International Symposium on Computational Medicine
June 15, 2012, Beijing, China
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Overview？
The International Symposium on Computational Medicine is one of the frontier forums of the Chinese Academy of Sciences. The aim of this symposium is to bring together researchers with an interest in complex networks based on brain imaging techniques (MRI, Diffusion MRI, fMRI, fNIRS, EEG/MEG) and their applications to brain disorders. In this meeting, we will discuss new frontiers in brain mapping and brain connectivity, and their application to neuropsychiatric disease.
The main topics are as follows:
Anatomical and Functional Brain Networks
Foundations of Brain Connectivity Methods
Clinical Applications of Brain Networks
All speakers of this symposium are the active experts in this field and are invited only. For the last symposium, please also refer to ISCM2008 and ISCM 2009.
Confirmed Speakers？
Dr. Jeff Anderson, University of Utah, USA
Jeffrey Anderson, MD PhD studied abstract mathematics and neuroscience at Northwestern University before completing residency and fellowship in neuroradiology at the University of Utah. Dr. Anderson is Director of Functional Imaging at the University of Utah, and runs the Brain Network Laboratory. He is author of 2 national bestselling science thrillers.
Title: The relationship between BOLD signal complexity and local connectivity
[Abstract]
Very low frequency blood oxygen level dependent (BOLD) fluctuations have emerged as a valuable tool for describing brain anatomy, neuropathology, and development. Such fluctuations exhibit power law frequency dynamics, with largest amplitude at lowest frequencies. The biophysical mechanisms generating such fluctuations are poorly understood. In publicly available data from 1019 subjects of age 7-30, BOLD fluctuations exhibit temporal complexity that is linearly related to local connectivity (regional homogeneity), consistently and significantly covarying across subjects and across gray matter regions. During late neurodevelopment, BOLD fluctuations were unchanged with age in association cortex while becoming more random throughout the rest of the brain. These data suggest that local interconnectivity may play a key role in establishing the complexity of low frequency BOLD fluctuations underlying functional MRI connectivity. Stable low frequency power dynamics may emerge through segmentation and integration of connectivity during development of distributed large-scale brain networks.
Dr. Huafu Chen, University of Electronic Science and Technology of China, China
He is a professor at University of Electronic Science Technology of China (UESTC). 1993.9-1996.7, in Applied Mathematics at Sichuan University, China. 2000.3-2004.12, Biomedical Engineer at University of Electronic Science and Technology of China; 2005.9-2006.3 Visiting Professor at Texas University, USA.
His research areas include Brain pattern recognition technique and cognitive application; Dynamic Brain network; Multi- model method and application in Nervous disease and mental disease. And he also got some major awards include Natural Science Award , Ministry of Education of China (2009); Science Progress Award , Ministry of Education of China(2010), Outstanding youth project of National science found (2011) for his contribution to this field; He has published more than 70 peer-reviewed articles
Title: Multiple-modalities Functional and Structural Brain Network
[Abstract]
Brain Network of Epilepsy: Functional Connectivity Network in mesial temporal lobe epilepsy(TLE) , Altered FC and SC of DMN in mTLE FCN and SCN coupling in Generalized tonic-clonic seizures (GTCS); Brain Network of Social Anxiety Disorder(SAD): Altered RSNs in SAD, Altered Effective Connectivity in SAD, Multiple Brain Network in SAD; Brain Network of Human: Topological Fractionation of Resting-State and Large-scale brain networks in board game experts.
Dr. Julien Doyon, University of Montreal, Canada
After completing his doctoral studies in 1988 at the Montreal Neurological Institute, McGill University under the supervision of Dr. Brenda Milner, Dr. Doyon accepted an academic position as assistant professor in the Department of Psychology at Laval University. He then joined, as professor, the Department of Psychology and the Research Center at the Geriatric Institute, University of Montreal, in July 2000. At present, he is the Scientific Director of the Functional Neuroimaging Unit at the University of Montreal, Co-director of the Laboratoire International de Neuroimagerie et Modélisation (LINeM), Université de Montréal – INSERM, and Director of the Quebec Bio-Imaging Network (QBIN).
