Theses and Dissertations from UMD
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New submissions to the thesis/dissertation collections are added automatically as they are received from the Graduate School. Currently, the Graduate School deposits all theses and dissertations from a given semester after the official graduation date. This means that there may be up to a 4 month delay in the appearance of a give thesis/dissertation in DRUM
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Item Examining Associations between Neural Sensitivity to Social Feedback with Trait and State Loneliness in Adolescents(2024) Alleluia Shenge, Victoire; Redcay, Elizabeth; Psychology; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Loneliness can be defined as the negative emotional response to an experience of discrepancy between the desired and actual quality or quantity of one’s relationships. Loneliness is associated with many negative outcomes, including depression and self-harm. This phenomenon tends to increase in adolescence and adolescents with autism spectrum disorder (ASD) are at even greater risk for developing loneliness during this time than their neurotypical peers. The present study examined how neural sensitivity to both positive and negative feedback from peers is related to loneliness and social experiences among adolescents with and without autism. In a sample of 94 adolescents (22 autistic and 72 non-autistic) ages 11-14, we used an innovative ecologically valid paradigm for fMRI task along with real-world experience sampling to assess self-reported interaction quality and state loneliness, as well as surveys to examine reports of “trait” (or stable levels of) loneliness.The results indicated group differences in both state and trait loneliness, with the autistic group showing high levels of loneliness. In addition, the autistic group had lower interaction quality compared to their non-autistic peers. However, we did not find support for associations between neural sensitivity to feedback and interaction quality or loneliness across our full group. This work provides an important first step in understanding the relation between loneliness, neural sensitivity to social feedback and social experiences and can further inform intervention for adolescents at risk for negative mental health outcomes depending on which mechanism shows an association effect on social experiences and lonelinessItem Predictors of Peer Interaction Success for Autistic and Non-Autistic Youth(2024) McNaughton, Kathryn; Redcay, Elizabeth; Neuroscience and Cognitive Science; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Successful peer interactions are a crucial component of mental health and well-being for autistic and non-autistic youth. Factors that influence successful peer interactions are particularly relevant to investigate in middle childhood and adolescence, a developmental period in which peer interactions take on increased importance for mental health. Research into social interactions can involve both individual-level and interindividual-level understanding of interaction outcomes. Individual-level predictors can yield insight into the way one’s own characteristics predict social interaction outcomes, for example, informing theories about how an individual’s social motivation may predict their social enjoyment. However, because research into social interaction challenges and success in autism has historically focused on individual-level contributions of autistic individuals to social interaction outcomes, it is also important to understand interindividual-level mechanisms, such as the similarity or synchrony between individuals, to understand the role both non-autistic and autistic individuals play in shaping social interactions and their outcomes. Therefore, the overarching goal of this dissertation is to evaluate potential neural and behavioral predictors of peer interaction success in autistic and non-autistic youth during middle childhood and adolescence at the individual and interindividual level. First, I demonstrate that neural sensitivity to social-interactive reward is an individual-level predictor of peer interaction enjoyment. Next, I move beyond individual-level neural predictors to interindividual-level neural predictors, providing evidence for how neural similarity to peers may differentially relate to day-to-day interaction success across different interaction types, such as interactions with peers. Finally, I establish smiling synchronization as an interindividual predictor of peer interaction enjoyment. These studies span the neural and behavioral levels of analysis, providing insight into how these levels of analysis can be investigated from both an individual and interindividual perspective. The findings advance understanding of factors that predict peer interaction success, leading to better understanding of opportunities to support successful peer interactions through individual and interindividual interventions with autistic and non-autistic youth.Item Statistical Network Analysis of High-Dimensional Neuroimaging Data With Complex Topological Structures(2023) Lu, Tong; Chen, Shuo SC; Mathematical Statistics; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)This dissertation contains three projects that collectively tackle statistical challenges in the field of