Browsing by Author "LaConte, Stephen M."

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    Biomimetic sonar design and the investigation of the role of peripheral dynamics for target classification in bat biosonar
    Sutlive, Joseph Vinson (Virginia Tech, 2020-12-17)
    The biosonar system of bats has many unique adaptations which allow for navigation in extremely cluttered environments. One such adaptation is the rapid motion of the pinna and noseleaf observed in certain families of old-world bats (Rhinolophidae and Hipposiderae). Little is known about the physical properties about this adaptation affects emitted pulses or incoming echoes. To explore the physical properties of biosonar systems utilizing dynamic peripheries, biomimetic sonar systems have been devised, which can be used to simulate the structural characteristics of the pinna and noseleaf geometry as well as the motor characteristics. Using this method, it was determined that the changing conformations of the biomimetic baffles were responsible for time-variant signatures in echoes. These signatures could be seen in echoes from a variety of both simple and complex target shapes. Then to further the capabilities of the device, an improved actuation system was devised using pneumatic actuation. This allowed for the baffles to make several unique motions as opposed to being restricted to one previously. It was also shown that the distinct motion profiles of the system led to distinct differences in the received acoustic signal. The features encoded by this system could lead to improvements in the development of improved sensing of smaller autonomous systems. GRANT INFORMATION: This work was supported by grants from the Office of Naval Research (ONR) and the Naval Engineering Education Consortium (NEEC). Additional support was provided by an East Asia and Pacific Summer Institutes (EAPSI) fellowship from the National Science Foundation (NSF).
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    Calculation of the effective atomic number for the iodine contrast agent of the varying concentrations
    Pen, Olga Vladimirovna (Virginia Tech, 2016-07-25)
    The author discusses the difficulties that arise with the determination of the concentration of the iodinated contrast agents in the blood stream via the traditional gray-scale computer tomography and searches for the new imaging modalities that would provide for better sensitivity. The topic of the energy-discriminative color CT is discussed as a potential solution and its suitability is evaluated by performing the experiments on the contrast materials phantom and the phantom containing the iohexol solutions of varying concentrations on the original CT system assembled by the author. A method of the effective atomic number mapping is discussed as a viable alternative to the traditional attenuation-based tomography. The dependency of the effective atomic number of the compound on the energy of the x-ray beam is a phenomenon well recorded in the literature, yet no formal study exists to correctly predict the effective atomic number for a given compound. An extensive physical model is developed based on the previously presented models and adaptations unique to the task in order to determine the effective atomic numbers for exact energies experimentally. The method is tested on different materials. The resultant effective atomic numbers for the water, oil, and iohexol-water solutions of varying concentrations are presented in the study. The effects of the k-edge on both the linear attenuation curve and the effective atomic number curve are discussed. The possible future venues of the research are presented in the final part of the thesis.
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    Cumulative Vulnerabilities: Substance Use in Adolescence and in Recovery
    Tomlinson, Devin Christine (Virginia Tech, 2023-07-27)
    Substance use and substance use disorders (SUDs) pose a significant health and economic concern in the United States. Conditions and comorbidities exist that are associated with substance use onset, continuation, and outcomes. In the theory of Reinforcer Pathology, we can categorize these conditions into vulnerabilities, or factors that may be associated with susceptibility to substance use onset and poorer outcomes in substance use recovery. The theory of vulnerabilities and reinforcer pathology is tested through three investigations. The first investigation sought to establish the relationship between cumulative vulnerabilities and adolescent substance use in a cross-sectional analysis. The second investigation evaluates the temporal relationship of cumulative vulnerabilities and substance use among adolescents. The final investigation establishes the relationship of cumulative vulnerabilities and substance use among individuals in recovery from Opioid Use Disorder. Collectively, these reports suggest that the intersection and cumulation of vulnerabilities to substance use and substance use disorders are directly related to substance use outcomes. Future research and reports in the substance use domain should consider these constructs, their accumulation, and their co-occurrence patterns.
