Browsing by Subject "neuroimaging"

Now showing 1 - 7 of 7
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    Behavioral Economics of Persecutory Delusions
    (2022-06) Kazinka, Rebecca
    Persecutory delusions cause significant distress in the individuals who experience them, yet as a field we are still working to understand their etiology. Persecutory delusions and suspiciousness have some overlap with mistrust, and thus this dissertation focuses on the use of trust games to examine behavior and neural mechanisms of spite sensitivity. Spite sensitivity is the fear that a partner is willing to take a hit in order to ensure that you do as well, as opposed to rational mistrust, in which a partner can make a gain but at your expense. The benefit of using social decision making tasks such as the Trust Game and its adaptation, the Minnesota Trust Game, is that 1) a computational model can be developed based on the given parameters of the task to understand beliefs and motivations and 2) it can provide a well-controlled task to examine principles of trust and spite sensitivity in neuroimaging. The goal of this dissertation is to provide evidence that spite sensitivity is an important construct to understand persecutory ideation and distrust, both in psychiatric populations but also the general population. I show that while the Trust Game identifies decreased trust in individuals with psychosis, the Minnesota Trust Game identifies that spite sensitivity is distinct from rational mistrust and can be measured computationally. Further examination in a group of individuals with psychosis show a neural dissociation between spite sensitivity and rational mistrust as well. We did not find this dissociation in a community sample of twins, yet did find a relationship between twin discordance in a computational measure of spite sensitivity and a personality measure of suspiciousness. Altogether, this dissertation provides a foundation for the use of spite sensitivity as a construct to understand persecutory ideation.
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    Functional and Structural Connectivity of Limbic and Interpersonally Relevant Regions in Non-Suicidal Self-Injury
    (2019-08) Westlund Schreiner, Melinda
    Non-suicidal self-injury (NSSI) commonly begins in adolescence and is associated with an array of negative outcomes including suicide. Research has only begun to explore the neurobiological mechanisms associated with this behavior, most often among adults with borderline personality disorder. However, research is urgently needed to study NSSI among adolescents in order to understand potential neurobiological correlates. Applications of this knowledge would potentially be used to identify neurobiologically informed intervention strategies targeting these deficits and restore healthy neurodevelopmental trajectories. The present study implemented a multi-modal approach to understanding neural functioning by examining structural and functional connectivity in adolescents with versus without NSSI. Given previous clinical findings on NSSI, this study focused on brain regions implicated in negative affect and interpersonal sensitivity, the amygdala and dorsal anterior cingulate (dACC) respectively. Overall, the NSSI group showed widespread differences in both functional and structural connectivity compared to controls. These patterns were suggestive of possible influence of negative affect on emotional memory, planning of motor movements, and interpersonal relationships. Additionally, the NSSI group showed impairments in structural connectivity consistent with those seen in major depressive disorder and anxiety disorders. Given the paucity of neurobiological research on NSSI, this study represents an important first step in furthering the understanding of this behavior in adolescents and will aid in generating hypotheses for future work.
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    MicroRNA and Neuroimaging Biomarkers of Neuropathic Pain Severity After Spinal Cord Injury: Results from a Robotic-Assisted Gait Training Study
    (2022-07) Kowalski, Jesse
    Spinal cord injury (SCI) results in chronic neuroinflammation which contributes to altered neural function and the development of neuropathic pain. Differential expression of microRNA regulators of neuroinflammatory pathways and alterations in brain structure and functional connectivity may contribute to the development or severity of neuropathic pain. Exercise has been shown to reduce neuroinflammation and chronic pain and alter brain structure in human and animal models, yet little is known about how exercise interventions influence pain processing in human populations with SCI. This doctoral dissertation aimed to identify 1) novel microRNA biomarkers of neuropathic pain, 2) neuropathic pain-related alterations in brain functional connectivity, and 3) the efficacy of an exercise intervention of robotic-assisted gait training to reduce neuropathic pain and alter brain volume in individuals with SCI. Successful identification of underlying mechanisms of neuropathic pain and potential exercise induced mitigation of these factors will guide the development of targeted interventions and provide useful biomarkers to predict and optimize prognosis, and subsequent care management for individuals with SCI.
