CIBM SP EPFL-UNIGE SECTION

Network Analysis & Functional MR Imaging

Section Head: Prof. Dimitri Van De Ville (EPFL)

The Network 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 syste

RESEARCH TOPICS

The role of the hippocampus in encoding memories and tracking personal goals

Description: Our brains are continuously monitoring what we have already accomplished, are currently pursuing, and are planning to tackle in the future. In this complex and dynamic process, the hippocampus plays a crucial role, in particular demonstrating a specific organization of the episodic memory information along its longitudinal axis, transitioning from more detailed representations (e.g., immediate goals) in the posterior part, to more generalized information (e.g. temporallly distant goals) in the anterior ones. This study investigates the role of the hippocampus in distinguishing goals over time. For this, we analyze functional Magnetic Resonance Imaging (fMRI) scans of healthy subjects acquired complex spatial navigation tasks, to investigate brain activation during the accomplishment of specific goals.

Investigators: Maria Giulia Preti (EPFL), Dimitri Van De Ville (EPFL)

Collaborators: Alison Montagrin (UNIGE), Victor Ferat (UNIGE), Sophie Schwartz (UNIGE)

Investigating memory consolidation during sleep with functional MRI

Description: Sleep has a crucial role in memory consolidation, but the specific brain mechanisms allowing for the development of memory traces remain still largely unknown. A unique dataset of lucid dreamers who were recorded with functional MRI during training of a hand motor sequence both awake and during sleep is used to analyze dynamic fMRI signatures during different sleep stages, and investigate memory consolidation mechanisms.

Investigators: Maria Giulia Preti (EPFL), Dimitri Van De Ville (EPFL)

Collaborators: Sophie Schwartz (UNIGE), Célia Lacaux (UNIGE) , Delphine Oudiette (INSERM, Paris)

Brain dynamics with functional MR imaging

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)

Graph signal processing of the brain

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)

Dynamics of functional connectivity in epilepsy

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)

Brain connectivity and Alzheimer's Disease

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)

Predicting structural and functional damage and recovery in stroke from MRI and CT scans

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)