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Advisory Committee Chair

Gregg L Vaughn

Advisory Committee Members

Gregory A Franklin

Frank M Skidmore

Earl Wells

Document Type

Dissertation

Date of Award

2020

Degree Name by School

Doctor of Philosophy (PhD) School of Engineering

Abstract

Neurodegenerative diseases are an increasingly significant source of health problems in recent years. The prevalence and severity of these diseases is only expected to grow with the aging of the population. There is a large and and growing research effort to understand, diagnose and develop treatments for this class of diseases. Many of these diseases are not well understood, and there is a dearth of approved biomarkers and other techniques to definitively diagnose these diseases. The field of computational neuroimaging has been developed as part of the effort to study and diagnose these diseases. This field has undergone explosive growth in recent years due to advances in magnetic resonance imaging techniques, high performance computation, image processing algorithms, and machine learning techniques. A wide variety of software packages have been created to analyze neurological images, with new packages being regularly added. Numerous analysis approaches have been employed, including traditional image processing techniques, statistical algorithms, machine learning techniques, morphological analysis, and various combinations of these approaches. This dissertation presents a new technique in the field of computational neuroimaging to perform morphological analysis of brain images. This new technique has been named Radial Fiber Atrophy (RFA), and is an extension to traditional Tensor Based Morphometry (TBM). Statistical analysis of the RFA metric has been performed using data sets from the Parkinson’s Progression Marker Initiative (PPMI). Results of this analysis have shown that the RFA metric is capable of indicating statistical differences related to the future progression of Parkinson’s Disease (PD). Progression of PD is a significant research area that has traditionally resisted solution. It is anticipated that this RFA metric should be highly applicable to research into many other neurodegenerative diseases, and may be potentially useful as part of the diagnosis processes in individual patients.

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