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With opioid use disorder (OUD) being an epidemic, it is important to investigate the mechanisms as to why this is so. This study established a self-administration paradigm to model and investigate the mechanisms of polysubstance, sequential use in conjunction with the analysis of withdrawal symptomatology driven by opioid withdrawal. The independent variables were dichotomized into the control group (food/cocaine) and the experimental group (oxycodone/cocaine). We hypothesized that more cocaine would be self-administered on the first day of oxycodone withdrawal. In addition, we hypothesized that somatic signs of withdrawal would increase at 16 hours post-oxycodone self-administration. Finally, we hypothesized that cocaine intake during oxycodone withdrawal would potentiate subsequent oxycodone self-administration. Our findings revealed that animals readily discriminated between the active (food or oxycodone) and inactive levers - but will however require more animals to achieve the appropriate power. Further, the average cocaine infusions across phases exhibited significance between the oxycodone/cocaine and food/cocaine group, with the average cocaine infusions being lower in food than in oxycodone-experienced animals. This implies that the exacerbation of the sequential co-use pattern in this case yields an increase in cocaine infusions that may be driven by oxycodone withdrawal. Further, to characterize withdrawal from oxycodone self-administration, somatic signs were examined at either 0 or 16 hrs following completion of oxycodone self-administration. The oxycodone/cocaine group exhibited significantly lower body temperature at 16 hrs of oxycodone withdrawal compared to 0 hrs. No differences in somatic signs of withdrawal in the food/cocaine group was found between the two timepoints. Oxycodone withdrawal was not found to potentiate any subsequent self-administration of oxycodone. Future research is needed to uncover neurobiological underpinnings of motivated polysubstance use in order to discover novel pharmacotherapeutic treatments to decrease co-use of drugs of abuse. Overall, this study is of importance as it is the first to establish a working preclinical model of a clinically-relevant pattern of polysubstance use. By doing so, it enables an exceptional opportunity to examine co-use in a highly-controlled setting.
ContributorsUlangkaya, Hanaa Corsino (Author) / Gipson-Reichardt, Cassandra (Thesis director) / Olive, M. Foster (Committee member) / Department of Psychology (Contributor) / School of Life Sciences (Contributor) / Barrett, The Honors College (Contributor)
Created2020-05
Description
Alzheimer’s disease (AD) is a progressive cognitive and behavior disorder that is characterized by the deposition of extracellular Aβ plaques, intracellular neurofibrillary tangles, and neuroinflammation. Aβ is generated by cleavage of the amyloid precursor protein (APP) by β-secretase (BACE1) and, subsequently, y- secretase. In recent years, there has been an increasing interest in studying and understanding inflammation as a therapeutic target for AD. Inflammation manifests in the brain in the form of activated microglia and astrocytes. These cells are able to release high levels of inflammatory cytokines such as Tumor Necrosis Factor-α (TNF-α). TNF-α is a major cytokine, which is involved in early inflammatory events and plays a role in the progression of AD pathology. There are currently no treatments that target chronic neuroinflammation. However, previous work in our laboratory with transgenic mice modeling AD suggested that the anti-cancer drug lenalidomide could lower neuroinflammation and slow AD progression, though the cellular and molecular mechanisms are yet to be elucidated. Here we hypothesized that lenalidomide can modulate TNF-α production in microglia and decrease amyloidogenesis. Using immortal cell lines mimicking several brain cell types, we discovered that lenalidomide is likely to decrease inflammation by modulating microglia cells rather than neurons or astrocytes. In addition, the drug may prevent the overexpression of BACE1 upon inflammation, thus blocking the overproduction of Aβ. If confirmed, these results could lead to a better understanding of how inflammation regulates Aβ synthesis and provide novel cellular and molecular therapeutic targets to control the progression AD.
ContributorsGujju, Manasa (Author) / DeCourt, Boris (Thesis director) / Olive, M. Foster (Committee member) / Department of Psychology (Contributor) / School of Life Sciences (Contributor) / Barrett, The Honors College (Contributor)
Created2018-05
Description
Nicotine addiction remains a prevalent public health issue, and the FDA has released a statement outlining the systematic reduction of nicotine to non-zero levels in the coming years. Current research has not yet established the effects of abrupt nicotine dose reduction on vulnerability to relapse, nor has abrupt nicotine dose reduction been evaluated in terms of behavioral economic characteristics of demand and elasticity been evaluated for reduced doses of nicotine. Using a rat model, we first evaluated the comparability of between- and within-session protocols for establishing characteristics of demand and elasticity for nicotine to shorten experimental timelines for this study and future studies. We then tested environmental enrichment and sex as factors of elasticity of demand for nicotine. Using a rat model of relapse to cues, we also examined the effects of nicotine dose-reduction on vulnerability to relapse. We found differences in maximum consumption and demand between the between- and within-session protocols, as well as sex differences in elasticity of demand on the within-session protocol where male demand was more elastic than female demand. Additionally, we found that enrichment significantly increased elasticity of demand for nicotine for both males and females. Finally, preliminary analyses revealed that nicotine dose reduction yields more inelastic demand and higher maximum consumption, and these outcomes predict increased time to extinction of the association between nicotine and contingent cues, and increased rates of relapse. These studies highlight the usefulness and validity of within-session protocols, and also illustrate the necessity for rigorous testing of forced dose reduction on nicotine vulnerability.