Title: Determinants of Brain Plasticity Associated with Motor Sequence Learning.
[Abstract]
Motor skill learning refers to the process by which movements, either produced alone or in a sequence, come to be performed effortlessly through repeated practice. From a functional neuroanatomy viewpoint, Doyon and colleagues (2002, 2003, 2005, 2009) have proposed that interactions between the cortico-striatal and cortico-cerebellar systems are critical for establishing the motor routines used to acquire motor skilled behaviors. With extended practice, however, activity within the cortico-striatal network is then believed to be sufficient for maintaining the neural representation of a sequence of movements (i.e., motor sequence learning) in long-term memory. In this presentation, I will present the results of a series of studies that aimed to investigate the consolidation process of such motor abilities; a phase during which the memory undergoes “off-line” transformations allowing an initially labile trace to become fixed into the physical structure of the brain through a cascade of events occurring at both cellular and systems levels. I will focus on the behavioral determinants, neural substrates and sleep characteristics associated with this mnemonic process.
Dr.Simon Eickhoff, Heinrich-Heine University Düsseldorf, Germany
Simon B. Eickhoff is currently Professor for Cognitive Neuroscience at the Institute of Clinical Neuroscience and Medical Psychology, Heinrich-Heine University Düsseldorf and leads the research group "Brain Network Modelling" at the Research Center Jülich. Following medical training in Aachen, Sheffield and London, received his PhD in Neuroanatomy and later worked as assistant professor and clinical psychiatrist at the RWTH Aachen. To date, he has authored more than 120 peer-reviewed journal articles and has an h-index of 31. His main interests are the integrated analysis of brain structure, function and connectivity using multi-modal approaches as well as the neurobiology of cognitive control mechanisms and social cognition.
Title: Meta-analytic approaches to mapping the brain, its connections and functions
[Abstract]
Whereas the potential inference from any single neuroimaging study is limited to method-inherent drawbacks, the high degree of standardization in neuroimaging research allows to pooling and integration of activation results from several thousends of experiments. Moreover, several large-scale databases of neurimaging results have emerged over the last years, that compile this walth of information. In this talk, I would like to outline, how emerging meta-analytic tools may be use to draw on these resources and provide new insights into several aspects pertaining to the organization of the human brain: i) The localization of brain functions and its relationship to task-specific confounds ii) The functional roles underlying, e.g., morphometric, structural findings, including formal inference for functional decoding iii) The identification of functional connectivity in a task-based state through the mapping of co-activations, which may complement information of interactions in the unconstrained, endogeneously driven task-free "resting" state iv) The delineation of cortical modules by data-driven clustering of co-activation patterns, which, in combination with the other described methods described in this talk, entails the possibility for functional brain mapping and atlasing.
Dr. Yong Fan, Institute of Automation, CAS, China
Dr. Yong Fan is an investigator and a professor of pattern recognition and intelligent systems at the Institute of Automation of the Chinese Academy of Sciences. He received his Ph.D. degree from the Chinese Academy of Science in 2003 and finished postdoctoral training at the University of Pennsylvania in 2006. He is a recipient of President Scholarship of the Chinese Academy of Sciences (2002), National Institute of Health Career Development Award (2009). He is a senior member of IEEE and a member of Sigma Xi. His research interests include medical image analysis, pattern recognition, and their applications to studies of neuropsychiatric disorders. In medical image processing and analysis journals or conferences, he has co-authored over 50 papers, including “Neuroimage One of the Top Ten Cited Articles of 2008” and a paper awarded “Editors Choice Award of the Organization for Human Brain Mapping”.
