Master of Science, The Ohio State University, 2024, Environment and Natural Resources

Ring-necked Pheasants (Phasianus colchicus, herein referred to as pheasant) are an introduced game bird that occupy a contemporary niche in Ohio's agricultural ecosystems, serving as an analogue for the native prairie Galliformes. After their introduction in the 1800's pheasants reached a peak density in the 1930's and then began a steady decline. This decline is attributable to the advent of commercial farming and crop subsidies introduced by Roosevelt's New Deal (1933). These subsidies led to drastic land use change in rural Ohio, replacing grassland and fallow fields with large commercial farms. Like the rest of North America, loss of nesting and winter habitat to commercial agriculture has led to the decline of pheasants in Ohio. This loss of habitat has also led to fragmentation and reduced habitat connectivity between suitable patches. Our current understanding of pheasants' response to land cover lacks the context of scale and habitat connectivity. These concepts are important for conservation as changes in arrangement and surrounding cover type may render some habitat unusable despite being the preferred cover type. My objectives were to find suitable patches, connections between them, and areas that would improve connectivity. To better inform the conservation of pheasant, I investigated pheasants' response to cover type and its scale of effect along with habitat connectivity. Using a novel multiscale framework, I analyzed landscape suitability by modeling the influence of cover types on pheasant occupancy and density. To quantify habitat connectivity, I used combination of circuit theory and graph theory to find areas of high importance for connectivity. Conservation Reserve Programs and grassland were both positively related to pheasant occupancy and density at a relatively fine scale, and developed areas and forests had a large negative impact at a broad scale. Additionally, the majority of the state has a low degree of habitat connectivity for pheasant. (open full item for complete abstract)

Committee: William Peterman (Advisor); Stephen Matthews (Committee Member); Robert Gates (Advisor) Subjects: Ecology; Wildlife Conservation; Wildlife Management

Doctor of Philosophy, The Ohio State University, 2022, Psychology

Multiple sclerosis (MS) is a chronic neurological disease of the central nervous system (CNS) characterized by widespread inflammation, neurodegeneration, and reparation failures. Amongst its sequelae, slowed processing speed remains the earliest predictor of disease burden. MS causes heterogeneous and often subtle changes to functional and structural connections in the brain, even before symptoms manifest. Harnessing neuroimaging-based biomarkers to predict individual prognosis may facilitate patient-centered preventative care before cognitive decline becomes life-limiting. Through leveraging machine learning approaches within a cross-validation framework, we can build models from high dimensional functional and structural whole-brain connectivity to predict individual-level cognition. The present study used neuroimaging data from 64 people with relapsing-remitting MS to construct a multimodal structure-function connectome. We used a data-driven iterative pipeline to train and test models to make continuous predictions of processing speed and quantified model performance through prediction accuracy. Behaviorally, processing speed was significantly correlated with both disease severity and depression scores, confirming shared variance between cognitive and clinical function. However, the multimodal connectome did not yield significant predictions of processing speed in the current sample, and predicted processing speed did not correlate significantly with observed disease severity and depression scores. Separate functional and structural connectomes also did not explain meaningful variance in processing speed. This is the first study to apply machine learning regression techniques in a systematic way across two brain parcellations and both multimodal and unimodal connectomes to make individual-level predictions of cognition in people with MS. Although this study fused structural and functional connectivity using one method, alternative data-driven approaches for bui (open full item for complete abstract)

Committee: Ruchika Prakash (Advisor); Charles Emery (Committee Member); David Osher (Committee Member) Subjects: Clinical Psychology

Master of Science, The Ohio State University, 2021, Environment and Natural Resources

