Daniel Spencer, Rajarshi Guhaniyogi and Raquel Prado
06/26/2020 12:25 AM
Statistics
Brain activation and connectivity analyses in task-based functional magnetic
resonance imaging (fMRI) experiments with multiple subjects are currently at the
forefront of data-driven neuroscience. In such experiments, interest often lies in
understanding activation of brain voxels due to external stimuli and strong association
or connectivity between the measurements on a set of pre-speci ed groups of brain
voxels, also known as regions of interest (ROI). This article proposes a joint Bayesian
additive mixed modeling framework that simultaneously assesses brain activation and
connectivity patterns from multiple subjects. In particular, fMRI measurements from
each individual obtained in the form of a multi-dimensional array/tensor at each time
are regressed on functions of the stimuli. We impose a low-rank parallel factorization
(PARAFAC) decomposition on the tensor regression coefficients corresponding to the
stimuli to achieve parsimony. Multiway stick breaking shrinkage priors are employed to
infer activation patterns and associated uncertainties in each voxel. Further, the model
introduces region speci c random effects which are jointly modeled with a Bayesian
Gaussian graphical prior to account for the connectivity among pairs of ROIs.
Empirical investigations under various simulation studies demonstrate the effectiveness
of the method as a tool to simultaneously assess brain activation and connectivity. The
method is then applied to a multi-subject fMRI dataset from a balloon-analog
risk-taking experiment, showing the effectiveness of the model in providing interpretable
joint inference on voxel-level activations and interregional connectivity associated with
how the brain processes risk.