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    Decoding the Brain’s Surface to Track Deeper Activity
    Tenzer, Mark L.; Lisinsk, Jonathan M.; LaConte, Stephen M. (Frontiers Media, 2022-03-17)
    Neural activity can be readily and non-invasively recorded from the scalp using electromagnetic and optical signals, but unfortunately all scalp-based techniques have depth-dependent sensitivities. We hypothesize, though, that the cortex’s connectivity with the rest of the brain could serve to construct proxy signals of deeper brain activity. For example, functional magnetic resonance imaging (fMRI)-derived models that link surface connectivity to deeper regions could subsequently extend the depth capabilities of other modalities. Thus, as a first step toward this goal, this study examines whether or not surface-limited support vector regression of resting-state fMRI can indeed track deeper regions and distributed networks in independent data. Our results demonstrate that depth-limited fMRI signals can in fact be calibrated to report ongoing activity of deeper brain structures. Although much future work remains to be done, the present study suggests that scalp recordings have the potential to ultimately overcome their intrinsic physical limitations by utilizing the multivariate information exchanged between the surface and the rest of the brain.
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    The Development and Validation of a Neural Model of Affective States
    McCurry, Katherine Lorraine (Virginia Tech, 2015-09-23)
    Emotion dysregulation plays a central role in psychopathology (B. Bradley et al., 2011) and has been linked to aberrant activation of neural circuitry involved in emotion regulation (Beauregard, Paquette, & Lévesque, 2006; Etkin & Schatzberg, 2011). In recent years, technological advances in neuroimaging methods coupled with developments in machine learning have allowed for the non-invasive measurement and prediction of brain states in real-time, which can be used to provide feedback to facilitate regulation of brain states (LaConte, 2011). Real-time functional magnetic resonance imaging (rt-fMRI)-guided neurofeedback, has promise as a novel therapeutic method in which individuals are provided with tailored feedback to improve regulation of emotional responses (Stoeckel et al., 2014). However, effective use of this technology for such purposes likely entails the development of (a) a normative model of emotion processing to provide feedback for individuals with emotion processing difficulties; and (b) best practices concerning how these types of group models are designed and translated for use in a rt-fMRI environment (Ruiz, Buyukturkoglu, Rana, Birbaumer, & Sitaram, 2014). To this end, the present study utilized fMRI data from a standard emotion elicitation paradigm to examine the impact of several design decisions made during the development of a whole-brain model of affective processing. Using support vector machine (SVM) learning, we developed a group model that reliably classified brain states associated with passive viewing of positive, negative, and neutral images. After validating the group whole-brain model, we adapted this model for use in an rt-fMRI experiment, and using a second imaging dataset along with our group model, we simulated rt-fMRI predictions and tested options for providing feedback.
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    Development of a Minipig Model of BINT From Blast Exposure Using a Repeatable Mobile Shock Expansion Tube
    McNeil, Elizabeth M.; Walilko, Timothy; Hulbert, Lindsey E.; VanMeter, John W.; LaConte, Stephen M.; VandeVord, Pamela J.; Zai, Laila; Bentley, Timothy B. (Oxford University Press, 2021-10-22)
    Introduction: The Office of Naval Research (ONR) sponsored the Blast Load Assessment Sense and Test (BLAST) program to provide an approach to operationally relevant monitoring and analysis of blast exposure for optimization of service member performance and health. Of critical importance in this effort was the development of a standardized methodology for preclinical large animal studies that can reliably produce outcome measures that cannot be measured in human studies to support science-based guidelines. The primary advantage of this approach is that, because animal studies report physiological measures that correlate with human neuropathology, these data can be used to evaluate potential risks to service members by accounting for the anatomical and physiological differences between humans and large animal models. This article describes the methodology used to generate a comprehensive outcome measure dataset correlated with controlled blast exposure. Methods and materials: To quantify outcomes associated with a single exposure to blast, 23 age- and weight-matched Yucatan minipigs were exposed to a single blast event generated by a large-bore, compressed gas shock tube. The peak pressure ranged from 280 to 525 kPa. After a post-exposure 72-hour observation period, the physiological response was quantified using a comprehensive set of neurological outcome measures that included neuroimaging, histology, and behavioral measures. Responses of the blast-exposed animals were compared to the sham-treated cohort to identify statistically significant and physiologically relevant differences between the two groups. Results: Following a single exposure, the minipigs were assessed for structural, behavioral, and cellular changes for 3 days after exposure. The following neurological changes were observed: Structural- Using Diffusion Tensor Imaging, a statistically significant decrement (P < .001) in Fractional Anisotropy across the entire volume of the brain was observed when comparing the exposed group to the sham group. This finding indicates that alterations in brain tissue following exposure are not focused at a single location but instead a diffuse brain volume that can only be observed through a systematic examination of the neurological tissue. Cellular- The histopathology results from several large white matter tract locations showed varied cellular responses from six different stains. Using standard statistical methods, results from stains such as Fluoro-Jade C and cluster of differentiation 68 in the hippocampus showed significantly higher levels of neurodegeneration and increased microglia/macrophage activation in blast-exposed subjects. However, other stains also indicated increased response, demonstrating the need for multivariate analysis with a larger dataset. Behavioral- The behavior changes observed were typically transient; the animals' behavior returned to near baseline levels after a relatively short recovery period. Despite behavioral recovery, the presence of active neurodegenerative and inflammatory responses remained. Conclusions: The results of this study demonstrate that (1) a shock tube provides an effective tool for generating repeatable exposures in large animals and (2) exposure to blast overpressure can be correlated using a combination of imaging, behavioral, and histological analyses. This research demonstrates the importance of using multiple physiological indicators to track blast-induced changes in minipigs. The methodology and findings from this effort were central to developing machine-learning models to inform the development of blast exposure guidelines.