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    Noninvasive Neuroimaging Of Responses To Transcranial Magnetic Stimulation
    (2018-05) Cline, Christopher
    Transcranial magnetic stimulation (TMS) and electroencephalography (EEG) provide means to noninvasively measure and modulate activity in the brain. EEG has the potential to infer user intent from measured signals, making it possible to build brain-computer interfaces for augmentative and alternative communication and control of devices that do not rely on intact motor function. TMS offers the ability to transiently perturb neural activity with good temporal and spatial precision, and to modulate longer-term excitability and network function, with various applications in both neuroscientific research and clinical treatment. However, both EEG and TMS have limitations, due in a large part to their noninvasiveness. EEG-based BCIs face issues with inconsistent inference of intent estimated from low-SNR measurements, which degrades the speed and accuracy of BCI control. Likewise, current TMS approaches face issues with variability in responses to stimulation, based on lack of precise targeting information and knowledge of underlying mechanisms of stimulation effects, resulting in inefficient or inconsistently effective clinical neuromodulation interventions. In this work, I describe several efforts to address these issues using approaches combining TMS and EEG. To improve our understanding of factors influencing successful motor imagery based BCI control, I applied TMS targeted at perturbing specific neural circuits and measuring resulting changes in BCI control. Conversely, I also explored factors influencing responses to TMS and how EEG can be used to inform stimulation via measurements of stimulation response and estimation of pre-stimulation brain state.
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    Perception and Processing of Pitch and Timbre in Human Cortex
    (2018-04) Allen, Emily
    Pitch and timbre are integral components of auditory perception, yet our understanding of how they interact with one another and how they are processed cortically is enigmatic. Through a series of behavioral studies, neuroimaging, and computational modeling, we investigated these attributes. First, we looked at how variations in one dimension affect our perception of the other. Next, we explored how pitch and timbre are processed in the human cortex, in both a passive listening context and in the presence of attention, using univariate and multivariate analyses. Lastly, we used encoding models to predict cortical responses to timbre using natural orchestral sounds. We found that pitch and timbre interact with each other perceptually, and that musicians and non-musicians are similarly affected by these interactions. Our fMRI studies revealed that, in both passive and active listening conditions, pitch and timbre are processed in largely overlapping regions. However, their patterns of activation are separable, suggesting their underlying circuitry within these regions is unique. Finally, we found that a five-feature, subjectively derived encoding model could predict a significant portion of the variance in the cortical responses to timbre, suggesting our processing of timbral dimensions may align with our perceptual categorizations of them. Taken together, these findings help clarify aspects of both our perception and processing of pitch and timbre.
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    Personality, psychosis, and connectivity: Neuroimaging endophenotypes in the psychotic spectrum
    (2016-05) Grazioplene, Rachael
    The link between diagnoses of psychotic disorders and altered structural and functional brain connectivity is well established, yet little is known about the degree to which similar neural features predict traits linked to psychosis-proneness in the general population. Moreover, intelligence is too rarely considered as a covariate in neural endophenotype studies, despite its known protective role against psychopathology in general and its associations with broad aspects of neural structure and function. To determine whether psychosis-linked personality traits are linearly associated with putative psychosis endophenotypes, this dissertation examines white matter and functional connectivity correlates of Psychoticism, Absorption, and Openness to Experience in a large community sample, covarying for sex, age, and IQ. Findings support the hypothesis that the white matter correlates of the shared variance of these traits overlap substantially with the frontal lobe white matter connectivity patterns characteristic of psychotic spectrum disorders. Positive schizotypy did predict connectivity in hypothesized functional networks, but also appears positively associated with average coherence across all intrinsic networks. These findings provide biological support for the notion that liability to psychosis is distributed throughout the population, is evident in measureable neural features, and manifests as normal personality variation at subclinical levels.
