[SFdS] Information du groupe BFA
WG Risk - Wednesday, January 30, 2019 - David BOLIN

Dear All,

We have the pleasure, thanks to the support of the ESSEC IDS dpt, Institut des Actuaires, LabEx MME-DII, and the group BFA (SFdS), to invite:

Prof. David BOLIN
University of Gothenburg & Chalmers University, Sweden

who will speak on

A Bayesian General Linear Modeling Approach
to Cortical Surface fMRI Data Analysis

Date and place: Wednesday, January 30, at: 12:30 pm, EEE - ESSEC La Défense room 202 / at 7:30pm, ESSEC Asia Pacific -Level 3, classroom 7

Abstract: Cortical surface fMRI (cs-fMRI) has recently grown in popularity versus traditional volumetric fMRI, as it allows for more meaningful spatial smoothing and is more compatible with the common assumptions of isotropy and stationarity in Bayesian spatial models. However, as no Bayesian spatial model has been proposed for cs-fMRI data, most analyses continue to employ the classical, voxel-wise general linear model (GLM). Here, we propose a Bayesian GLM for cs-fMRI, which employs a class of spatial processes based on stochastic partial differential equations to model latent activation fields. Bayesian inference is performed using integrated nested Laplacian approximations (INLA), which is a computationally efficient alternative to Markov Chain Monte Carlo. To identify regions of activation, we propose an excursions set method based on the joint posterior distribution of the latent fields, which eliminates the need for multiple comparisons correction. Finally, we address a gap in the existing literature by proposing a Bayesian approach for multi-subject analysis. The methods are validated and compared to the classical GLM through simulation studies and a motor task fMRI study from the Human Connectome Project. The proposed Bayesian approach results in smoother activation estimates, more accurate false positive control, and increased power to detect truly active regions.

Kind regards,
Jeremy Heng, Olga Klopp, Marie Kratz, Isabelle Wattiau
SFdS - Société Française de Statistique
©2019 SFdS