ContributorsCabrera-Brown, Gabriella Paula (Author) / Gipson-Reichardt, Cassandra (Thesis director) / Olive, M. Foster (Committee member) / Davis, Mary (Committee member) / Sanford School of Social and Family Dynamics (Contributor) / Department of Psychology (Contributor) / Barrett, The Honors College (Contributor)
Created2017-12
Description
Open source image analytics and data mining software are widely available but can be overly-complicated and non-intuitive for medical physicians and researchers to use. The ASU-Mayo Clinic Imaging Informatics Lab has developed an in-house pipeline to process medical images, extract imaging features, and develop multi-parametric models to assist disease staging and diagnosis. The tools have been extensively used in a number of medical studies including brain tumor, breast cancer, liver cancer, Alzheimer's disease, and migraine. Recognizing the need from users in the medical field for a simplified interface and streamlined functionalities, this project aims to democratize this pipeline so that it is more readily available to health practitioners and third party developers.
ContributorsBaer, Lisa Zhou (Author) / Wu, Teresa (Thesis director) / Wang, Yalin (Committee member) / Computer Science and Engineering Program (Contributor) / W. P. Carey School of Business (Contributor) / Barrett, The Honors College (Contributor)
Created2016-12
Description
Cases of heroin use and overdose are on the rise in the United States which has created what some call a public health crisis. Previous studies have investigated the beneficial effect of social interaction recovering addicts, and in animal models of addiction, social interaction can prevent or reverse the conditioned rewarding effects of cocaine. This study sought to determine if social interaction would prevent or diminish a conditioned preference for a heroin-paired context. Following establishment of baseline place preference, adult male Sprague-Dawley rats underwent once daily conditioning with either saline, heroin (1 mg/kg), or the animal's cage-mate for a total of 8 conditioning sessions. Assessment of post-conditioning place preference revealed that both the heroin injections and the presence of the cage-mate produced a place preference . In contrast to the findings of previous studies using cocaine as the conditioning drug, it was determined that rats preferred the heroin-paired context over that paired with the cage-mate.. These findings suggest that the protective effects of social interaction found in prior studies using cocaine as the conditioning drug may not extend to opiates, perhaps a result of stronger contextual conditioning and/or rewarding effects of this class of abused drugs.
ContributorsMarble, Krista Lillian (Author) / Olive, M. Foster (Thesis director) / Tomek, Seven (Committee member) / Department of Psychology (Contributor) / Barrett, The Honors College (Contributor)
Created2017-12
Description
The RAS/MAPK (RAS/Mitogen Activated Protein Kinase) pathway is a highly conserved, canonical signaling cascade that is highly involved in cellular growth and proliferation as well as cell migration. As such, it plays an important role in development, specifically in development of the nervous system. Activation of ERK is indispensable for the differentiation of Embryonic Stem Cells (ESC) into neuronal precursors (Li z et al, 2006). ERK signaling has also shown to mediate Schwann cell myelination of the peripheral nervous system (PNS) as well as oligodendrocyte proliferation (Newbern et al, 2011). The class of developmental disorders that result in the dysregulation of RAS signaling are known as RASopathies. The molecular and cell-specific consequences of these various pathway mutations remain to be elucidated. While there is evidence for altered DNA transcription in RASopathies, there is little work examining the effects of the RASopathy-linked mutations on protein translation and post-translational modifications in vivo. RASopathies have phenotypic and molecular similarities to other disorders such as Fragile X Syndrome (FXS) and Tuberous Sclerosis (TSC) that show evidence of aberrant protein synthesis and affect related pathways. There are also well-defined downstream RAS pathway elements involved in translation. Additionally, aberrant corticospinal axon outgrowth has been observed in disease models of RASopathies (Xing et al, 2016). For these reasons, this present study examines a subset of proteins involved in translation and translational regulation in the context of RASopathy disease states. Results indicate that in both of the tested RASopathy model systems, there is altered mTOR expression. Additionally the loss of function model showed a decrease in rps6 activation. This data supports a role for the selective dysregulation of translational control elements in RASopathy models. This data also indicates that the primary candidate mechanism for control of altered translation in these modes is through the altered expression of mTOR.
