International Symposium on Computational Medicine November 20-21, 2009, Beijing, China
The nternational Symposium on Computational Medicine is one of the frontier forums of the Chinese Academy of Sciences. The symposium of this year will focus on the advances of neural circuits and brain networks based on brain imaging techniques (MRI, Diffusion MRI, fMRI, fNIRS, EEG/MEG) and their applications to brain disorders. All speakers of this symposium are the active experts in this field and are invited only.
Professor Michael Breakspear
Professor Michael Breakspear is a consultant psychiatrist with a PhD in computational neuroscience. He is Chair of the Division of Mental Health Research at the Queensland Insitute of Medical Research, Australia. He employs functional neuroimaging and computational modelling in order to improve our understanding and diagnosis of severe mental illnesses.
Title: Multistable and heirarchical cortical dynamics
[Abstract] Recordings of cortical activity across the entire hierarchy of spatial scales in the brain evidence the hallmarks of complex dynamics. At the largest scale, the human alpha rhythm (~10 Hz) is a multistable phenomenon with erratic, but well well defined switching behaviour between different temporal modes of activity. Betaoscillations (15-30 Hz) exhibit erratic fluctuations that appear drawn from an underlying "super-Gaussian" process. In this talk, these phenomena will be defined and evidence for their existence reviewed. I will then present a model of spiking neural populations defined on a heirarchically-organised architecture. This enables us to understand the role of spike-time dependent plasticity in the generation of these non-trivial large-scale fluctuations in neuronal activity and the conditions under which such activity can become super-critical, hence exhibiting seizure-like spiking behaviour. I will also overview current work which seeks to integrate information from diffusion-tensor imaging in order to understand the mechanisms underlying sub-optimal information processing due to white matter changes in vascular dementia.
Professor James Gee
Professor James Gee is Associate Professor of Radiologic Science and Computer and Information Science, Director of the HHMI-NIBIB Interfaces Program in Biomedical Imaging and Informational Sciences, and Co-Director of the Translational Biomedical Imaging Center, all at the University of Pennsylvania.His current work includes applications of image analysis to study the biomechanics of moving organs; the normal development and pathological correlates of brain and lung structure; and the correlation between brain structural changes and cognitive deficits in central nervous system disorders.
Title:Tract-specific analysis of brain white matter
[Abstract] We present a new model-based framework for the statistical analysis of diffusion imaging data associated with specific white matter tracts. The framework takes advantage of the fact that several of the major white matter tracts are thin sheet-like structures that can be effectively modeled by medial representations. The approach involves segmenting major tracts and fitting them with deformable geometric medial models. The medial representation makes it possible to average and combine tensor-based features along directions locally perpendicular to the tracts, thus reducing data dimensionality and accounting for errors in normalization. The framework enables the analysis of individual white matter structures, and provides a range of possibilities for computing statistics and visualizing differences between cohorts.
Professor Jen-Chuen Hsieh
Professor Jen-Chuen Hsieh is a brain scientist with a PhD in clinical neuroscience from Karolinska Institute, Sweden, and MD from National Yang-Ming University, Taiwan. He is the director of Brain Research Center of National Yang-Ming University/University System of Taiwan, also heading the Laboratory of Integrated Brain Research of Taipei Veterans General Hospital, a national medical center of Taiwan. His team employs MRI, MEG/EEG, PET, SPECT, NIR, and TMS for fundamental and clinical research of cognitive neuroscience and various neuropsychiatric diseases.
Title:The brain connecting the mind and the mind emboding the brain
[Abstract] Nothing defines the function of a neuron better then its anatomical and effective connections; and nothing describes the connections of a neuron better than in a functional and dynamic way. The recent advancements of DTI and DSI have revealed the anatomical architecture of the brain with good spatial resolution. Functional connectivity of resting fMRI has also been profusely studied in both cognitive and clinical settings. However, the functional essence of neuro-information communication can be most heuristically disclosed by dynamic scenario of the brain cascade. In this talk, I will present our recent work using MEG and connectivity modeling to investigate the human mirror neuron system in the context of affective social neuroscience and the patterning of affective psychiatric disorders.
Professor Xiaoping Hu
Professor Xiaoping Hu obtained his Ph.D. in medical physics, with a focus on MRI, from the University of Chicago in 1988. In January 2002, he moved to Emory University/Georgia Institute of Technology to become Professor of Biomedical Engineering, Georgia Research Alliance Endowed Eminent Scholar in Biomedical Imaging, and Director of the Biomedical Imaging Technology Center. His research interest lies in the development and biomedical application of magnetic resonance imaging/spectroscopy, particularly in neuroimaging. He was named a Fellow of the International Society for Magnetic Resonance in Medicine and also a fellow of Institute of Electrical and Electronic Engineers and a fellow of American Institute of Medical and Biological Engineering.
