A Bayesian Double Fusion Model for Resting-State Brain Connectivity Using Joint Functional and Structural Data.

Kang H, Ombao H, Fonnesbeck C, Ding Z, Morgan VL
Brain Connect. 2017 7 (4): 219-227

PMID: 28316255 · PMCID: PMC5444499 · DOI:10.1089/brain.2016.0447

Current approaches separately analyze concurrently acquired diffusion tensor imaging (DTI) and functional magnetic resonance imaging (fMRI) data. The primary limitation of these approaches is that they do not take advantage of the information from DTI that could potentially enhance estimation of resting-state functional connectivity (FC) between brain regions. To overcome this limitation, we develop a Bayesian hierarchical spatiotemporal model that incorporates structural connectivity (SC) into estimating FC. In our proposed approach, SC based on DTI data is used to construct an informative prior for FC based on resting-state fMRI data through the Cholesky decomposition. Simulation studies showed that incorporating the two data produced significantly reduced mean squared errors compared to the standard approach of separately analyzing the two data from different modalities. We applied our model to analyze the resting state DTI and fMRI data collected to estimate FC between the brain regions that were hypothetically important in the origination and spread of temporal lobe epilepsy seizures. Our analysis concludes that the proposed model achieves smaller false positive rates and is much robust to data decimation compared to the conventional approach.

MeSH Terms (14)

Adult Bayes Theorem Brain Diffusion Tensor Imaging Epilepsy, Temporal Lobe Female Functional Neuroimaging Humans Magnetic Resonance Imaging Male Middle Aged Neural Pathways Spatio-Temporal Analysis Young Adult

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