The Institute of Electrical and Electronics Engineers Signal Processing Society (IEEE SPS) has appointed the School of Engineering Professor Distinguished Lecturer.
Dimitri Van De Ville, professor and head of the medical image processing laboratory, has just been awarded the title of Distinguished Lecturer for a period of two years. Through various conferences, he will contribute to the visibility and reputation of his field of research and our institution. A first presentation will be given at the Indian Institute of Technology by videoconference on February 2 at 1:00 pm. Link to watch the conference.
The IEEE SPS Distinguished Lecturer program allows renowned researchers and authors in the field of signal processing to give various lectures. Speakers are nominated by technical committees, chapter chairs, editorial boards and selected by the Society’s Award Board. Five Distinguished Lecturers have been appointed for the period 2021-2022.
The next lectures by Professor Van De Ville will take place on 26 March 2021 at 10.30 am (IEEE Signal Processing Society, Gujarat Chapter, Nano Electronics and Bioscience Symposium) and on 9 June 2021 at 5 pm (29th IEEE Conference on Signal Processing and Communications Applications).
Dimitri Van De Ville’s talk
“Graph Signal Processing for Computational Neuroimaging”
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 using tractography on diffusion-weighted MRI data, while functional graphs map out statistical interdependencies between pairs of regional activity traces from resting-state functional MRI. 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. In addition, we also make a case to introduce the graph modularity matrix at the core of GSP, in order to incorporate knowledge about graph community structure when processing signals on the graph, but without the need for community detection. Finally, recent work will highlight how the spatial resolution of this type of analyses can be increased to the voxel level, representing a few hundredth thousands of nodes.