Title: Instantaneous and Causal Connectivity in Resting State Brain Networks Derived from fMRI Data
[Abstract] This study investigated functional connectivity (FC) based on instantaneous correlation and effective connectivity (EC) based on Granger causality of four important networks at resting state derived from functional magnetic resonance imaging data – default mode network (DMN), hippocampal cortical memory network (HCMN), dorsal attention network (DAN) and fronto-parietal control network (FPCN). The use of correlation-purged Granger causality (CPGC), a measure recently introduced by us, not only enabled the simultaneous evaluation of FC and EC of all networks using a single multivariate model, but also accounted for the interaction between them resulting from the smoothing of neuronal activity by hemodynamics. FC was visualized using a force-directed layout upon which causal interactions were overlaid. FC results revealed that DAN was very tightly coupled compared to other networks while the DMN formed the core network around which the other networks amalgamated. Bidirectional causal interactions showed that posterior cingulate and posterior inferior parietal lobule of DMN acted as major transit hubs for information exchange. The unidirectional causal paths revealed that hippocampus and anterior prefrontal cortex (aPFC) received major inputs, indicating memory encoding and cognitive integration, respectively. Major outputs emanated from anterior insula and middle temporal area which were directed at aPFC, and might carry information about interoceptive awareness and external environment, respectively, into aPFC for integration. This supports the hypothesis that aPFC-seeded FPCN may act as a control network. Finally, even though circumstantial evidence has given rise to networks with different nomenclature, there was high degree of interaction (especially causal) between different networks.
Professor Rolf Kotter
Professor Rolf Kotter is the Chair of Neurophysiology and Neuroinformatics, UMCN. His research interests include analysis and mathematical modelling of complex processes in neuroscience; experimental and computational investigation of structural and functional brain connectivity at cellular and regional levels.
Title: Patterns of cortical degeneration in an elderly cohort with cerebral small vessel disease
[Abstract] Emerging non-invasive neuroimaging techniques allow for the morphometric analysis of patterns of grey and white matter degeneration in vivo, which may help explain and predict the occurrence of cognitive impairment and Alzheimer's disease. A single center prospective follow-up study (Radboud University Nijmegen Diffusion tensor and Magnetic resonance imaging Cohort study (RUN DMC)) was performed involving 503 non-demented elderly individuals (50- 85 yrs.) with a history of symptomatic cerebral small vessel disease (SVD). Age was associated with a global reduction in cortical thickness, and this was preferential for ventrolateral prefrontal cortex, auditory cortex, Wernicke’s area, superior temporal lobe, and primary visual cortex. Right and left hemispheres differed in the thickness of language-related areas. White matter (WM) lesions were generally negatively correlated with cortical thickness, with the notable exception of some superior frontal and parietal regions, which were positively correlated. The observed pattern of age-related decline may explain problems in memory and executive functions, which are already well-documented in individuals with SVD. The additional gray matter loss affecting visual and auditory cortex, and specifically the head region of primary motor cortex, may indicate morphological correlates of impaired sensorimotor functions. The paradoxical positive relationship between WM lesion volume and cortical thickness in some areas may reflect early compensatory hypertrophy. This study raises a further interest in the mechanisms underlying cerebral gray and white matter degeneration in association with SVD, which will require further investigation with diffusion weighted and longitudinal MR studies.
Professor Fang Fang
Professor Fang Fang is in the Psychology Department at Peking University. He obtained a Ph.D. in Cognitive and Biological Psychology at the University of Minnesota in 2006 with Sheng He and Daniel Kersten as advisors, and was a Postdoctoral Research Associate between 2006 and 2007 with Daniel Kersten, Sheng He and Gordon Legge. His research seeks to understand the neural mechanisms of vision, attention and consciousness by combining neuroimaging, psychophysical and computational techniques.
Title: Retinotopically specific reorganization of visual cortex for tactile pattern recognition
[Abstract] Although previous studies have shown that Braille reading and other tactile-discrimination tasks activate the visual cortex of blind and sighted people, it is not known whether this kind of cross-modal reorganization is influenced by retinotopic organization. We have addressed this question by studying S, a visually impaired adult with the rare ability to read print visually and Braille by touch. S had normal visual development until age six years, and thereafter severe acuity reduction due to corneal opacification, but no evidence of visual-field loss. Functional magnetic resonance imaging (fMRI) revealed that, in S's early visual areas, tactile information processing activated what would be the foveal representation for normally-sighted individuals, and visual information processing activated what would be the peripheral representation. Control experiments showed that this activation pattern was not due to visual imagery. S's high-level visual areas which correspond to shape- and object-selective areas in normally-sighted individuals were activated by both visual and tactile stimuli. The retinotopically specific reorganization in early visual areas suggests an efficient redistribution of neural resources in the visual cortex.