Migratory bird populations can be limited by events in disparate parts of the world. Despite that roughly two-thirds of a migrant's annual cycle is spent migrating and on the winter grounds, these periods are poorly studied, limiting our ability to design effective conservation strategies. An understanding of basic migratory and winter ecology is critical for a full annual cycle approach to the conservation of rapidly declining species. The Eastern whip-poor-will (hereafter, whip-poor-will) is a rapidly declining (-70% from 1966-2016) nightjar, yet data remains elusive for the species outside of the breeding season where they are highly vocal. We extracted data from 52 archival GPS tags from individuals across the Midwestern U.S. to understand large-scale migratory movements and space use on the wintering grounds. We also used satellite imagery and stable-Carbon (measuring habitat moisture) and -Nitrogen (relative trophic level) isotope ratios from winter-grown claws to analyze how land use and habitat moisture impact home range size and relative trophic level. Whip-poor-wills circumvented the Gulf of Mexico, and populations across a large latitudinal gradient came together in eastern Texas in early October, resulting in increasing spatial overlap throughout migration. Migratory connectivity was low (MC = 0.22), with extensive overlap of core wintering areas in southern Mexico and Guatemala. The overlap of wintering areas by individuals across a large latitudinal span suggests that whip-poor-wills are telescopic migrants, although a single line of weak evidence pointed towards a leapfrog migration pattern. We examined predictors of home range size at three spatial scales (broader geographic region, site, and home range), and found that forest fragmentation in the site and the presence of agriculture in the home range were positively related to home range size. These results suggest that both landscape configuration and composition variables within the site and t (open full item for complete abstract)

Committee: Chris Tonra (Advisor); Stephen Matthews (Committee Member); Mazeika Sullivan (Committee Member) Subjects: Environmental Science

Doctor of Philosophy, Case Western Reserve University, 2024, Epidemiology and Biostatistics

Functional connectivity (FC) is the degree of synchrony of time series between distinct, spatially separated brain regions. While traditional FC analysis assumes the temporal stationarity throughout a brain scan, there is growing recognition that connectivity can change over time and is not stationary, leading to the concept of dynamic FC (dFC). Resting-state functional magnetic resonance imaging (fMRI) can assess dFC using the sliding window method with the correlation analysis of fMRI signals. Accurate statistical inference of sliding window correlation must consider the autocorrelated nature of the time series. Currently, the dynamic consideration is mainly confined to the point estimation of sliding window correlations. Using in vivo resting-state fMRI data, we first demonstrate the non-stationarity in both the cross-correlation function (XCF) and the autocorrelation function (ACF). Then, we propose the variance estimation of the sliding window correlation considering the nonstationary of XCF and ACF. This approach provides a means to dynamically estimate confidence intervals in assessing dynamic connectivity. Using simulations, we compare the performance of the proposed method with other methods, showing the impact of dynamic ACF and XCF on connectivity inference. We further apply our proposed method on two in vivo resting-state fMRI data, one for health subjects, one for tumor patients. We show the additional information can be obtained for statistical inference using our method. We also map temporal fluctuations of FC in brain tumor patients and look at the test-retest reliability. We demonstrate the feasibility of performing resting-state functional connectivity studies in intraoperative settings with high spatial-temporal resolution. Accurate variance estimation used in this analysis can help in addressing the critical issue of false positivity and negativity.

Committee: Abdus Sattar (Committee Chair); Xiaofeng Zhu (Advisor); Curtis Tatsuoka (Committee Member); Douglas Martin (Committee Member); Stefan Posse (Committee Member) Subjects: Biostatistics

Doctor of Education (Educational Leadership), Youngstown State University, 2024, Department of Teacher Education and Leadership Studies

Teacher-student relationships have long been identified as an important factor in the success of a student, and in the investment of the teacher. Extensive research has been conducted on the perception and outcomes of teacher-student relationships from the perspective of the teacher and that of the students. However, research on the outcomes, insights, and expectation of teacher-student relationships from the perspective of parents, specifically parents of urban elementary students, is scarce. This study aimed to address the research gap by exploring the lived experiences of urban elementary parents through a phenomenological, qualitative study. Three semi-structured focus groups were held in which the researcher interviewed and facilitated discussions with parents of a child currently in kindergarten- second-grade in an urban school district in Northeastern Ohio. The 16 parent participants were asked to describe characteristics and expectations of high-quality teacher-student relationships, reflecting on their lived experiences and current state as a parent of an elementary student. Participants shared personal anecdotes, reflected on the impact of teachers in their own life, and emphasized the formative role teachers play in all aspects of a child's life. Responses indicated five primary themes when determining expectation and characteristics of teacher-student relationships from the perspective of parents: 1. Strong and consistent communication, 2. Teacher care and connection, 3. Teacher awareness and understanding to individual situations, 4. Holding students and families to high-expectations, and 5. Student engagement and recognition of individualized learning. The results of the study suggest that the teacher-student relationship is pivotal in making meaningful connections and creating a sense of belongingness and interconnectivity for students. Implications of the study indicate the importance of viewing the teacher-student relationship from a comprehensive l (open full item for complete abstract)

