Bayesian Joint Modeling of Multiple Brain Functional Networks

Abstract

Investigating the similarity and changes in brain networks under different mental conditions has become increasingly important in neuroscience research. A standard separate estimation strategy fails to pool information across networks and hence has reduced estimation accuracy and power to detect between-network differences. Motivated by an fMRI Stroop task experiment that involves multiple related tasks, we develop an integrative Bayesian approach for jointly modeling multiple brain networks that provides a systematic inferential framework for network comparisons. The proposed approach explicitly models shared and differential patterns via flexible Dirichlet process-based priors on edge probabilities. Conditional on edges, the connection strengths are modeled via Bayesian spike-and-slab prior on the precision matrix off-diagonals. Numerical simulations illustrate that the proposed approach has increased power to detect true differential edges while providing adequate control on false positives and achieves greater network estimation accuracy compared to existing methods. The Stroop task data analysis reveals greater connectivity differences between task and fixation that are concentrated in brain regions previously identified as differentially activated in Stroop task, and more nuanced connectivity differences between exertion and relaxed task. In contrast, penalized modeling approaches involving computationally burdensome permutation tests reveal negligible network differences between conditions that seem biologically implausible. Supplementary materials for this article, including a standardized description of the materials available for reproducing the work, are available as an online supplement.

KEYWORDS:

• Brain networks
• Dirichlet process
• Multiple graphical models
• Spike-and-slab prior
• Stroop task

Supplementary Materials

The supplementary materials contain the detailed posterior computation steps, description of the network metrics used for Stroop task analysis, the results of the 40 node simulations, additional boxplots for performance metrics for simulations, additional details on the Stroop Task data analysis, and a Matlab GUI to implement the method.

Additional information

Funding

This work was supported by the European Community Marie Curie Action IRG (Call FP7-PEOPLE-2009-RG, project 249329 “NBC-EFFORT”). Research reported in this publication was supported by the National Institute of Mental Health of the National Institutes of Health under Award Number RO1 MH105561 and R01MH079448. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.