Dr. Gaolang Gong
McConnell Brain Imaging Center
Montreal Neurological Institute and Hospital
Mcgill University, Montreal, QC, Canada
Title:Revealing the patterns of human brain anatomical connectivity by diffusion MRI tractography
[Abstract] The topological architecture of anatomical complex networks in human brain represents the underlying substrates of functional states. Diffusion MRI tractography provides a unique technique facilitating the in-vivo mapping of human brain anatomical connectivity and has been recently utilized to explore the anatomical networks across the human brain. The tractography-derived anatomical networks exhibit non-trivial topological attributes (e.g. small-worldness, hub regions, and modular organization), revealed by advanced graph theoretical approaches. Furthermore, the topological attributes of these anatomical networks have showed statistically significant dependence on age and gender, as well as other specific cognitive and behavioral abilities across populations. These intriguing findings provide new insights into the substrates that underlie functional differences across subjects.
Professor Bin Hu
Professor Bin Hu is the dean of School of Information Science and Engineering, Lanzhou University; the leader of Intelligent Contextual Computing Group in Pervasive Computing Centre, Reader, Birmingham City University, visiting professor in Beijing University of Posts and Telecommunications, China and ETH Switzerland. He received MSc. in Computer Science, Beijing University of Technology and Ph.D. in Computer Science, Institute of Computing Technology, Chinese Academy of Science. His research fields are Pervasive Computing, CSCW and Semantic Web and has published more than 60 papers in peer reviewed journals and conferences. He has been also editor/guest editor in about 10 high ranking journals and principal investigator in national/international funded research projects, e.g. FP7.
Title: Enhance Ubiquitous Affective Learning using EEG Approach
[Abstract] People’s moods heavily influence their way of communicating and their acting and productivity, which also plays a crucial role in learning process.
Along with ubiquitous computing technologies development, ubiquitous affective learning is a booming subject in e-learning paradigm. Emotions of students need to be recognized and interpreted so as to motivate students and deepen their learning, and this is also a prerequisite in e-learning. Normally, affective learning has investigated some technologies to understand learners’ emotions through detecting their face, voice and eyes motion, etc.
Our research focuses on how to enhance interactive learning between learners and tutors, and understand learners’ emotions through an EEG approach. We have developed an e-learning environment and recorded EEG signals of learners while they are surfing in the website. We presented an ontology based model for analyzing learner’s alpha wave( a component of EEG signals) to infer the meaning of its representation, then to understand learners’ emotions in learning process. The outcomes of the research can contribute to evaluation of e-learning systems and deepen understanding of learners’ emotion in learning process.
Professor Xintian Hu
After graduation from the University of Science and Technology of China in 1988, he continued his education by enrolling in a graduate program in Kunming Institute of Zoology. Two and half years later, under the direction of Professor Jingxia Cai, he received his master’s degree in neuroscience and became a junior faculty in the institute. In 1994, he went to Princeton University in the United States to seek higher education. Supervised by Dr. Charles Gross，who discovered the world famous“face cell” and is a member of the National Academy of Sciences of the United States, Xintian Hu studied the spatial information processing function in the prefrontal cortex of the monkey. Part of the data was published in the Science magazine. Having received his PhD degree in 2000, he took a postdoctoral position at Baylor College of Medicine to study the neuro-control mechanisms of movements under the direction of David Sparks, a renowned neuroscientist in eye movement control study. In 2005, he was awarded the one hundred persons project of the Chinese Academy of Sciences and took current position at Kunming Institute of Zoology as a full professor. Now he continues his research on the brain mechanisms of sensory, movement information processing and their integration. Another research direction in his lab is to develope non-human primate models of mental disorders.
Title: Non human primate models of depression and Parkinson’s diseases
[Abstract] Animal models are an indispensable tool in understanding Human diseases. Nonhuman primates are an excellent animal model to study human mental disorders because of their phylogenetic affinity to human, their dependence on social relationships, their ability to engage in complex cognitive processes and their similarities to human beings in central nervous system (CNS) function. Recently, two kinds of primate models of mental disorders have been successfully developed in our lab.
Professor Hao Lei
Prof. Lei got his Ph. D’s degree from University of Manitoba, Canada. He then did postdoctoral training in Dartmouth College and University of Minnesota. He is currently the head of the Magnetic Resonance Imaging (MRI) in State Key Laboratory of Magnetic Resonance and Atomic and Molecular Physics, Wuhan Institute of Physics and Mathematics, Chinese Academy of Sciences. Active research interests of Dr. Lei’s group is developing novel MRI and in vivo magnetic resonance spectroscopy (MRS) technology and applying MRI/MRS technology as tools to study metabolism and function the brain, and animal models of neurological diseases. He is the author of more then 50 peer-reviewed scientific papers.