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    Development of Mechanical Optical Clearing Devices for Improved Light Delivery in Optical Diagnostics
    Vogt, William C. (Virginia Tech, 2013-09-12)
    Biomedical optics is a rapidly expanding field of research focusing on the development of methods to detect, diagnose, and treat disease using light. While there are a myriad of optical systems that have been developed for biological tissue imaging, optical diagnostics, and optical therapeutics, all of these methods suffer severely limited penetration depths due to attenuation of light by tissue constituent chromophores, including cells, water, blood, and protein structures. Tissue optical clearing is a recent area of study within biomedical optics and photonics, where chemical agents have been used to alter tissue optical properties, reducing optical absorption and scattering and enabling light delivery to and collection from deeper tissue regions. However, there are concerns as to the safety and efficacy of these chemical clearing agents in vivo, especially in the skin, where the projective barrier function of the stratum corneum must be removed. Mechanical optical clearing is a recently developed technology which utilizes mechanical loading to reversibly modify light transport through soft tissues, and much of the work published on this technique has focused on applications in skin tissue. This clearing technique enables deeper light delivery into soft tissues but does not require use of exogenous chemicals, nor does it compromise the skin barrier function. While this clearing effect is thought to be resultant from interstitial water and blood transport, the underlying mechanism has not been concretely identified nor characterized. The hypothesis of this body of work was that interstitial transport of tissue chromophores (e.g. water and blood) causes intrinsic optical property changes, reduces tissue optical absorption and scattering, and improves light delivery in diagnostic applications. To test this hypothesis, we first developed a mathematical framework to simulate mechanical optical clearing, using both mechanical finite element models and optical Monte Carlo simulations. By directly simulating interstitial water transport in response to loading, data from mechanical simulations was combined with optical Monte Carlo simulations, which enabled prediction of light transmission measurements made during mechanical indentation experiments. We also investigated changes in optical properties during mechanical indentation using diffuse reflectance spectroscopy. These studies used controlled flat indentation by a fiberoptic probe to dynamically measure intrinsic optical properties as they changed over time. Finally, we apply mechanical optical clearing principles to functional near-infrared spectroscopy for neuroimaging. By building a prototypical mechanical optical clearing device for measuring cerebral hemodynamics, we demonstrated that mechanical optical clearing devices modify measured cerebral hemodynamic signals in human subjects, improving signal quality.
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    Development of MRI-based Yucatan Minipig Brain Template
    Norris, Caroline N. (Virginia Tech, 2019-04-05)
    Yucatan minipigs have become increasingly common animal models in neuroscience where recent studies, investigating blast-induced traumatic brain injury, stroke, and glioblastoma, aim to uncover brain injury mechanisms [1-3]. Magnetic Resonance Imaging (MRI) has the potential to validate and optimize unknown parameters in controlled populations. The key to group-level MRI analysis within a species is to align (or register) subject scans to the same volumetric space using a brain template. However, large animal brain templates are lacking, which limits the use of MRI as an effective research tool to study group effects. The objective of this study was to create an MRI-based Yucatan minipig brain template allowing for uniform group-level analysis of this animal model in a standard volumetric space to characterize brain mechanisms. To do this, 5-7 month old, male Yucatan minipigs were scanned using a 3 Tesla whole-body scanner (Siemens AG, Erlangen) in accordance with IACUC. T1-weighted anatomical volumes (resolution = 1×1×1 mm3; TR = 2300 ms; TE= 2.89 ms; TI = 900 ms; FOV = 256 mm2 ; FA = 8 deg) were collected with a three-dimensional magnetization prepared rapid acquisition gradient echo (MPRAGE) pulse sequence [4]. The volumes were preprocessed, co-registered, and averaged using both linear and non-linear registration algorithms in AFNI [5] to create four templates (n=58): linear brain, non-linear brain, linear head, and non-linear head. To validate the templates, tissue probability maps (TPMs) and variance maps were created, and landmark variation was measured. TPMs computed in FSL [6] and AFNI show enhanced tissue probability and contrast in the non-linear template. Additionally, variance maps showed a more uniform spatial variance in the non-linear template compared to the linear. Registration variation within the brain template was within 1.5 mm and displayed improved landmark variation in the non-linear brain template. External evaluation subjects (n=12), not included in the template, were registered to the four templates to assess functionality. The results indicate that the developed templates provide acceptable registration accuracy to enable population comparisons. With these templates, researchers will be able to use MRI as a tool to further neurological discovery and collaborate in a uniform space.
