Covariance and graphical models of brain connectivity

Functional connectivity captures brain interactions via fluctuations in the observed activity. We use graphical modelling to extract connectomes, i.e. graphs of brain connectivity, with statistically well-posed and controled methods.

Learning brain connectivity faces the complexity of brain graphs, that is in sharp contrast with the small amount of data at the subject level. At the group level, connectivity information can be convolved with inter-subject variability. To tackle these challenges, our research adapts cutting-edge methods from the statistical learning literature to the specificities of brain imaging.

Selected publications:

Inter-subject bio-markers from connectivity require statistical modelling of the distributed variability in the connectomes. Univariate statistics are ill-suited for these problems, and we develop matrix-variate probilistic models that lead to well-posed statistical tests maximizing the edge-level detection power.

Selected publications:

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