Title: Probing Neural Activities in Awake, Freely Moving Rodents with Manganese Enhanced Magnetic Resonance Imaging
[Abstract] In the central nervous system, divalent manganese ion (Mn2+) behaves as an analogy of calcium ion (Ca2+), since it can enter neurons via voltage-gated calcium channel in an activity-depend manner. Once in the neurons, Mn2+ can also be transported along the axons in microtubules, and it can travel across synapses. Mn2+ is also paramagnetic, and thus can be used as a contrast agent in magnetic resonance imaging. Taking advantage of these properties of Mn2+, manganese enhanced magnetic resonance imaging (MEMRI) has been pursued as a novel functional imaging technique, and is widely used for neuronal tract tracing and for functional studies of the brain nowadays. In this presentation, examples of the use of MEMRI in probing neural activities in awake, freely moving rodents are given. The advantages and disadvantages of the technique in comparison to other functional imaging techniques are discussed. It is shown that neural activities associated with hungry, neuropathic pain and exposure to enriched environment can be measured by MEMRI.
Dr. Yonghui Li is a postdoctoral research fellow in the Research Center for Computational Medicine, the Institute of Automation, Chinese Academy of Sciences since June 2009. He obtained a Ph.D. in the Institute of Automation in 2009. His research interests mainly focus on diffusion MRI and its application in human brain. By combining DTI technique and graph theory, his recent study reported significant association between the structural properties of brain anatomical network and individual intellectual performance in healthy young subjects, which may provide new clues for understanding the mechanism of intelligence.
Title: Brain anatomical network and Intelligence
[Abstract] Intuitively, higher intelligence might be assumed to correspond to more efficient information transfer in the brain, but no direct evidence has been reported from the perspective of brain networks. We performed extensive analyses to test the hypothesis that individual differences in intelligence are associated with brain structural organization, and in particular that higher scores on intelligence tests are related to greater global efficiency of the brain anatomical network. We constructed binary and weighted brain anatomical networks in each of 79 healthy young adults utilizing diffusion tensor tractography and calculated topological properties of the networks using a graph theoretical method. Based on their IQ test scores, all subjects were divided into general and high intelligence groups and significantly higher global efficiencies were found in the networks of the latter group. Moreover, we showed significant correlations between IQ scores and network properties across all subjects while controlling for age and gender. Specifically, higher intelligence scores corresponded to a shorter characteristic path length and a higher global efficiency of the networks, indicating a more efficient parallel information transfer in the brain. The results were consistently observed not only in the binary but also in the weighted networks, which together provide convergent evidence for our hypothesis. Our findings suggest that the efficiency of brain structural organization may be an important biological basis for intelligence.
Professor Chingpo Lin
Dr. Chingpo Lin is Associate Professor of the Institute of Neuroscience & Institute of Radiological Science, and also the Head of Laboratory of MRI Core Facility, in National Yang-Ming University, Taiwan. He obtained a Ph.D. in Electrical Engineering, National Taiwan University, Taiwan, R.O.C.
Title: In vivo mapping of neural connections using MRI
[Abstract] Knowledge of functional roles of brain areas and associated connections are of critical importance in the understanding of normal brain functions as well as brain abnormalities. The connectivity pattern is formed by structural links of synapses and associated fiber pathways and the underlying causal relationships measured as cross-correlations or coherence. Neural activity, and by extension neural codes, are constrained by connectivity. It is so shallow and clear to understand the importance of the links of neuronal connection with brain activity and consequent brain functions.
For decades neuroscientists exhibited less attentions in the connections due to the lack of non-invasive technique. With the advent of diffusion MRI, a technique that can probe direction-dependent diffusivity of water molecules to reflect the microstructural tissue status, the architecture of human neuronal bundles can be more thoroughly scrutinized in vivo. The neuronal microstructure as measured by diffusion tensor imaging (DTI) appears the importance of the connection in the proper transfer of information among brain regions. New studies have shown its close coupling with the cognitive functions, not only behavioral deviation or dysfunction but also personal variation in cognitive performance such as memory performance, language learning, and reading ability etc.
During the last decade, our research effort dedicated on the development of the diffusion imaging technique as well as its applications to neuroscience. In technique development, we have developed new imaging and neural tracking methods, validated the accuracy of diffusion MRI in mapping neural connection, optimized the imaging protocols for clinical usage, and visualized the neuronal network. In neuroscience and clinical applications, we have parcellated sub-regions of human corpus callosum based on its cortical connections, identified brain regions that link to behavioral variations on healthy subjects and patients including psychiatric disorders and drug abuse, and shown the association of brain tumor and peritumoral tracts. More recently, correlation between structural and functional connectivity, the spontaneous fluctuations of brain regions, is under actively studied.
