Matching Items (110)
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Description
Beta-Amyloid(Aβ) plaques and tau protein tangles in the brain are now widely recognized as the defining hallmarks of Alzheimer’s disease (AD), followed by structural atrophy detectable on brain magnetic resonance imaging (MRI) scans. However, current methods to detect Aβ/tau pathology are either invasive (lumbar puncture) or quite costly and not widely available (positron emission tomography (PET)). And one of the particular neurodegenerative regions is the hippocampus to which the influence of Aβ/tau on has been one of the research projects focuses in the AD pathophysiological progress.
In this dissertation, I proposed three novel machine learning and statistical models to examine subtle aspects of the hippocampal morphometry from MRI that are associated with Aβ /tau burden in the brain, measured using PET images. The first model is a novel unsupervised feature reduction model to generate a low-dimensional representation of hippocampal morphometry for each individual subject, which has superior performance in predicting Aβ/tau burden in the brain. The second one is an efficient federated group lasso model to identify the hippocampal subregions where atrophy is strongly associated with abnormal Aβ/Tau. The last one is a federated model for imaging genetics, which can identify genetic and transcriptomic influences on hippocampal morphometry. Finally, I stated the results of these three models that have been published or submitted to peer-reviewed conferences and journals.
ContributorsWu, Jianfeng (Author) / Wang, Yalin (Thesis advisor) / Li, Baoxin (Committee member) / Liang, Jianming (Committee member) / Wang, Junwen (Committee member) / Wu, Teresa (Committee member) / Arizona State University (Publisher)
Created2022
![189274-Thumbnail Image.png](https://d1rbsgppyrdqq4.cloudfront.net/s3fs-public/styles/width_400/public/2023-08/189274-Thumbnail%20Image.png?versionId=P.lmJyttEUQhj_oKTGcxHjot2098A6or&X-Amz-Content-Sha256=UNSIGNED-PAYLOAD&X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Credential=AKIASBVQ3ZQ42ZLA5CUJ/20240616/us-west-2/s3/aws4_request&X-Amz-Date=20240616T120725Z&X-Amz-SignedHeaders=host&X-Amz-Expires=120&X-Amz-Signature=2778a6e8a8a04924169c43e53a9e359fd69e147d7b714bf865e19d51c74420b4&itok=OhsFPShr)
Description
Structural Magnetic Resonance Imaging analysis is a vital component in the study of Alzheimer’s Disease pathology and several techniques exist as part of the existing research conducted. In particular, volumetric approaches in this field are known to be beneficial due to the increased capability to express morphological characteristics when compared to manifold methods. To aid in the improvement of the field, this paper aims to propose an intrinsic volumetric conic system that can be applied to bounded volumetric meshes to enable a more effective study of subjects. The computation of the metric involves the use of heat kernel theory and conformal parameterization on genus-0 surfaces extended to a volumetric domain. Additionally, this paper also explores the use of the ’TetCNN’ architecture on the classification of hippocampal tetrahedral meshes to detect features that correspond to Alzheimer’s indicators. The model tested was able to achieve remarkable results with a measured classification accuracy of above 90% in the task of differentiating between subjects diagnosed with Alzheimer’s and normal control subjects.
ContributorsGeorge, John Varghese (Author) / Wang, Yalin (Thesis advisor) / Hansford, Dianne (Committee member) / Gupta, Vikash (Committee member) / Arizona State University (Publisher)
Created2023
Description
Weight stigma is a prevalent issue that has detrimental effects on health for both adolescents and parents. Adolescents are in a formative stage of life, so it is important to understand how parents may impact adolescents’ own experience with weight stigma. Past research has examined adolescent coping, body image, and associated stigma in the context of the parent-child relationship. This cross-sectional study examined self-reported weight stigma experience and internalization within 42 parent/adolescent dyads to provide greater understanding of how adolescents and parents are experiencing and internalizing weight stigma independently and transversely.
