Biostatistics Weekly Seminar

Functional magnetic resonance imaging (fMRI) is a non-invasive indirect measure of neural activity, which is commonly used to study the function, organization and connectivity of the brain. Given its high dimensionality and complex spatiotemporal structure, fMRI data is often analyzed in a “massive univariate” framework wherein a separate model is fit at every location (e.g. voxel or vertex) of the brain. This approach ignores spatial dependencies, leading to inefficient estimates and a lack of power to detect effects, particularly in individual subjects. A statistically principled alternative is spatial Bayesian models, which impose spatial priors on the latent signal. For computational feasibility, stationary and isotropic gaussian Markov random field (GMRF) spatial priors are a common choice. The underlying signal in fMRI data is primarily localized to the gray matter of the cortical surface and subcortical/cerebellar structures. In its original volumetric form, the spatial fields of this signal exhibit clear deviations from the assumptions of stationarity and isotropy due to cortical folding and the presence of nuisance tissue classes (white matter and cerebral spinal fluid). It is therefore preferable to build spatial models directly on the cortical surface, a 2-dimensional manifold, and subcortical/cerebellar gray matter regions (collectively referred to as “grayordinates”). In this talk, I will discuss my group’s work developing spatial Bayesian models for common types of fMRI analysis. I will also discuss the software we have developed to facilitate the adoption of grayordinates neuroimaging data and spatial Bayesian modeling for fMRI. Finally, I will present an application using a task fMRI study of amyotrophic lateral sclerosis (ALS), in which we uncovered new features of disease progression.

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27 April 2022
1:30pm