Dr. Bing Liu
Dr. Bing Liu is an assistant professor in the Research Center for Computational Medicine, the Institute of Automation, Chinese Academy of Sciences. She obtained a Ph.D. in the Institute of Automation in 2007. Since June 2007, she has worked as an assistant professor in the institute. Her research interests mainly focus on bioinformatics study for complex diseases and imaging genetics. By combining imaging and genetic techniques, her recent study reported the connections between the genetic variations and the specific brain morphology, even and the property of brain network.
Title:Default network connectivity and the COMT gene
[Abstract] Previous studies have supported the concept that the default network is an intrinsic brain system that participates in internal modes of cognition. Neural activity and connectivity within the default network, which are correlated with cognitive ability even at rest, may be plausible intermediate phenotypes that will enable us to understand the genetic mechanisms of individuals’ cognitive function or the risk for genetic brain diseases. Using resting functional magnetic resonance imaging (fMRI) and imaging genetic paradigms, we investigated whether individual default network connectivity was modulated by COMT val158met in 57 healthy young subjects. Compared with COMT heterozygous individuals, homozygous val individuals showed significantly decreased prefrontal-related connectivities, which primarily occurred between prefrontal regions and the posterior cingulated/restrosplenial cortices. Further analyses of the topological characteristics of the default network showed homozygous val individuals had significantly fewer node degrees in the prefrontal regions. This finding may partially elucidate previous reports that the COMT val variant is associated with inefficient prefrontal information processing and poor cognitive performance. Our findings suggest that default network connectivity that involves the prefrontal cortex is modulated by COMT val158met through differential effects on prefrontal dopamine levels.
Professor Tianming Liu
Dr. Tianming Liu is an Assistant Professor of Computer Science at the University of Georgia (UGA),and also affiliated with the UGA Bioimaging Research Center (BIRC), UGA Institute of Bioinformatics (IOB), UGA Faculty of Engineering, UGA Biomedical Health and Sciences Institute (BHSI) and UGA Neuroscience PhD Program. Dr. Liu received PhD in computer science from Shanghai Jiaotong University in 2002, and a postdoc in neuroimaging at the University of Pennsylvania (2002-2004) and Harvard Medical School (2004-2005). Dr. Liu’s research interests include neuroimaging, neuroimage computing, computational neuroscience, bioimaging informatics, biomedical informatics, and biomedical image analysis.
Title: Joint modeling of cortical folding and connectivity patterns
[Abstract] The ability to study the human brain via non-invasive neuroimaging techniques has been significantly hampered by the lack of effective quantitative descriptors of the complex and variable brain anatomy and function, within and across individuals. Currently, the common practice in fMRI studies is to report stereotaxic coordinates in relation to the same atlas for the activations in different brains, despite the tremendous variability in cortical anatomy, connectivity and function. Our research objective is to jointly model cortical folding, structural and functional connectivity patterns to obtain a structured representation of brain architecture and function that is made up of data-driven elementary features from Diffusion Tensor Imaging (DTI) and Resting state functional Magnetic Resonance Imaging (R-fMRI) data at the individual brain level. In this talk, we will introduce our recent and ongoing work on modeling of cortical folding patterns, structural connectivity patterns, and functional connectivity patterns, as well as joint modeling of them, towards the ultimate goal of in vivo segregation of the human brain and the construction of individualized brain coordinate system.
Dr.Qiu Anqi received her Ph. D. degree at Johns Hopkins University in 2006. Her thesis topic was “intrinsic and extrinsic analysis in computational anatomy” that looked into anatomical descriptors and shape variations related to neuropsychiatric and neurodegenerative diseases in anatomical magnetic resonance imaging (MRI). After her graduation, she continued her postdoctoral training in both MRI acquisition and analysis. In 2007, she joined division of bioengineering at National University of Singapore as an assistant professor and launched her own lab “Computational Functional Anatomy Laboratory”. In the last two years, she published 20 peer-reviewed journal papers and four of them were selected as cover in journals of NeuroImage and Human Brain Mapping. She was invited to contribute her work to a special issue of NeuroImage in Mathematics in Brain Mapping in 2009.
Title: Relationships between Hippocampal Shape, Cortical Thickness, Integrity of White Matter Tracts in Schizophrenia.
[Abstract] The evidence from both postmortem and neuroimaging studies suggests the hippocampus as being central to the neuropathology and pathophysiology of schizophrenia. Disruption in the hippocampal-cortex connectivity has been implicated in the correlation between hippocampal volume loss and frontal-lobe dysfunction as well as reduction in functional connectivity of these regions during the resting state. In this talk, we will discuss anatomical relationships between hippocampal shape, cortical thickness, and hippocampal-cortical disconnectivity using an advanced brain mapping technique, large deformation diffeomorphic metric mapping (LDDMM), for images acquired using MRI and DTI. We will discuss roles of such relationship in the development of schizophrenia.
