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

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) (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.

Effect Size and Power in fMRI Group Analysis

“…However, this was not observed for the proportion of voxels deemed active or false negative rates. It remains possible that surface based analysis will be preferable if a spatial model is used instead; see, for example Mejia et al (2017). However, this topic is outside the scope of the current manuscript.…”

Template Independent Component Analysis: Targeted and Reliable Estimation of Subject-level Brain Networks Using Big Data Population Priors

“…Bayesian computation techniques, such as integrated nested Laplace approximations (INLA) (Rue et al 2009), could then be used to compute the necessary posterior quantities. Such an approach has previously been used in the context of identifying brain areas activated during a task fMRI experiment (Mejia et al 2017).…”

Functional Brain Response to Emotional Musical Stimuli in Depression, Using INLA Approach for Approximate Bayesian Inference

“…Let T be the number of time points in the fMRI time series and let N be the number of voxels in the brain. For a subject, the following GLM model is present (Mejia et al, 2017):…”

“…INLA method can compute approximations to the posterior distributions and manage large data sets in shorter time by using the sparsity of Gaussian Markov Random Fields (GMRFs). Moreover, INLA is faster than MCMC (Rue et al, 2017) and can be easily implemented using R-INLA package (Mejia, Yue, Bolin, Lindren, & Lindquist, 2017).…”