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    Uncovering Disturbed Microstructure, Disrupted Microarchitecture, and Altered Network Topology in Traumatic Brain Injury
    (2019-05) Mahan, Margaret
    Background. Traumatic Brain Injury (TBI) is a debilitating condition, with long-term sequelae, affecting a considerable portion of the population. Typically, TBI transpires from a direct impact to the head as well as from the onset of acceleration and deceleration forces following abrupt changes in head position. Both localized and diffuse damage generated after injury are subject to a cascade of events altering normal biological function, culminating in heterogeneous pathophysiology. Considerable attempts to tackle this heterogeneity have been made. However, this has lead to numerous TBI classifications primarily rooted in subjective measurements, such as the Glasgow Coma Scale score, loss of consciousness duration, and post-traumatic amnesia, each known to be poor indicators of TBI. Physical measures, such as clinical findings from Computed Tomography (CT) or Magnetic Resonance Imaging (MRI) scans, are indispensable for assessing TBI, but they lack reliability and specificity in diagnosis and prognosis of TBI. Part of this deficiency arises from the reliance on subjective interpretation of these scans. A more significant issue arises as a result of inadequate methods that are unable to capture the extent and heterogeneity of damage. Objective. To extensively capture the heterogeneous pathophysiology in TBI using objective and physical measurements to advance candidate neuroimaging biomarkers for TBI, which will aid in overcoming the critical barriers that currently exist in diagnosis and prognosis of TBI. Contributions. For my first contribution, I developed and validated an algorithm that incorporates natural language processing methods to automate the extraction of TBI clinical findings from head CT scan radiology reports. The output of my algorithm is a structured summary of twenty-seven standardized TBI clinical findings with their respective status. My algorithm was validated using physician annotators as the gold standard and was found to be equivalent, sensitive, specific, and fast. For my second contribution, I examined the shape of the diffusion tensor, explored microstructural disturbances using voxelwise tract-based spatial statistics, quantified the location of these disturbances, and established an ordered ranking of diffusion measurements using machine learning methods. I discovered widespread changes in diffusion measurements, often affecting long-range connections, indicating disturbances in microstructural integrity after TBI. Lastly, I reported that structural diffusion measurements were better able to differentiate TBI from control groups. For my third contribution, I evaluated the construction of TBI structural networks using spectral graph theory and statistical network analysis to identify the most indicative diffusion measurements representing disrupted microarchitecture. I set out to uncover distinctions between diffusion measurements used in the construction of TBI structural networks, locate network disruptions, and discover preliminary network topologies. Using vastly different methodologies from my second contribution, I again found that structural diffusion measurements were better able to differentiate TBI from control groups. In addition, I discovered disrupted connections were primarily located in the parietal and temporal lobes, with geometric diffusion measurements more prevalent in these disruptions. These results indicate a dual representation of microarchitectural disruption in TBI structural networks. For my fourth contribution, I assessed TBI structural and functional networks using graph theory. I examined five measures of network topology: construction, integration, segregation, influence, and resilience. I discovered altered network integration and network influence in TBI structural networks, in addition to altered network resilience in TBI structural and functional networks, with no alterations to network segregation. My results indicate altered information exchange capacity, participation capacity, and flexibility of the TBI brain network. Discussion. The nearly universal acquisition of head CT scans following trauma necessitates the inclusion of their findings in discerning injuries. These head CT scans are often accompanied by a radiology report containing expert-level interpretation in unstructured narrative text format, which makes retrieving information challenging. The outcome of my first contribution addresses this and can be used to partition patients into meaningful groups based on physical measurements. A notable portion of TBI patients do not have clinical findings on hospital admission head CT scans yet display pathologies on MRI consistent with microstructural damage. Efforts to capture this damage have been extensive. However, there are discrepancies in identifying the location of microstructural damage and the direction of changes in measurements quantifying it. Part of this is due to an incomplete assessment of the diffusion tensor shape. The outcome of my second contribution addresses this deficiency, provides an assessment of underlying pathophysiological mechanisms associated with microstructural disturbances, and postulates candidate predictors of microstructural damage in TBI. The construction and study of connectomes, a field known as connectomics, provides a theoretical framework for understanding the brain as a complex network. Since microstructural disturbances in TBI lead to disruptions to the structural network, quantifying these disruptions are crucial for describing TBI pathophysiology, yet this quantification has not yet been previously tackled. The outcome of my third contribution addresses this and establishes more suitable methods for TBI structural network construction, which may aid in the precise identification of vulnerable brain regions. Quantification of diffuse injury over the entire structural network is required to appreciate network dysfunction. Naturally, this can be addressed by assessing alterations to connectome topology. The outcome of my fourth contribution addresses this and establishes the impact of alterations to network topology in TBI, which may lead to improvements in patient monitoring by assessing the progression of these topological alterations towards standardized levels. Impact. In summary, my dissertation is uniquely positioned to create valuable contributions towards identifying neuroimaging biomarkers that will catalyze research on TBI. I expect my contributions will lead to a significant shift in current TBI research by adopting novel network analysis and machine learning methods that go beyond conventional approaches and ultimately improve the lives of the TBI-afflicted.