Professor Martin Walter
Professor Martin Walter obtained his MD at the Otto v. Guericke University Mageburg after studies of Medicine and Philosophy at the Universities of Lyon (France) and Magdeburg (Germany). He obtained his clinical training in medicine, neurosurgery and psychiatry at the University Medical Centers in Magdeburg, Kansas City and Zurich and received his MD-PhD at the graduate school for neurobiological foundations of brain disorders in Magdeburg. Dr. Walter is clinical assistant professor and head of the Clinical Affective Neuroimaging Laboratory at the Department of Psychiatry in Magdeburg and adjunct professor at the Department of Radiology, UMDNJ, Newark. His work focuses on methodological advances in non invasive psychiatric neuroimaging, especially using (rs)fMRI, MRS and DTI in magnetic field up to 7 Tesla.
Title: Multimodal imaging of altered baseline processing in major depression using combined resting state fMRI and MR- spectroscopy.
[Abstract] Recent investigations have pointed out the key role of medial prefrontal cortex (MPFC) in the pathophysiology of major depression. Together with a well defined set of subcortical structures, seems to exert an altered functional network architecture that leads to symptoms such as increased anxiety or anhedonia. While functional MRI could reveal altered responses in these regions during experimental tasks, basic alterations of the molecular and cellular level have been reported here by post mortem studies. Combining molecular and functional MR methods, we can now directly investigate the molecular underpinnings of altered functional responses. As part of the default mode network, MPFC responses appear as negative BOLD responses with amplitudes that can be predicted by local concentrations of substances such as GABA, glutamate and glutamine. While in healthy controls, these functional responses seem to be correlated with GABA levels in MPFC, patients with major depression show altered functional responses that can be related to deficits in glutamate and glutamine concentrations. Such metabolic alterations link the observed functional deviances to a generally disturbed metabolic baseline level which explains the rather unspecific functional abnormalities upon external stimulation in patients during most fMRI designs. Recent findings from resting state fMRI now allow us to relate these metabolic impairments in the glutamatergic system to altered functional architectures that connect MPFC differently to other key regions such as anterior insula.
Dr. Zhengyi Yang
Dr.Yang is a Postdoctoral Research Fellow in the Centre for Advanced Imaging at the University of Queensland, Australia. His research interest includes medical image processing, biomechanics of spine, computer haptics and rapid prototyping. He is currently working on the image processing for The Australian Mouse Brain Mapping Consortium and attenuation correction/image reconstruction for PET/MRI scanners.
Title: MR Constrained 3D Reconstruction of Mouse Brain Histology Image.
[Abstract] The 3D reconstruction of mouse brain volume from sections obtained with optical microscopy is very important in neuroscience research. It is difficult, time-consuming and labor intensive because of volume distortions and section imperfections. A robust methodology for automatic or semi-automatic 3D reconstruction of histology volumes of mouse brain is highly desired. One approach to this is to perform histology reconstruction with the guidance of information extracted from anatomical images, like high-resolution magnetic resonance (MR) images. We present an iterative method employing a series of morphological segmentation, local rigid registration, and deformable registration to perform MR constrained 3D reconstruction of histology images of mouse brain.
Professor Chunshui Yu
Deputy director of radiology,Xuan Wu Hospital,Beijing.
Title: Antidepressant Effect on Functional Connectivity of Cingulate Subregions in Major Depressive Disorder.
[Abstract] Background: The cingulate cortex can be divided anatomically based on attributed functions into different subregions, some of which are related to emotion processing. We hypothesize that the resting-state functional connectivities (FCs) of different cingulate subregions are different, the FCs of the emotion-related cingulate subregions are abnormal in major depressive disorder (MDD) and these abnormalities could be reversed after fluoxetine treatment. Methods: Eighteen medication-free patients with first depressive episode and 20 matched healthy controls underwent fMRI scans at baseline while resting quietly. And 12 patients were examined again after 8-week fluoxetine treatment. FCs of the subgenual (sACC) and pregenual anterior cingulate cortex (pACC), the anterior (aMCC) and posterior midcingulate cortex (pMCC), the dorsal (dPCC) and ventral posterior cingulate cortex (vPCC), and the retrosplenial cortex (RSC) were compared between depressed patients and healthy controls. The effects of antidepressant treatment on the altered FCs of cingulate subregions were also investigated. Results: In healthy subjects, the FCs of different cingulate subregions differ significantly. At baseline scan, all cingulate subregions (including sACC, pACC, aMCC, pMCC, dPCC, vPCC, and RSC) showed abnormalities in the FCs in patients with MDD compared with those in matched healthy controls. Moreover, some of these altered FCs were reversed after 8-week fluoxetine treatment in MDD patients. Conclusions: The FCs of different cingulate subregions differ distinctly in healthy controls. MDD involves more extensive cingulate subregions than we expected. Our findings also support that quantitative measurements of the FCs of cingulate subregions might have the potential to monitor the therapeutic outcomes in MDD.
