Magneto-encephalography (MEG) and intracranial electro-encephalography (iEEG) provide unique access to the brain activity in human. In particular, it enables the study of communication between brain regions when performing high-level cognitive tasks, like decision making and learning [Brovelli J Neurosci 2017]. This projects aims to uncover the distributed organization of information processing at work, in particular the interactions between brain regions that can be quantified using the concept of `effective connectivity'. A possible direction is to transpose recent work on functional MRI (fMRI) to characterize the brain dynamics using both spatial and temporal information in fMRI signals [Gilson Network Neuroscience 2020]. Another direction is the development of Bayesian estimation methods for complex network dynamics that account for the richness of activity patterns in MEG and iEEG, for example using the stan package (https://mc-stan.org/) or the dynamic causal model (DCM) [Friston Brain Connect 2011]. Effective connectivity captures the dyncamic state of brain activity and can then be used as a signature to be related to behavioral variables, like performance scores in congitive tasks.
The research will be conducted at the Institut de Neurosciences de la Timone (INT), in Marseille. The project will be conducted in close collaboration with the experimental team that collected the data. The Aix-Marseille University, the CNRS/INSERM laboratories and the APHM hospital center also offer a multidisciplinary environment for neuroscience. Projects range from behavior and cognition to neuropathologies, with strong interactions between the experimental and theoretical teams.