Search results

(1 - 3 of 3)
DEVELOPMENT OF BIOMARKERS OF SMALL VESSEL DISEASE IN AGING
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.
Show less
DEVELOPMENT AND EVALUATION OF MRI TEMPLATES OF THE MIITRA ATLAS
Title
DEVELOPMENT AND EVALUATION OF MRI TEMPLATES OF THE MIITRA ATLAS
Creator
RIDWAN, ABDUR RAQUIB
Date
2021
Description
Digital human brain atlases play a pivotal role in conducting wide range of neuroimaging studies and are commonly used as references for...
Show moreDigital human brain atlases play a pivotal role in conducting wide range of neuroimaging studies and are commonly used as references for spatial normalization in voxel-wise analysis, region-of interest analyses, automated tissue-segmentation, functional connectivity analyses, etc. A brain atlas typically consists of MRI-based multi-modal templates and semantic labels delineating brain regions according to the characteristics of the underlying tissue. In recent times there has been a plethora of magnetic resonance imaging (MRI) studies on older adults without dementia to explore the role of brain characteristics associated with cognitive functions in old age with the ultimate goal to develop strategies for prevention of cognitive decline. Increasing the accuracy in terms of sensitivity and specificity of such neuroimaging studies require an atlas with a comprehensive set of high-quality templates representative of the brain characteristics typical of older adults and detailed labels accurately mapping brain regions of interest. However, such an atlas has not been constructed for older adults without dementia. Hence this thesis aims to build high quality MRI templates which are the cornerstone resources needed for the development of a comprehensive, high quality, multi-channel, longitudinal, probabilistic digital human brain atlas for older adults termed as Multi-channel Illinois Institute of Technology and Rush University Aging (MIITRA) atlas. This dissertation focuses on a) to develop and evaluate a high performing 1mm isotropic structural T1-weighted brain template, b) to investigate the development and evaluation of a spatio-temporally consistent longitudinal structural T1-weighted template of the older adult brain, c) to develop and evaluate an unbiased 0.5 mm isotropic super-resolved high resolution and detail-preserving structural T1 weighted template of the older adult brain, d) to develop an unbiased 0.5 mm super-resolved high resolution and detail-preserving structural PD weighted and T2-weighted template of the older adult brain, e) to investigate and provide future directions in the development of a 0.5 mm super-resolved high resolution DTI template of the older adult brain, and f) to construct a novel approach in the development of MRI templates using both space and frequency information of spatially normalized older adult data. The thesis based on the aforementioned foundational points was constructed as follows: Firstly, this thesis presents the development of a 1mm isotropic T1-weighted structural template of the older adult brain utilizing state of the art registration algorithm ANTs with parameters carefully optimized for older adults, in an iterative groupwise spatial normalization framework. The preprocessing steps were also thoroughly investigated to ensure high quality data. It was demonstrated through systematic comparison of this new template to several other standardized and study-specific T1-weighted templates that a) it exhibited high image sharpness, b) allowed for high spatial normalization accuracy and detection of smaller inter-group morphometric differences compared to other standardized templates, c) had similar performance to that of study-specific templates and d) was highly representative of the older adult brain. Secondly, with the acquired technical know-how from the aforementioned research findings a new method was introduced for the construction of a spatio-temporally consistent longitudinal template based on high quality cross-sectional older adult data from a large cohort. The new template was compared to templates generated with previously published methods in terms of spatio-temporal consistency and image quality and was shown to have superior performance. In addition, a novel approach was introduced for image quality enhancement of the longitudinal templates utilizing both space and frequency information. Thirdly, the thesis presents a method that involves a) thoroughly refining registration parameters, b) patch-based tissue-guided sparse-representation approach in a super-resolved unbiased minimum deformation space to construct and evaluate an unbiased 0.5 mm isotropic super-resolved high resolution and detail-preserving structural T1 weighted template of the older adult brain. This method accounts for misregistration specially in the cortical regions, ensuring sharp delineation of structures representative of the older adult brain. The new template developed using this approach maintained high anatomical consistency with sharp and detailed cortical features in the brain and exhibited higher image sharpness compared to other high-resolution standardized templates and allowed for high spatial normalization accuracy when used as a reference for normalization of older adult data. Additionally, this approach of template building was investigated on DTI tensors of older adult participants, and the constructed DTI template was shown to perform better than templates developed using the best approach currently present in the literature. Finally, the thesis presents the development of an unbiased 0.5 mm super-resolved high resolution and detail-preserving structural PD weighted and T2-weighted template of the older adult brain, from nonlocal super-resolution based upsampled PD and T2w older adult participant data, using this new template building approach.
