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Abstract Details
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Activity Number:
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424
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Type:
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Contributed
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Date/Time:
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Tuesday, August 2, 2011 : 2:00 PM to 3:50 PM
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Sponsor:
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Section on Bayesian Statistical Science
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| Abstract - #303364 |
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Title:
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A Graph-Theoretic Analysis of Brain Connectivity in Schizophrenia Patients Using a Bayesian Hierarchical Model
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Author(s):
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Lijun Zhang*+ and DuBois Bowman
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Companies:
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Emory University and Emory University
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Address:
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Dept. of Biostatistics and Bioinformatics, Atlanta, GA, 30322,
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Keywords:
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Functional connectivity ;
graph theoretic ;
Bayesian hierarchical model
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Abstract:
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Functional connectivity in the brain reflects associations in neural activity between distinct brain regions and has been actively studied in healthy subjects as well as in patients with psychiatric disorders. Recently, graph theoretic metrics have been applied to characterize functional networks, addressing properties such as centrality, clustering, efficiency, and small world. We propose a Bayesian framework for obtaining group-level estimates to build graph theoretic properties of task-related functional networks. Moreover, our approach enables inferences about group comparisons in functional connectivity properties. We apply our methods to an fMRI study to evaluate differences in working-memory related functional connectivity between schizophrenia patients and healthy controls.
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