Title: Discriminative analysis of functional networks on Grassmann manifolds
[Abstract]
Neuroimaging based functional brain connectivity is typically investigated by using independent component analysis (ICA) or graph theoretical analysis of brain regional temporal correlations. Many neuroimaging studies have focused on statistical analysis of independent components, regional temporal correlations, or graph properties at group level and demonstrated that neuropsychiatric disorders are often associated with aberrant functional brain connectivity. However, it is of interest in clinical studies to identify brain connectivity patterns which are able to achieve individual diagnosis with sufficiently high specificity and sensitivity. This talk will introduce a pattern classification technique for discriminative analysis of functional networks on Grassmann manifolds and its application to neuroimaging studies of schizophrenia.
Dr. Xu Lei, Southwest University, China
He is a Associate Professor at School of Psychology, Southwest University, Chongqing, China. He received the B.S.degree in Information and Computational Science from University of Electronic Science and Technology of China (UESTC), Chengdu, in 2005. In 2011, he received the Ph.D. degree in Biomedical Engineering from UESTC. He is the author or coauthor of more than 30 scientific articles. His research interests include brain rest-state networks; data-driven EEG/fMRI fusion; EEG inverse problem, Dynamic Brain network and Bayesian inference.
Title: Multimodal Functional Network Connectivity
[Abstract]
EEG and fMRI recordings measure the functional activity of multiple coherent networks distributed in the cerebral cortex. Identifying network interaction from the complementary neuroelectric and hemodynamic signals may help to explain the complex relationships between different brain regions.
In this presentation, I will introduce multimodal functional network connectivity (mFNC), which fuses EEG and fMRI in a network space. First, functional networks (FNs) are extracted using spatial independent component analysis (ICA) in each modality separately. Then the interactions among FNs in each modality are explored by Granger causality analysis (GCA). Finally, fMRI FNs are matched to EEG FNs in the spatial domain using network-based source imaging (NESOI). Investigations of both synthetic and real data demonstrate that mFNC has the potential to reveal the underlying neural networks of each modality separately and in their combination. With mFNC, comprehensive relationships among FNs might be unveiled for the deep exploration of neural activities and metabolic responses in a specific task or neurological state.
Dr. Tianming Liu, University of Georgia, USA
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: Connectomics Signatures for Characterization of Brain Conditions
[Abstract]
Human connectomes constructed via neuroimaging data offer a complete description of macro-scale structural/functional connectivity within the brain. Assessing connectome-wide structural and functional connectivities not only can fundamentally advance our understanding of brain organization and function, but also have ultimate importance to systematically and comprehensively characterize many devastating brain conditions. Here, we constructed structural connectomes of 240 brains and assessed the connectome-wide functional connectivity alterations in mild cognitive impairment, schizophrenia and post-traumatic stress disorder, in comparison with their healthy controls. By applying genomics signatures discovery approaches, we discovered informative and robust functional connectomics signatures that can distinctively characterize these brain conditions from their healthy controls. Our results suggest that connectomics signatures could be a general, powerful platform for characterization of many brain conditions in the future.
Dr. Ruthger Righart, Institute for Stroke and Dementia, Munich, Germany
Ruthger Righart is research fellow at the Institute for Stroke and Dementia, Munich, Germany. He works in the neuroimaging group and performs research on anatomical imaging and specifically measures of cortical thickness in different patient populations. Previously he has also undertaken functional imaging studies (fMRI and ERP) particularly in the domain of visual perception (face perception). He has obtained his PhD in cognitive neuroscience at Tilburg University, The Netherlands.
Title: Cerebral atrophy in small vessel disease
[Abstract]
Brain atrophy has been recognized as a major predictor of cognitive decline and dementia. There have been no systematic studies directly examining the relationship between incident subcortical infarcts and morphological alterations in connected cortical areas. To investigate this question we applied serial brain imaging in CADASIL patients. Using measures of fiber tracking and cortical thickness we observed that incident subcortical infarcts result in focal cortical thinning in connected areas. Additionally, we found that deficits in processing speed relate to regional cortical atrophy in frontal areas. The research altogether underlines the importance of cortical morphology analyses in subcortical ischaemic vascular disease.