Committee: Jane Beese EdD (Committee Chair); Nate Myers PhD (Committee Member); Jake Protivnak PhD (Committee Member) Subjects: Early Childhood Education; Education; Educational Leadership; Educational Theory; Elementary Education; Higher Education; School Administration; Teaching

Doctor of Philosophy, The Ohio State University, 2024, Industrial and Systems Engineering

This dissertation addresses two applied problems relating to images. The first relates to images of pipeline corrosion and the second relates to images of the human brain and individuals with Attention-Deficit/Hyperactivity Disorder (ADHD). The corrosion of oil and gas pipelines is important because there are thousands of leaks every year costing billions of dollars for cleanups. ADHD is important because a substantial fraction of the world population has the disorder causing significant suffering and hundreds of billions of dollars of losses to the world economy. To address both image analysis problems, novel statistical and operations research techniques are proposed which have potentially wide applicability. Relating to pipeline corrosion, an established simulation method is called the “voxel” method which permits predictions about how images and pipelines or other media will change as corrosion evolves. In most realistic cases, we find that the parameter values or “inputs” (Xs) needed to run the simulation are unknown. We only have the images which are essentially outputs (Ys) which can be generated by real world experiments or simulations. The phenomenon of having incomplete inputs for simulation is common in many engineering and science situations and a critical challenge for both people and artificial intelligence. We and others have called this important subject, “empirical forward-inverse estimation” since we can gather data (empirically) in the forward manner progressing from assumed inputs (Xs) to measured outputs (Ys) and then generate inverse predictions from Ys to Xs. With (hopefully) accurately estimated X values, the experimental setup or simulation can then predict the future corrosion evolution and whether repair in critically needed. Relating to forward-inverse analyses, 24 variants of an established two stage method or framework are studied in relation to enhanced inverse prediction accuracy for two test cases including pipeline corrosion (open full item for complete abstract)

Committee: Theodore T. Allen (Advisor); William (Bill) Notz (Committee Member); Samantha Krening (Committee Member); Marat Khafizov (Committee Member) Subjects: Engineering; Industrial Engineering; Materials Science; Statistics

Doctor of Philosophy, The Ohio State University, 2023, Psychology

This dissertation investigates the neural representation of complex social information by employing multivariate methods such as functional connectivity classification analyses and intersubject representational similarity analyses (IS-RSA). The present research examines the functional network associated with social interactions as well as the association between neural similarity and individual differences in emotional reactivity and empathy. Study 1 utilizes multivariate pattern analyses (MVPA) to classify different categories of social interactions based on connectivity patterns between brain regions involved in social perception. Study 2 uses IS-RSA to investigate how individual differences in emotional reactivity modulates the neural representation of different social interactions. Study 3 explores how idiosyncrasies in behavioral measures of empathy are associated with neural synchrony during the observation of naturalistic social scenes depicting specific characters and various types of interactions. Study 1 demonstrates that contextual categorical information about social interactions is better classified by a network of regions rather than within a single region of interest. Studies 2 and 3 suggest that idiosyncrasies in trait-like attributes such as empathy or emotional reactivity reflects differences in neural representation of complex social information. This research contributes to our understanding of how social information is processed in the brain and sheds light on the impact of individual differences on social perception.

Committee: Dylan Wagner (Advisor); Baldwin Way (Committee Member); Steven Spencer (Committee Member) Subjects: Neurosciences; Psychology; Social Psychology

Doctor of Philosophy, The Ohio State University, 2023, Speech and Hearing Science

Two essential components of language are semantics, or the meaning of language, and phonology, or the sounds that make up our words. Researchers have long sought to investigate the neural correlates of semantics and phonology; however, questions remain related to the specific brain regions comprising each network as well as the degree to which these networks coincide. Moreover, patterns of reorganization following injury to these networks in populations such as those with post-stroke aphasia remain unclear. Across three manuscripts, this dissertation addresses these questions, emphasizing the influence of aging on the language networks in the brain as well as reorganization during the process of recovery from post-stroke aphasia. Recent work examining the semantic and phonological networks in the brain has focused on neurologically intact younger adults. Considering many people who experience acquired language impairments are older adults, the first manuscript in this dissertation presents the results of a scoping review addressing the regions comprising the semantic and phonological brain networks in this aging population. The review finds that these brain networks are consistent with the networks of younger adults but may have subtle differences that should be further explored in a full systematic review or meta-analysis. The second manuscript in this dissertation specifically examines the resting-state functional connectivity of the inferior frontal gyrus, a region that has been implicated in both semantic and phonological brain networks and is often damaged in cases of post-stroke aphasia. Compared with younger adults, we again found subtle differences that may be accounted for in part by theories of age-related de-lateralization of the dominant left hemisphere. We next correlated significant resting-state functional connectivity with behavioral tasks targeting semantics and phonology, which did not support theories of semantic specialization at the anterior inf (open full item for complete abstract)

