Friday, September 10, 2021 10:00am to 11:00am
About this Event
Structure-Function Coupling Enables Exploring Brain Organization, Task Decoding, and Individual Fingerprinting presented by Dimitri Van De Ville, Professor of Bioengineering at the École polytechnique fédérale de Lausanne (EPFL) and the University of Geneva.
This research seminar is hosted by the Department of Computer Science and the Goergen Institute for Data Science
Abstract: State-of-the-art magnetic resonance imaging (MRI) provides unprecedented opportunities to study brain structure (anatomy) and function (physiology). Based on such data, graph representations can be built where nodes are associated to brain regions and edge weights to strengths of structural or functional connections. In particular, structural graphs capture major neural pathways in white matter, while functional graphs map out statistical interdependencies between pairs of regional activity traces. Network analysis of these graphs has revealed emergent system-level properties of brain structure or function, such as efficiency of communication and modular organization.
In this talk, graph signal processing (GSP) will be presented as a novel framework to integrate brain structure, contained in the structural graph, with brain function, characterized by activity traces that can be considered as time-dependent graph signals. Such a perspective allows to define novel meaningful graph-filtering operations of brain activity that take into account smoothness of signals on the anatomical backbone. This allows to define a new measure of “coupling” between structure and function based on how activity is expressed on structural graph harmonics. To provide statistical inference, we also extend the well-known Fourier phase randomization method to generate surrogate data to the graph setting. This new measure reveals a behaviorally relevant spatial gradient, where sensory regions tend to be more coupled with structure, and high-level cognitive ones less so. Next to these population-level results, the coupling measures can also be successfully deployed to decode different tasks with high accuracy or identify individuals. Interestingly, the most informative regions for task and fingerprinting are again the more unimodal and multimodal ones, respectively. If time allows, some recent work will be highlighted how the spatial resolution of this type of analysis can be increased to the voxel level, representing a few hundredth thousands of nodes.
Bio: Dimitri Van De Ville received his M.S. and Ph.D. degrees in Computer Science from Ghent University, Belgium in 1998 and 2002, respectively. From 2002 to 2005, he was a post-doctoral fellow at the Biomedical Imaging Group of Prof. Michael Unser at the Ecole Polytechnique Fédérale de Lausanne (EPFL), Switzerland. In 2005, he became responsible for the Signal Processing Unit at the University of Geneva (UniGE) and Geneva University Hospital (HUG) as part of the Centre d’Imagerie BioMédicale (CIBM), a large imaging initiative of the Lemanic academic institutions. In 2009, he was awarded an SNSF professorship and he started a joint tenure-track professorship at the EPFL (Institute of Bioengineering, School of Engineering) and the UniGE (Department of Radiology and Medical Informatics, Faculty of Medicine). Since 2015 he’s Associate Professor of Bioengineering at both institutions and his lab is located at the newly established Campus Biotech in Geneva.
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