Invited Speakers
Gael Varoquaux
Research Faculty, INRIA - Parietal Team, CEA Institute, Saclay, France
Dr. Gael Varoquaux is a tenured computer-science researcher at INRIA. His research develops statistical learning tools for functional neuroimaging data with application to cognitive mapping of the brain as well as the study of brain pathologies. In addition, he is heavily invested in software development for data science, as project-lead for scikit-learn, one of the reference machine-learning toolboxes, and on joblib, Mayavi, and nilearn. Dr. Varoquaux has contributed key methods to learn functional brain atlases and connectome structure from task-based and rest fMRI, and methods for statistical mapping and decoding of functional brain imaging. He has a PhD in quantum physics and is a graduate from Ecole Normale Superieure, Paris.
Neda Jahanshad
Assistant Professor, Imaging Genetics Center, Keck School of Medicine, University of Southern California, Los Angeles, CA USA
Dr. Neda Jahanshad is an Assistant Professor of Neurology at the Imaging Genetics Center, part of the Mark and Mary Stevens Neuroimaging and Informatics Institute at the University of Southern California. Her research interests lie in diffusion MRI-based structural connectivity and developing protocols for large-scale meta-analyses of brain structure and connectivity for the ENIGMA Consortium. Among many areas of application, Dr. Jahanshad primary emphases include the discovery of genetic influences on brain structure, and monitoring the effects of infectious disease on the brain. She holds a PhD in Biomedical Engineering from the University of California Los Angeles, and undergraduate degrees from Johns Hopkins University.
Detailed Programme
| 09:00 - 09:15 | Welcome |
| 09:15 - 9:45 | Invited Speaker: Gael Varoquaux Extracting neuro-phenotypes from the brain at rest Abstract: As functional brain imaging probes brain mechanisms, hope is that it can capture markers of subjects' psychiatric status. However, to form a simple and objective measure of neuro-psychiatric state, it must avoid complex psychological experimental paradigms. For this purpose, studying the brain at rest is ideal. Indeed, resting-state fMRI is a promising source of functional biomarkers as, unlike typical task-based fMRI paradigms, it can be applied to diminished populations.I will discuss a model of brain interactions at rest, the connectome, representing these interactions as a graph between brain regions. Predicting subject phenotypes, such as neuro-psychiatric states, based on their connectome implies a complex classification pipeline to learn and compare connectome. I will present our efforts to understand the different modeling steps: How to define functional brain regions? How to capture functional interactions in a subject? How to compare it across subjects? I will detail theoretical and experimental validation of each step. Validation of these choices is very hard, as it often relies on assumptions about the data. Based on our understanding of the various steps, we have built a full pipeline that predicts Autism from rest-fMRI on unseen scanning site in the ABIDE dataset. To our knowledge, this is the first prediction of a clinically-relevant diagnosis status that carries over in inhomogeneous acquisitions settings. This full-blown experiment, on 871 subjects, also highlights what are the important choices in a population-level connectome analysis. Slides |
| 09:45 - 10:05 | Multiple-View Spectral Clustering for Group-wise Functional Community Detection Nathan Cahill, Harmeet Singh, Chao Zhang, Daryl Corcoran, Alison Prengaman, Paul Wenger, John Hamilton, Peter Bajorski and Andrew Michael Rochester Institute of Technology Slides |
| 10:05 - 10:25 | An Empirical Study of Continuous Connectivity Degree Sequence Equivalents Daniel Moyer, Boris Gutman, Joshua Faskowitz, Neda Jahanshad and Paul M. Thompson University of Southern California Slides |
| 10:30 - 11:00 | Coffee Break |
| 11:00 - 11:20 | Invited Speaker: Neda Jahanshad The genetics of brain structural connectivity Slides |
| 11:20 - 11:40 | Cortical Geometry Network and Topology Markers for Parkinson's Disease Diagnosis Amanmeet Garg, Donghuan Lu, Karteek Popuri and Mirza Faisal Beg Simon Fraser University Slides |
| 11:40 - 12:00 | Comparison of Brain Networks with Unknown Correspondences Sofia Ira Ktena, Sarah Parisot, Jonathan Passerat-Palmbach and Daniel Rueckert Imperial College London Slides |
| 12:00 - 12:20 | Kernel classification of connectomes based on earth mover's distance between graph spectra Yulia Dodonova, Mikhail Belyaev, Anna Tkachev, Dmitry Petrov and Leonid Zhukov Kharkevich Institute for Information Transmission Problems, Skolkovo Institute of Science and Technology, National Research University Higher School of Economics Slides |
| 12:30 - 12:45 | Closing remarks |