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Little is known about how cognitive and brain aging patterns differ in older adults with autism spectrum disorder (ASD). However, recent evidence suggests that individuals with ASD may be at greater risk of pathological aging conditions than their neurotypical (NT) counterparts. A growing body of research indicates that older adults with ASD may experience accelerated cognitive decline and neurodegeneration as they age, although studies are limited by their cross-sectional design in a population with strong age-cohort effects. Studying aging in ASD and identifying biomarkers to predict atypical aging is important because the population of older individuals with ASD is growing. Understanding the unique challenges faced as autistic adults age is necessary to develop treatments to improve quality of life and preserve independence. In this study, a longitudinal design was used to characterize cognitive and brain aging trajectories in ASD as a function of autistic trait severity. Principal components analysis (PCA) was used to derive a cognitive metric that best explains performance variability on tasks measuring memory ability and executive function. The slope of the integrated persistent feature (SIP) was used to quantify functional connectivity; the SIP is a novel, threshold-free graph theory metric which summarizes the speed of information diffusion in the brain. Longitudinal mixed models were using to predict cognitive and brain aging trajectories (measured via the SIP) as a function of autistic trait severity, sex, and their interaction. The sensitivity of the SIP was also compared with traditional graph theory metrics. It was hypothesized that older adults with ASD would experience accelerated cognitive and brain aging and furthermore, age-related changes in brain network topology would predict age-related changes in cognitive performance.
For both cognitive and brain aging, autistic traits and sex interacted to predict trajectories, such that older men with high autistic traits were most at risk for poorer outcomes. In men with autism, variability in SIP scores across time points trended toward predicting cognitive aging trajectories. Findings also suggested that autistic traits are more sensitive to differences in brain aging than diagnostic group and that the SIP is more sensitive to brain aging trajectories than other graph theory metrics. However, further research is required to determine how physiological biomarkers such as the SIP are associated with cognitive outcomes.
ContributorsSullivan, Georgia (Author) / Braden, Blair (Thesis advisor) / Kodibagkar, Vikram (Thesis advisor) / Schaefer, Sydney (Committee member) / Wang, Yalin (Committee member) / Arizona State University (Publisher)
Created2022
Description
The purpose of this study was to determine the feasibility of a mindfulness-based intervention among pregnant women (12-20 weeks’ gestation) using a mobile meditation app, Calm. This study involved 100 participants who were recruited nationally due to the COVID-19 pandemic. This study was reviewed and approved by the Institutional Review Board of Arizona State University (STUDY STUDY00010467). All participants were provided an informed consent document and provided electronic consent prior to enrollment and participation in this study. This study was a randomized, controlled trial (trial registration: ClinicalTrials.gov NCT04264910). Participants randomized to the intervention group were asked to participate in a minimum of 10 minutes of daily meditation using a mindfulness meditation mobile app (i.e., Calm) for the duration of their pregnancy. Participants randomized to the standard of care control group were given access to the app after they gave birth. Both the intervention and control groups were administered surveys that measured feasibility outcomes, perceived stress, mindfulness, self-compassion, impact from COVID-19, pregnancy-related anxiety, depression, emotional regulation, sleep, and childbirth experience at four time points: baseline (12-20 weeks gestation), midline (24 weeks gestation), postintervention (36 weeks gestation), and follow-up survey (3-5 weeks postpartum). Data is currently being analyzed for publication.
ContributorsLister, Haily (Author) / Huberty, Jennifer (Thesis director) / Larkey, Linda (Committee member) / School of Mathematical and Natural Sciences (Contributor) / Barrett, The Honors College (Contributor)
Created2021-05
Description
As robots become increasingly integrated into the environments, they need to learn how to interact with the objects around them. Many of these objects are articulated with multiple degrees of freedom (DoF). Multi-DoF objects have complex joints that require specific manipulation orders, but existing methods only consider objects with a single joint. To capture the joint structure and manipulation sequence of any object, I introduce the "Object Kinematic State Machines" (OKSMs), a novel representation that models the kinematic constraints and manipulation sequences of multi-DoF objects. I also present Pokenet, a deep neural network architecture that estimates the OKSMs from the sequence of point cloud data of human demonstrations. I conduct experiments on both simulated and real-world datasets to validate my approach. First, I evaluate the modeling of multi-DoF objects on a simulated dataset, comparing against the current state-of-the-art method. I then assess Pokenet's real-world usability on a dataset collected in my lab, comprising 5,500 data points across 4 objects. Results showcase that my method can successfully estimate joint parameters of novel multi-DoF objects with over 25% more accuracy on average than prior methods.
