Subventions et des contributions :

Subvention ou bourse octroyée s'appliquant à plus d'un exercice financier. (2017-2018 à 2022-2023)

Communication dynamics in the human connectome

The human brain is a complex network of anatomically connected and functionally interacting neuronal populations. Collective communication among these distributed elements is thought to support patterned neural activity, as well as flexible cognitive operations and complex behavior. Recent innovations in noninvasive neuroimaging have resulted in increasingly detailed maps of human brain connectivity patterns (connectomes). Concomitant analytic advancements in network science and statistics have offered new insight into the organizational principles of brain networks.

Despite considerable progress, we do not yet understand how neural signalling in brain networks translates to individual variation in behaviour and performance. First, most modern research has focused on static properties of brain networks, but fundamental theoretical models of how communication processes unfold on these networks are still lacking. Second, much of our knowledge about brain network organization comes from studies performed on composite connectomes averaged over many individuals, obscuring the link between network function and individual differences in behaviour.

The goal of this proposal is to investigate how communication processes on individual connectomes give rise to observable neural activity patterns and to individual variation in cognitive-behavioral phenotypes. First, we will continue to develop a theoretical and computational framework to relate brain structure and function, by conceptualizing brain function as the result of communication processes on the connectome. Second, we will customize these models to individual human connectomes and relate network features to individuality in cognition and behavior.

This work will establish a principled, theoretical foundation for how communication processes unfold on the human connectome and give rise to individual cognitive characteristics. These findings have the potential to uncover network mechanisms of mental disorders, thus addressing a problem of great societal importance. The proposed project integrates two vibrant scientific disciplines, providing unique educational opportunities for trainees.