Doctor of Philosophy, The Ohio State University, 2012, Integrated Biomedical Science Graduate Program

Autism Study: A decrease in interaction between brain regions is observed in individuals with autism spectrum disorder (ASD), which is believed to be related to restricted neural network access in ASD. Propranolol, a beta-adrenergic antagonist, has revealed benefit during performance of tasks involving flexibility of access to networks, a benefit also seen in ASD. Our goal was to determine the effect of propranolol on functional connectivity in ASD during a verbal decision making task as compared to nadolol, thereby accounting for the potential spurious fMRI effects due to peripheral hemodynamic effects of propranolol. Ten ASD subjects underwent fMRI scans after administration of placebo, propranolol or nadolol, while performing a phonological decision making task. Comparison of functional connectivity between pre-defined ROI-pairs revealed a significant increase with propranolol compared to nadolol, suggesting a potential imaging marker for the cognitive effects of propranolol in ASD. Cocaine Withdrawal Study: Recent research revealed decreased access to semantic and associative networks in acute cocaine withdrawal. In autism, such behavioral outcomes are associated with decreased functional connectivity using functional magnetic resonance imaging. Therefore, we wished to determine whether connectivity is also decreased in acute cocaine withdrawal. Eight subjects in acute cocaine withdrawal were compared to controls for connectivity in language areas while performing a word categorization task. Acute withdrawal subjects had significantly less overall connectivity during semantic categorization and a trend towards less connectivity during phonological categorization. This is of interest due to recent research revealing pharmacological effects on connectivity in autism.

Committee: David Beversdorf MD (Advisor); Petra Schmalbrock PhD (Advisor); Doug Scharre MD (Committee Member); Michael Knopp PhD (Committee Member); Virginia Sanders PhD (Committee Member) Subjects: Neurosciences

Doctor of Philosophy, The Ohio State University, 2006, Biophysics

Functional MRI was used to examine brain activation during language processing and the effect of L-Dopa on brain hemodynamics and language. Firstly, we wished to determine the effect of L-Dopa on brain hemodynamics. Since fMRI signal is based on cerebral blood flow, oxygenation and cerebral blood volume changes, drug administration could interfere with the coupling of neural activation with these parameters, independent of neuronal activity. To obtain this information a theoretical model of a relationship between BOLD signal and CBF was used. The results revealed no significant changes induced by drug in baseline CBF. Therefore, this was not used as a covariate in the subsequent studies of language. Secondly, we examined the semantic priming and dopamine effects on brain activation. We intended to implement a protocol for language function imaging, explore different paradigm designs in fMRI, and examine brain activation and the effect of L-Dopa. Behavioral measurements demonstrated a significant priming effect for all semantic distances. Imaging results showed activation in a network known to be involved in language processing and attention. The block and event related paradigms were explored and compared, revealing the importance of design selection in fMRI. No drug or temporal effects were found on the activation maps, suggesting that more sensitive techniques must be used to detect these changes. Lastly, fMRI was used to study functional connectivity associated with semantic and phonological processing. The goal was to explore the interaction between language network components and to determine if they are affected by administration of L-Dopa. Activation patterns for the two language processes were obtained and compared to previous findings. The functional connectivity, calculated as the correlation between the time series data of two brain areas was determined and revealed that language areas were activated more synchronously for phonological tasks than for sema (open full item for complete abstract)

Committee: David Beversdorf (Advisor) Subjects: Biophysics, General

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

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, 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, 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, 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

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, 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

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

PhD, University of Cincinnati, 2018, Arts and Sciences: Physics

With the advances in MRI, it has become possible to noninvasively observe function and structure of the developing brain in vivo. Functional magnetic resonance imaging (fMRI) of the brain is a non-invasive way to assess brain function using MRI signal changes associated with neuronal activity. The most widely used method is based on BOLD (Blood Oxygenation Level Dependent) signal changes caused by hemodynamic and metabolic neuronal responses. Functional connectivity has been defined as inter-regional temporal correlations among spontaneous BOLD fluctuations in different regions of the brain during a task as well as when the brain is idle. By identifying brain regions that exhibit highly correlated BOLD signal fluctuations, we can infer that the regions are functionally connected and co-activation during a particular task or at rest (fcMRI) suggests that these regions work together as part of a functional brain network. This method is now being used widely to study brain networks but has seen limited use in studies of the developing brain, particularly in infants. Functionally connected brain regions can be specified as components of integrated networks that enable specific sensory or cognitive brain functions. These brain networks demonstrate the basic connectivity pattern between brain regions, which can be represented mathematically using graph-theoretical approaches. Graph theory provides a convenient quantitative and visual format to sketch the topological organization of brain connectivity representing complex brain networks. Graph theory analysis also naturally provides quantitative descriptors of both global and regional topological properties of brain graphs. While this approach is now widely used with functional MRI data as a means of studying the topology of functional brain networks, it has not been applied to study the development of brain networks from birth, nor in the premature infant brain. The main goal of this dissertation is to use novel functiona (open full item for complete abstract)

