoaneurysm Perceived as a Left Lung Mass

    Directory of Open Access Journals (Sweden)

    Ugur Gocen

    2013-02-01

    Full Text Available Left ventricular pseudo-aneurysm is a rare complication of aneurysmectomy. We present a case of surgically-treated left ventricular pseudo-aneurysm which was diagnosed three years after coronary artery bypass grafting and left ventricular aneurysmectomy. The presenting symptoms, diagnostic evaluation and surgical repair are described. [Cukurova Med J 2013; 38(1.000: 123-125

  16. Automatic determination of the regional ejection fraction of the left ventricle (gated bloodpool)

    International Nuclear Information System (INIS)

    Feser, J.A.

    1982-01-01

    The left ventricular volume curve and the ejection fraction are calculated according to the ''sliding region of interest'' method in which the ventricle contour is redetermined for every single picture of the various phases of the heart beat. The original set of data, consisting of 32 pictures in 64 x 64 matrix resolution, is processed by a three-dimensional filtering process in space (x,y) and time (t). The ventricle contour is determined by convolution of the filtered images with a 7-point Laplacian operator in 4 independent directions. The atrial and ventricular phase histograms are then calculated on the basis of this contour. (WU) [de

  17. Shifted inferior frontal laterality in women with major depressive disorder is related to emotion-processing deficits.

    Science.gov (United States)

    Briceño, E M; Weisenbach, S L; Rapport, L J; Hazlett, K E; Bieliauskas, L A; Haase, B D; Ransom, M T; Brinkman, M L; Peciña, M; Schteingart, D E; Starkman, M N; Giordani, B; Welsh, R C; Noll, D C; Zubieta, J-K; Langenecker, S A

    2013-07-01

    Facial emotion perception (FEP) is a critical human skill for successful social interaction, and a substantial body of literature suggests that explicit FEP is disrupted in major depressive disorder (MDD). Prior research suggests that weakness in FEP may be an important phenomenon underlying patterns of emotion-processing challenges in MDD and the disproportionate frequency of MDD in women. Method Women with (n = 24) and without (n = 22) MDD, equivalent in age and education, completed a FEP task during functional magnetic resonance imaging. The MDD group exhibited greater extents of frontal, parietal and subcortical activation compared with the control group during FEP. Activation in the inferior frontal gyrus (IFG) appeared shifted from a left >right pattern observed in healthy women to a bilateral pattern in MDD women. The ratio of left to right suprathreshold IFG voxels in healthy controls was nearly 3:1, whereas in the MDD group, there was a greater percentage of suprathreshold IFG voxels bilaterally, with no leftward bias. In MDD, relatively greater activation in right IFG compared with left IFG (ratio score) was present and predicted FEP accuracy (r = 0.56, p imaging-to-assessment translational applications in MDD.

  18. Inferior Frontal Cortex Contributions to the Recognition of Spoken Words and Their Constituent Speech Sounds.

    Science.gov (United States)

    Rogers, Jack C; Davis, Matthew H

    2017-05-01

    Speech perception and comprehension are often challenged by the need to recognize speech sounds that are degraded or ambiguous. Here, we explore the cognitive and neural mechanisms involved in resolving ambiguity in the identity of speech sounds using syllables that contain ambiguous phonetic segments (e.g., intermediate sounds between /b/ and /g/ as in "blade" and "glade"). We used an audio-morphing procedure to create a large set of natural sounding minimal pairs that contain phonetically ambiguous onset or offset consonants (differing in place, manner, or voicing). These ambiguous segments occurred in different lexical contexts (i.e., in words or pseudowords, such as blade-glade or blem-glem) and in different phonological environments (i.e., with neighboring syllables that differed in lexical status, such as blouse-glouse). These stimuli allowed us to explore the impact of phonetic ambiguity on the speed and accuracy of lexical decision responses (Experiment 1), semantic categorization responses (Experiment 2), and the magnitude of BOLD fMRI responses during attentive comprehension (Experiment 3). For both behavioral and neural measures, observed effects of phonetic ambiguity were influenced by lexical context leading to slower responses and increased activity in the left inferior frontal gyrus for high-ambiguity syllables that distinguish pairs of words, but not for equivalent pseudowords. These findings suggest lexical involvement in the resolution of phonetic ambiguity. Implications for speech perception and the role of inferior frontal regions are discussed.

  19. The right inferior frontal gyrus processes nested non-local dependencies in music.

    Science.gov (United States)

    Cheung, Vincent K M; Meyer, Lars; Friederici, Angela D; Koelsch, Stefan

    2018-02-28

    Complex auditory sequences known as music have often been described as hierarchically structured. This permits the existence of non-local dependencies, which relate elements of a sequence beyond their temporal sequential order. Previous studies in music have reported differential activity in the inferior frontal gyrus (IFG) when comparing regular and irregular chord-transitions based on theories in Western tonal harmony. However, it is unclear if the observed activity reflects the interpretation of hierarchical structure as the effects are confounded by local irregularity. Using functional magnetic resonance imaging (fMRI), we found that violations to non-local dependencies in nested sequences of three-tone musical motifs in musicians elicited increased activity in the right IFG. This is in contrast to similar studies in language which typically report the left IFG in processing grammatical syntax. Effects of increasing auditory working demands are moreover reflected by distributed activity in frontal and parietal regions. Our study therefore demonstrates the role of the right IFG in processing non-local dependencies in music, and suggests that hierarchical processing in different cognitive domains relies on similar mechanisms that are subserved by domain-selective neuronal subpopulations.

  20. Verbal memory impairment in new onset bipolar disorder: Relationship with frontal and medial temporal morphology.

    Science.gov (United States)

    Chakrabarty, Trisha; Kozicky, Jan-Marie; Torres, Ivan J; Lam, Raymond W; Yatham, Lakshmi N

    2015-06-01

    Verbal memory (VM) impairment is a trait feature of bipolar I disorder (BDI) that is present at illness onset and associated with functional outcome. However, little is known about the morphological abnormalities underlying this deficit early in the disease course. This study examined the neurobiological correlates of VM impairment in euthymic newly diagnosed patients, with attention to frontal and medial temporal (MT) structures known to contribute to VM. Euthymic patients with BDI recently recovered from their first episode of mania (n = 42) were compared with healthy subjects (n = 37) using measures of the California Verbal Learning Test (CVLT-II) associated with frontal and MT functioning. A subset of participants had 3T MRI scan (n = 31 patient group, n = 30 healthy subject group). Hippocampal and prefrontal volumes were analyzed using FreeSurfer 5.1 and correlated with their corresponding CVLT-II subscores. Patients showed decreased performance in total learning as well as short and long delay verbal recall. Consistent with MT dysfunction, they also showed deficits in recognition discriminability and learning slope. In the patient group only, left hippocampal volumes were negatively correlated with these measures. These results suggest that anomalous MT functioning is involved with VM impairment early in the course of BDI.