Section Head: Prof. Dimitri Van De Ville (EPFL)
The Graph Analysis & Functional MR Imaging (EPFL-UNIGE) Section pursues the development and integration of innovative data-processing tools at various stages of the acquisition, analysis, and interpretation pipeline of neuroimaging data, in particular, using (functional) magnetic resonance imaging, electroencephalography, and optical techniques. We aim at obtaining new insights into brain function & dysfunction by approaches that are based on modeling the brain as a network and as a dynamical system.
Description: We develop novel methods to explore dynamic functional connectivity, based on functional MRI. This involves the use of sophisticated mathematical and graph analysis tools aiming to build meaningful functional brain networks and disentangle the dynamics of brain states.
Investigators: Dimitri Van De Ville (EPFL) , Maria Giulia Preti (EPFL)
Description: We are among the first ones to apply the framework of graph signal processing to brain MR data, to reveal new insights about the coupling between function and structure.
Investigators: Dimitri Van De Ville (EPFL) , Maria Giulia Preti (EPFL)
Description: By combining functional MRI and EEG, we investigate the features of functional connectivity dynamics characterizing epilepsy.
Investigators: Dimitri Van De Ville (EPFL) , Maria Giulia Preti (EPFL)
Collaborators: Serge Vulliemoz (HUG), Giannarita Iannotti (HUG), Margitta Seek (HUG)
Description: We investigate dynamic functional connectivity patterns by resting-state functional MRI at 7T, combined with PET and quantitative susceptibility mapping MRI, to test for interactive effects between functional connectivity and the genetic risk of Alzheimer’s Disease.
Investigators: Dimitri Van De Ville (EPFL) , Maria Giulia Preti (EPFL)
Collaborators: Paul G. Unschuld (Psychiatric University Hospital Zürich), Francis C. Quevenco (University of Zürich)
Description: We investigate brain functional and structural connectivity after stroke in a longitudinal setting and correlate it with behavioral measures of performance, to unravel the mechanisms of post-stroke recovery. Further, we explore the use of deep learning to predict the permanent infarcted brain area from perfusion images of acute stroke.
Investigators: Dimitri Van De Ville (EPFL) , Maria Giulia Preti (EPFL)
Collaborators: Emmanuel Carrera (HUG), Mitsouko Van Assche (HUG), Elisabeth Dirren (HUG), Julian Klug (HUG)
Description: We use sophisticated deep learning techniques to improve the reproducibility of radiological acquisitions across different scanners.
Investigators: Dimitri Van De Ville (EPFL) , Maria Giulia Preti (EPFL)
Collaborators: Xavier Montet (HUG), Jeremy Hofmeister (HUG), Sandra Marcadent (EPFL)