ContributorsMillett, Emma (Author) / McEntee, Mindy (Thesis director) / Adams, Marc (Committee member) / Barrett, The Honors College (Contributor) / College of Health Solutions (Contributor)
Created2022-12
![168404-Thumbnail Image.png](https://d1rbsgppyrdqq4.cloudfront.net/s3fs-public/styles/width_400/public/2022-08/168404-Thumbnail%20Image.png?versionId=2Tz_0X2bFoBuyGz8EAvbhKVdswCkgM_l&X-Amz-Content-Sha256=UNSIGNED-PAYLOAD&X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Credential=AKIASBVQ3ZQ42ZLA5CUJ/20240616/us-west-2/s3/aws4_request&X-Amz-Date=20240616T025721Z&X-Amz-SignedHeaders=host&X-Amz-Expires=120&X-Amz-Signature=48e0ac918c14e05d7b158473abf786a6a56e33fdf7636993cbb5f8ae9e4ae7ad&itok=1CLJNF1j)
Description
Communicating with computers through thought has been a remarkable achievement in recent years. This was made possible by the use of Electroencephalography (EEG). Brain-computer interface (BCI) relies heavily on Electroencephalography (EEG) signals for communication between humans and computers. With the advent ofdeep learning, many studies recently applied these techniques to EEG data to perform various tasks like emotion recognition, motor imagery classification, sleep analysis, and many more.
Despite the rise of interest in EEG signal classification, very few studies have explored the MindBigData dataset, which collects EEG signals recorded at the stimulus of seeing a digit and thinking about it. This dataset takes us closer to realizing the idea of mind-reading or communication via thought. Thus classifying these signals into the respective digit that the user thinks about is a challenging task. This serves as a motivation to study this dataset and apply existing deep learning techniques to study it.
Given the recent success of transformer architecture in different domains like Computer Vision and Natural language processing, this thesis studies transformer architecture for EEG signal classification. Also, it explores other deep learning
techniques for the same. As a result, the proposed classification pipeline achieves comparable performance with the existing methods.
ContributorsMuglikar, Omkar Dushyant (Author) / Wang, Yalin (Thesis advisor) / Liang, Jianming (Committee member) / Venkateswara, Hemanth (Committee member) / Arizona State University (Publisher)
Created2021
![161945-Thumbnail Image.png](https://d1rbsgppyrdqq4.cloudfront.net/s3fs-public/styles/width_400/public/2021-11/161945-Thumbnail%20Image.png?versionId=z5CQRIfaX2JVznCjIQLdH8ESZ._dNAjZ&X-Amz-Content-Sha256=UNSIGNED-PAYLOAD&X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Credential=AKIASBVQ3ZQ42ZLA5CUJ/20240615/us-west-2/s3/aws4_request&X-Amz-Date=20240615T235520Z&X-Amz-SignedHeaders=host&X-Amz-Expires=120&X-Amz-Signature=f35df4023d9495d80cb0f210f3a4cf85325f57a9b98448b3162179183fd263d1&itok=GONxiotc)
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
Graph matching is a fundamental but notoriously difficult problem due to its NP-hard nature, and serves as a cornerstone for a series of applications in machine learning and computer vision, such as image matching, dynamic routing, drug design, to name a few. Although there has been massive previous investigation on high-performance graph matching solvers, it still remains a challenging task to tackle the matching problem under real-world scenarios with severe graph uncertainty (e.g., noise, outlier, misleading or ambiguous link).In this dissertation, a main focus is to investigate the essence and propose solutions to graph matching with higher reliability under such uncertainty. To this end, the proposed research was conducted taking into account three perspectives related to reliable graph matching: modeling, optimization and learning. For modeling, graph matching is extended from typical quadratic assignment problem to a more generic mathematical model by introducing a specific family of separable function, achieving higher capacity and reliability. In terms of optimization, a novel high gradient-efficient determinant-based regularization technique is proposed in this research, showing high robustness against outliers. Then learning paradigm for graph matching under intrinsic combinatorial characteristics is explored. First, a study is conducted on the way of filling the gap between discrete problem and its continuous approximation under a deep learning framework. Then this dissertation continues to investigate the necessity of more reliable latent topology of graphs for matching, and propose an effective and flexible framework to obtain it. Coherent findings in this dissertation include theoretical study and several novel algorithms, with rich experiments demonstrating the effectiveness.