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    Dynamic Causal Modeling Across Network Topologies
    Zaghlool, Shaza B. (Virginia Tech, 2014-04-03)
    Dynamic Causal Modeling (DCM) uses dynamical systems to represent the high-level neural processing strategy for a given cognitive task. The logical network topology of the model is specified by a combination of prior knowledge and statistical analysis of the neuro-imaging signals. Parameters of this a-priori model are then estimated and competing models are compared to determine the most likely model given experimental data. Inter-subject analysis using DCM is complicated by differences in model topology, which can vary across subjects due to errors in the first-level statistical analysis of fMRI data or variations in cognitive processing. This requires considerable judgment on the part of the experimenter to decide on the validity of assumptions used in the modeling and statistical analysis; in particular, the dropping of subjects with insufficient activity in a region of the model and ignoring activation not included in the model. This manual data filtering is required so that the fMRI model's network size is consistent across subjects. This thesis proposes a solution to this problem by treating missing regions in the first-level analysis as missing data, and performing estimation of the time course associated with any missing region using one of four candidate methods: zero-filling, average-filling, noise-filling using a fixed stochastic process, or one estimated using expectation-maximization. The effect of this estimation scheme was analyzed by treating it as a preprocessing step to DCM and observing the resulting effects on model evidence. Simulation studies show that estimation using expectation-maximization yields the highest classification accuracy using a simple loss function and highest model evidence, relative to other methods. This result held for various data set sizes and varying numbers of model choice. In real data, application to Go/No-Go and Simon tasks allowed computation of signals from the missing nodes and the consequent computation of model evidence in all subjects compared to 62 and 48 percent respectively if no preprocessing was performed. These results demonstrate the face validity of the preprocessing scheme and open the possibility of using single-subject DCM as an individual cognitive phenotyping tool.
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    Early childhood investment impacts social decision-making four decades later
    Luo, Yi; Hétu, Sébastien; Lohrenz, Terry; Hula, Andreas; Dayan, Peter; Ramey, Sharon L.; Sonnier-Netto, Mary Elizabeth; Lisinski, Jonathan; LaConte, Stephen M.; Nolte, Tobias; Fonagy, Peter; Rahmani, Elham; Montague, P. Read; Ramey, Craig T. (Nature Research, 2018-11-20)
    Early childhood educational investment produces positive effects on cognitive and non-cognitive skills, health, and socio-economic success. However, the effects of such interventions on social decision-making later in life are unknown. We recalled participants from one of the oldest randomized controlled studies of early childhood investment—the Abecedarian Project (ABC)—to participate in well-validated interactive economic games that probe social norm enforcement and planning. We show that in a repeated-play ultimatum game, ABC participants who received high-quality early interventions strongly reject unequal division of money across players (disadvantageous or advantageous) even at significant cost to themselves. Using a multi-round trust game and computational modeling of social exchange, we show that the same intervention participants also plan further into the future. These findings suggest that high quality early childhood investment can result in long-term changes in social decision-making and promote social norm enforcement in order to reap future benefits.
