Global ETD Search

1	Accuracy of sampling methods in morphometric studies of the sural nerve in man / Cai, Zhao. January 1997 (has links) (PDF) Thesis (M.Med.Sc.)--University of Adelaide, Dept. of Medicine, 1999? / Addendum pasted onto back end paper. Bibliography: leaves 129-143. Myelinated neurofibrils.
2	A technique for examining longitudinal and cross sections of teased nerve fibres and its application to human and experimental neuropathy / Cai, Zhao. January 2002 (has links) (PDF) Thesis (Ph.D.)--University of Adelaide, Dept. of Medicine, 2002? / Includes bibliographical references (leaves 194-225). Myelinated neurofibrils. Myelin sheath
3	A technique for examining longitudinal and cross sections of teased nerve fibres and its application to human and experimental neuropathy Cai, Zhao. January 2002 (has links) (PDF) Includes bibliographical references (leaves 194-225) A new method is described that enables longitudinal and cross sections of an individual nerve fibre to be cut at multiple specified sites along the fibre by use of an unique marker system. The method is particularly useful for the correlative study of myelin-axon relationships Myelinated neurofibrils Myelin sheath Diseases Diagnosis
4	A technique for examining longitudinal and cross sections of teased nerve fibres and its application to human and experimental neuropathy / a thesis submitted by Zhao Cai. Cai, Zhao January 2002 (has links) Includes bibliographical references (leaves 194-225) / ix, 225, vii leaves : ill. (some col.) ; 30 cm. / Title page, contents and abstract only. The complete thesis in print form is available from the University Library. / A new method is described that enables longitudinal and cross sections of an individual nerve fibre to be cut at multiple specified sites along the fibre by use of an unique marker system. The method is particularly useful for the correlative study of myelin-axon relationships / Thesis (Ph.D.)--University of Adelaide, Dept. of Medicine, 2002? Myelinated neurofibrils Myelin sheath Diseases Diagnosis.
5	Structural white matter abnormalities in never-medicated patients with first-episode schizophrenia : a diffusion tensor imaging study / Cheung, Vinci, January 2008 (has links) Thesis (Ph. D.)--University of Hong Kong, 2008. / Also available online.
6	Structural white matter abnormalities in never-medicated patients with first-episode schizophrenia a diffusion tensor imaging study / Cheung, Vinci, January 2008 (has links) Thesis (Ph. D.)--University of Hong Kong, 2008.
7	Structural white matter abnormalities in never-medicated patients withfirst-episode schizophrenia: a diffusiontensor imaging study Cheung, Vinci, 張穎思 January 2008 (has links) published_or_final_version / Psychiatry / Doctoral / Doctor of Philosophy Schizophrenia Myelinated neurofibrils. Brain - Magnetic resonance imaging. Diffusion tensor imaging.
8	A numerically stable model for simulating high frequency conduction block in nerve fiber Kieselbach, Rebecca 26 July 2011 (has links) Previous studies performed on myelinated nerve fibers have shown that a high frequency alternating current stimulus can block impulse conduction. The current threshold at which block occurs increases as the blocking frequency increases. Cable models based on the Hodgkin-Huxley model are consistent with these results. Recent experimental studies on unmyelinated nerve have shown that at higher frequencies, the block threshold decreases. When the block threshold is plotted as a function of frequency the resulting graph is distinctly nonmonotonic. Currently, all published models do not explain this behavior and the physiological mechanisms that create it are unknown. This difference in myelinated vs. unmyelinated block thresholds at high frequencies could have numerous clinical applications, such as chronic pain management. A large body of literature has shown that the specific capacitance of biological tissue decreases at frequencies in the kHz range or higher. Prior research has shown that introducing a frequency-dependent capacitance (FDC) to the Hodgkin-Huxley model will attenuate the block threshold at higher frequencies, but not to the extent that was seen in the experiments. This model was limited by the methods used to solve its higher order partial differential equation. The purpose of this thesis project is to develop a numerically stable method of incorporating the FDC into the model and to examine its effect on block threshold. The final, modified model will also be compared to the original model to ensure that the fundamental characteristics of action potential propagation remain unchanged. Numerical stability Axon model Computational modeling Neurofibrils Myelinated neurofibrils Finite differences Neurophysiology
9	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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