  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) (Worsley and Friston 1995). Here, we
propose a 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. The proposed Bayesian approach
results in smoother activation estimates, more accurate false positive control,
and increased power to detect truly active regions.