Committee: Stacy M. Harnish (Advisor); Jennifer Brello (Committee Member); David E. Osher (Committee Member) Subjects: Neurosciences; Psychology; Rehabilitation; Speech Therapy

Master of Science, Miami University, 2023, Electrical and Computer Engineering

Distributed Unmanned Aerial Vehicle (UAV) based communication networks (UCNs) are robust, next-generation communication system that ensures mobile on-demand service. Distributed multi-agent reinforcement learning (MARL) can be a practical approach to solving time-coupled sequential decision problems associated with such UCNs while achieving scalability. However, a collaborative impact study of information exchanges among UAVs on performance and UCNs with cooperate-compete relationships has yet to be well studied. In this thesis, we aim for the stepwise traversal of UAVs to maximize connected users, formulated as a time-coupled mixed-integer non-convex optimization problem. A multi-agent deep Q-learning (MA-DQL) algorithm with different reward functions featuring four information exchange levels is proposed to solve the overall connected user problem. Another proposed approach incorporates correlated equilibrium from game theory with multi-agent deep Q-learning to solve individual connected user problems. Extensive simulation results are conducted to compare and contrast the connectivity performance of different levels and correlated MA-DQL. The findings demonstrate that exchanging state information with a task-specific reward function design produces the best performance for stationary and dynamic user distribution. In addition, results indicate that correlated MA-DQL has an optimal management strategy in an environment with UAVs in contention.

Committee: Ran Zhang (Advisor); Bryan Van Scoy (Committee Member); Miao Wang (Committee Member) Subjects: Computer Engineering; Electrical Engineering

Master of Science, The Ohio State University, 2023, Environment and Natural Resources

The federally threatened eastern massasauga rattlesnake (Sistrurus catenatus) occurs across the Great Lakes region of the midwestern United States in increasingly small and fragmented populations. While massasaugas are relatively well-studied among snakes, much is still unknown about their baseline habitat requirements, as well as how they move across heterogeneous landscapes. One of the most stable remaining populations outside the species strongholds of Michigan and Ontario is found at a wildlife area in northern Ohio. My research objectives were to: 1) identify land use practices and habitat features that best predict massasauga occurrence at the wildlife area; and 2) determine how the wildlife area is functionally connected for massasaugas given the amount of active agricultural production still taking place on the landscape and the species' tendency not to travel great distances. During the 2022 field season, I used adapted-Hunt drift fence technique (AHDriFT) camera arrays and timed constrained visual encounter surveys to assess massasauga occupancy and created single-species integrated occupancy models to establish which covariates best predicted occupancy. Massasaugas were more likely to occupy sites with a higher proportion of open herbaceous habitat, sites with a higher proportion of marginal habitat features like infrequently mowed ditches and field margins, and sites that had been out of agricultural production for a longer time. I created a series of cumulative kernel density surfaces using three different dispersal kernels to analyze functional connectivity for massasaugas at the wildlife area. I also examined the potential impact of agriculture on connectivity by using three alternative resistance values for agriculture in the resistance surface. The probability of detecting dispersing massasaugas was highest in and around the heavily occupied center of the wildlife area. Using the mean rank for each of the 45 agricultural fields across the nine (open full item for complete abstract)

Committee: William Peterman (Advisor); Christopher Tonra (Committee Member); Stephen Matthews (Committee Member); Gabriel Karns (Committee Member) Subjects: Conservation; Ecology; Wildlife Conservation; Wildlife Management