Show less
Development and evaluation of high resolution MRI templates and labels of the MIITRA atlas
Title
Development and evaluation of high resolution MRI templates and labels of the MIITRA atlas
Creator
Niaz, Mohammad Rakeen
Date
2022
Description
A digital human brain atlas consisting of MRI-based multi-modal templates and semantic labels delineating brain regions are commonly used as...
Show moreA digital human brain atlas consisting of MRI-based multi-modal templates and semantic labels delineating brain regions are commonly used as references for spatial normalization in a wide range of neuroimaging studies. Magnetic resonance imaging (MRI) studies of the aging brain is of significant interest in recent times to explore the role of brain characteristics associated with cognitive functions. The introduction of advanced image reconstruction techniques, and the recent trend in MRI acquisitions at submillimeter in-plane resolution have resulted in an easier availability of MRI data on older adults at high spatial resolution. An atlas with a comprehensive set of high-resolution templates representative of the older adult brain and detailed labels accurately mapping brain regions can increase the sensitivity and specificity of such neuroimaging studies. Additionally, most neuroimaging studies can benefit from a high-resolution atlas with templates where fine brain structures are resolved and, where the transition between different tissue can be more accurately defined. However, such an atlas is not publicly available for older adults. Hence the goal of this thesis is to develop a comprehensive, high-resolution digital human brain atlas for older adults termed as Multi-channel Illinois Institute of Technology and Rush University Aging (MIITRA) atlas.This dissertation aims a) to develop a new technique based on the principles of super-resolution for the construction of high-resolution structural and diffusion tensor templates, and evaluate the templates for use in studies on older adults, b) to construct and evaluate high-resolution structural and diffusion tensor templates constructed using the method developed in (a) for the MIITRA atlas using MRI data collected on 400 nondemented older adults, c) to investigate and develop a technique for the construction of high-resolution labels and evaluate the performance of gray matter labels constructed using this technique in segmenting the gray matter of older adults, and d) to develop and evaluate a comprehensive set of high-resolution labels using the technique developed in (c) for the MIITRA atlas using data on 400 non-demented older adults. Based on the aforementioned points, the thesis is structured as follows: Firstly, this thesis presents a novel approach for the construction of a high-resolution T1-weighted structural template based on the principles of super resolution. This method introduced a forward mapping technique to minimize signal interpolation, and a weighted averaging method to account for residual misregistration. The new template was shown to resolve finer brain structures compared to a lower resolution template constructed using the same data. It was demonstrated through systematic comparison of this new template to several other standardized templates of different resolutions that a) it exhibited high image sharpness, b) was free of image artifacts, c) allowed for high spatial normalization accuracy and detection of smaller inter-group morphometric differences compared to other standardized templates, d) was highly representative of the older adult brain. This novel approach was further modified for the construction of a high spatial resolution diffusion tensor imaging template. The new DTI template is the first high spatial resolution population-based DTI template of the older adult brain and exhibits high image quality, high sharpness, is free of artifacts, resolves fine white matter structures, and provides higher spatial normalization accuracy of older adult DTI data compared to other available DTI templates. Secondly, the aforementioned techniques were utilized in the development of high resolution T1-weighted and DTI templates, and tissue probability maps for the MIITRA atlas using high quality MRI images on 400 diverse, community cohort of non-demented older adults. Thirdly, a novel approach for generating high resolution gray matter labels is presented that involves a) utilization of the super resolution technique to ensure sharp delineation of structures, and b) a multi atlas based correction technique to reduce errors due to misregistration. High-resolution gray matter labels were constructed using the super resolution technique. When used for regional segmentation of the gray matter of older adults, the new gray matter labels of the showed high overlap, high geometric correlation, and low dissimilarity with the manually edited reference labels, demonstrating that there is a high agreement between the new labels and the manually edited Freesurfer labels. Finally, this thesis presents the development of a comprehensive array of gyral-based, cytoarchitecture-based, and functional connectivity-based gray matter labels in MIITRA space utilizing the aforementioned techniques. These labels include gyral-based, cytoarchitecture-based, and functional connectivity-based labels which will enhance the functionality of the MIITRA atlas. The new labels will also enhance the interoperability of MIITRA with the source atlases.
Show less