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

Hakmook Kang*, Hernando Ombao, Christopher Fonnesbeck, Zhaohua Ding, Victoria L. Morgan

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

16 Scopus citations


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.

Original languageEnglish (US)
Pages (from-to)219-227
Number of pages9
JournalBrain Connectivity
Issue number4
StatePublished - May 2017

Bibliographical note

Publisher Copyright:
© Copyright 2017, Mary Ann Liebert, Inc. 2017.


  • Diffusion Tensor Image
  • Functional Connectivity
  • Functional Magnetic Resonance Imaging
  • Space-Time Structure
  • Structural Connectivity

ASJC Scopus subject areas

  • General Neuroscience


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