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- All Subjects: Alzheimer's Disease
- Creators: College of Health Solutions
- Creators: Wang, Yalin
Description
Alzheimer's Disease (AD) is the most common form of dementia observed in elderly patients and has significant social-economic impact. There are many initiatives which aim to capture leading causes of AD. Several genetic, imaging, and biochemical markers are being explored to monitor progression of AD and explore treatment and detection options. The primary focus of this thesis is to identify key biomarkers to understand the pathogenesis and prognosis of Alzheimer's Disease. Feature selection is the process of finding a subset of relevant features to develop efficient and robust learning models. It is an active research topic in diverse areas such as computer vision, bioinformatics, information retrieval, chemical informatics, and computational finance. In this work, state of the art feature selection algorithms, such as Student's t-test, Relief-F, Information Gain, Gini Index, Chi-Square, Fisher Kernel Score, Kruskal-Wallis, Minimum Redundancy Maximum Relevance, and Sparse Logistic regression with Stability Selection have been extensively exploited to identify informative features for AD using data from Alzheimer's Disease Neuroimaging Initiative (ADNI). An integrative approach which uses blood plasma protein, Magnetic Resonance Imaging, and psychometric assessment scores biomarkers has been explored. This work also analyzes the techniques to handle unbalanced data and evaluate the efficacy of sampling techniques. Performance of feature selection algorithm is evaluated using the relevance of derived features and the predictive power of the algorithm using Random Forest and Support Vector Machine classifiers. Performance metrics such as Accuracy, Sensitivity and Specificity, and area under the Receiver Operating Characteristic curve (AUC) have been used for evaluation. The feature selection algorithms best suited to analyze AD proteomics data have been proposed. The key biomarkers distinguishing healthy and AD patients, Mild Cognitive Impairment (MCI) converters and non-converters, and healthy and MCI patients have been identified.
ContributorsDubey, Rashmi (Author) / Ye, Jieping (Thesis advisor) / Wang, Yalin (Committee member) / Wu, Tong (Committee member) / Arizona State University (Publisher)
Created2012
Description
Current research attempts to address the increasing prevalence of Alzheimer’s disease (AD) by
finding causes and treatments to revert misfolded proteins and ceasing progression due to
Diabetes Mellitus (DM). The goal of this review is to highlight the contribution of misfolded Tau
protein to AD through neurofibrillary tangles solely, and in conjunction with known causative
agents such as 𝛽-amyloid protein. Finally, it interprets the association of Tau with DM and its
effects on the progression of AD.
finding causes and treatments to revert misfolded proteins and ceasing progression due to
Diabetes Mellitus (DM). The goal of this review is to highlight the contribution of misfolded Tau
protein to AD through neurofibrillary tangles solely, and in conjunction with known causative
agents such as 𝛽-amyloid protein. Finally, it interprets the association of Tau with DM and its
effects on the progression of AD.
ContributorsTavani, Jennifer Renee (Author) / Houtchens, Jason (Thesis director) / Lisenbee, Cayle (Committee member) / College of Health Solutions (Contributor) / Barrett, The Honors College (Contributor)
Created2020-05
Description
Alzheimer’s disease (AD) is a neurodegenerative disease resulting in loss of cognitive function and is not considered part of the typical aging process. Recently, research is being conducted to study environmental effects on AD because the exact molecular mechanisms behind AD are not known. The associations between various toxins and AD have been mixed and unclear. In order to better understand the role of the environment and toxic substances on AD, we conducted a literature review and geospatial analysis of environmental, specifically wastewater, contaminants that have biological plausibility for increasing risk of development or exacerbation of AD. This literature review assisted us in selecting 10 wastewater toxic substances that displayed a mixed or one-sided relationship with the symptoms or prevalence of Alzheimer’s for our data analysis. We utilized data of toxic substances in wastewater treatment plants and compared them to the crude rate of AD in the different Census regions of the United States to test for possible linear relationships. Using data from the Targeted National Sewage Sludge Survey (TNSSS) and the Centers for Disease Control and Prevention (CDC), we developed an application using R Shiny to allow users to interactively visualize both datasets as choropleths of the United States and understand the importance of this area of research. Pearson’s correlation coefficient was calculated resulting in arsenic and cadmium displaying positive linear correlations with AD. Other analytes from this statistical analysis demonstrated mixed correlations with AD. This application and data analysis serve as a model in the methodology for further geospatial analysis on AD. Further data analysis and visualization at a lower level in terms of scope is necessary for more accurate and reliable evidence of a causal relationship between the wastewater substance analytes and AD.