Committee: Scott Holland Ph.D. (Committee Chair); L. C. R. Wijewardhana Ph.D. (Committee Chair); Howard Jackson Ph.D. (Committee Member); Stephanie Merhar (Committee Member); Jean Tkach Ph.D. (Committee Member); Jason Woods Ph.D. (Committee Member) Subjects: Radiology

MS, University of Cincinnati, 2017, Medicine: Clinical and Translational Research

Executive dysfunction is observed in adolescents with localization-related and generalized epilepsy, and likely contributes to poor outcomes in social, academic, behavioral and quality of life domains. Identification of neuroimaging biomarkers of brain network connectivity could provide insight into the neural mechanisms of executive dysfunction in epilepsy and facilitate more objective assessment of disruptions in executive function related brain networks. Neural correlates of working memory, a specific component of executive function involving short-term retention and manipulation of information, can be assessed by task based functional MRI (fMRI) studies such as the n-back task. N-back tasks are very demanding and require participants to continually adjust the information held in working memory to incorporate the most recently presented stimulus while simultaneously rejecting or ignoring more temporally distant stimuli. As adolescents with epilepsy are at a greater risk of working memory disability, we hypothesized that fMRI activation in brain regions involved in working memory would be significantly different in children with epilepsy relative to healthy controls and that activation patterns would correlate with neuropsychological measures of executive dysfunction. We performed a prospective case- control study of 29 adolescents with MRI non-lesional epilepsy and 20 healthy controls. Both groups performed standardized measures of executive function and questionnaires (parent and child Behavior Rating Inventory of Executive Function - BRIEF) to rate executive function and behavior. All participants performed an n-back fMRI task with 2-back and 0-back components. We performed group analysis of brain functional activation, ROI based analyses of working memory specific brain regions and functional connectivity analyses of the known working memory networks. Adolescents with epilepsy scored poorly on working memory neuropsycholgical tests Wechsler Intelligence Scale (open full item for complete abstract)

Committee: Erin Nicole Haynes Dr. P.H. (Committee Chair); Mekibib Altaye Ph.D. (Committee Member); Avani Chandrakant Modi Ph.D. (Committee Member); Jennifer Vannes Ph.D. (Committee Member) Subjects: Surgery

Master of Sciences, Case Western Reserve University, 2014, Systems Biology and Bioinformatics

The purpose of my efforts was to develop efficient yet comprehensive data analysis tools for the study of biological rhythms in the mammalian neural networks. I focused on two contexts in great detail; namely, the respiratory system and small neural networks. In Chapter 2, I present a computational approach that can be used to study the relations between heartbeat and respiration using non-invasive recordings from these systems. We found that coupling was asymmetric with respiration affecting the heart beat more strongly than vice versa. In Chapter 3, I present a measure that can be used to study functional connectivity from ensemble recordings. We show that our method, the Spike Triggered Average of the Postsynaptic Phase, reliably resolves pairwise interactions between neurons. Performance in terms of accuracy and running time were evaluated for multiple methods. We found that our measure matched or outperformed alternative approaches in various categories.

Committee: Roberto Galan (Advisor); Thomas Dick (Committee Member); Masaru Miyagi (Committee Member) Subjects: Applied Mathematics; Biomedical Research; Biostatistics

PhD, University of Cincinnati, 2011, Arts and Sciences: Mathematical Sciences

Narrative comprehension is a fundamental cognitive skill that involves the coordination of different functional brain regions. To investigate the network structure among the brain regions supporting this cognitive function, a Spectral Bayesian Network with Bayesian model averaging is developed based on the spectral density estimation of the functional Magnetic Resonance Imaging (fMRI) time series recorded from multiple brain regions. In this approach, the neural interactions and temporal dependence among different brain regions are measured by spectral density matrices after a Fourier transform of the fMRI signals to the frequency domain. A Bayesian model averaging method is then applied to build the network structure from a set of candidate networks. Using this model, brain networks of three distinct age groups are constructed to assess the dynamic change of network connectivity with respect to age. Networks of multivariate time series are also simulated from vector autoregressive models to compare the performances of the SBN with existing methods in learning network structure from time series data. In addition to the network modeling of the functional interactions among brain regions, the quantification of the functional connectivity between two brain regions is also very important for understanding how the functions of the human brain develop. Using spectral coherence and partial spectral coherence, the overall and direct functional connectivity strengths among the language-related neural circuits are computed based on fMRI time series data collected in 313 children ranging in age from 5 to 18 years in a story comprehension experiment. The age or gender effects on both the pair wise direct link and connection strength are studied to access children's development of brain functions for story comprehension. In addition, the connectivity differences between the left and right hemispheres, and the connections in both hemispheres that are directly related to the child (open full item for complete abstract)