ContributorsHilbert, Alexander Robert (Author) / Newbern, Jason (Thesis director) / Olive, M. Foster (Committee member) / Bjorklund, Reed (Committee member) / School of Life Sciences (Contributor) / Barrett, The Honors College (Contributor)
Created2017-05
Description
Video object segmentation (VOS) is an important task in computer vision with a lot of applications, e.g., video editing, object tracking, and object based encoding. Different from image object segmentation, video object segmentation must consider both spatial and temporal coherence for the object. Despite extensive previous work, the problem is still challenging. Usually, foreground object in the video draws more attention from humans, i.e. it is salient. In this thesis we tackle the problem from the aspect of saliency, where saliency means a certain subset of visual information selected by a visual system (human or machine). We present a novel unsupervised method for video object segmentation that considers both low level vision cues and high level motion cues. In our model, video object segmentation can be formulated as a unified energy minimization problem and solved in polynomial time by employing the min-cut algorithm. Specifically, our energy function comprises the unary term and pair-wise interaction energy term respectively, where unary term measures region saliency and interaction term smooths the mutual effects between object saliency and motion saliency. Object saliency is computed in spatial domain from each discrete frame using multi-scale context features, e.g., color histogram, gradient, and graph based manifold ranking. Meanwhile, motion saliency is calculated in temporal domain by extracting phase information of the video. In the experimental section of this thesis, our proposed method has been evaluated on several benchmark datasets. In MSRA 1000 dataset the result demonstrates that our spatial object saliency detection is superior to the state-of-art methods. Moreover, our temporal motion saliency detector can achieve better performance than existing motion detection approaches in UCF sports action analysis dataset and Weizmann dataset respectively. Finally, we show the attractive empirical result and quantitative evaluation of our approach on two benchmark video object segmentation datasets.
ContributorsWang, Yilin (Author) / Li, Baoxin (Thesis advisor) / Wang, Yalin (Committee member) / Cleveau, David (Committee member) / Arizona State University (Publisher)
Created2013
Description
Learning from high dimensional biomedical data attracts lots of attention recently. High dimensional biomedical data often suffer from the curse of dimensionality and have imbalanced class distributions. Both of these features of biomedical data, high dimensionality and imbalanced class distributions, are challenging for traditional machine learning methods and may affect the model performance. In this thesis, I focus on developing learning methods for the high-dimensional imbalanced biomedical data. In the first part, a sparse canonical correlation analysis (CCA) method is presented. The penalty terms is used to control the sparsity of the projection matrices of CCA. The sparse CCA method is then applied to find patterns among biomedical data sets and labels, or to find patterns among different data sources. In the second part, I discuss several learning problems for imbalanced biomedical data. Note that traditional learning systems are often biased when the biomedical data are imbalanced. Therefore, traditional evaluations such as accuracy may be inappropriate for such cases. I then discuss several alternative evaluation criteria to evaluate the learning performance. For imbalanced binary classification problems, I use the undersampling based classifiers ensemble (UEM) strategy to obtain accurate models for both classes of samples. A small sphere and large margin (SSLM) approach is also presented to detect rare abnormal samples from a large number of subjects. In addition, I apply multiple feature selection and clustering methods to deal with high-dimensional data and data with highly correlated features. Experiments on high-dimensional imbalanced biomedical data are presented which illustrate the effectiveness and efficiency of my methods.
ContributorsYang, Tao (Author) / Ye, Jieping (Thesis advisor) / Wang, Yalin (Committee member) / Davulcu, Hasan (Committee member) / Arizona State University (Publisher)
Created2013
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
Retinotopic map, the map between visual inputs on the retina and neuronal activation in brain visual areas, is one of the central topics in visual neuroscience. For human observers, the map is typically obtained by analyzing functional magnetic resonance imaging (fMRI) signals of cortical responses to slowly moving visual stimuli on the retina. Biological evidences show the retinotopic mapping is topology-preserving/topological (i.e. keep the neighboring relationship after human brain process) within each visual region.
Unfortunately, due to limited spatial resolution and the signal-noise ratio of fMRI, state of art retinotopic map is not topological.
The topic was to model the topology-preserving condition mathematically, fix non-topological retinotopic map with numerical methods, and improve the quality of retinotopic maps.
The impose of topological condition, benefits several applications.
With the topological retinotopic maps, one may have a better insight on human retinotopic maps, including better cortical magnification factor quantification, more precise description of retinotopic maps, and potentially better exam ways of in Ophthalmology clinic.
ContributorsTu, Yanshuai (Author) / Wang, Yalin (Thesis advisor) / Lu, Zhong-Lin (Committee member) / Crook, Sharon (Committee member) / Yang, Yezhou (Committee member) / Zhang, Yu (Committee member) / Arizona State University (Publisher)
Created2022