high-dimensional brain connectome data analysis and enhance our understanding of the intricate workings of the human brain. Project 1 proposes a novel network method for detecting brain-disease-related alterations in voxel-pair-level brain functional connectivity with spatial constraints, thus improving spatial specificity and sensitivity. Its effectiveness is validated through extensive simulations and real data applications in nicotine addiction and schizophrenia studies. Project 2 introduces a multivariate multiple imputation method specifically designed for voxel-level neuroimaging data in high dimensions based on Bayesian models and Markov chain Monte Carlo processes. According to both synthetic data and real neurovascular water exchange data extracted from a neuroimaging dataset in a schizophrenia study, our method indicates high imputation accuracy and computational efficiency. Project 3 develops a multi-level network model based on graph combinatorics that captures vector-to-matrix associations between brain structural imaging measures and functional connectomic networks. The validity of the proposed model is justified through extensive simulations and a real structure-function imaging dataset from UK Biobank. These three projects contribute innovative methodologies and insights that advance neuroimaging data analysis, including improvements in spatial specificity, statistical power, imputation accuracy, and computational efficiency when revealing the brain’s complex neurological patterns.Item BRAIN BASIS OF HUMAN SOCIAL INTERACTION: NEUROCOGNITIVE FUNCTIONS AND META-ANALYSIS(2023) Merchant, Junaid Salim; Redcay, Elizabeth; Neuroscience and Cognitive Science; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Social interactions, or the reciprocal exchange between socially engaged individuals, plays a central role in shaping human life. Social interactions are fundamental for neurocognitive development, and a key factor contributing to mental and physical health. Despite their importance, research investigating the neurocognitive systems associated with human social interaction is relatively new. Human neuroimaging research has traditionally used approaches that separate the individual from social contexts, thereby limiting the ability to examine brain systems underlying interactive social behavior. More recent work has begun incorporating real-time social contexts, and have implicated an extended network of brain regions associated with social interaction. However, open questions remain about the neurocognitive processes that are critical for social interactions and the brain systems that are commonly engaged. The current dissertation aims to address these gaps in our understanding through a set of studies using computational and data-driven approaches. Study 1 examined the relationship between social interaction and mentalizing, which is the ability to infer the mental states of others that is considered to be critically important for social interactions. Prior work has demonstrated that mentalizing and social interaction elicit brain activity spatially overlapping areas, but spatial overlap is not necessarily indicative of a common underlying process. Thus, Study 1 utilized multivariate approaches to examine the similarity of brain activity patterns associated with mentalizing outside of social contexts and when interacting with a peer (regardless of mentalizing) as a means for inferring a functional relationship between the two. Study 2 investigated brain regions commonly engaged across social interactive contexts using coordinate-based meta-analysis, which is an approach for aggregating findings across neuroimaging literature. This involved an exhaustive search strategy to find fMRI and PET studies that utilize social interactive approaches, and calculated spatial convergence across studies as a means to uncover brain regions that are reliably implicated during social interaction. The results from Studies 1 and 2 offer major advancements for a neuroscientific understanding of social interaction by demonstrating a functional link with mentalizing and through elucidating brain systems that are commonly reported in studies using social interactive approaches.Item INVESTIGATING THE RELATION BETWEEN PATTERN SEPARATION AND HIPPOCAMPAL SUBREGION ACTIVATION(2022) Dunstan, Jade; Riggins, Tracy; Psychology; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Pattern separation is a key component of episodic memory as it allows us to distinguish between similar events that share overlapping features. Therefore, understanding the development of pattern separation processes can help elucidate individual differences in memory development. Research in children and adults has shown relations between hippocampal structure and pattern separation, indexed behaviorally through a mnemonic discrimination task where participants distinguished between similar stimuli. However, there has been less research investigating relations between hippocampal function and pattern separation processes, all in adult samples. Thus, the current study sought to pilot a child-friendly mnemonic discrimination fMRI paradigm in adults before recruiting a child sample. Results provided some evidence of pattern separation processes as greater differences in activation for Targets relative to Lures predicted better memory performance. Future