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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
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
Communications around sustainability have been found to be incongruent with eliciting the transformative change required to address global climate change and its' repercussions. Recent research has been exploring storytelling in sustainability, specifically with an emphasis on reflexive and emancipatory methods. These methods encourage embracing and contextualizing complexity and intend to target entire cognitive hierarchies. This study explores the possibility of using emancipatory and reflexive storytelling as a tool to change attitudes pertaining to the Valley Metro Light Rail, an example of a complex sustainability mitigation effort. I explore this in four steps: 1) Conducted a pre-survey to gauge preexisting attitudes and predispositions; 2) Provided a narrative that uses storytelling methodologies of reflexivity and emancipation through a story about the light rail; 3) Conducted a post-survey to gauge attitude shift resulting from the narrative intervention; 4) Facilitated a focus group discussion to examine impact qualitatively. These steps intended to provide an answer to the question: How does emancipatory and reflexive storytelling impact affective, cognitive and conative attitudes regarding local alternative transportation? By using tripartite attitude model, qualitative and quantitative analysis this paper determines that reflexive and emancipatory storytelling impacts attitudinal structures. The impact is marginal in the survey response, though the shift indicated a narrowing of participant responses towards one another, indicative of participants subscribing to emancipation and reflexivity of their held attitudes. From the group discussion, it was evident from qualitative responses that participants engaged in emancipating themselves from their held attitudes and reflected upon them. In doing so they engaged in collaboration to make suggestions and suggest actions to help those with experiences that differed from their own. Though this research doesn’t provide conclusive evidence, it opens the door for future research to assess these methodologies as a tool to elicit shared values, beliefs and norms, which are necessary for collective action leading to transformative change in response to global climate change.
ContributorsSwanson, Jake Ryan (Author) / Roseland, Mark (Thesis advisor) / Larson, Kelli (Committee member) / Calhoun, Craig (Committee member) / Schoon, Michael (Committee member) / Arizona State University (Publisher)
Created2023
Description
Understanding the dynamic interactions between humans and wildlife is essential to establishing sustainable wildlife-based ecotourism (WBE). Animal behavior exists within a complex feedback loop that affects overall ecosystem function, tourist satisfaction, and socioeconomics of local communities. However, the specific value that animal behavior plays in provisioning ecosystem services has not been thoroughly evaluated. People enjoy activities that facilitate intimate contact with animals, and there are many perceived benefits associated with these experiences, such as encouraging pro-environmental attitudes that can lead to greater motivation for conservation. There is extensive research on the effects that unregulated tourism activity can have on wildlife behavior, which include implications for population health and survival. Prior to COVID-19, WBE was developing rapidly on a global scale, and the pause in activity caused by the pandemic gave natural systems the chance to recover from environmental damage from over-tourism and provided insights into how tourism could be less impactful in the future. Until now it has been undetermined how changes in animal behavior can alter the relationships and socioeconomics of this multidimensional system. This dissertation provides a thorough exploration of the behavioral, ecological, and economic parameters required to model biosocial interactions and feedbacks within the whale watching system in Las Perlas Archipelago, Panama. Through observational data collected in the field, this project assessed how unmanaged whale watching activity is affecting the behavior of Humpback whales in the area as well as the socioeconomic and conservation contributions of the industry. Additionally, it is necessary to consider what a sustainable form of wildlife tourism might be, and whether the incorporation of technology will help enhance visitor experience while reducing negative impacts on wildlife. To better ascertain whether this concept of this integration would be favorably viewed, a sample of individuals was surveyed about their experiences about using technology to enhance their interactions with nature. This research highlights the need for more deliberate identification and incorporation of the perceptions of all stakeholders (wildlife included) to develop a less-impactful WBE industry that provides people with opportunities to establish meaningful relationships with nature that motivate them to help meet the conservation challenges of today.
