A Bayesian Hierarchical Framework for Modeling of Resting-State fMRI Data

• Activity Number: 674
• Type: Contributed
• Date/Time: Thursday, August 5, 2010 : 10:30 AM to 12:20 PM
• Sponsor: Section on Bayesian Statistical Science
• Author(s): Shuo Chen*+ and DuBois Bowman and Lijun Zhang
• Companies: Emory University and Emory University and Emory University
• Keywords: fMRI ; resting-state ; fALFF ; Bayesian

Abstract

Functional magnetic resonance imaging (fMRI) has emerged as a powerful technique to investigate the neuropathophysiology of major psychiatric disorders. Examining the so-called default mode of resting state brain function has revealed a variety of brain networks. Altered resting-state fMRI characteristics are associated with mental illnesses such as major depressive disorder. Bowman et al. (2008) proposed a Bayesian spatial model for detecting task-related neural activity and functional connectivity between distinct brain locations. In this study, we extend that model for resting-state fMRI profiles by first representing the temporal profiles by fractional amplitude of low frequency fluctuation or projecting coefficients, then applying a Bayesian hierarchical model to estimate the parameters of the variance-covariance matrix that reflect the connectivity between various brain regions.