ContributorsYu, Tianshu (Author) / Li, Baoxin (Thesis advisor) / Wang, Yalin (Committee member) / Yang, Yezhou (Committee member) / Yang, Yingzhen (Committee member) / Arizona State University (Publisher)
Created2021
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Description
Graph matching is a fundamental but notoriously difficult problem due to its NP-hard nature, and serves as a cornerstone for a series of applications in machine learning and computer vision, such as image matching, dynamic routing, drug design, to name a few. Although there has been massive previous investigation on high-performance graph matching solvers, it still remains a challenging task to tackle the matching problem under real-world scenarios with severe graph uncertainty (e.g., noise, outlier, misleading or ambiguous link).In this dissertation, a main focus is to investigate the essence and propose solutions to graph matching with higher reliability under such uncertainty. To this end, the proposed research was conducted taking into account three perspectives related to reliable graph matching: modeling, optimization and learning. For modeling, graph matching is extended from typical quadratic assignment problem to a more generic mathematical model by introducing a specific family of separable function, achieving higher capacity and reliability. In terms of optimization, a novel high gradient-efficient determinant-based regularization technique is proposed in this research, showing high robustness against outliers. Then learning paradigm for graph matching under intrinsic combinatorial characteristics is explored. First, a study is conducted on the way of filling the gap between discrete problem and its continuous approximation under a deep learning framework. Then this dissertation continues to investigate the necessity of more reliable latent topology of graphs for matching, and propose an effective and flexible framework to obtain it. Coherent findings in this dissertation include theoretical study and several novel algorithms, with rich experiments demonstrating the effectiveness.
ContributorsYu, Tianshu (Author) / Li, Baoxin (Thesis advisor) / Wang, Yalin (Committee member) / Yang, Yezhou (Committee member) / Yang, Yingzhen (Committee member) / Arizona State University (Publisher)
Created2021
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Description
Alzheimer's disease (AD) is a neurodegenerative disease that damages the cognitive abilities of a patient. It is critical to diagnose AD early to begin treatment as soon as possible which can be done through biomarkers. One such biomarker is the beta-amyloid (Aβ) peptide which can be quantified using the centiloid (CL) scale. For identifying the Aβ biomarker, A deep learning model that can model AD progression by predicting the CL value for brain magnetic resonance images (MRIs) is proposed. Brain MRI images can be obtained through the Alzheimer's Disease Neuroimaging Initiative (ADNI) and Open Access Series of Imaging Studies (OASIS) datasets, however a single model cannot perform well on both datasets at once. Thus, A regularization-based continuous learning framework to perform domain adaptation on the previous model is also proposed which captures the latent information about the relationship between Aβ and AD progression within both datasets.
ContributorsTrinh, Matthew Brian (Author) / Wang, Yalin (Thesis advisor) / Liang, Jianming (Committee member) / Su, Yi (Committee member) / Arizona State University (Publisher)
Created2022
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Description
The Population Receptive Field (pRF) model is widely used to predict the location (retinotopy) and size of receptive fields on the visual space. Doing so allows for the creation of a mapping from locations in the visual field to the associated groups of neurons in the cortical region (within the visual cortex of the brain). However, using the pRF model is very time consuming. Past research has focused on the creation of Convolutional Neural Networks (CNN) to mimic the pRF model in a fraction of the time, and they have worked well under highly controlled conditions. However, these models have not been thoroughly tested on real human data. This thesis focused on adapting one of these CNNs to accurately predict the retinotopy of a real human subject using a dataset from the Human Connectome Project. The results show promise towards creating a fully functioning CNN, but they also expose new challenges that must be overcome before the model could be used to predict the retinotopy of new human subjects.
