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- Title
- DEVELOPMENT OF BIOMARKERS OF SMALL VESSEL DISEASE IN AGING
- Creator
- Makkinejad, Nazanin
- Date
- 2021
- Description
-
Age-related neuropathologies including cerebrovascular and neurodegenerative diseases play a critical role in cognitive dysfunction, and...
Show moreAge-related neuropathologies including cerebrovascular and neurodegenerative diseases play a critical role in cognitive dysfunction, and development of dementia. Designing methodologies for early prediction of these diseases are much needed. Since multiple pathologies commonly coexist in brains of older adults, clinical diagnosis lacks the specificity to isolate the pathology of interest, and gold standard is determined only at autopsy. Magnetic resonance imaging (MRI) provides a non-invasive tool to study abnormalities in brain characteristics that is unique to each pathology. Utilizing ex-vivo MRI for brain imaging proves to be useful as it eliminates two important biases of in-vivo MRI. First, no additional pathology would develop between imaging and pathologic examination, and second, frail older adults would not be excluded from MRI.Hence, the aims of this dissertation were two-fold: to study brain correlates of age- related neuropathologies, and to develop and validate classifiers of small vessel diseases by combining ex-vivo MRI and pathology in a large community cohort of older adults. The structure of the project is as follows.First, the association of amygdala volume and shape with transactive response DNA-binding protein 43 (TDP-43) pathology was investigated. Using a regularized regression technique, higher TDP-43 was associated with lower amygdala volume. Also, shape analysis of amygdala showed unique patterns of spatial atrophy associated with TDP-43 independent of other pathologies. Lastly, using linear mixed effect models, amygdala volume was shown to explain an additional portion of variance in cognitive decline above and beyond what was explained by the neuropathologies and demographics.Second, the previous study was extended to analyze other subcortical regions including the hippocampus, thalamus, nucleus accumbens, caudate, and putamen, and was also conducted in a larger dataset. The results showed unique contribution of TDP-43, neurofibrillary tangles (hallmark characteristic of Alzheimer’s disease pathology), and atherosclerosis (a cerebrovascular pathology) to atrophy on the surface of subcortical structures. Understanding the independent effects of each pathology on volume and shape of different brain regions can form a basis for the development of classifiers of age-related neuropathologies.Third, an in-vivo classifier of arteriolosclerosis was developed and validated. Arteriolosclerosis is one of the main pathologies of small vessel disease, is associated with cognitive decline and dementia, and currently has no standard biomarker available. In this work, the classifier was developed ex-vivo using machine learning (ML) techniques and was then translated to in-vivo. The in-vivo classifier was packaged as a software called ARTS, which outputs a score that is the likelihood of arteriolosclerosis when the required input is given to the software. It was tested and validated in various cohorts and showed to have high performance in predicting the pathology. It was also shown that higher ARTS score was associated with greater cognitive decline in domains that are specific to small vessel disease.Fourth, motivated by current trends and superiority of deep learning (DL) techniques in classification tasks in computer vision and medical imaging, a preliminary study was designed to use DL for training an ex-vivo classifier of arteriolosclerosis. Specifically, convolutional neural networks (CNNs) were applied on 3 Tesla ex-vivo MR images directly without providing prior information of brain correlates of arteriolosclerosis. One interesting aspect of the results was that the network learnt that white matter hyperintense lesions contributed the most to classification of arteriolosclerosis. These results were encouraging, and more future work will exploit the capability of DL techniques alongside the traditional ML approaches for more automation and possibly better performance.Finally, a preliminary classifier of arteriolosclerosis and small vessel atherosclerosis was developed since the existence of both pathologies in brain have devastating effects on cognition. The methodology was similar to the one used for development of arteriolosclerosis classifier with minor differences. The classifier showed a good performance in-vivo, although the testing needs to be assessed in more cohorts.The comprehensive study of age-related neuropathologies and their contribution to abnormalities of subcortical brain structures offers a great potential to develop a biomarker of each pathology. Also, the finding that the MR-based classifier of arteriolosclerosis showed high performance in-vivo demonstrate the potential of ex-vivo studies for development of biomarkers that are precise (because they are based on autopsy, which is the gold standard) and are expected to work well in-vivo. The implications of this study include development of biomarkers that could potentially be used in refined participant selection and enhanced monitoring of the treatment response in clinical drug and prevention trials.
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