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Joint Bayesian Estimation of Voxel Activation and Inter-regional Connectivity in fMRI Experiments

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Abstract

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-specified 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 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-specific 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 inter-regional connectivity associated with how the brain processes risk. The proposed method is also validated through simulation studies and comparisons to other methods used within the neuroscience community.

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Authors and Affiliations

  1. Department of Statistics, University of California, Santa Cruz, CA, USA

    Daniel Spencer, Rajarshi Guhaniyogi & Raquel Prado

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  1. Daniel Spencer
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  2. Rajarshi Guhaniyogi
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Correspondence to Daniel Spencer.

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The research of Rajarshi Guhaniyogi is partially supported by grants from the Office of Naval Research (ONR-BAA N000141812741) and the National Science Foundation (DMS-1854662). Raquel Prado was partially supported by NSF grant SES-1853210.

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Spencer, D., Guhaniyogi, R. & Prado, R. Joint Bayesian Estimation of Voxel Activation and Inter-regional Connectivity in fMRI Experiments. Psychometrika 85, 845–869 (2020). https://doi.org/10.1007/s11336-020-09727-0

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