ContributorsBurgard, Braeden (Author) / Wang, Yalin (Thesis director) / Ta, Duyan (Committee member) / Barrett, The Honors College (Contributor) / School of International Letters and Cultures (Contributor) / Computer Science and Engineering Program (Contributor) / School of Mathematical and Statistical Sciences (Contributor)
Created2022-05
![189311-Thumbnail Image.png](https://d1rbsgppyrdqq4.cloudfront.net/s3fs-public/styles/width_400/public/2023-08/189311-Thumbnail%20Image.png?versionId=WAdVPOgaSGC1wKZmy_JfIjv9HK.fr.bD&X-Amz-Content-Sha256=UNSIGNED-PAYLOAD&X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Credential=AKIASBVQ3ZQ42ZLA5CUJ/20240615/us-west-2/s3/aws4_request&X-Amz-Date=20240615T094237Z&X-Amz-SignedHeaders=host&X-Amz-Expires=120&X-Amz-Signature=d2343856df3add97638e2b3e14beba50f14019b90bd2dfea57e58970782fbb20&itok=6Xjy1yIu)
The Association Between Time to Eat and Students Fruit & Vegetable Consumption, Selection, and Waste
Description
Background: Studies have examined student fruit/vegetable (FV) consumption, selection, and waste related to lunch duration and found that longer duration at lunch was associated with greater consumption, selection, and reduced waste. However, few studies have investigated the relationship between time to eat and FVs. The aim of this research is to analyze the relationship between objective time to students took to eat (“time to eat”) as it relates to their fruit and vegetable consumption, selection, and plate waste.in elementary, middle, and high schools. Methods: A secondary analysis of cross-sectional study of 37 Arizona schools to discover the differences in the selection, consumption, and waste of FVs from students (Full N = 2226, Elementary N = 630, Middle School N = 699, High School N = 897) using objective time to eat measures. Zero-inflated negative binomial regressions examined differences in FV grams selected, consumed, and wasted adjusted for sociodemographics including race, ethnicity, eligibility for free or reduced lunch, academic year, and sex and clustering for students within schools. Results are presented across school level (elementary, middle, and high school).
Results: The average time taken to eat ranged from 10-12 minutes for all students. The association of time to eat and lunch duration were not closely related (r=0.03, p = 0.172). In the count model for every additional minute spent, there was a 0.5% greater likelihood of selecting FVs for elementary kids among those who took any FVs. In the zero-inflated model, it was found that there was a statistically significant relationship between time spent eating and the selection of fruits and vegetables. For the total sample and high schoolers, a minute more of eating time was associated with a 4.3% and 8.8% greater odds of selecting FV. This means that longer eating time increased the likelihood of choosing fruits and vegetables. The results indicated that the longer students took to eat, the higher the likelihood of consuming more of FVs. Each 10 more minutes spent eating (i.e., time to eat) is associated with a 5% increase in grams of FV selected relative to mean (for those that chose FV) over 1 week this equates to 32 g increase of FV selected. However, for middle schoolers, the time to eat was not found to be significant in relation to the grams of fruits and vegetables consumed. There was some significance in the sociodemographic factors such as gender (all) and other (middle school). There was a relationship between time taken to eat and waste as a proportion for fruits and vegetables. For example, among those among the students who wasted something (as a proportion of selection), each additional 10 minutes of eating time was associated with a .6% decrease in waste relative to the mean (for those who chose fruits and vegetables) over a week, resulting in a decrease in waste percentage of 16.5%. Among high schoolers, males had a slightly higher odds of wasting a proportion of fruits and vegetables.
Conclusions: This study aimed to examine the association between the time students take to eat during lunch and their fruit and vegetable (FV) consumption, selection, and plate waste. The findings revealed that the time to eat was related to FV consumption, depending on the school level. However, it was not significantly associated with FV selection or waste. The study emphasized the need for further research on time to eat, distinguishing it from the duration of lunch. Longer lunch periods and adequate time could influence better food choices, increased FV consumption, and reduced waste. The study highlighted the importance of interventions and school policies promoting healthier food choices and providing sufficient time for students to eat. Future research should validate these findings and explore the impact of socialization opportunities on promoting healthier eating habits. Understanding the relationship between lunch duration, time to eat, and students' dietary behaviors can contribute to improved health outcomes and inform effective strategies in school settings.
ContributorsDandridge, Christina Marie (Author) / Adams, Marc (Thesis advisor) / Whisner, Corrie (Committee member) / Bruening, Meg (Committee member) / Arizona State University (Publisher)
Created2023