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Clustered-Temporal Bayesian Models for Brain Connectivity in Neuroimaging Data

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posted on 01.12.2019, 00:00 by Nairita Ghosal
Functional connectivity can be measured by considering co-activation of brain regions in resting-state functional magnetic resonance imaging. We explore contrasting fuctional connectivity between subjects with Autism Spectrum Disorder and controls using resting state fMRI data. Bayesian models are developed to explore differential connectivity using cross-correlated functional connectivity between region of interest pairs. Additionally, a regional-temporal model is proposed to directly model time sequence of resting state fMRI measurements at each brain region. We have implemented dynamic linear model to capture temporal structure of the data and the potential correlation between connected regions is modeled using hidden Potts model with latent variable. We apply the proposed models to analyse Autism Brain Image Data Exchange data set.

History

Advisor

Basu, Sanjib

Chair

Basu, Sanjib

Department

Public Health Sciences-Epidemiology and Biostatistics

Degree Grantor

University of Illinois at Chicago

Degree Level

Doctoral

Degree name

PhD, Doctor of Philosophy

Committee Member

Awadalla, Saria Bhaumik, Dulal Bhaumik , Runa L. Berbaum, Michael

Submitted date

December 2019

Thesis type

application/pdf

Language

en

Issue date

05/12/2019

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