Professor Shan Yu
Dr. Shan Yu received her Bachelor of Engineering degree in Biomedical Instrumentation from Shanghai Jiao Tong University, and her PhD degree in Science of Engineering from University of Nice – Sophia Antipolis. She joined INRIA as a research scientist in 1993. Her interest of research was on image processing and patter recognition with application specialized in automatic interpretation of remote sensing images. From 2000 to 2009, Dr. Yu took leave from INRIA and joined the Medical Center of Columbia University and the New York State Psychiatric Institute. Since then, Dr. Yu’s research interest has been focused on understanding normal brain development, and the neural basis of psychiatric disorders using functional human brain image data (PET, fMRI).
Title: A Functional MRI Study of the Effects of Stimulant Medication in ADHD Youth.
[Abstract] Psychostimulants improve distractibility, hyperactivity, and impulsivity in children who have Attention-Deficit/Hyperactivity Disorder (ADHD). Identifying the neural basis for the effects of stimulants has important implications for improving our understanding of the pathophysiology of ADHD and for developing more effective therapeutic interventions. We examined the influence of stimulants on brain activity in children with ADHD as they performed the Stroop Word-Color Interference task. Brain activity was assessed using fMRI during performance of the Stroop task. Correlations of stimulant-induced changes in brain activity and behavioral measures were also assessed. Our fMRI data analysis results showed that stimulant medications improve suppression of default-mode activity during the more challenging attentional task, improving attentional symptoms in direct proportion to the improvement in this suppressor activity. The results also showed that stimulants did not introduce significant new activity compared with activations in healthy control subjects.
Professor Yuemin Zhu
Professor Yueming Zhu is a researcher of the CNRS (Centre National de la Recherche Scientifique) of France. He is currently is a permanent Research Director of the CNRS, and Chine affaire manager in the INSA Lyon. He has been appointed Honorary Professor of Shanghai Jiaotong University (SJTU), Harbin Institute of Technology (HIT), and Chongqing Three Gorges University, China. Prof. Zhu obtained his Ph.D. from the INSA (Institut National des Sciences Appliquées), Lyon, France in 1988. He is currently principal investigator of several research projects on magnetic resonance imaging (MRI) including diffusion-tensor MRI (DT-MRI). His research interests include image representation, reconstruction, correction, denoising, registration, segmentation, quantification, visualization, and fusion.
Title: Development of some DTI data processing techniques.
[Abstract] Diffusion tensor magnetic resonance imaging (DT-MRI) or simply diffusion tensor imaging (DTI) has gained considerable attention in recent years in several fields such as neuroimaging, medical imaging, clinic research, and image processing. Data from DTI contains rich information, but is more complex than conventional scalar-valued images. The processing of DTI data poses new problems and supplementary difficulties. This talk presents several DTI data processing techniques ranging from denoising of diffusion-weighted (DW) images to fiber tracking. Within this framework, emphasis is put on the processing of DTI data acquired in clinical conditions. Concerning the processing of DW images, sparse representation and regularization approaches are described. For clinically acquired DTI data, interpolation is often needed in order to compensate for lower spatial resolution. We were led to develop two interpolation methods, respectively for vector fields and tensor fields. A method, which achieves simultaneously interpolation and denoising of 3-D primary vector fields, is presented. To interpolate tensor fields, we present a method that takes into account inherent relation between tensor components, which allows us to not only preserve the advantages of Log-Euclidean or Riemannian interpolation such as symmetric positive-definite matrix and monotonically interpolated tensor determinant, but also minimize the decreasing effect of Fractional Anisotropy (FA) values caused by existing interpolation methods such as Euclidean, Cholesky and Log-Euclidean methods.
