Online Program Home
My Program

Abstract Details

Activity Number: 78 - Bayesian Generalized Linear Models for Medicine
Type: Contributed
Date/Time: Sunday, July 29, 2018 : 4:00 PM to 5:50 PM
Sponsor: Section on Bayesian Statistical Science
Abstract #329991 Presentation
Title: A Bayesian General Linear Modeling Approach to Cortical Surface fMRI Data Analysis
Author(s): Yu Yue* and Amanda Mejia and David Bolin and Finn Lindgren and Martin A Lindquist
Companies: Baruch College, CUNY and Indiana University and Gothenburg University and The University of Edinburgh and Johns Hopkins University
Keywords: spatial statistics; Bayesian smoothing; integrated nested Laplace approximation; stochastic partial differential equation; brain imaging

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. Here, we propose a novel Bayesian GLM for cs-fMRI, which employs a class of sophisticated spatial processes to flexibly model latent activation fields. We use integrated nested Laplacian approximation (INLA), a highly accurate and efficient Bayesian computation technique (Rue et al. 2009). 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 novel 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.

Authors who are presenting talks have a * after their name.

Back to the full JSM 2018 program