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    Early influences of microbiota on white matter development in germ-free piglets
    Ahmed, Sadia; Travis, Sierrah; Díaz-Bahamonde, Francisca; Porter, Demisha; Henry, Sara; Ravipati, Aditya; Booker, Aryn; Ding, Hanzhang; Ju, Jing; Ramesh, Ashwin; Pickrell, Alicia M.; Wang, Maosen; LaConte, Stephen M.; Howell, Brittany R.; Yuan, Lijuan; Morton, Paul D. (Frontiers, 2021-12-27)
    Abnormalities in the prefrontal cortex (PFC), as well as the underlying white matter (WM) tracts, lie at the intersection of many neurodevelopmental disorders. The influence of microorganisms on brain development has recently been brought into the clinical and research spotlight as alterations in commensal microbiota are implicated in such disorders, including autism spectrum disorders, schizophrenia, depression, and anxiety via the gut-brain axis. In addition, gut dysbiosis is common in preterm birth patients who often display diffuse WM injury and delayed WM maturation in critical tracts including those within the PFC and corpus callosum. Microbial colonization of the gut aligns with ongoing postnatal processes of oligodendrogenesis and the peak of brain myelination in humans; however, the influence of microbiota on gyral WM development remains elusive. Here, we develop and validate a neonatal germ-free swine model to address these issues, as piglets share key similarities in WM volume, developmental trajectories, and distribution to humans. We find significant region-specific reductions, and sexually dimorphic trends, in WM volume, oligodendrogenesis, and mature oligodendrocyte numbers in germ-free piglets during a key postnatal epoch of myelination. Our findings indicate that microbiota plays a critical role in promoting WM development during early life when the brain is vulnerable to environmental insults that can result in an array of disabilities manifesting later in life.
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    Extracting Feature Vectors From Event-Related fMRI Data to Enable Machine Learning Analysis
    Soldate, Jeffrey S. (Virginia Tech, 2022-10-05)
    Linear models are the dominant means of extracting summaries of events in fMRI for feature vector based machine learning. While they are both useful and robust, they are limited by the assumptions made in modeling. In this work, we examine a number of feature extraction techniques adjacent to linear models that account for or allow wider variation. Primarily, we construct mixed effects models able to account for variation between stimuli of the same class and perform empirical tests on the resulting feature extraction – classifier system. We extend this analysis to spatial temporal models as well as summary models. We find that mixed effects models increase classifier performance at the cost of increased uncertainty in prediction estimates. In addition, these models identify similar regions of interest in separating classes. While they currently require knowledge hidden during testing, we present these results as an optimum to be reached in additional works.
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    Eye Movements and Hemodynamic Response during Emotional Scene Processing: Exploring the Role of Visual Perception in Intrusive Mental Imagery
    Roldan, Stephanie Marie (Virginia Tech, 2017-06-05)
    Unwanted and distressing visual imagery is a persistent and emotionally taxing symptom characteristic of several mental illnesses, including depression, schizophrenia, and posttraumatic stress disorder. Intrusive imagery symptoms have been linked to maladaptive memory formation, abnormal visual cortical activity during viewing, gaze pattern deficits, and trait characteristics of mental imagery. Emotional valence of visual stimuli has been shown to alter perceptual processes that influence the direction of attention to visual information, which may result in enhanced attention to suboptimal and generalizable visual properties. This study tested the hypothesis that aberrant gaze patterns to central and peripheral image regions influence the formation of decontextualized visual details which may facilitate involuntary and emotionally negative mental imagery experiences following a stressful or traumatic event. Gaze patterns and hemodynamic response from occipital cortical locations were recorded while healthy participants (N = 39) viewed and imagined scenes with negative or neutral emotional valence. Self-report behavioral assessments of baseline vividness of visual imagery and various cognitive factors were combined with these physiological measures to investigate the potential relationship between visual perception and mental recreation of negative scenes. Results revealed significant effects of task and valence conditions on specific fixation measures and hemodynamic response patterns in ventral visual areas, which interacted with cognitive factors such as imagery vividness and familiarity. Findings further suggest that behaviors observed during mental imagery reveal processes related to representational formation over and above perceptual performance and may be applied to the study of disorders such as PTSD.
