Conférence de Bertrand Thirion dans le cadre du Colloquium du DEC.
Functional neuroimaging offers a unique view on brain functional organization, which is broadly characterized by two features: the segregation of brain territories into functionally specialized regions, and the integration of these regions into networks of coherent activity. Functional Magnetic Resonance Imaging yields a spatially resolved, yet noisy view of this organization. It also yields useful measurements of brain integrity to compare populations and characterize brain diseases.
To extract information from these data, a popular strategy is to rely on supervised classification settings, where signal patterns are used to predict the experimental task performed by the subject during a given experiment, which is a proxy for the cognitive or mental state of this subject. In this talk we will describe how the reliance on large data copora changes the picture: it boosts the generalizability of the results and provides meaningful priors to analyze novel datasets. We will discuss the challenges posed by these analytic approaches, with an emphasis on computational aspects, and how the use of non-labelled data can be further used to improve the model learned from brain activity data.
Bertrand Thirion est chercheur à l'INRIA et modélisateur du cerveau, responsable de l'équipe-projet Parietal sur la Modélisation de la structure, du fonctionnement et de la variabilité du cerveau à partir d'IRM à haut champ.Cliquer ICI pour fermer
Dernière mise à jour : 07/03/2017