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Description
This dissertation constructs a new computational processing framework to robustly and precisely quantify retinotopic maps based on their angle distortion properties. More generally, this framework solves the problem of how to robustly and precisely quantify (angle) distortions of noisy or incomplete (boundary enclosed) 2-dimensional surface to surface mappings. This framework builds upon the Beltrami Coefficient (BC) description of quasiconformal mappings that directly quantifies local mapping (circles to ellipses) distortions between diffeomorphisms of boundary enclosed plane domains homeomorphic to the unit disk. A new map called the Beltrami Coefficient Map (BCM) was constructed to describe distortions in retinotopic maps. The BCM can be used to fully reconstruct the original target surface (retinal visual field) of retinotopic maps. This dissertation also compared retinotopic maps in the visual processing cascade, which is a series of connected retinotopic maps responsible for visual data processing of physical images captured by the eyes. By comparing the BCM results from a large Human Connectome project (HCP) retinotopic dataset (N=181), a new computational quasiconformal mapping description of the transformed retinal image as it passes through the cascade is proposed, which is not present in any current literature. The description applied on HCP data provided direct visible and quantifiable geometric properties of the cascade in a way that has not been observed before. Because retinotopic maps are generated from in vivo noisy functional magnetic resonance imaging (fMRI), quantifying them comes with a certain degree of uncertainty. To quantify the uncertainties in the quantification results, it is necessary to generate statistical models of retinotopic maps from their BCMs and raw fMRI signals. Considering that estimating retinotopic maps from real noisy fMRI time series data using the population receptive field (pRF) model is a time consuming process, a convolutional neural network (CNN) was constructed and trained to predict pRF model parameters from real noisy fMRI data
ContributorsTa, Duyan Nguyen (Author) / Wang, Yalin (Thesis advisor) / Lu, Zhong-Lin (Committee member) / Hansford, Dianne (Committee member) / Liu, Huan (Committee member) / Li, Baoxin (Committee member) / Arizona State University (Publisher)
Created2022
Description
Neural tissue is a delicate system comprised of neurons and their synapses, glial cells for support, and vasculature for oxygen and nutrient delivery. This complexity ultimately gives rise to the human brain, a system researchers have become increasingly interested in replicating for artificial intelligence purposes. Some have even gone so far as to use neuronal cultures as computing hardware, but utilizing an environment closer to a living brain means having to grapple with the same issues faced by clinicians and researchers trying to treat brain disorders. Most outstanding among these are the problems that arise with invasive interfaces. Optical techniques that use fluorescent dyes and proteins have emerged as a solution for noninvasive imaging with single-cell resolution in vitro and in vivo, but feeding in information in the form of neuromodulation still requires implanted electrodes. The implantation process of these electrodes damages nearby neurons and their connections, causes hemorrhaging, and leads to scarring and gliosis that diminish efficacy. Here, a new approach for noninvasive neuromodulation with high spatial precision is described. It makes use of a combination of ultrasound, high frequency acoustic energy that can be focused to submillimeter regions at significant depths, and electric fields, an effective tool for neuromodulation that lacks spatial precision when used in a noninvasive manner. The hypothesis is that, when combined in a specific manner, these will lead to nonlinear effects at neuronal membranes that cause cells only in the region of overlap to be stimulated. Computational modeling confirmed this combination to be uniquely stimulating, contingent on certain physical effects of ultrasound on cell membranes. Subsequent in vitro experiments led to inconclusive results, however, leaving the door open for future experimentation with modified configurations and approaches. The specific combination explored here is also not the only untested technique that may achieve a similar goal.
ContributorsNester, Elliot (Author) / Wang, Yalin (Thesis advisor) / Muthuswamy, Jitendran (Committee member) / Towe, Bruce (Committee member) / Arizona State University (Publisher)
Created2022
Description
Cocaine induces long-lasting changes in mesolimbic ‘reward’ circuits of the brain after cessation of use. These lingering changes include the neuronal plasticity that is thought to underlie the chronic relapsing nature of substance use disorders. Genes involved in neuronal plasticity also encode circular RNAs (circRNAs), which are stable, non-coding RNAs formed through the back-splicing of pre-mRNA. The Homer1 gene family, which encodes proteins associated with cocaine-induced plasticity, also encodes circHomer1. Based on preliminary evidence from shows cocaine-regulated changes in the ratio of circHomer1 and Homer1b mRNA in the nucleus accumbens (NAc), this study examined the relationship between circHomer1 and incentive motivation for cocaine by using different lengths of abstinence to vary the degree of motivation. Male and female rats were trained to self-administer cocaine (0.75 mg/kg/infusion, IV) or received a yoked saline infusion. Rats proceeded on an increasingly more difficult variable ratio schedule of lever pressing until they reached a variable ratio 5 schedule, which requires an average of 5 lever presses, and light and tone cues were delivered with the drug infusions. Rats were then tested for cocaine-seeking behavior in response to cue presentations without drug delivery either 1 or 21 days after their last self-administration session. They were sacrificed immediately after and circHomer1 and Homer1b expression was then measured from homogenate and synaptosomal fractions of NAc shell using RT-qPCR. Lever pressing during the cue reactivity test increased from 1 to 21 days of abstinence as expected. Results showed no group differences in synaptic circHomer1 expression, however, total circHomer1 expression was downregulated in 21d rats compared to controls. Lack of change in synaptic circHomer1 was likely due to trends toward different temporal changes in males versus females. Total Homer1b expression was higher in females, although there was no effect of cocaine abstinence. Further research investigating the time course of circHomer1 and Homer1b expression is warranted based on the inverse relationship between total circHomer1and cocaine-seeking behavior observed in this study.
