Clustered-Temporal Bayesian Models for Brain Connectivity in Neuroimaging Data
thesisposted on 2019-12-01, 00:00 authored 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.
DepartmentPublic Health Sciences-Epidemiology and Biostatistics
Degree GrantorUniversity of Illinois at Chicago
Degree namePhD, Doctor of Philosophy
Committee MemberAwadalla, Saria Bhaumik, Dulal Bhaumik , Runa L. Berbaum, Michael
Submitted dateDecember 2019