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Activity Number: 329 - Advances of Statistical Methodologies in Mental Health and Related Field: Some Recent Issues and Solutions
Type: Topic Contributed
Date/Time: Tuesday, August 1, 2017 : 10:30 AM to 12:20 PM
Sponsor: Mental Health Statistics Section
Abstract #324222
Title: Bayesian Approaches for Modeling Functional Connectivity in Neuroimaging
Author(s): Sanjib Basu* and Nairita Ghosal
Companies: University of Illinois At Chicago and University of Illinois at Chicago
Keywords: Autism ; Dirichlet Process ; fMRI ; spatial dependence
Abstract:

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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