GitHub Repository: https://github.com/komal-agrawal/AD_GIS.git
GitHub Repository: https://github.com/komal-agrawal/AD_GIS.git
ContributorsAgrawal, Komal (Author) / Scotch, Matthew (Thesis director) / Halden, Rolf (Committee member) / College of Health Solutions (Contributor, Contributor) / Barrett, The Honors College (Contributor)
Created2019-05
Description
Objective: To examine the change in caregiver burden, stress, and heart rate variability (HRV) scores when family caregivers (FCG) of patients with Alzheimer’s disease (AD) used heart rate variability biofeedback (HRVB). Additional factors that could potentially moderate the effects of HRVB, such as education and income level, were separately examined.
Methods: An 8-week HRVB intervention was compared to a music listening control (MLC) condition for 30 family caregivers (FCGs) of individuals with Alzheimer’s disease (AD) (and related dementias: ADRD). Analysis per education and income level were separately conducted.
Results: The HRVB intervention with higher education and lower-income individuals showed more favorable HRV outcomes (noted to be slightly decreased in higher-income individuals). Perceived stress was reduced for both intervention groups, and caregiver burden levels decreased for all income groups, particularly in those with lower incomes.
Discussion: Future researchers should increase the sample size, explore stratification based on income and education levels, and consider gender-based divisions, as these factors could yield valuable insights.
ContributorsMathews, Megan (Author) / Vizcaino, Maricarmen (Thesis director) / Larkey, Linda (Committee member) / James, Darith (Committee member) / Barrett, The Honors College (Contributor) / College of Health Solutions (Contributor)
Created2023-12
Description
Statistical Shape Modeling is widely used to study the morphometrics of deformable objects in computer vision and biomedical studies. There are mainly two viewpoints to understand the shapes. On one hand, the outer surface of the shape can be taken as a two-dimensional embedding in space. On the other hand, the outer surface along with its enclosed internal volume can be taken as a three-dimensional embedding of interests. Most studies focus on the surface-based perspective by leveraging the intrinsic features on the tangent plane. But a two-dimensional model may fail to fully represent the realistic properties of shapes with both intrinsic and extrinsic properties. In this thesis, severalStochastic Partial Differential Equations (SPDEs) are thoroughly investigated and several methods are originated from these SPDEs to try to solve the problem of both two-dimensional and three-dimensional shape analyses. The unique physical meanings of these SPDEs inspired the findings of features, shape descriptors, metrics, and kernels in this series of works. Initially, the data generation of high-dimensional shapes, here, the tetrahedral meshes, is introduced. The cerebral cortex is taken as the study target and an automatic pipeline of generating the gray matter tetrahedral mesh is introduced. Then, a discretized Laplace-Beltrami operator (LBO) and a Hamiltonian operator (HO) in tetrahedral domain with Finite Element Method (FEM) are derived. Two high-dimensional shape descriptors are defined based on the solution of the heat equation and Schrödinger’s equation. Considering the fact that high-dimensional shape models usually contain massive redundancies, and the demands on effective landmarks in many applications, a Gaussian process landmarking on tetrahedral meshes is further studied. A SIWKS-based metric space is used to define a geometry-aware Gaussian process. The study of the periodic potential diffusion process further inspired the idea of a new kernel call the geometry-aware convolutional kernel. A series of Bayesian learning methods are then introduced to tackle the problem of shape retrieval and classification. Experiments of every single item are demonstrated. From the popular SPDE such as the heat equation and Schrödinger’s equation to the general potential diffusion equation and the specific periodic potential diffusion equation, it clearly shows that classical SPDEs play an important role in discovering new features, metrics, shape descriptors and kernels. I hope this thesis could be an example of using interdisciplinary knowledge to solve problems.