Dr. Timothy Rittman, University of Cambridge, UK
He is pursuing a career in clinical cognitive neuroscience, combining clinical experience with the powerful tools of computational neuroscience. Alongside this effort he maintains an active interest in the education of medical students and doctors in the broadest sense and in the promotion of global health issues.
Title: Clinically Relevant Changes in Functional Connectivity Using ICA and Resting State fMRI in Neurodegenerative Tauopathies
[Abstract]
A key feature of neurodegenerative diseases is their selectivity for specific anatomical and functional networks. In order to investigate the association between underlying pathology, changes in functional networks, and clinical measures, we examined two neurodegenerative diseases associated with tau pathology and age-matched control subjects. Progressive Supranuclear Palsy (PSP) causes an akinetic-rigid syndrome with a characteristic vertical gaze palsy and executive cognitive dysfunction with markedly reduced verbal fluencies; Corticobasal Degeneration (CBD) presents with asymmetric limb dystonia, apraxia and rigidity often with features of the alien limb phenomenon, visuospatial disturbance, and variable cognitive changes. I will discuss how we have applied Independent Component Analysis to resting state fMRI scans with a Goodness of Fit Measure to look for changes in disease and to correlate with clinical measures. In particular, we have found changes in networks relevant to the disease processes that correlate with clinical disease measures. In addition, I will describe how we applied a regression model to identify regions within networks contributing to altered connectivity. Our findings support the use of functional connectivity measures as biomarkers in neurodegenerative disease that give information about both the disease pathology and clinical features.
Viviana Siless, Parietal Team, INRIA Saclay-Ile-de-France, Saclay, France
Title: Joint T1 and Brain Fiber Log-demons Registration using Currents to Model Geometry
[Abstract]
Within inter-individual comparison, registration should align images as well as cortical and external structures such as sulcal lines and fiber in brain imaging. In image-based registration, neural fibers appear uniformly white giving no information to the registration. Tensor-based registration improves white-matter alignment, however misregistration may also persist in regions where the tensor field appears uniform.
We propose an hybrid approach by extending the diffeomorphic Geometric Demons algorithm which combines the iconic registration with geometric constraints. The algorithm work in the log-domain to efficiently compute the geometry deformation field. We represent the shape of objects of interest in the space of currents which is sensitive to location and shape.
We present results from simultaneously registering T1 images and 65 fiber bundles consistently extracted in 12 subjects. We compare our results with non-linear T1, tensor and multi-modal T1+Fractional Anisotropy (FA) registration algorithms.
Gael Varoquaux, Parietal Team, INRIA Saclay-Ile-de-France, Saclay, France
Title: Brain Region Identification Based on Multivariate Classification Methods
[Abstract]
FMRI provides high-dimensional --tens of thousands of voxels-- and noisy measurements of in-vivo brain activity. It is the workhorse of human brain mapping and has widely been used to define brain regions characterizing a cognitive task with mass-univariate statistical approaches. I will discuss how fMRI studies can be cast in classification task, or statistical learning, in order to apply multivariate methods to the extraction of brain regions. In particular, I will introduce the "recovery" problem, i.e. how to guaranty that the regions extracted not only predict well, but also are close to a ground truth. In addition, I'll discuss how these methods can be extended to resting-state studies, in which the subject is not performing a specific task.
Dr. Andrew Zalesky, University of Melbourne, Australia
He is currently the Melbourne Neuroscience Institute Fellow. He is based at the Melbourne Neuropsychiatry Centre, a centre in the Department of Psychiatry at the University of Melbourne. He was an ARC Fellow (APD) at the same centre from 2009-2011. In 2008 he was an ARC International Fellow.
