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Abstract Details
Activity Number:
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637
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Type:
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Invited
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Date/Time:
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Thursday, August 4, 2011 : 10:30 AM to 12:20 PM
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Sponsor:
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International Society of Bayesian Analysis
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Abstract - #300291 |
Title:
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Bayesian Variable Selection for Identifying Genetic Effects on Functional Connectivity
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Author(s):
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Brian Reich*+ and Michele Guindani and Abel Rodriguez and Vince Calhoun
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Companies:
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North Carolina State University and The University of Texas MD Anderson Cancer Center and University of California at Santa Cruz and University of New Mexico
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Address:
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2311 Stinson Drive, Raleigh, NC, 27695,
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Keywords:
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fMRI data ;
Bayesian variable selection ;
functional connectivity ;
Graphical models
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Abstract:
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Functional magnetic resonance imaging (fMRI) data are often used to identify regions of the brain that are functionally connected while performing a cognitive task. Connectivity patterns vary across subjects according to subject-specific characteristics, e.g., the subject having been diagnosed with a form of schizophrenia. Our objective is to identify genetic pathways that affect a subject's functional connectivity in response to a series of external stimuli. We model each subject's connectivity using a graphical model, with potentially a different set of edges for each subject. We assume that the probability of each pair of regions being connected depends on a set of subject-specific genetic covariates. This gives a high-dimensional model, as the number potential region pairs and the number of genetic variables are both large. Therefore, we propose a Bayesian variable selection technique to identify a sparse model for functional connectivity. The approach is illustrated on a a set of genetic and fMRI data from a population of healthy and schizophrenic patients.
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