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Title: Applications of NODDI for imaging in vivo white matter pathology in neurodegenerative diseases
Author: Zhang, Jiaying
Awarding Body: UCL (University College London)
Current Institution: University College London (University of London)
Date of Award: 2019
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This thesis aims to evaluate: 1) the feasibility of advanced diffusion Magnetic Resonance Imaging (MRI) technique − Neurite orientation dispersion and density imaging (NODDI) for providing in vivo imaging evidence of white matter (WM) pathology at both preclinical and clinical stages of neurodegenerative diseases; 2) the added value of this advanced technique - NODDI over the standard diffusion MRI technique - Diffusion Tensor Imaging (DTI). Monitoring WM pathology is vital in coping with this challenge brought by neurodegenerative diseases as abnormal axonal transport has been identified in neurodegenerative diseases. In vivo imaging evidence using DTI suggests that patients with neurodegenerative diseases have abnormal WM microstructure compared to normal controls. Whilst sensitive, DTI metrics lack tissue specificity to biological features due to the simplicity of the model, therefore could not inform more on the disease pathology. In contrast, NODDI could provide biologically meaningful metrics that have been validated with histological measures in human neural tissue. Therefore, investigating the potential of NODDI in clinical studies of neurodegenerative diseases could greatly increase our knowledge and benefit our understanding of the disease pathology. In this thesis, we chose pre-manifest Huntington's disease and young onset Alzheimer's disease as the disease models to represent the preclinical and clinical stages respectively. We demonstrated the feasibility of NODDI in not only detecting WM abnormalities at both preclinical and clinical stages of neurodegenerative diseases but also tracking the longitudinal progression of WM microstructural deficits at the clinical stage. We also demonstrated the clinical relevance of NODDI by evaluating the correlations between the clinical assessments and NODDI metrics. Compared with DTI, we found that NODDI could provide more information on disease-specific WM pathology.
Supervisor: Not available Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID:  DOI: Not available