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Probabilistic Models for Brain Image Collection, Classication, and Functional Connectivity.

Creative Commons 'BY' version 4.0 license

The use of functional neuroimaging to evaluate brain disorders has become pervasive in the scientific community. The technique provides researchers with a means to evaluate dynamic in-vivo brain function. Over the last thirty years of using neuroimaging techniques to evaluate brain disorders, there is evidence suggesting some illnesses are characterized by differences in regional brain function whereas others by differences in regional connectivity. Disorders with gross anatomical and functional changes such as Alzheimer's disease and traumatic brain injury are often visually discernible in brain scans and differences quantifiable using typical mass univariate analysis techniques. Conversely, disorders with subtle functional changes (e.g. depression) or subtle changes in how the brain communicates (e.g. schizophrenia) are less amiable to existing analysis techniques. Detecting these subtle differences in molecular imaging data, often plagued by noisy measurements from the imaging system, further impedes our ability to gain valuable insights into brain disorders. In this dissertation we use a variety of tools from machine learning and probabilistic modeling to develop new models for decreasing noise in data captured from our imaging systems, improve feature extraction for detecting differences in regional brain function, and evaluate group-based functional connectivity models and their performance in settings with small sample sizes. Each of these models are presented separately with experiments designed to show improvements over existing methodologies and measures of accuracy in both disease classification and recovering gold-standard functional relationships in the brain.

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