Section: New Results

Cohort-level brain mapping: learning cognitive atoms to single out specialized regions

Participants : Gaël Varoquaux [Correspondant] , Bertrand Thirion, Yannick Schwartz.

Functional Magnetic Resonance Imaging (fMRI) studies map the human brain by testing the response of groups of individuals to carefully-crafted and contrasted tasks in order to delineate specialized brain regions and networks. The number of functional networks extracted is limited by the number of subject-level contrasts and does not grow with the cohort. Here, we introduce a new group-level brain mapping strategy to differentiate many regions reflecting the variety of brain network configurations observed in the population. Based on the principle of functional segregation, our approach singles out functionally-specialized brain regions by learning group-level functional profiles on which the response of brain regions can be represented sparsely. We use a dictionary-learning formulation that can be solved efficiently with on-line algorithms, scaling to arbitrary large datasets. Importantly, we model inter-subject correspondence as structure imposed in the estimated functional profiles, integrating a structure-inducing regularization with no additional computational cost. On a large multi-subject study, our approach extracts a large number of brain networks with meaningful functional profiles (see Fig. 7 ).

More details can be found in [66] .

Figure 7. (Left) A brain functional atlas can be conceptualized as a parcellation of the brain volume into overlapping networks, where each functional network is characterized by a profile of activation for a set of functional contrasts. (Right) Such an atlas can be learned by applying an adapted dictionary learning to a set of images that display the activation observed in different subjects for a (very large) set of cognitive tasks.