Amanda Mejia, PhD, Department of Statistics, Indiana University Bloomington

"A spatial Bayesian approach to cortical surface task FMRI analysis"

Cortical surface functional magnetic resonance imaging (cs-fMRI) has recently experienced a rise in popularity relative to traditional 3-dimensional volumetric fMRI. Cs-fMRI offers removal of extraneous tissue types and improved alignment of cortical areas across subjects. Additionally, cs-fMRI is more compatible with common assumptions of spatial models, unlike volumetric fMRI data, which exhibits a complex spatial dependence structure due to cortical folding and the presence of multiple tissue types. However, no spatial Bayesian model has yet been developed for cs-fMRI data. Most analyses, therefore, rely on the classical general linear model (GLM), in which a linear regression model is fit separately at each location in the brain to estimate activation amplitude in response to each task. At each location, a hypothesis test is performed on the model coefficients to determine whether that location is “activated”, presenting a massive multiple comparisons problem.  This approach also fails to properly account for spatial dependence in the activation amplitudes of neighboring voxels.  In this paper, we propose a Bayesian GLM approach to estimate task activation using cs-fMRI data, which employs a class of sophisticated spatial processes to flexibly model latent fields of task activation. To perform the Bayesian computation, we use integrated nested Laplacian approximation (INLA), a highly accurate and computationally efficient alternative to Markov chain Monte Carlo. To identify regions of activation, we propose a novel joint posterior probability map (PPM) method, which eliminates the problem of multiple comparisons.  Finally, we extend the model from the single-subject to the multi-subject case to facilitate group inference. The method is validated through simulation studies and motor and gambling task fMRI studies from the Human Connectome Project.

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