Doctor of Philosophy, The Ohio State University, 2022, Mechanical Engineering

In recent years, the trend in the automotive industry has been favoring the reduction of fuel consumption in vehicles with the help of new and emerging technologies. This drive stemmed from the developments in communication technologies for Connected and Autonomous Vehicles (CAV), such as Vehicle to Infrastructure (V2I), Vehicle to Vehicle (V2V) and Vehicle to Everything (V2X) communication. Coupled with automated driving capabilities of CAVs, a new and exciting era has started in the world of transportation as each transportation agent is becoming more and more connected. To keep up with the times, research in the academia and the industry has focused on utilizing vehicle connectivity for various purposes, one of the most significant being fuel savings. Motivated by this goal of fuel saving applications of Connected Vehicle (CV) technologies, the main focus and contribution of this dissertation is developing and evaluating a complete Eco-Driving strategy for CAVs. Eco-Driving is a term used to describe the energy efficient use of vehicles. In this dissertation, a complete and comprehensive Eco-Driving strategy for CAVs is studied, where multiple driving modes calculate speed profiles ideal for their own set of constraints simultaneously to save fuel as much as possible while a High Level (HL) controller ensures smooth transitions between the driving modes for Eco-Driving. The first step in making a CAV achieve Eco-Driving is to develop a route-dependent speed profile called Eco-Cruise that is fuel optimal. The methods explored to achieve this optimally fuel economic speed profile are Dynamic Programming (DP) and Pontryagin's Minimum Principle (PMP). Using a generalized Matlab function that minimizes the fuel rate for a vehicle travelling on a certain route with route gradient, acceleration and deceleration limits, speed limits and traffic sign (traffic lights and STOP signs) locations as constraints, a DP based fuel optimal velocity profile is found. The ego CAV (open full item for complete abstract)

Committee: Levent Guvenc (Advisor); Mrinal Kumar (Committee Member); Bilin Aksun-Guvenc (Committee Member) Subjects: Automotive Engineering; Computer Science; Design; Energy; Engineering; Experiments; Mechanical Engineering; Systems Design; Technology; Transportation

MS, University of Cincinnati, 2022, Engineering and Applied Science: Biomedical Engineering

The objective of the current study is to investigate brain alterations in patients with localization-related epilepsy (LRE) compared to healthy controls using different brain activity measures including regional homogeneity (ReHo); functional connectivity density mapping (FCDM) with global (gFCD) and lobal (lFCD); amplitude of low-frequency fluctuations (ALFF) and fractional amplitude of low-frequency fluctuations (fALFF), based on resting-state fMRI data. ReHo, gFCD and lFCD, ALFF and fALFF were used to examine the alterations in the brain from 19 LRE patients and 19 healthy controls, by using the whole brain resting-state fMRI (rs-fMRI) data. For each method, a two sampled t-test was conducted; and all results were corrected for family wise error for group analysis. The LRE patients were placed into subgroups based on whether the seizure onset falls into the limbic regions. The two-samples t-test was conducted to investigate the brain alterations in the limbic LRE patients versus the healthy controls, the non-limbic LRE patients versus the healthy controls, and between these two epileptic patient groups. The current findings demonstrated ReHo, gFCD, lFCD, ALFF, and fALFF alterations in the LRE patients compared to healthy subjects during resting state. For both group analysis and for subgroup limbic analysis, we obtained specific brain regions with significantly altered ReHo, lFCD, ALFF, and fALFF values. The targeted brain regions with significant alterations might be contributing to the overall lower synchronization and more impaired functional activity in the processing of motor pathways, visual information, and emotional functions. The targeted brain regions were also in the salience network and the default mode network (DMN), as well as part of the language area, sensorimotor regions, and those associated with the posterior attention system. We also (open full item for complete abstract)

Committee: Jing-Huei Lee Ph.D. (Committee Member); Marepalli Rao Ph.D. (Committee Member); Zackary Cleveland Ph.D. (Committee Member) Subjects: Biomedical Research

Doctor of Philosophy, University of Toledo, 2021, Biology (Ecology)

Conserving and managing migratory species is inherently complicated due largely to their reliance on multiple landscapes at different stages of their annual cycle. The combination and degree to which each life stage (e.g., nascence through independence from adult care), geographical location (e.g., a large estuarine stopover site), or portion of the annual cycle (e.g., the nonbreeding period) influences a population is often unknown. Thus, resulting conservation strategies are often built with information representing a limited portion of a migratory species' annual range. This trend is concerning as recent studies demonstrate the influence of poorly studied life stages (e.g., the post-fledging period) and carryover effects (e.g., habitat quality and food availability influencing subsequent productivity) on population dynamics of migratory species. Previous research suggests that, like other migratory taxa, global populations of many migratory birds are declining at alarming rates, presenting an important and time-sensitive opportunity to develop full life-cycle conservation strategies and identify and mitigate key factors driving population declines in migratory species. This dissertation investigates the migratory ecology of Vermivora warblers and synthesizes findings in ecological, evolutionary, and conservation frameworks. Vermivora warblers are a species complex composed of two extant species of obligate Nearctic-Neotropical migrant warblers that are extremely closely related. Golden-winged warblers (Vermivora chrysoptera) and blue-winged warblers (Vermivora cyanoptera) breed and migrate throughout deciduous forests of eastern North America and occur throughout Central America, with golden-winged warblers also occurring in northern South America during the nonbreeding period. On the breeding grounds, golden-winged warblers and blue-winged warblers have overlapping distributions and regularly hybridize to produce viable young. Recent genomic evidence suggests (open full item for complete abstract)