ContributorsGUPTA, ANMOL (Author) / Gopalan, Nakul (Thesis advisor) / Zhang, Yu (Committee member) / Wang, Yalin (Committee member) / Arizona State University (Publisher)
Created2024
Description
In today's data-driven world, privacy is a significant concern. It is crucial to preserve the privacy of sensitive information while visualizing data. This thesis aims to develop new techniques and software tools that support Vega-Lite visualizations while maintaining privacy. Vega-Lite is a visualization grammar based on Wilkinson's grammar of graphics. The project extends Vega-Lite to incorporate privacy algorithms such as k-anonymity, l-diversity, t-closeness, and differential privacy. This is done by using a unique multi-input loop module logic that generates combinations of attributes as a new anonymization method. Differential privacy is implemented by adding controlled noise (Laplace or Exponential) to the sensitive columns in the dataset. The user defines custom rules in the JSON schema, mentioning the privacy methods and the sensitive column. The schema is validated using Another JSON Validation library, and these rules help identify the anonymization techniques to be performed on the dataset before sending it back to the Vega-Lite visualization server. Multiple datasets satisfying the privacy requirements are generated, and their utility scores are provided so that the user can trade-off between privacy and utility on the datasets based on their requirements. The interface developed is user-friendly and intuitive and guides users in using it. It provides appropriate feedback on the privacy-preserving visualizations generated through various utility metrics. This application is helpful for technical or domain experts across multiple domains where privacy is a big concern, such as medical institutions, traffic and urban planning, financial institutions, educational records, and employer-employee relations. This project is novel as it provides a one-stop solution for privacy-preserving visualization. It works on open-source software, Vega-Lite, which several organizations and users use for business and educational purposes.
ContributorsSekar, Manimozhi (Author) / Bryan, Chris (Thesis advisor) / Wang, Yalin (Committee member) / Cao, Zhichao (Committee member) / Arizona State University (Publisher)
Created2024
Description
Image denoising, a fundamental task in computer vision, poses significant challenges due to its inherently inverse and ill-posed nature. Despite advancements in traditional methods and supervised learning approaches, particularly in medical imaging such as Medical Resonance Imaging (MRI) scans, the reliance on paired datasets and known noise distributions remains a practical hurdle. Recent progress in noise statistical independence theory and diffusion models has revitalized research interest, offering promising avenues for unsupervised denoising. However, existing methods often yield overly smoothed results or introduce hallucinated structures, limiting their clinical applicability. This thesis tackles the core challenge of progressing towards unsupervised denoising of MRI scans. It aims to retain intricate details without smoothing or introducing artificial structures, thus ensuring the production of high-quality MRI images. The thesis makes a three-fold contribution: Firstly, it presents a detailed analysis of traditional techniques, early machine learning algorithms for denoising, and new statistical-based models, with an extensive evaluation study on self-supervised denoising methods highlighting their limitations. Secondly, it conducts an evaluation study on an emerging class of diffusion-based denoising methods, accompanied by additional empirical findings and discussions on their effectiveness and limitations, proposing solutions to enhance their utility. Lastly, it introduces a novel approach, Unsupervised Multi-stage Ensemble Deep Learning with diffusion models for denoising MRI scans (MEDL). Leveraging diffusion models, this approach operates independently of signal or noise priors and incorporates weighted rescaling of multi-stage reconstructions to balance over-smoothing and hallucination tendencies. Evaluation using benchmark datasets demonstrates an average gain of 1dB and 2% in PSNR and SSIM metrics, respectively, over existing approaches.
ContributorsVora, Sahil (Author) / Li, Baoxin (Thesis advisor) / Wang, Yalin (Committee member) / Zhou, Yuxiang (Committee member) / Arizona State University (Publisher)
Created2024
Description
Unsupervised learning of time series data, also known as temporal clustering, is a challenging problem in machine learning. This thesis presents a novel algorithm, Deep Temporal Clustering (DTC), to naturally integrate dimensionality reduction and temporal clustering into a single end-to-end learning framework, fully unsupervised. The algorithm utilizes an autoencoder for temporal dimensionality reduction and a novel temporal clustering layer for cluster assignment. Then it jointly optimizes the clustering objective and the dimensionality reduction objective. Based on requirement and application, the temporal clustering layer can be customized with any temporal similarity metric. Several similarity metrics and state-of-the-art algorithms are considered and compared. To gain insight into temporal features that the network has learned for its clustering, a visualization method is applied that generates a region of interest heatmap for the time series. The viability of the algorithm is demonstrated using time series data from diverse domains, ranging from earthquakes to spacecraft sensor data. In each case, the proposed algorithm outperforms traditional methods. The superior performance is attributed to the fully integrated temporal dimensionality reduction and clustering criterion.
