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

Published online by Cambridge University Press:  01 January 2025

Daniel Spencer Open the ORCID record for Daniel Spencer [Opens in a new window] ,
Rajarshi Guhaniyogi  and
Raquel Prado
Daniel Spencer*
Affiliation:
University of California
Rajarshi Guhaniyogi
Affiliation:
University of California
Raquel Prado
Affiliation:
University of California
*
Correspondence should be made to Daniel Spencer, Department of Statistics, University of California, Santa Cruz, CA, USA. Email: daspence@ucsc.edu; URL: https://users.soe.ucsc.edu/~daspence/
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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.

Keywords

Bayesian inferencebrain activationbrain connectivityfunctional magnetic resonance imaginggraphical modelingmultiway stick-breaking priorPARAFAC decompositiontensor response
Type
Theory and Methods
Information
Psychometrika , Volume 85 , Issue 4 , December 2020 , pp. 845 - 869
Copyright
Copyright © 2020 The Psychometric Society

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