Committee: Henry Streby (Advisor); David Andersen (Committee Member); Petra Wood (Committee Member); David Buehler (Committee Member); Jon Bossenbroek (Committee Member) Subjects: Animals; Biology; Ecology; Environmental Science; Wildlife Conservation; Zoology

PhD, University of Cincinnati, 2021, Medicine: Biomedical Informatics

Precision Medicine seeks to tailor treatments to the individual and thus aims to find the “right drug, for the right person, in the right dose”. This dissertation deals with computational approaches for drug discovery and repurposing that combine cheminformatics and predictive omics signatures for applications of precision medicine in brain disorders and renal cancer. Brain disorders and renal cancer represent two disease classes that are in dire need of new drugs and further advances in personalized medicine treatments. Brain disorders such as Alzheimer disease, schizophrenia, or Parkinson disease while representing a growing prevalence of disability worldwide have no successful treatments despite years of research. Another difficult to treat disease is renal cancer. Metastatic renal cancer exhibits a mere 12% 5-year survival rate, however a durable, complete response to treatments is rarely achieved. In this dissertation, chemical and omics domains are combined to develop a new computational framework for drug discovery and repurposing for brain disorders and renal cancer. The chemical domain includes cheminformatics and metabolomics, and the omics domain encompasses transcriptomics and proteomics. The approaches that are integrated into this dissertation include transcriptional signature connectivity analysis, structure activity relationship, as well as analysis of transcriptomic, metabolomic, and proteomic data. The results are disseminated through publicly available tools and software repositories to further advance research in precision medicine.

Committee: Jaroslaw Meller Ph.D. (Committee Chair); Maria Czyzyk-Krzeska M.D. Ph.D. (Committee Member); Robert McCullumsmith M.D. Ph.D. (Committee Member); Mario Medvedovic Ph.D. (Committee Member); Senthilkumar Sadhasivam M.D. (Committee Member) Subjects: Bioinformatics

Doctor of Philosophy, The Ohio State University, 2021, Agricultural, Environmental and Developmental Economics

In these essays, I study the soil-to-human mineral transmission and the role of markets in mitigating the linkages in the context of Nepal and Malawi in the first two chapters. And, in the last chapter I explore the adverse impact of rural connectivity on children's unhealthy dietary habits in rural Nepal. The first chapter examines the negative child health impacts of soil zinc (Zn) deficiency in Nepal. Soil Zn deficiency limits the Zn concentration in food crops, leading many to speculate that it underlies human Zn deficiency and child stunting, globally and particularly in South Asia. We find strong evidence that soil Zn deficiency does have a causal impact on child stunting in Nepal's Tarai region, the breadbasket of the country. Using conservative causal bounds, we find that a 1 part per million increase in plant-available soil Zn --- achievable with application of Zn-enriched fertilizer --- decreases child stunting by 9-10 percentage points in the long run, and 4-7 percentage points in the short run. Multiple statistical sensitivity tests indicate that these relationship are not manufactured by omitted, relevant variables. The association is strongest in the most isolated areas and in seasons where dependence on food crops is strongest, as expected if soil Zn deficiency reduces local crop Zn concentration and through that human Zn intake and status. The second chapter investigates the linkage between human selenium (Se) status and the local vs nearby market Se environment, and the role of markets in mitigating the linkage in Malawi. Using nationally representative data on human micronutrient concentration and gridded data on soil characteristics, we find that local soil type (with implications for crop Se concentration) and proximity to Lake Malawi (which holds Se-rich fish) are strongly predictive of human Se status. Furthermore, markets seem to aggregate Se from their catchment area and supply that Se to nearby families. Human Se intake and status ther (open full item for complete abstract)