studies will recruit a child sample to assess group differences in pattern separation processes as well as go beyond mean activation for the conditions by using techniques such as representational similarity analysis to assess patterns of representations for Targets, Lures, and Foils across the voxels of the hippocampus.Item Bayesian Methods and Their Application in Neuroimaging Data(2022) Ge, Yunjiang; Kedem, Benjamin; Chen, Shuo; Mathematics; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)The functional magnetic resonance imaging (fMRI) technique is widely used in the medical field because it allows the in vivo investigations of human cognition, emotions, and behaviors at the neural level. One primary objective is to study brain activation, which can be achieved through a conventional two-stage approach. We consider the individualized voxel-specific modeling in the first stage and group-level inference in the second stage. Existing methods, in general, rely on pre-determined parameters or domain knowledge, which may not properly incorporate the unique features from different studies or cohorts, and thus also leads to some gaps in the inference for activated regions. This dissertation focuses on Bayesian approaches to fill the gaps in statistical inference at all levels, as well as accounting for the various information carried out by the data. Cluster-wise statistical inference is the most widely used technique for fMRI data analyses. It consists of two steps: i) primary thresholding that excludes less significant voxels by a pre-specified cut-off (e.g., p<0.001); and ii) cluster-wise thresholding that is often obtained by counting the number of intra-cluster voxels which surpass a voxel-level statistical significance threshold. The selection of the primary threshold is critical because it determines both statistical power and false discovery rate. However, in most existing statistical packages, the primary threshold is selected based on prior knowledge (e.g., p<0.001) without considering the information in the data. Thus, in the first project, we propose a data-driven approach to algorithmically select the optimal primary threshold based on an empirical Bayes framework. We evaluate the proposed model using extensive simulation studies and real fMRI data. In the simulation, we show that our method can effectively increase statistical power while controlling the false discovery rate. We then investigate the brain response to the dose effect of chlorpromazine in patients with schizophrenia by analyzing fMRI scans and generating consistent results. In Chapter 3, we focus on controlling the FWER by conducting cluster-level inference. The cluster-extent measure can be sub-optimal regarding the power and false positive error rate because the supra-threshold voxel count neglects the voxel-wise significance levels and ignores the dependence between voxels. Based on the information that a cluster carries, we provide a new Integrated Cluster-wise significance Measure (ICM) for cluster-level significance determination in cluster-wise fMRI analysis by integrating cluster extent, voxel-level significance (e.g., p-values), and activation dependence between within-cluster voxels. We develop a computationally efficient strategy for ICM based on probabilistic approximation theories. Consequently, the computational load for ICM-based cluster-wise inference (e.g., permutation tests) is affordable. We validate the proposed method via extensive simulations and then apply it to two fMRI data sets. The results demonstrate that ICM can improve power with well-controlled FWER. The above chapters focus on the cluster-extent thresholding method, while the Bayesian hierarchical model can also efficiently handle high-dimensional neuroimaging data. Existing methods provide voxel-specific and pre-determined regional (region of interest (ROI)) inference. However, the activation clusters may be across multiple ROIs or vary from studies and study cohorts. To provide the inference and build the bridge between voxels, unknown activation clusters, targeted regions, and the whole brain, we propose the Dirichlet Process Mixture model with Spatial Constraint (DPMSC) in Chapter 4. The spatial constraint is based on the Euclidean distance between two voxels in the brain space. With such a constraint added at each iteration in Markov Chain Monte Carlo (MCMC), our DPMSC can efficiently remove the single voxel or small noise clusters, as well as provide a whole contiguous cluster that belongs to the same component in the mixture model. Finally, we provide a real data example and simulation studies based on various dataset features.Item THE ROLE OF THEORY OF MIND IN SOCIAL INTERACTION(2021) Alkire, Diana; Redcay, Elizabeth; Neuroscience and Cognitive Science; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Theory of mind (ToM) is assumed to be instrumental to social interactions, yet it is typically studied using non-interactive laboratory tasks. Standard measures are thus limited in their ability to characterize the cognitive and neural substrates of ToM in naturalistic social interactions, as well as the mechanisms explaining social-interactive difficulties in autism spectrum disorder (ASD). Across three studies, this dissertation aimed to highlight and bridge the disconnect between the study of ToM and its real-world