Activity Number:
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329
- Advances of Statistical Methodologies in Mental Health and Related Field: Some Recent Issues and Solutions
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Type:
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Topic Contributed
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Date/Time:
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Tuesday, August 1, 2017 : 10:30 AM to 12:20 PM
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Sponsor:
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Mental Health Statistics Section
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Abstract #324222
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Title:
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Bayesian Approaches for Modeling Functional Connectivity in Neuroimaging
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Author(s):
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Sanjib Basu* and Nairita Ghosal
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Companies:
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University of Illinois At Chicago and University of Illinois at Chicago
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Keywords:
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Autism ;
Dirichlet Process ;
fMRI ;
spatial dependence
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Abstract:
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Functional connectivity considers temporal dependence of activation patterns in functionally linked and anatomically separated brain regions which are in continuous communication with each other. Functional connectivity can be measured by considering co-activation of brain regions in resting-state functional magnetic resonance imaging (fMRI). We consider differences in functional connectivity between normal and autistic subjects and propose Bayesian semiparametric models that can incorporate latent clustering and spatial correlation. These proposed models performed better than their comparators in correctly detecting significant co-active brain regions in simulation studies. We apply these models to analyze functional connectivity in Autism Brain Imaging Data Exchange (ABIDE)
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Authors who are presenting talks have a * after their name.