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UBC Theses and Dissertations

Multimodal assessment of neurodegenerative diseases Sheikhi Shoshtari, Ava


There is growing recognition that accurate assessment of brain function includes activity at multiple temporal and spatial scales. In this thesis, we explored ways to combine clinically-relevant imaging information derived from subjects with neurodegenerative disease. In the first work, we investigated a two-step framework to determine both joint and unique biomarkers from structural and functional MRI in 18 healthy control (HC) and 12 Parkinson’s disease (PD) subjects. Three matrices (structural, functional, and structural/functional interactions) were derived from a subset of features in both modalities that were likely candidates for discrimination between PD and HC subjects. Finally, Least Absolute Shrinkage and Selection Operator (LASSO) regression was performed to determine if subjects’ clinical characteristics such as gender, smoking history, smell performance, Hoehn and Yahr Scale (H&Y Stage), and Unified Parkinson’s Disease Rating Scale (UPDRS) values, could be accurately predicted based on the imaging features. The results revealed that complementary biomarkers were most informative in predicting clinical scores in both groups. In the second work, for analyzing imaging data from subjects with Multiple Sclerosis (MS), we employed a joint Multimodal Statistical Analysis Framework, a data fusion approach that used Latent Variables (LV). We studied fusion of information from seven different imaging modalities: Myelin Water Imaging (MWI), Diffusion Tensor Imaging (DTI), resting state functional MRI (rsfMRI), cortical thickness of the right and left hemisphere, MS lesion load, and normalized brain volume from 47 subjects with MS. Decomposed common and unique information in each modality were acquired and their relationships with disease duration (DD), the Expanded Disability Status Scale (EDSS), and age, were analyzed through LASSO regression. We noted that common components of the seven modalities were the most accurate in predicting clinical indices. Results further revealed the regional importance of each modality by indicating a unique pattern of degeneration in MS and an asymmetry between the cortical thickness components in the two hemispheres. Our results demonstrate the power of utilizing multimodal imaging biomarkers in neurodegenerative diseases. Since structural imaging data is acquired along with functional data, we propose that fusion of information from both types of data should become part of routine analysis.

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