implementation. Study 1 assessed the relative importance of a range of social-cognitive, social-perceptual, and social-affective constructs in explaining variance in the social symptoms of ASD. Three standard, non-interactive ToM measures together explained only 6% of the variance in social symptoms, reinforcing the need for interactive approaches to studying ToM. Study 2 applied such an approach using a socially interactive neuroimaging paradigm to measure brain activation associated with both ToM and social interaction. In typically developing children aged 8-12, interacting with a peer, even in the absence of explicit ToM demands, engaged many of the same regions as did non-interactive ToM reasoning, consistent with the idea that social interaction elicits spontaneous ToM-related processes. Study 3 also investigated ToM in social interaction, this time at the behavioral level, by introducing a novel observational coding system that measures the use of (or failure to use) ToM in naturalistic conversation. Among dyads of typically developing and autistic children and adolescents, conversational ToM (cToM) did not predict interaction success. However, the cToM Negative subscale—capturing ToM-related violations of conversational norms—was negatively associated with two forms of non-interactive ToM: 1) recognizing complex emotions from facial expressions, and 2) spontaneously attributing mental states when describing abstract social animations. Furthermore, exploratory analyses revealed associations between cToM and brain activation during the socially interactive neuroimaging task used in Study 2. Findings across the three studies highlight the multifaceted nature of the ToM construct, the value of socially interactive approaches to studying ToM, and the importance of considering ToM alongside other social-cognitive and affective processes when investigating social interaction.Item Computing with Trajectories: Characterizing Dynamics and Connectivity in Spatiotemporal Neuroimaging Data(2020) Venkatesh, Manasij; Pessoa, Luiz; JaJa, Joseph F; Electrical Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Human functional Magnetic Resonance Imaging (fMRI) data are acquired while participants engage in diverse perceptual, motor, cognitive, and emotional tasks. Although data are acquired temporally, they are most often treated in a quasi-static manner. Yet, a fuller understanding of the mechanisms that support mental functions necessitates the characterization of dynamic properties. Firstly, we describe an approach employing a class of recurrent neural networks called reservoir computing, and show their feasibility and potential for the analysis of temporal properties of brain data. We show that reservoirs can be used effectively both for condition classification and for characterizing lower-dimensional "trajectories" of temporal data. Classification accuracy was approximately 90% for short clips of "social interactions" and around 70% for clips extracted from movie segments. Data representations with 12 or fewer dimensions (from an original space with over 300) attained classification accuracy within 5% of the full data. We hypothesize that such low-dimensional trajectories may provide "signatures" that can be associated with tasks and/or mental states. The approach was applied across participants (that is, training in one set of participants, and testing in a separate group), showing that representations generalized well to unseen participants. In the second part, we use fully-trained recurrent neural networks to capture and characterize spatiotemporal properties of brain events. We propose an architecture based on long short-term memory (LSTM) networks to uncover distributed spatiotemporal signatures during dynamic experimental conditions. We demonstrate the potential of the approach using naturalistic movie-watching fMRI data. We show that movie clips result in complex but distinct spatiotemporal patterns in brain data that can be classified using LSTMs (≈90% for 15-way classification), demonstrating that learned representations generalized to unseen participants. LSTMs were also superior to existing methods in predicting behavior and personality traits of individuals. We propose a dimensionality reduction approach that uncovers low-dimensional trajectories and captures essential informational properties of brain dynamics. Finally, we employed saliency maps to characterize spatiotemporally-varying brain-region importance. The spatiotemporal saliency maps revealed dynamic but consistent changes in fMRI activation data. Taken together, we believe the above approaches provide a powerful framework for visualizing, analyzing, and discovering dynamic spatially distributed brain representations during naturalistic conditions. Finally, we address the problem of comparing functional connectivity matrices obtained from temporal fMRI data. Understanding the correlation structure associated with multiple brain measurements informs about potential "functional groupings" and network organization. The correlation structure can be conveniently captured in a matrix format that summarizes the relationships among a set of brain measurements involving two regions, for example. Such functional connectivity matrix is an important component of many types of investigation focusing on network-level properties of the