ContributorsMadiraju, NaveenSai (Author) / Liang, Jianming (Thesis advisor) / Wang, Yalin (Thesis advisor) / He, Jingrui (Committee member) / Arizona State University (Publisher)
Created2018
Description
College students experience a considerable amount of stress. Unmanaged stress is associated with poor academic performance, health risk behaviors (i.e., inadequate sleep and physical activity, alcohol consumption, poor dietary behaviors), and poor mental health. Coping with stress has become a priority among universities. The most tested stress-related programs to date have been mindfulness-based and face-to-face. These programs demonstrated significant improvements in stress, mindfulness, and self-compassion among college students. However, they may be burdensome to students as studies report low attendance and low compliance due to class conflicts or not enough time. Few interventions have used more advanced technologies (i.e., mobile apps) as a mode of delivery. The purpose of this study is to report adherence to a consumer-based mindfulness meditation mobile application (i.e., Calm) and test its effects on stress, mindfulness, and self-compassion in college students. We will also explore what the relationship is between mindfulness and health behaviors.
College students were recruited using fliers on college campus and social media. Eligible participants were randomized to one of two groups: (1) Intervention - meditate using Calm, 10 min/day for eight weeks and (2) Control – no participation in mindfulness practices (received the Calm application after 12-weeks). Stress, mindfulness, and self-compassion and health behaviors (i.e., sleep disturbance, alcohol consumption, physical activity, fruit and vegetable consumption) were measured using self-report. Outcomes were measured at baseline and week eight.
Of the 109 students that enrolled in the study, 41 intervention and 47 control participants were included in analysis. Weekly meditation participation averaged 38 minutes with 54% of participants completing at least half (i.e., 30 minutes) of meditations. Significant changes between groups were found in stress, mindfulness, and self-compassion (all P<0.001) in favor of the intervention group. A significant negative association (p<.001) was found between total mindfulness and sleep disturbance.
An eight-week consumer-based mindfulness meditation mobile application (i.e., Calm) was effective in reducing stress, improving mindfulness and self-compassion among undergraduate college students. Mobile applications may be a feasible, effective, and less burdensome way to reduce stress in college students.
College students were recruited using fliers on college campus and social media. Eligible participants were randomized to one of two groups: (1) Intervention - meditate using Calm, 10 min/day for eight weeks and (2) Control – no participation in mindfulness practices (received the Calm application after 12-weeks). Stress, mindfulness, and self-compassion and health behaviors (i.e., sleep disturbance, alcohol consumption, physical activity, fruit and vegetable consumption) were measured using self-report. Outcomes were measured at baseline and week eight.
Of the 109 students that enrolled in the study, 41 intervention and 47 control participants were included in analysis. Weekly meditation participation averaged 38 minutes with 54% of participants completing at least half (i.e., 30 minutes) of meditations. Significant changes between groups were found in stress, mindfulness, and self-compassion (all P<0.001) in favor of the intervention group. A significant negative association (p<.001) was found between total mindfulness and sleep disturbance.
An eight-week consumer-based mindfulness meditation mobile application (i.e., Calm) was effective in reducing stress, improving mindfulness and self-compassion among undergraduate college students. Mobile applications may be a feasible, effective, and less burdensome way to reduce stress in college students.
ContributorsGlissmann, Christine (Author) / Huberty, Jennifer (Thesis advisor) / Sebren, Ann (Committee member) / Larkey, Linda (Committee member) / Lee, Chong (Committee member) / Arizona State University (Publisher)
Created2018
Description
Background: Although childhood engagement in physical activity has received growing attention, most children still do not meet the recommended daily 60 minutes of moderate to vigorous physical activity [MVPA]. Children of ethnic minorities are less likely to meet the guidelines. Interventions have been implemented in various settings to increase child physical activity levels, yet these efforts have not yielded consistent results. The purpose of this study was to assess the preliminary effects of a community-based intervention on light physical activity and MVPA among 6-11 year old children. Methods: The present study was part of a larger study called Athletes for Life [AFL], a family-based, nutrition-education and physical activity intervention. The present study focused on physical activity data from the first completed cohort of participants (n=29). This study was a randomized control trial in which participating children were randomized into a control (n=14) or intervention (n=15) group. Participants wore accelerometers at two time points. Intervention strategies were incorporated to increase child habitual physical activity. Analyses of covariance were performed to test for post 12-week differences between both groups on the average minutes of light physical activity and MVPA minutes per day.
