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Abstract Details
Activity Number:
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486
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Type:
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Invited
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Date/Time:
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Wednesday, August 3, 2011 : 10:30 AM to 12:20 PM
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Sponsor:
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ENAR
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Abstract - #300274 |
Title:
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Meeting Challenges for Modeling Brain Imaging Data: The Spatio-Temporal Perspective
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Author(s):
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DuBois Bowman*+ and Shuo Chen
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Companies:
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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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Imaging ;
spatial modeling ;
fMRI ;
resting-state analysis ;
mental health
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Abstract:
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Analyzing resting-state fMRI data has revealed brain networks that exhibit consistent properties across subjects. Moreover, altered resting-state network properties are associated with mental illnesses, including major depressive disorder (MDD). Most resting-state fMRI studies conduct network analyses; however, important information may exist in localized "activation" properties. A key challenge in characterizing localized activation properties for resting-state fMRI data is that first-order moments are not interpretable as activation statistics. We extend a Bayesian spatial model (Bowman et al., 2008) that was primarily intended for the analysis of task-induced neural processing, which yields inferences for both localized activation patterns and network properties. We first construct frequency-domain descriptors of the resting-state activity profiles, then use a Bayesian hierarchical model to estimate localized patterns of this measure as well as a covariance matrix reflecting the functional connectivity between brain regions. We perform estimation using MCMC implemented via Gibbs sampler. We apply our Bayesian model to data from a resting-state fMRI study of MDD.
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