ContributorsJohnson, Michael Christian (Author) / Neisewander, Janet L (Thesis advisor) / Perrone-Bizzozero, Nora (Thesis advisor) / Mangone, Marco (Committee member) / Arizona State University (Publisher)
Created2022
Description
The RNA editing enzyme adenosine deaminase acting on double stranded RNA 2 (ADAR2) converts adenosine into inosine in regions of double stranded RNA. Here, it was discovered that this critical function of ADAR2 was dysfunctional in amyotrophic lateral sclerosis (ALS) mediated by the C9orf72 hexanucleotide repeat expansion, the most common genetic abnormality associated with ALS. Typically a nuclear protein, ADAR2 was localized in cytoplasmic accumulations in postmortem tissue from C9orf72 ALS patients. The mislocalization of ADAR2 was confirmed using immunostaining in a C9orf72 mouse model and motor neurons differentiated from C9orf72 patient induced pluripotent stem cells. Notably, the cytoplasmic accumulation of ADAR2 coexisted in neurons with cytoplasmic accumulations of TAR DNA binding protein 43 (TDP-43). Interestingly, ADAR2 overexpression in mammalian cell lines induced nuclear depletion and cytoplasmic accumulation of TDP-43, reflective of the pathology observed in ALS patients. The mislocalization of TDP-43 was dependent on the catalytic activity of ADAR2 and the ability of TDP-43 to bind directly to inosine containing RNA. In addition, TDP-43 nuclear export was significantly elevated in cells with increased RNA editing. Together these results describe a novel cellular mechanism by which alterations in RNA editing drive the nuclear export of TDP-43 leading to its cytoplasmic mislocalization. Considering the contribution of cytoplasmic TDP-43 to the pathogenesis of ALS, these findings represent a novel understanding of how the formation of pathogenic cytoplasmic TDP-43 accumulations may be initiated. Further research exploring this mechanism will provide insights into opportunities for novel therapeutic interventions.
ContributorsMoore, Stephen Philip (Author) / Sattler, Rita (Thesis advisor) / Zarnescu, Daniela (Committee member) / Brafman, David (Committee member) / Van Keuren-Jensen, Kendall (Committee member) / Mangone, Marco (Committee member) / Arizona State University (Publisher)
Created2021
Description
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 RASopathies are a collection of developmental diseases caused by germline mutations in components of the RAS/MAPK signaling pathway and is one of the world’s most common set of genetic diseases. A majority of these mutations result in an upregulation of RAS/MAPK signaling and cause a variety of both physical and neurological symptoms. Neurodevelopmental symptoms of the RASopathies include cognitive and motor delays, learning and intellectual disabilities, and various behavioral problems. Recent noninvasive imaging studies have detected widespread abnormalities within white matter tracts in the brains of RASopathy patients. These abnormalities are believed to be indicative of underlying connectivity deficits and a possible source of the behavioral and cognitive deficits. To evaluate these long-range connectivity and behavioral issues in a cell-autonomous manner, MEK1 loss- and gain-of-function (LoF and GoF) mutations were induced solely in the cortical glutamatergic neurons using a Nex:Cre mouse model. Layer autonomous effects of the cortex were also tested in the GoF mouse using a layer 5 specific Rbp4:Cre mouse. Immunohistochemical analysis showed that activated ERK1/2 (P-ERK1/2) was expressed in high levels in the axonal compartments and reduced levels in the soma when compared to control mice. Axonal tract tracing using a lipophilic dye and an adeno-associated viral (AAV) tract tracing vector, identified significant corticospinal tract (CST) elongation deficits in the LoF and GoF Nex:Cre mouse and in the GoF Rbp4:Cre mouse. AAV tract tracing was further used to identify significant deficits in axonal innervation of the contralateral cortex, the dorsal striatum, and the hind brain of the Nex:Cre GoF mouse and the contralateral cortex and dorsal striatum of the Rbp4:Cre mouse. Behavioral testing of the Nex:Cre GoF mouse indicated deficits in motor learning acquisition while the Rbp4:Cre GoF mouse showed no failure to acquire motor skills as tested. Analysis of the expression levels of the immediate early gene ARC in Nex:Cre and Rbp4:Cre mice showed a specific reduction in a cell- and layer-autonomous manner. These findings suggest that hyperactivation of the RAS/MAPK pathway in cortical glutamatergic neurons, induces changes to the expression patterns of P-ERK1/2, disrupts axonal elongation and innervation patterns, and disrupts motor learning abilities.
ContributorsBjorklund, George Reed (Author) / Newbern, Jason M (Thesis advisor) / Neisewander, Janet (Committee member) / Smith, Brian (Committee member) / Orchinik, Miles (Committee member) / Mangone, Marco (Committee member) / Arizona State University (Publisher)
Created2018
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