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Recently, there has been increased interest in fusing multimodal imaging tobetter understand brain organization. Specifically, accounting for knowledge ofanatomical pathways connecting brain regions should lead to desirable outcomessuch as increased accuracy in functional brain network estimates and greaterreproducibility of topological features across scanning sessions. Despite theclear merits, major challenges persist in integrative analyses including anincomplete understanding of the structure-function relationship andinaccuracies in mapping anatomical structures due to deficiencies in existingimaging technology. Clearly advanced network modeling tools are needed toappropriately incorporate anatomical structure in constructing brain functionalnetworks. We propose a hierarchical Bayesian Gaussian graphical modelingapproach that estimates the functional networks via sparse precision matriceswhose degree of edge-specific shrinkage is informed by anatomical structure andan independent baseline component. The approach flexibly identifies functionalconnections supported by structural connectivity knowledge. This enables robustbrain network estimation even in the presence of mis-specified anatomicalknowledge, while accommodating heterogeneity in the structure-functionrelationship. We implement the approach via an efficient optimization algorithmyielding maximum a posteriori estimates. Extensive numerical studies reveal theclear advantages of our approach over competing methods in accuratelyestimating brain functional connectivity, even when the anatomical knowledge ismis-specified. An application of the approach to the PhiladelphiaNeurodevelopmental Cohort (PNC) study reveals gender based connectivitydifferences across multiple age groups, and higher reproducibility in theestimation of network metrics compared to alternative methods.

Integrative Bayesian Analysis of Brain Functional Networks Incorporating Anatomical Knowledge