brain, including clustering brain states, characterizing dynamic functional states, performing participant identification (so-called "fingerprinting"), understanding how tasks reconfigure brain networks, and inter-subject correlation analysis. In these investigations, some notion of proximity or similarity of functional connectivity matrices is employed, such as their Euclidean distance or Pearson correlation (by correlating the matrix entries). We propose the use of a geodesic distance metric that reflects the underlying non-Euclidean geometry of functional correlation matrices. The approach is evaluated in the context of participant identification (fingerprinting): given a participant's functional connectivity matrix based on resting-state or task data, how effectively can the participant be identified? Using geodesic distance, identification accuracy was over 95% on resting-state data and exceeded the Pearson correlation approach by 20%. For whole-cortex regions, accuracy improved on a range of tasks by between 2% and as much as 20%. We also investigated identification using pairs of subnetworks (say, dorsal attention plus default mode), and particular combinations improved accuracy over whole-cortex participant identification by over 10%. The geodesic distance also outperformed Pearson correlation when the former employed a fourth of the data as the latter. Finally, we suggest that low-dimensional distance visualizations based on the geodesic approach help uncover the geometry of task functional connectivity in relation to that during resting-state. We propose that the use of the geodesic distance is an effective way to compare the correlation structure of the brain across a broad range of studies.Item THE NEURAL CORRELATES OF SOCIAL MOTIVATION IN AUTISM SPECTRUM DISORDER DURING A REAL-TIME PEER INTERACTION(2018) Kirby, Laura Anderson; Redcay, Elizabeth; Psychology; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Autism Spectrum Disorder (ASD) is characterized by difficulties with social motivation and social interaction. However, the neural underpinnings of these processes are poorly understood, and past studies investigating this subject have significant methodological limitations. This study is the first to investigate the neural correlates of social interaction in children and adolescents diagnosed with ASD using a naturalistic “chat” paradigm that mimics real-world reciprocal conversations. Despite core weaknesses in social interaction, participants with ASD showed similar brain activation to their neurotypical counterparts while initiating conversations and receiving replies from peers. Two notable group differences emerged, however. Participants with ASD showed blunted responses in the amygdala while initiating conversations and receiving replies, and they showed hyperactive responses in the temporal parietal junction (TPJ) while initiating conversations with peers. Findings have implications for how we understand social motivational and social cognitive weaknesses in ASD.Item The Impact of Acute Aerobic Exercise on Semantic Memory Activation in Healthy Older Adults(2018) Won, Junyeon; Smith, Jerome C; Kinesiology; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Background: A growing body of exercise literature use functional magnetic resonance imaging (fMRI) technique to measure the effects of exercise on the brain. Findings suggest that regular participation of long-term exercise is associated with enhanced cognitive function. However, fundamental questions regarding the beneficial effects of acute exercise on semantic memory have been ignored. Purpose: This study investigated the effects of a single session of exercise on brain activation during recognition of Famous names and Non-Famous names compared to seated-rest in healthy older adults (age 65-85) using fMRI. We also aimed to measure whether there are differences in brain activation during retrieval of Famous names from three distinct time epochs (Remote, Enduring, and Recent) following acute exercise. Methods: Using a within-subjects counterbalanced design, 30 participants (ages 55-85) will undergo two experimental visits on separate days. During each visit, participants will engage in 30-minutes of rest or stationary cycling exercise immediately followed by the famous name discrimination task (FNT). Neuroimaging and behavioral data will be processed using AFNI (version 17.1.06) and SPSS (version 23), respectively. Results: HR and RPE were significantly higher during exercise. Acute exercise was associated with significantly greater semantic memory activation (Famous > Non-Famous) in five out of nine regions (p-value ranged 0.027 to 0.046). In an exploratory epoch analysis, five out of 14 brain regions activated ruing the semantic memory task showed significantly greater activation intensity following the exercise intervention (Enduringly Famous > Non-Famous). Conclusions: Enhanced semantic memory processing is observed following acute exercise, characterized by greater fMRI response to Famous than Non-Famous names. Enduringly Famous names exhibited significantly greater activation after exercise compared to Non-Famous names. These findings suggest that exercise may improve semantic memory retrieval in healthy older adults, and may lead to enhancement of cognitive function.
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