Browsing by Subject "Functional MRI"

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    Approaches to Anatomical and Functional Brain Connectivity Analysis with Applications to Adolescent Major Depressive Disorder
    (2018-09) Chu, Shu-Hsien
    Magnetic Resonance Imaging (MRI) has been extensively utilized in brain studies. Diffusion MRI (dMRI) measures brain microstructure and functional MRI (fMRI) reveals neural activity in vivo. Neuroimaging studies can be performed from various spatial perspectives such as voxel, region, connectivity between a pair of regions, and connectome which is a network consisting of brain regions as nodes and connectivity as edges. In a brain, information is processed by the combined interactions of neurons, ensembles of neurons, and collaborating brain regions, which form a special (small-world) topological structure. Network analysis offers tools for characterizing and studying the topological structure of brain networks. In addition to the network analysis established using fMRI or dMRI separately, joint analysis has shown favorable benefits in leveraging the advantages from dMRI and fMRI. However, it is difficult to combine information from dMRI and fMRI and create a joint network. This thesis presents solutions for three problems based on an interdisciplinary framework combining domain knowledge, neuroimaging techniques, signal processing, graph theory, machine learning and statistical analysis. First, a joint model is proposed to create function-specific structural networks, i.e., joint networks, from both dMRI and fMRI simultaneously. Function-specific structural networks inherit the detailed neuron connectome from dMRI and the functional specificity from fMRI, which potentially can improve the statistical power and the limitation of small sample size in clinical applications. Secondly, anatomical features including connectivity and network topological measures established from dMRI data are analyzed using statistical tools, along-track analysis and machine learning techniques to reveal alterations in brain network for adolescents with major depressive disorder (MDD). Last, wavelet-filtered functional connectivity and network topology features are extracted from fMRI data to characterize the correlation of neural activity between brain regions. The functional features are analyzed using statistical tools and false discovery rate control to discover neurological responders to selective serotonin reuptake inhibitors (SSRIs) and neurological correlations with clinical improvement in treating depression. The identified features add new knowledge to the current understanding of the underlying mechanisms of adolescent MDD and the responses to SSRIs and may be further developed and utilized in monitoring disease progression and effectiveness of therapy. Applications in MDD show how network analysis, signal processing and machine learning are utilized to reveal spatial, temporal and frequency information in brain activity, connectivity and network topology.
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    Neural mechanisms of visual context processing in healthy adults and those with Schizophrenia
    (2014-12) Schallmo, Michael-Paul
    The brain's response to a visual stimulus depends in part on the context in which it appears. For example, objects appearing within similar-looking backgrounds tend to evoke smaller neural responses than those seen in isolation. While it is known that schizophrenia (SZ) may reduce visual context effects, the neural mechanisms involved are not fully understood. This dissertation uses functional magnetic resonance imaging (fMRI) and visual behavioral tasks to examine the role of context during normal visual processing, and how context processing is affected by SZ.Chapter 1 provides an overview of the forms of contextual modulation that will be addressed later, and their impairment in SZ. Chapter 2 describes a series of five experiments probing how factors such as stimulus geometry, presentation timing, and attention affect the fMRI response to small groups of visual stimuli. In primary visual cortex, the relative strength of contextual modulation was found to increase when subjects directed their attention away from the stimuli. Further, fMRI responses to parallel center and surrounding stimuli did not show the predicted sensitivity to center contrast.In Chapters 3 and 4, the effect of spatial context during early visual processing in SZ patients was assessed using behavioral measures. Surround suppression of perceived contrast was examined in Chapter 3 among SZ patients and their unaffected relatives, as well as subjects with bipolar affective disorder (BP), relatives of BP subjects, and healthy controls. Weaker surround suppression was observed in SZ versus control subjects, while BP patients showed an intermediate deficit. These deficits did not depend on the configuration of surrounding stimuli. Normal performance was observed among relatives of SZ and BP subjects, indicating deficits in surround suppression were not associated with a genetic risk for these disorders. Chapter 4 examined how SZ impairs the ability to detect visual contours in cluttered backgrounds. Contours were presented in more- or less-similar backgrounds, in order to assess contextual modulation. While SZ patients performed worse than healthy controls or SZ relatives when detecting contours, performance in SZ was less influenced by background context. These experiments were designed to explore the neural basis of visual context processing in healthy adults, and to help uncover how SZ impairs these processes. The large body of research into the neurophysiology of human vision provides powerful tools with which to study how SZ may disrupt neural processing. Studying visual context processing may ultimately help to uncover computational principles conserved across many neural systems, and aid in identifying new targets for the treatment of mental disorders.