Title: Disrupted Anatomical Connectivity in Schizophrenia
[Abstract]
I will present recent evidence for widespread disruption to axonal fiber connectivity in individuals with schizophrenia. Diffusion imaging coupled with axonal fiber tracking was performed to chart high-resolution connectivity maps of the brain's anatomical connectional architecture. These connectivity maps were then analyzed for differences between a large sample of people with schizophrenia and healthy controls. Our results point to a multifaceted pathophysiology in schizophrenia encompassing axonal as well as putative synaptic mechanisms. I will discuss the computational and statistical methods developed to map brain connectivity and identify disrupted connections in the schizophrenia group. I will also discuss the significance of these findings in regards to understanding the biological basis of schizophrenia.
Scientific Program？
June 15, Friday, 2nd Meeting Room, 13th floor, Automation Building
8:30-8:40 Opening Ceremony By Tianzi Jiang
8:40-10:00 Session 1: Brain Networks and Connectivity I
Chair: Tianzi Jiang, Institute of Automation, Chinese Academy of Sciences, China
8:40-9:20 Simon B. Eickhoff, Heinrich-Heine University Düsseldorf, Germany
Title: Meta-analytic approaches to mapping the brain, its connections and functions
9:20-10:00 Jeff Anderson, University of Utah, USA
Title: The relationship between BOLD signal complexity and local connectivity
10:00-10:20 Coffee Break
10:20-12:10 Session 2: Brain Networks and Connectivity II
Chair: Yong Fan, Institute of Automation, Chinese Academy of Sciences, China
10:20-11:00 Gael Varoquaux, Parietal Team, INRIA Saclay-Ile-de-France, Saclay, France
Title: Brain Region Identification Based on Multivariate Classification Methods
11:00-11:40 Julien Doyon, University of Montreal, Canada
Title: Determinants of Brain Plasticity Associated with Motor Sequence Learning
11:40-12:10 Xu Lei, Southwest University, China
Title: Multimodal Functional Network Connectivity
12:10 Group Photo
12:20-14:00 Lunch (Dining Room, 2nd Floor, CASIA)
14:00-15:30 Session 3: Altered Brain Networks in Brain Disorders I
Chair: Yong Liu, Institute of Automation, Chinese Academy of Sciences, China
14:00-14:30 Tianming Liu, University of Georgia, USA
Title: Connectomics Signatures for Characterization of Brain Conditions
14:30-15:00 Huafu Chen, University of Electronic Science and Technology of China, China
Title: Multiple-modalities Functional and Structural Brain Network
15:00-15:30 Timothy Rittman, University of Cambridge, UK
Title: Clinically Relevant Changes in Functional Connectivity using ICA and Resting State fMRI in Neurodegenerative Tauopathies
15:30-15:50 Coffee Break
15:50-17:40 Session 4: Altered Brain Networks in Brain Disorders II
Chair: Yue Cui, Institute of Automation, Chinese Academy of Sciences, China
15:50-16:20 Yong Fan, Institute of Automation, Chinese Academy of Sciences, China
Title: Discriminative analysis of functional networks on Grassmann manifolds
16:20-16:50 Ruthger Righart, Institute for Stroke and Dementia, Munich, Germany
Title: Cerebral atrophy in small vessel disease
16:50-17:05 Viviana Siless, Parietal Team, INRIA Saclay-Ile-de-France, Saclay, France
Title: Joint T1 and Brain Fiber Log-demons Registration using Currents to Model Geometry
17:05-17:20 Yajing Zhang, Johns Hopkins University, USA
Title: Population-specific, sharpness-preserving brain atlas for accurate registration and its application in brain development
17:40-17:50 Closing Remarks (Tianzi Jiang)
17:50-18:30 Short Visiting of NLPR
18:30-20:00 Dinner (Dining Room, 2nd Floor, CASIA)
Register
All the attendes are welcome, please register here
Address？
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: 010-62629189 (Ms. Gangqin Zhang, gangqin.zhang@ia.ac.cn)