Committee: Mark Partridge (Advisor); Leah Bevis (Advisor); Abdoul Sam (Committee Member) Subjects: Agricultural Economics; Economics; Health

Doctor of Philosophy, The Ohio State University, 2021, Neuroscience Graduate Studies Program

Tasks in everyday life are not completed in isolation. We each uniquely maneuver in an environment rich with information that undoubtedly influences our behaviors. For example, searching for your keys in the kitchen does not happen in the absence of drawers, counter tops, plates on the table, a stack of mail etc. Rather this contextual information can influence your search. This dissertation is focused on exploring how the contexts we are exposed to during a task can affect how information is processed, and eventually behavioral outcomes. Two specific types of context will be explored: spatial and Gestalt grouping cues. Additionally, due to individual differences in task context utilization, I sought to explore a method that could be used to study brain-behavior relationships. The first study examines how context may not be learned when faced with increased task demands. When exposed to the same spatial layout of a target and distractors on a computer screen multiple times, participants become faster at finding the target when searching through repeated displays, i.e. the contextual cueing effect. However, when a secondary task had to be completed immediately after the search task, subjects did not always exhibit the expected search facilitation for repeated displays. It is speculated that the attenuation of cueing due to the secondary task results from attentional resources being redirected during the critical consolidation period after the search concludes. Thus, a spatial context was not always able to influence performance. The second study examines how individuals can overcome visual working memory capacity limitations through the use of an illusory grouping context. Illusory objects like the Kanizsa triangle, have been shown to produce benefits to visual working memory performance, possibly by allowing the inducers forming the object to be perceived as an individual unit rather than separate distractors, but it was unknown exactly how the triangle led to b (open full item for complete abstract)

Committee: Andrew Leber PhD (Advisor); Julie Golomb PhD (Committee Member); Benedetta Leuner PhD (Committee Member); Zeynep Saygin PhD (Committee Member) Subjects: Cognitive Psychology; Neurosciences; Psychology

Master of Science, The Ohio State University, 2021, Psychology

Multiple Sclerosis is a common, neurodegenerative disorder characterized by the accumulation of gray and white matter lesions within the central nervous system and presenting with progressive disability within the physical, sensory, and cognitive domains. Understanding the relationship between cognitive dysfunction, which affects an estimated 70% of people with multiple sclerosis (PwMS), and overall disease burden (commonly measured by the Expanded Disability Status Scale; EDSS) is exceedingly important for informing research interventions to preserve patient quality of life. The current study utilized resting-state fMRI to derive connectome-based predictive models (CPM) of two cognitive domains affected in PwMS (information processing speed and working memory) and determine the contribution of model-derived metrics in explaining variance associated with EDSS. We were able to successfully derive a model of processing speed (rs= .41, p= .03), but not working memory, perhaps due to processing speed deficits emerging earlier and more prominently in PwMS. fMRI-derived processing speed metrics uniquely accounted for 13.19% of the variance in explaining EDSS. In contrast, behavioral performance on processing speed measures, working memory measures, and calculated total lesion volume did not explain a significant amount of variance in EDSS, suggesting that functional connections associated with processing speed ability may importantly contribute to patient disease burden. This work therefore supports the hypothesis that models of MS disease burden may benefit from functional inputs in addition to structural and behavioral ones, though future work is needed to establish the mechanisms of MS-related disease processes on functional connections, cognition, and disease.

Committee: Ruchika Prakash PhD (Advisor); Zeynep Saygin PhD (Committee Member); Jasmeet Hayes PhD (Committee Member) Subjects: Clinical Psychology; Cognitive Psychology; Neurosciences; Psychology

Master of Sciences, Case Western Reserve University, 0, EECS - Computer and Information Sciences

