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1	Advanced Modeling of Longitudinal Spectroscopy Data Kundu, Madan Gopal January 2014 (has links) Indiana University-Purdue University Indianapolis (IUPUI) / Magnetic resonance (MR) spectroscopy is a neuroimaging technique. It is widely used to quantify the concentration of important metabolites in a brain tissue. Imbalance in concentration of brain metabolites has been found to be associated with development of neurological impairment. There has been increasing trend of using MR spectroscopy as a diagnosis tool for neurological disorders. We established statistical methodology to analyze data obtained from the MR spectroscopy in the context of the HIV associated neurological disorder. First, we have developed novel methodology to study the association of marker of neurological disorder with MR spectrum from brain and how this association evolves with time. The entire problem fits into the framework of scalar-on-function regression model with individual spectrum being the functional predictor. We have extended one of the existing cross-sectional scalar-on-function regression techniques to longitudinal set-up. Advantage of proposed method includes: 1) ability to model flexible time-varying association between response and functional predictor and (2) ability to incorporate prior information. Second part of research attempts to study the influence of the clinical and demographic factors on the progression of brain metabolites over time. In order to understand the influence of these factors in fully non-parametric way, we proposed LongCART algorithm to construct regression tree with longitudinal data. Such a regression tree helps to identify smaller subpopulations (characterized by baseline factors) with differential longitudinal profile and hence helps us to identify influence of baseline factors. Advantage of LongCART algorithm includes: (1) it maintains of type-I error in determining best split, (2) substantially reduces computation time and (2) applicable even observations are taken at subject-specific time-points. Finally, we carried out an in-depth analysis of longitudinal changes in the brain metabolite concentrations in three brain regions, namely, white matter, gray matter and basal ganglia in chronically infected HIV patients enrolled in HIV Neuroimaging Consortium study. We studied the influence of important baseline factors (clinical and demographic) on these longitudinal profiles of brain metabolites using LongCART algorithm in order to identify subgroup of patients at higher risk of neurological impairment. / Partial research support was provided by the National Institutes of Health grants U01-MH083545, R01-CA126205 and U01-CA086368 Spectroscopy Functional Data Analysis Longitudinal Functional Data Analysis Brownian Bridge Longitudinal CART Longitudinal Regression Tree HIV Brain metabolites HIV neuroimaging consortium LongPEER PEER Decomposition based penalty NAA Creatine Myo-inositol Choline Glutamine and Glutamate White matter Gray matter Basal ganglia LongCART neurological disorder Global deficit score GSVD General Singular Value Decomposition Microbial metabolites -- Research HIV infections -- Complications Myelinated neurofibrils Research -- Methodology Trees (Graph theory) -- Research Biometry -- Research -- Methodology Cerebral cortex Central nervous system -- Abnormalities Spectrum analysis -- Research HIV (Viruses) -- Research -- Analysis Creatine Choline Glutamine

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Advanced Modeling of Longitudinal Spectroscopy Data