ContributorsFan, Yonghui (Author) / Wang, Yalin (Thesis advisor) / Lepore, Natasha (Committee member) / Turaga, Pavan (Committee member) / Yang, Yezhou (Committee member) / Arizona State University (Publisher)
Created2021
Description
Alzheimer’s disease (AD) and Frontotemporal lobe dementia (FTLD) are types of dementia that have distinct differences. To help identify some of the neural differences, researchers use diffusion tensor imaging (DTI) techniques to assist with diagnosing patients and track progression over time. The major objective of this experiment was to use the advanced diffusion tensor imaging techniques of Fractional Anisotropy (FA) and Free water (FW) to help differentiate between AD and FTLD neurodegeneration. The scope of this experiment was to examine literature research on AD and FTLD by gathering the mean values of (FA) and (FW) to identify diffusivity susceptibility in the specific brain regions of the Uncinate Fasciculus (UF) and the Superior Temporal Gyrus (STG). The methods used were the Alzheimer’s Disease Neuroimaging Initiative (ADNI) and the Frontotemporal Lobe Degenerative Neuroimaging Initiative (FTLD): These data repositories provide researchers with study data to define the progression of AD and FTLD. Next, an imaging analysis was used to calculate the average FA and FW through each slice of the brain regions UF and STG in standard space. Then FreeSurfer segmented Superior Temporal Gyrus and the JHU ICBM Atlas of the Uncinate Fasciculus were used as a set of tools for analysis and visualization of structural and functional brain imaging data for processing the cross-sectional and longitudinal data. We calculated 95% Confidence intervals for mean FW and FA at each slice and direction across 21 participants within each dementia group to determine regions of overlap and nonoverlap. Results indicated that for the FA and FW graphs in the x and z directions among UF and STG regions, there were more non-overlap regions between the AD and FTLD in the FW graphs across x and z-directions in particular the UF. Our results indicate that there may be concomitant decline in white and gray matter regions in dementia, and FW may be more sensitive detecting AD related neurodegeneration in the UF and STG regions.
ContributorsMalone, Joshua (Author) / Ofori, Edward (Thesis director) / Schaefer, Sydney (Committee member) / Barrett, The Honors College (Contributor) / College of Health Solutions (Contributor)
Created2022-05
Description
Alzheimer’s disease (AD), is a chronic neurodegenerative disease that usually starts slowly and gets worse over time. It is the cause of 60% to 70% of cases of dementia. There is growing interest in identifying brain image biomarkers that help evaluate AD risk pre-symptomatically. High-dimensional non-linear pattern classification methods have been applied to structural magnetic resonance images (MRI’s) and used to discriminate between clinical groups in Alzheimers progression. Using Fluorodeoxyglucose (FDG) positron emission tomography (PET) as the pre- ferred imaging modality, this thesis develops two independent machine learning based patch analysis methods and uses them to perform six binary classification experiments across different (AD) diagnostic categories. Specifically, features were extracted and learned using dimensionality reduction and dictionary learning & sparse coding by taking overlapping patches in and around the cerebral cortex and using them as fea- tures. Using AdaBoost as the preferred choice of classifier both methods try to utilize 18F-FDG PET as a biological marker in the early diagnosis of Alzheimer’s . Addi- tional we investigate the involvement of rich demographic features (ApoeE3, ApoeE4 and Functional Activities Questionnaires (FAQ)) in classification. The experimental results on Alzheimer’s Disease Neuroimaging initiative (ADNI) dataset demonstrate the effectiveness of both the proposed systems. The use of 18F-FDG PET may offer a new sensitive biomarker and enrich the brain imaging analysis toolset for studying the diagnosis and prognosis of AD.
ContributorsSrivastava, Anant (Author) / Wang, Yalin (Thesis advisor) / Bansal, Ajay (Thesis advisor) / Liang, Jianming (Committee member) / Arizona State University (Publisher)
Created2017
Description
The apolipoprotein E (APOE) e4 genotype is the most prevalent known genetic risk factor for Alzheimer's disease (AD). In this paper, we examined the longitudinal effect of APOE e4 on hippocampal morphometry in Alzheimer's Disease Neuroimaging Initiative (ADNI). Generally, atrophy of hippocampus has more chance occurs in AD patients who carrying the APOE e4 allele than those who are APOE e4 noncarriers. Also, brain structure and function depend on APOE genotype not just for Alzheimer's disease patients but also in health elderly individuals, so APOE genotyping is considered critical in clinical trials of Alzheimer's disease. We used a large sample of elderly participants, with the help of a new automated surface registration system based on surface conformal parameterization with holomorphic 1-forms and surface fluid registration. In this system, we automatically segmented and constructed hippocampal surfaces from MR images at many different time points, such as 6 months, 1- and 2-year follow up. Between the two different hippocampal surfaces, we did the high-order correspondences, using a novel inverse consistent surface fluid registration method. At each time point, using Hotelling's T^2 test, we found significant morphological deformation in APOE e4 carriers relative to noncarriers in the entire cohort as well as in the non-demented (pooled MCI and control) subjects, affecting the left hippocampus more than the right, and this effect was more pronounced in e4 homozygotes than heterozygotes.
ContributorsLi, Bolun (Author) / Wang, Yalin (Thesis advisor) / Maciejewski, Ross (Committee member) / Liang, Jianming (Committee member) / Arizona State University (Publisher)
Created2015