November 20, Friday, Lecture Hall, 13th floor, Automation Building
9:00-9:30 a.m. Opening Ceremony Chair: Tianzi Jiang, Institute of Automation, CAS, China
Speakers from CAS, NSFC, CASIA …
9:30-10:20 Session 1: Functional Brain Networks
Chair: Dewen Hu,National University of Defense Technology, China
*9:30-10:00 Xiaoping Hu(Keynote Speaker), Georgia Tech and Emory University, USA
Title: Instantaneous and Causal Connectivity in Resting State Brain Networks Derived from fMRI Data
*10:00-10:20 Fang Fang,the Psychology Department at, Peking University, China
Title:Retinotopically Specific Reorganization of Visual Cortex for Tactile Pattern Recognition
10:20-10:50 Coffee Break
10:50-12:00 Session 2: Anatomical Connectivity Based on Diffusion Tensor Imaging Chair: Yong He,Beijing Normal University, China
*10:50-11:20 James Gee(Keynote Speaker),PICSL, University of Pennsylvania, USA
Title: Tract-specific Analysis of Brain White Matter
*11:20-11:40 Gaolang Gong, Montreal Neurological Institute, Mcgill University, Canada
Title: Revealing the Patterns of Human Brain Anatomical Connectivity by Diffusion MRI Tractography
*11:40-12:00 Yueming Zhu,CNRS, France
Title: Development of some DTI Data Processing Techniques
12:00-14:00 Lunch (Dining Room, 2nd Floor, CASIA)
14:00-15:30 Session 3: Brain Connectivity and Human Cognition Chair: Andrew CN Chen, Capital Medical University, China
*14:00-14:30 Jen-Chuen Hsieh(Keynote Speaker), Brain Research Center of National Yang-Ming University, Taiwan
Title: The Brain Connects the Mind, the Mind Embodies the Brain
*14:30-14:50 Tianming Liu,Department of Computer Science, the University of Georgia, USA
Title: Joint Modeling of Cortical Folding and Connectivity Patterns
*14:50-15:10 Yonghui Li, Institute of Automation, CAS, China
Title: Brain Anatomical Network and Intelligence
*15:10-15:30 Martin Walter, Department of Psychiatry, Otto v. Guericke University, Germany
Title: Multimodal Imaging of Altered Baseline Processing in Major Depression using Combined Resting State fMRI and MR- spectroscopy
15:30-16:00 Coffee Break
16:00-17:30 Session 4: Brain Connectivity in Brain Diseases Chair: Tianzi Jiang, Institute of Automation, Chinese Academy of Sciences, China
*16:00-16:30 Rolf Kotter (Keynote Speaker), Donders Institute,Radboud Univ. Nijmegen Med Ctr, the Netherlands
Title: Patterns of Cortical Degeneration in an Elderly Cohort with Cerebral Small Vessel Disease
*16:30-16:50 Chunshui Yu,Tianjin Medical University, China
Title: Antidepressant Effect on Functional Connectivity of Cingulate Subregions in Major Depressive Disorder
*16:50-17:10 Anqi Qiu, National University of Singapore
Title: Relationships between Hippocampal Shape, Cortical Thickness, Integrity of White Matter Tracts in Schizophrenia
*17:10-17:30 Shan Yu, LIAMA, INRIA and CASIA, China
Title: A Functional MRI Study of the Effects of Stimulant Medication in ADHD Youth
18:00-20:00 Dinner at Baijia Da Yuan Restaurant (tentative)
November 21,Saturday, Lecture Hall, 13th floor, Automation Building
9:00-10:30 Session 5: Neural Circuits Chair: Hong Li, Southwest University, Chongqing, China
*9:00-9:30 Michael Breakspear (Keynote Speaker), Queensland Institute of Medical Research, Australia
Title: Multistable and Hierarchical Cortical Dynamics
*9:30-9:50 Xintian Hu, Kunming Institute of Zoology, Chinese Academy ofSciences,China
Title: Non Human Primate Models of Depression and Parkinson’s Diseases
*9:50-10:10 Bing Liu, Institute of Automation, CAS，China
Title: Default Network Connectivity and the COMT Gene
*10:10-10:30 Bin Hu, Lanzhou University, China
Title: Enhance Ubiquitous Affective Learning using EEG Approach
10:30-11:00 Coffee Break
11:00-12:00 Session 6: Brain Connectivity in Animals Chair: Rong Xue, Institute of Biophysics, CAS, China
*11:00-11:20 Ching-Po Lin, Dept. of Biomedical Image and Radiological Science, National Yang-Ming University, Taiwan
Title: In Vivo Mapping of Neural Connections using MRI
*11:20-11:40 Zhengyi Yang, CAI, the University of Queensland, Australia
Title: MR Constrained 3D Reconstruction of Mouse Brain Histology Image
*11:40-12:00 Hao Lei, Wuhan Institute of Physics and Mathematics,CAS,China
Title: Probing Neural Activities in Awake, Freely Moving Rodents with Manganese Enhanced Magnetic Resonance Imaging
12:00-14:00 Lunch (Dining Room, 2nd Floor, CASIA)
14:30-17:30 Free Discussion (Coffee Hall, 13th Floor, CASIA)
17:30-19:30 Dinner (Dining Room, 2nd Floor, CASIA)
19:30-21:30 Event (TBA)
Lecture Hall, 13th floor, Automation Building
Institute of Automation, Chinese Academy of Sciences(CASIA)
No.95, Zhong Guan Cun East Road, Beijing, 100190, P.R. China
Tel: 010-62659278 (Ms. Jingtao Lv, email@example.com
010-62629189 (Ms. Gangqin Zhang, firstname.lastname@example.org)
National Laboratory Pattern Recognition Institute of Automation
Chinese Academy of Sciences 95 Zhongguancun East Road,Hai Dian District
Beijing 100190,P.R.China TEL：62629189 E-mail:email@example.com