All ETDs from UAB

Advisory Committee Chair

Adrienne C Lahti

Advisory Committee Members

Allan C Dobbins

David C Knight

Charity J Morgan

Kristina M Visscher

Document Type

Dissertation

Date of Award

2018

Degree Name by School

Doctor of Philosophy (PhD) School of Engineering

Abstract

Schizophrenia is a heterogeneous psychiatric disorder that affects approximately 1% of the population. Due to its heterogeneity, critical gaps in knowledge about the disorder persist as the underlying neural mechanisms and etiology remain poorly understood. In order to fill this gap in knowledge, identification of an imaging biomarker characteristic of schizophrenia’s pathophysiology is critical in not only furthering understanding of the disorder, but also improving patient prognosis and outcome. Attempts toward filling this critical gap in knowledge and thus uncovering the neurobiological basis of schizophrenia are continually being made with neuroimaging and electrophysiological studies. The primary objective of this dissertation research was to use multimodal neuroimaging data, specifically structural and functional magnetic resonance imaging (MRI) at 7 Tesla and magnetoencephalography (MEG), to examine structural, functional, and electrophysiological alterations in first-episode patients with schizophrenia. In the first study, we used a multimodal fusion approach to examine resting-state structural and functional relationships that can distinguish patients from controls. We found grey matter basal ganglia, somatosensory, parietal lobe, and thalamus volume abnormalities associated with ventricular cerebrospinal fluid volume; occipital and frontal lobe white matter volume abnormalities associated with temporal lobe function; and grey matter frontal, temporal, parietal, and occipital lobe volume abnormalities associated with caudate function. In the second study, we utilized the complementary modalities of functional MRI and MEG to examine resting-state functional connectivity alterations characteristic of schizophrenia. We found patients exhibited hyperconnectivity between the caudate and auditory network and hypoconnectivity between sensorimotor components using fMRI. In MEG, we found patients demonstrated hypoconnectivity between sensorimotor and task positive networks in the delta frequency band but no differences in connectivity among the theta, alpha, beta, and gamma bands. These studies indicate the importance of using multimodal neuroimaging techniques when examining schizophrenia in order to uncover information about different aspects of the brain that may be missed in single modality analyses and therefore hindering our ability to identify an imaging biomarker of schizophrenia.

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Engineering Commons

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