This is the program for the 2010 Joint Statistical Meetings in Vancouver, British Columbia.
Abstract Details
Activity Number:
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674
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Type:
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Contributed
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Date/Time:
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Thursday, August 5, 2010 : 10:30 AM to 12:20 PM
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Sponsor:
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Section on Bayesian Statistical Science
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Abstract - #309278 |
Title:
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A Bayesian Hierarchical Framework for Modeling of Resting-State fMRI Data
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Author(s):
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Shuo Chen*+ and DuBois Bowman and Lijun Zhang
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Companies:
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Emory University and Emory University and Emory University
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Address:
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1518 Clifton Rd., N.E., 3rd Floor, Atlanta, GA, 30322,
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Keywords:
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fMRI ;
resting-state ;
fALFF ;
Bayesian
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Abstract:
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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.
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