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    Impaired Behavioral and Pathological Outcomes Following Blast Neurotrauma
    Sajja, Venkata Siva Sai Sujith (Virginia Tech, 2013-08-30)
    Blast-induced neurotrauma (BINT) is a major societal concern due to the complex expression of neuropathological disorders after exposure to blast. Disruptions in neuronal function, proximal in time to the blast exposure, may eventually contribute to the late emergence of the clinical deficits. Besides complications with differential clinical diagnosis, the biomolecular mechanism underlying BINT that gives rise to cognitive deficits is poorly understood. Some pre-clinical studies have demonstrated cognitive deficits at an acute stage following blast overpressure (BOP) exposure. However, the behavioral deficit type (e.g., short term memory) and the mechanism underlying injury prognosis that onsets the cognitive deficits remains to be further investigated. An established rodent model of blast neurotrauma was used in order to study impaired behavioral and neuropathological outcomes following blast. Anesthetized rats were exposed to a calibrated BOP using a blast simulator while control animals were not exposed to BOP. Behavioral changes in short term memory and anxiety were assessed with standard behavioral techniques (novel objected recognition paradigm and light and dark box test) at acute and chronic stages (range: 3 hours -- 3 months). In addition, brains were assayed for neurochemical changes using proton magnetic resonance spectroscopy (MRS) and neuropathology with immunohistochemistry in cognitive regions of brain (hippocampus, amygdala, frontal cortex and nucleus accumbens) Early metabolic changes and oxidative stress were observed along with a compromise in energy metabolism associated with sub-acute (7 days following BOP exposure) active neurodegeneration and glial scarring. Data suggested GABA shunting pathway was activated and phospholipase A2 regulated arachadonic acid pathway may be involved in cellular death cascades. In addition, increased myo-inositol levels in medial pre-frontal cortex (PFC) further supported the glial scarring and were associated with impaired working memory at a sub-acute stage (7 days) following BOP exposure. Chronic working memory issues and anxiety associated behavior could be related to chronic activation of microglia in hippocampus and astrocytes in amygdala respectively. Furthermore, these results from MRS could be directly translated into clinical studies to provide a valuable insight into diagnosis of BINT, and it is speculated that gliosis associated markers (myo-inositol) may be a potential biomarker for blast-induced memory impairment.
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    Improving Fast-Scan Cyclic Voltammetry and Raman Spectroscopy Measurements of Dopamine and Serotonin Concentrations via the Elastic Net
    Long, Hunter Wayne (Virginia Tech, 2016-06-30)
    Dopamine and serotonin are two neurotransmitters known to both play a very important role in the human brain. For example, the death of dopamine producing neurons in a region of the brain known as the substantia nigra are known to cause the motor symptoms of Parkinson's disease. Also, many antidepressants are believed to work by increasing the extracellular level of serotonin in the brain. For the first time, it is now possible to measure the release of these two chemicals at sub-second time resolution in a human brain using a technique known as fast-scan cyclic voltammetry, for example from patients undergoing deep brain stimulation (DBS) electrode implantation surgery. In this work, we aimed to assess the feasibility of obtaining veridical dual measurements of serotonin and dopamine from substrates with mixtures of both chemicals. In the wet lab, data was collected on known concentrations of dopamine and serotonin and then used to make models capable of estimating the concentration of both chemicals from the voltammograms recorded in the patients. A method of linear regression known as the elastic net was used to make models from the wet lab data. The wetlab data was used to compare the performance of univariate and multivariate type models over various concentration ranges from 0-8000nM of dopamine and serotonin. Cross validation revealed that the multivariate model outperformed the univariate model both in terms of the linear correlation between predictions and actual values, and pH induced noise. The pH induced noise for the univariate model was 3.4 times greater for dopamine and 4.1 times greater for serotonin than the multivariate model. Raman spectroscopy was also investigated as a possible alternative to fast-scan cyclic voltammetry. Raman spectroscopy could have several benefits over fast-scan cyclic voltammetry, including the ability to chronically implant the measurement probe into a patient's brain and make observations over a long period of time. Raman spectroscopy data was collected on known concentrations of dopamine to investigate its potential in making in vivo measurements, however this data collection failed. Therefore, simulations were made which revealed the potential of the elastic net algorithm to determine the Raman spectra of several neurotransmitters simultaneously, even when they are in mixtures and the spectra are obstructed by the noisy background. The multivariate type model outperformed the univariate type model on Raman spectroscopy data and was able to predict dopamine with an error of 805nM RMS and serotonin with an error of 475nM RMS after being trained on concentrations smaller than 5uM of both dopamine and serotonin. In addition, the original Raman spectra of both neurotransmitters was extracted from the noise and reproduced very accurately by this method.
