All ETDs from UAB

Advisory Committee Chair

Adrienne C Lahti

Advisory Committee Members

Charity J Morgan

Kristina M Visscher

Timothy M Wick

Document Type

Thesis

Date of Award

2015

Degree Name by School

Master of Biomedical Engineering (MBE) School of Engineering

Abstract

Schizophrenia, a psychiatric disorder affecting approximately 1% of the population, is often characterized as a disorder of dysconnectivity. Evaluation of the dysconnectivity hypothesis of schizophrenia has been extensively examined via functional neuroimaging studies in order to enhance insight into the disorder’s pathology. In particular, resting-state functional connectivity analyses have reported widespread aberrant network connectivity between brain regions in patients; however, consistencies in the directionality of abnormalities are often variable across studies. Recent investigations have begun to analyze functional connectivity dynamically as it is likely these inconsistencies may be a result of the static nature of traditional connectivity measures. In this study, we assess dynamic functional network connectivity in patients with schizophrenia and matched healthy controls through implementation of group independent component and sliding window analyses. Patients completed a resting-state functional magnetic resonance imaging scan while unmedicated and after six weeks of treatment. Data were preprocessed and decomposed into independent components via group independent component analysis and identified as resting-state networks. Sliding window analysis was subsequently performed on post-processed time courses with variable tapered window sizes (30s, 40s, 44s, 50s, and 60s) and resultant windowed correlation matrices were clustered into discrete connectivity states. Results demonstrated widespread aberrant (increased and decreased) connectivity differences in unmedicated patients across window sizes; however, the vast majority of connectivity differences were most prominent within a single state at small window sizes. Exploratory analyses of connectivity state statistics indicate that unmedicated patients tend to spend less time in states typified by sparse connectivity. Additionally, results found patients tended to dwell longer in smaller windowed states typified by patient hypo-connectivity. Ultimately, our results demonstrate the importance of implementing dynamic analyses to gain connectivity details not obtainable with traditional connectivity analyses. In addition, implementation of these analyses not only improves insight into the role of network dysconnectivity in schizophrenia, but also provides a promising indication of progress towards biomarker identification.

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