Brain functional connectivity measures are used to study interactions between brain regions in various neurological disorders such as Alzheimer's Disease and epilepsy. In particular, high-resolution electrophysiological signal data recorded from intracranial electrodes, such as stereotactic electroencephalography (SEEG) signal data, is often used to characterize the properties of brain connectivity in neurological disorders. For example, SEEG data is used to lateralize the epileptogenic zone and characterize seizure networks in epilepsy. However, there are several computational challenges associated with efficient and scalable analysis of signal data in neurological disorders due to the large volume and complexity of signal data. In order to address the challenges associated with processing and analyzing signal datasets, we have developed an integrated platform called Neuro-Integrative Connectivity (NIC) platform that integrates and streamlines multiple data processing and analysis steps into a single tool. In particular, in this thesis we have developed a suite of new approaches covering signal data format, indexing structure, and Apache Spark libraries to support efficient and scalable signal data management for applications in neurological disorders such as epilepsy. Our evaluations demonstrate the utility of Apache Spark in supporting neuroscience Big Data application; however, our results also demonstrate that Apache Spark is not well suited for all types of computational tasks associated with signal data management. Therefore, the overall objective of this thesis is to identify specific computational tasks that benefit from the use of main memory-based Apache Spark methods in neuroscience Big Data applications. The new NIC platform developed in this thesis is a significant resource for the brain connectivity research community as it has applications in real world settings for advancing research in neurological disorders using signal data.

Committee: Satya Sahoo (Advisor); Jing Li (Committee Chair); An Wang (Committee Member) Subjects: Bioinformatics; Computer Science

Doctor of Philosophy (PhD), Ohio University, 2020, Clinical Psychology (Arts and Sciences)

Background: Normal healthy aging is associated with changes in cognition, particularly decline in working memory and fluid intelligence. Decrements in fluid intelligence are predictive of greater functional decline and higher rates of mortality. Thus, interventions aimed at improving working memory performance in older adults may improve everyday functioning and overall quality of life. Transcranial direct current stimulation (tDCS) is a form of non-invasive neurostimulation that has been shown to improve working memory performance, though there is considerable variability in response to neurostimulation. Objective: We examined the effectiveness of tDCS applied to the left dorsolateral prefrontal cortex (DLPFC) for improving verbal and visuospatial working memory performance in older adults. Further, we explored whether effectiveness was moderated by general cerebral integrity, as assessed by either fluid intelligence or within- or between-connectivity of resting state brain networks. Methods: We conducted a within-subjects crossover design with three neurostimulation conditions: anode stimulation to DLPFC, cathode stimulation to DLPFC, and sham stimulation. Working memory was assessed with an N-back task. Results: Twenty minutes of tDCS (anode or cathode) improved verbal N-back reaction time compared to sham stimulation. Contrary to expectations, only cathodal tDCS improved visuospatial N-back reaction time compared to sham stimulation. We found weak evidence that within- and between-connectivity of intrinsic resting state networks plays a moderating effect on response to neurostimulation. Conclusion: These results suggest tDCS can improve working memory performance in older adults with a potentially larger effect for individuals with decreased cerebral integrity.

Committee: Julie Suhr (Advisor) Subjects: Clinical Psychology; Neurosciences; Psychology

MS, University of Cincinnati, 2020, Engineering and Applied Science: Computer Engineering

Functional magnetic resonance imaging (fMRI) data has been collected and studied in the neuroscience community for more than two decades. Methods have been developed to model fMRI data as a network, referred to as a functional connectivity (FC) network, consisting of a matrix of pairwise correlation of the blood-oxygen-level-dependent (BOLD) signal measured at different brain regions. FC fingerprinting is a recently introduced problem where the goal is to identify subjects based on FC. Given reference fMRI scans from a set of subjects and an unidentified target fMRI scan from a subject in this set, the goal is to identify which subject the target scan was collected from by computing the correlation between the target FC and the reference FCs. The reference FC with the highest similarity is identified as the target FC. FC fingerprinting studies have reported near 100% accuracies, suggesting that the FC captures subject-specific neuronal activity signature effectively. However, recently we and others have shown that fingerprinting accuracy decreases as the number of subjects increases. Our previous work provided multiple contributions in the context of the FC fingerprinting problem. We showed using silhouette analysis that the reason for the decrease in FC fingerprinting accuracy was due to cluttering of FCs in high-dimensional space. We introduced an intelligent feature selection framework which used a subset of the elements in the FC to improve accuracy. We observed that effective features exhibited a block structure where connectivity between certain groups of regions were more effective for fingerprinting than others. In this thesis our objective is to extract a group-level block-structure from FCs where block-level connectivity can then be used for fingerprinting. One promising direction is to use a non-negative matrix factorization (NMF), a data modeling tool used to extract the inherent structure in data. One variant of NMF, fast matrix tri-factori (open full item for complete abstract)

Committee: Gowtham Atluri Ph.D. (Committee Chair); Raj Bhatnagar Ph.D. (Committee Member); Boyang Wang (Committee Member) Subjects: Computer Science