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    The inefficiency of open-loop fMRI experiments
    Norfleet, David George (Virginia Tech, 2023-06-29)
    The default mode network (DMN) is a highly cited neural network whose functional roles are not well understood. Until recently, event related fMRI experiments used to study the DMN could only be conducted in an open-loop format. The purpose of this study was to demonstrate the potential statistical advantages of real-time fMRI studies to conduct closed-loop experiments to directly test putative DMN functions. Using both fMRI simulations and large archival datasets, we demonstrate that open-loop designs are less statistically powerful than closed-loop experiments that can trigger stimuli at controlled levels of brain activity. When simulating event scheduling on resting state data, DMN levels were normally distributed, but the event timing proved to be ineffective in capturing the highest and lowest DMN values on average across subjects. Statistical differences in DMN levels collected by the Human Connectome Project-Aging (HCP-A) during a Go/NoGo task were also reported, along with the network's distributional effects across subjects. When examining DMN levels in 136 subjects more prone to commission errors the mean DMN levels were reported to be higher during and prior to incorrect NoGo responses. Exploring DMN levels in these same individuals reacting to a Go task also revealed differing measurement patterns when compared to all 711 subjects in the study. Additionally, the distribution of total DMN levels across all participants, as well as during a Go or NoGo trial, showed a shift in the mean towards deactivation. Furthermore, the peak at this location was greater and revealed that increased sampling occurred at the mean and under sampling at the tails. Overall, the cumulative findings in this study were successful in providing statistical arguments to support propositions for more powerful closed-loop experimentation in fMRI.
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    The influence and manipulation of resting-state brain networks in alcohol use disorder
    Myslowski, Jeremy Edward (Virginia Tech, 2024-01-25)
    Alcohol use disorder is common, and treatments are currently inadequate. Some of the acute effects of alcohol on the brain, such as altering the decision-making and future thinking capacities, mirror the effects of chronic alcohol use. Therefore, interventions that can address these shortcomings may be useful for reducing the negative effects of alcohol use disorder in combination with other therapies. The signature of those interventions may also be evident in the signature of large-scale, dynamic brain networks, which can show whether an intervention is effective. One such intervention is episodic future thinking, which has been shown to reduce delay discounting and orient people toward pro-social, long-term outcomes. To better understand decision making in high-risk individuals, we examined delay discounting in an adolescent population. When the decision-making faculties were challenged with difficult choices, adolescents made decisions inconsistent with their predicted preference, complemented by increased brain activity in the central executive network and salience network. Using these results and the hypothesis that the default mode network would be implicated in future thinking and intertemporal choice, we examined the neural effects of a brief behavioral intervention, episodic future thinking, that seeks to address these impairments. We showed that episodic future thinking has both acute and longer-lasting effects on consequential brain networks at rest and during delay discounting compared to a control episodic thinking condition in alcohol use disorder. Our failure to show group differences in default mode network prompted us to scrutinize it more carefully, from a position where we could measure the ability to self-regulate the network rather than its resting-state tendency. We implemented a real-time fMRI experiment to test the degree to which people along the alcohol use severity spectrum can self-regulate this network. Our results showed that default mode network suppression is impaired as alcohol use disorder severity increases. In the process, we showed that direct examination of resting-state networks with these methods will provide more information than measuring them at rest alone. We also characterized the default mode network along the real-time fMRI pipeline to show the whole-brain spatial pattern of regions associated and unassociated with the network. Our results indicate that resting-state brain networks are important markers for outcomes in alcohol use disorder and that they can be manipulated under experimental conditions, potentially to the benefit of the afflicted individual.
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    The Limits of Perceived Control:  Novel Task-Based Measures of Control under Effort and in Anhedonia
    Toole, Holly Sullivan (Virginia Tech, 2020-05-14)
    Previous research presents a paradox in relation to the value of exerting personal control such that personal control is generally reinforcing, but its value may also be limited in some individuals and under certain circumstances. Across two studies, this dissertation takes a step towards exploring the limitations of perceived control at the process-level by manipulating perceived control via the provision of choice. Manuscript 1 examined limitations of perceived control in the context of effort costs and found that actual control, but not illusory control, may be necessary to enhance motivation in the context of physical effort, suggesting that perceived control may be limited in the context of effort. Manuscript 2 examined limitations of perceived control in relation to self-reported symptoms of anhedonia and found that responsivity to personal control was diminished in those with higher levels of anhedonia. Together these studies examined factors associated with limitations in appetitive personal control and suggest avenues for future research exploring perceived control processes and how they may interface with reward processes, which has potential implications for developing interventions to alleviate reward-related deficits found in anhedonia.