Results: The accelerometer data demonstrated no significant difference in light physical activity or MVPA mean minutes per day between the groups. Few children reported engaging in activities sufficient for meeting the physical activity guidelines outside the AFL program. Of the 119 total distributed child physical activity tracker sheets (7 per family), 55 were returned. Of the 55 returned physical activity tracker sheets, parents reported engaging in physical activity with their children only 7 times outside of the program over seven weeks.
Conclusion: The combined intervention strategies implemented throughout the 12-week study did not appear to be effective at increasing habitual mean minutes per day spent engaging in light and MVPA among children beyond the directed program. Methodological limitations and low adherence to intervention strategies may partially explain these findings. Further research is needed to test successful strategies within community programs to increase habitual light physical activity and MVPA among 6-11 year old children.
Results: The accelerometer data demonstrated no significant difference in light physical activity or MVPA mean minutes per day between the groups. Few children reported engaging in activities sufficient for meeting the physical activity guidelines outside the AFL program. Of the 119 total distributed child physical activity tracker sheets (7 per family), 55 were returned. Of the 55 returned physical activity tracker sheets, parents reported engaging in physical activity with their children only 7 times outside of the program over seven weeks.
Conclusion: The combined intervention strategies implemented throughout the 12-week study did not appear to be effective at increasing habitual mean minutes per day spent engaging in light and MVPA among children beyond the directed program. Methodological limitations and low adherence to intervention strategies may partially explain these findings. Further research is needed to test successful strategies within community programs to increase habitual light physical activity and MVPA among 6-11 year old children.
ContributorsQuezada, Blanca (Author) / Crespo, Noe (Thesis advisor) / Huberty, Jennifer (Committee member) / Vega-Lopez, Sonia (Committee member) / Arizona State University (Publisher)
Created2015
Description
Yersinia enterocolitica is a major foodborne pathogen found worldwide that causes approximately 87,000 human cases and approximately 1,100 hospitalizations per year in the United States. Y. enterocolitica is a very unique pathogen with the domesticated pig acting as the main animal reservoir for pathogenic bio/serotypes, and as the primary source of human infection. Similar to other gastrointestinal infections, Yersinia enterocolitica is known to trigger autoimmune responses in humans. The most frequent complication associated with Y. enterocolitica is reactive arthritis - an aseptic, asymmetrical inflammation in the peripheral and axial joints, most frequently occurring as an autoimmune response in patients with the HLA-B27 histocompatability antigen. As a foodborne illness it may prove to be a reasonable explanation for some of the cases of arthritis observed in past populations that are considered to be of unknown etiology. The goal of this dissertation project was to study the relationship between the foodborne illness -Y. enterocolitica, and the incidence of arthritis in individuals with and without contact with the domesticated pig.
ContributorsBrown, Starletta (Author) / Hurtado, Ana M (Thesis advisor) / Chowell-Puente, Gerardo (Committee member) / Hill, Kim (Committee member) / Arizona State University (Publisher)
Created2015
Description
Understanding the complexity of temporal and spatial characteristics of gene expression over brain development is one of the crucial research topics in neuroscience. An accurate description of the locations and expression status of relative genes requires extensive experiment resources. The Allen Developing Mouse Brain Atlas provides a large number of in situ hybridization (ISH) images of gene expression over seven different mouse brain developmental stages. Studying mouse brain models helps us understand the gene expressions in human brains. This atlas collects about thousands of genes and now they are manually annotated by biologists. Due to the high labor cost of manual annotation, investigating an efficient approach to perform automated gene expression annotation on mouse brain images becomes necessary. In this thesis, a novel efficient approach based on machine learning framework is proposed. Features are extracted from raw brain images, and both binary classification and multi-class classification models are built with some supervised learning methods. To generate features, one of the most adopted methods in current research effort is to apply the bag-of-words (BoW) algorithm. However, both the efficiency and the accuracy of BoW are not outstanding when dealing with large-scale data. Thus, an augmented sparse coding method, which is called Stochastic Coordinate Coding, is adopted to generate high-level features in this thesis. In addition, a new multi-label classification model is proposed in this thesis. Label hierarchy is built based on the given brain ontology structure. Experiments have been conducted on the atlas and the results show that this approach is efficient and classifies the images with a relatively higher accuracy.
ContributorsZhao, Xinlin (Author) / Ye, Jieping (Thesis advisor) / Wang, Yalin (Thesis advisor) / Li, Baoxin (Committee member) / Arizona State University (Publisher)
Created2016