ContributorsSurrey, Katie (Author) / Gerber, Leah (Thesis advisor) / Guzman, Hector (Committee member) / Minteer, Ben (Committee member) / Schoon, Michael (Committee member) / Arizona State University (Publisher)
Created2023
Description
University-level sustainability education in Western academia attempts to focus on eliminating future harm to people and the planet. However, Western academia as an institution upholds systems of oppression and reproduces settler colonialism. This reproduction is antithetical to sustainability goals as it continues patterns of Indigenous erasure and extractive relationships to the Land that perpetuate violence towards people and the planet. Sustainability programs, however, offer several frameworks, including resilience, that facilitate critical interrogations of social-ecological systems. In this thesis, I apply the notion of resilience to the perpetuation of settler colonialism within university-level sustainability education. Specifically, I ask: How is settler colonialism resilient in university-level sustainability education? How are, or could, sustainability programs in Western academic settings address settler colonialism? Through a series of conversational interviews with faculty and leadership from Arizona State University School of Sustainability, I analyzed how university-level sustainability education is both challenging and shaped by settler colonialism. These interviews focused on faculty perspectives on the topic and related issues; the interviews were analyzed using thematic coding in NVivo software. The results of this project highlight that many faculty members are already concerned with and focused on challenging settler colonialism, but that settler colonialism remains resilient in this system due to feedback loops at the personal level and reinforcing mechanisms at the institutional level. This research analyzes these feedback loops and reinforcing mechanisms, among others, and supports the call for anti-colonial and decolonial reconstruction of curriculum, as well as a focus on relationship building, shifting of mindset, and school-wide education on topics of white supremacy, settler colonialism, and systems of oppression in general.
ContributorsBills, Haven (Author) / Klinsky, Sonja (Thesis advisor) / Goebel, Janna (Committee member) / Schoon, Michael (Committee member) / Arizona State University (Publisher)
Created2022
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
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
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
Description
Transorbital surgery has gained recent notoriety due to its incorporation into endoscopic skull base surgery. The body of published literature on the field is cadaveric and observation. The pre-clinical studies are focused on the use of the endoscope only. Furthermore the methodology utilised in the published literature is inconsistent and does not embody the optimal principles of scientific experimentation. This body of work evaluates a minimally invasive novel surgical corridor - the transorbital approach - its validity in neurosurgical practice, as well as both qualitatively and quantitatively assessing available technological advances in a robust experimental fashion. While the endoscope is an established means of visualisation used in clinical transorbital surgery, the microscope has never been assessed with respect to the transorbital approach. This question is investigated here and the anatomical and surgical benefits and limitations of microscopic visualisation demonstrated. The comparative studies provide increased knowledge on specifics pertinent to neurosurgeons and other skull base specialists when planning pre-operatively, such as pathology location, involved anatomical structures, instrument maneuvrability and the advantages and disadvantages of the distinct visualisation technologies. This is all with the intention of selecting the most suitable surgical approach and technology, specific to the patient, pathology and anatomy, so as to perform the best surgical procedure. The research findings illustrated in this body of work are diverse, reproducible and applicable. The transorbital surgical corridor has substantive potential for access to the anterior cranial fossa and specific surgical target structures. The neuroquantitative metrics investigated confirm the utility and benefits specific to the respective visualisation technologies i.e. the endoscope and microscope. The most appropriate setting wherein the approach should be used is also discussed. The transorbital corridor has impressive potential, can utilise all available technological advances, promotes multi-disciplinary co-operation and learning amongst clinicians and ultimately, is a means of improving operative patient care.
ContributorsHoulihan, Lena Mary (Author) / Preul, Mark C. (Thesis advisor) / Vernon, Brent (Thesis advisor) / O' Sullivan, Michael G.J. (Committee member) / Lawton, Michael T. (Committee member) / Santarelli, Griffin (Committee member) / Smith, Brian (Committee member) / Arizona State University (Publisher)
Created2021