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    A Machine Learning Approach for the Objective Sonographic Assessment of Patellar Tendinopathy in Collegiate Basketball Athletes
    Cheung, Carrie Alyse (Virginia Tech, 2021-06-07)
    Patellar tendinopathy (PT) is a knee injury resulting in pain localized to the patellar tendon. One main factor that causes PT is repetitive overloading of the tendon. Because of this mechanism, PT is commonly seen in "jumping sports" like basketball. This injury can severely impact a player's performance, and in order for a timely return to preinjury activity levels early diagnosis and treatment is important. The standard for the diagnosis of PT is a clinical examination, including a patient history and a physical assessment. Because PT has similar symptoms to injuries of other knee structures like the bursae, fat pad, and patellofemoral joint, imaging is regularly performed to aid in determining the correct diagnosis. One common imaging modality for the patellar tendon is gray-scale ultrasonography (GS-US). However, the accurate detection of PT in GS-US images is grader dependent and requires a high level of expertise. Machine learning (ML) models, which can accurately and objectively perform image classification tasks, could be used as a reliable automated tool to aid clinicians in assessing PT in GS-US images. ML models, like support vector machines (SVMs) and convolutional neural networks (CNNs), use features learned from labelled images, to predict the class of an unlabelled image. SVMs work by creating an optimal hyperplane between classes of labelled data points, and then classifies an unlabelled datapoint depending on which side of the hyperplane it falls. CNNs work by learning the set of features and recognizing what pattern of features describes each class. The objective of this study was to develop a SVM model and a CNN model to classify GS-US images of the patellar tendon as either normal or diseased (PT present), with an accuracy around 83%, the accuracy that experienced clinicians achieved when diagnosing PT in GS-US images that were already clinically diagnosed as either diseased or normal. We will also compare different test designs for each model to determine which achieved the highest accuracy. GS-US images of the patellar tendon were obtained from male and female Virginia Tech collegiate basketball athletes. Each image was labelled by an experienced clinician as either diseased or normal. These images were split into training and testing sets. The SVM and the CNN models were created using Python. For the SVM model, features were extracted from the training set using speeded up robust features (SURF). These features were then used to train the SVM model by calculating the optimal weights for the hyperplane. For the CNN model, the features were learned by layers within the CNN as were the optimal weights for classification. Both of these models were then used to predict the class of the images within the testing set, and the accuracy, sensitivity and precision of the models were calculated. For each model we looked at different test designs. The balanced designs had the same amount of diseased and normal images. The designs with Long images had only images taken in the longitudinal orientation, unlike Long+Trans, which had both longitudinal and transverse images. The designs with Full images contained the patellar tendon and surrounding tissue, whereas the ROI images removed the surrounding tissue. The best designs for the SVM model were the Unbalanced Long designs for both the Full and ROI images. Both designs had an accuracy of 77.5%. The best design for the CNN model was the Balanced Long+Trans Full design, with an accuracy of 80.3%. Both of the models had more difficulty classifying normal images than diseased images. This may be because the diseased images had a well defined feature pattern, while the normal images did not. Overall, the CNN features and classifier achieved a higher accuracy than the SURF features and SVM classifier. The CNN model is only slightly below 83%, the accuracy of an experienced clinician. These are promising results, and as the data set size increases and the models are fine tuned, the accuracy of the model will only continue to increase.
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    Magnetic Resonance Imaging Movies for Multivariate Analysis of Speech
    McRoberts, Katherine (Virginia Tech, 2013-09-04)
    The complex human motor function of speech presents a scientifically interesting, yet relatively unexplored, means to study brain-behavior relationships. Fortunately, magnetic resonance imaging (MRI), which has been proven to characterize soft tissue excellently, has recently become a promising technique for the study of speech. MRI\'s contributions in speech research could lead to new and individualized treatment for speech disorders. Although many studies have shown that MRI can capture information about speech, this project sought to determine what covert information could be disclosed from MRI movies through multivariate analysis. The articulation of phoneme pairs was imaged using a novel sequence, and simultaneously recorded. The data were then analyzed using support vector machine (SVM) analysis and canonical correlation analysis (CCA). Determination of classification accuracy through SVM analysis revealed that phoneme pairs were distinguishable from one another consistently over 90% of the time using information found from MRI movie clips of the speech. Additionally, study of the SVM weights demonstrated that SVM could identify regions of the vocal tract that are used to form auditory distinctions between the phonemes. Finally, CCA revealed relationships between images and the frequencies in corresponding audio waveforms; once again, the speech articulators were identified as lending maximum correlation to the sound profile. These promising results demonstrate that multivariate analysis can uncover information that is known to be true concerning speech production. These analyses may perhaps even contribute to existing knowledge and thus provide a platform from which to advance the treatment of speech dysfunction.