Applications of DTI and NODDI-MRI in Diabetes Mellitus and Multiple Sclerosis

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Saudi Digital Library
Type 2 diabetes (T2DM) and multiple sclerosis (MS) affect the central nervous system (CNS). Both diseases are major risk factors for brain damage and degeneration, challenging healthcare providers worldwide. Magnetic Resonance Imaging (MRI) can visualise brain structural abnormalities in patients with T2DM and MS, but conventional MRI cannot quantify microstructural changes. As a result, there has been a surge in interest in utilising imaging tools to quantify microstructural changes, such as advanced diffusion MRI models, to understand white matter abnormalities in MS and T2DM. Diffusion tensor imaging (DTI) has been used to quantify microstructural abnormalities in T2DM and MS. Despite the sensitivity of DTI in detecting microstructural changes in white matter, DTI indices are influenced by fibre orientation dispersion, which may lead to the incomplete or erroneous characterisation of the microstructural changes occurring within a voxel. A biophysical diffusion model known as neurite orientation dispersion and density imaging (NODDI) has been proposed to overcome this DTI limitation. My original contribution to knowledge is 1) a current systematic review of DTI studies that detected brain microstructural changes in diabetes; 2) a meta-analysis of NODDI studies in characterising white matter changes in MS; 3) optimising an up-to-date NODDI scanning protocol at the University of Nottingham; 4) using DTI/NODDI metrics derived from the UK Biobank to investigate brain white matter alterations in T2DM; 5) assessing the correlation between the altered DTI/NODDI metrics and metabolic profile in patients with T2DM. I initially optimised the NODDI scanning protocol for this original PhD project. I tested four scanning protocols on a 3T Philips MRI scanner at Sir Peter Mansfield Imaging Centre (SPMIC). The research team agreed that Protocol #3 was the best choice. In parallel, I performed a systematic review to investigate the impact of diabetes on brain microstructure measured by DTI and correlated it with cognitive and metabolic tests. I reviewed 38 DTI studies on both types of diabetes. This review showed that diabetes affects brain microstructure, suggesting its impact on cognitive abilities. I aimed to meta-analyse studies using NODDI metrics to detect the white matter neuroaxonal pathology in MS. The intracellular volume fraction in the white matter lesions and normal-appearing white matter were significantly reduced compared to healthy white matter, suggesting underlying damage or loss of neurites. After the COVID-19 pandemic halted my prospective patient study, I evaluated the microstructural impact of T2DM on brain white matter using DTI and NODDI metrics derived from UK Biobank. I evaluated the relationship between white matter alterations and disease duration/glycated haemoglobin (HbA1c). The study showed that T2DM was associated with subtle but global white matter microstructural changes, as indicated by the alterations of DTI and NODDI parameters. There were weak but statistically significant associations between altered DTI and NODDI parameters in participants with T2DM and glycaemic control/disease duration. T2DM and MS were associated with white matter alterations detected by DTI and NODDI. Although the findings of this thesis demonstrated that DTI still has a potential value as a clinical biomarker in T2DM, NODDI could biophysically characterise white matter neuroaxonal pathology in T2DM and MS, which contributes to DTI parameters.
This thesis work has used a novel Microstructural imaging technique to investigate the white matter changes in type 2 diabetes and multiple sclerosis.
Microstructural imaging, diffusion imaging, type 2 diabetes, white matter, neu-rite density, orientation dispersion, isotropic volume fraction