Committee: Siva Sivaganesan PhD (Committee Chair); James Deddens PhD (Committee Member); Scott Holland PhD (Committee Member); Paul Horn PhD (Committee Member); Xiaodong Lin PhD (Committee Member); Seongho Song PhD (Committee Member) Subjects: Statistics

Doctor of Engineering, Cleveland State University, 2009, Fenn College of Engineering

Traditional brain activation studies using neuroimaging such as functional magnetic imaging (fMRI) have shown that muscle fatigue at submaximal intensity level is associated with increased brain activity in various cortical regions from low- to high-order motor centers. However, how these areas might interact remain unclear since previous activation studies related to motor control could not reveal information of between-area interaction. This issue can be addressed by evaluating brain activation data using the framework of connectivity analysis. Three types of brain connectivity, functional connectivity (FC), effective connectivity (EC) and structural connectivity (SC) have been examined to investigate the effect of voluntary muscle fatigue on the interaction within the cortical motor network. The aim of the study was to propose a new framework to reveal adaptive interactions among motor regions during progressive muscle fatigue. We hypothesized that the brain would exhibit fatigue-related alterations in the FC and EC. Ten healthy subjects performed repetitive handgrip contractions (3.5s ON/6.5s OFF) for 20 minutes at 50% maximal voluntary force (MVC) level using the right hand (fatigue task). Significant MVC reduction occurred at the end of the fatigue task, indicating muscle fatigue. Histogram and quantile analysis confirmed that FC of the brain increased in the severe fatigue stage (the last 100s of the fatigue task) compared with the minimal fatigue stage (the first 100s of the fatigue task). Structural equation modeling (SEM) was used to evaluate the EC of the brain during fatigue. We found the path from the prefrontal cortex (PFC) to the supplementary motor area (SMA) decreased during fatigue while the path from the premotor area (PMA) to the primary motor cortex (M1) increased. We also found supporting evidence from SC analysis using diffusion tensor image (DTI). The new framework of connectivity analysis, combining the work of SC, FC and EC, provides greate (open full item for complete abstract)

Committee: Guang H. Yue Ph.D. (Committee Chair); George Chatzimavroudis Ph.D. (Committee Member); Fuqin Xiong Ph.D. (Committee Member); Andrew Slifkin Ph.D. (Committee Member); Yuping Wu Ph.D. (Committee Member) Subjects: Biomedical Research

Doctor of Philosophy, The Ohio State University, 2013, Mathematics

In a series of papers, Hansen and Jaworski explored a very general model for choosing random mappings with exchangeable in-degrees. The special case in which the in-degrees are obtained by conditioning independent random variables with a specially chosen negative binomial distribution on their sum corresponds in distribution to a process for choosing random mappings which exhibits preferential attachment - images are chosen one at a time, and vertices chosen as images earlier in the process are more likely to be chosen again. They consider the functional digraph as a random object, and study its properties. We generalize Hansen and Jaworski's preferential attachment model to a new setting: our model produces digraphs with labeled arcs and uniform out-degree k; other than a few technical wrinkles, the graph obtained by ignoring arc directions, loops, and multiple arcs is essentially a preferential attachment version of the k-out graph model first studied by Fenner and Frieze. This dissertation splits nicely in to three parts: first, we build a collection of analytical tools for studying our mapping; second, we study structural properties of its induced graph, including minimum vertex degree, vertex connectivity, and the k-core; finally, we present a very strong measurement of the differences and similarities between our model and a limiting case that is uniformly random.

Committee: Boris Pittel (Advisor); Jeffery McNeal (Committee Member); Neil Falkner (Committee Member) Subjects: Mathematics

Master of Sciences (Engineering), Case Western Reserve University, 2013, Biomedical Engineering

Forced-rate lower-extremity exercise has recently emerged as a potential safe and low-cost therapy for Parkinson's Disease (PD). The efficacy is believed to be dependent on high rates of exercise. In this study, we use functional connectivity (FC) MRI to further elucidate the mechanism underlying this rate-dependent effect. PD subjects were randomized to complete 8 weeks of forced, voluntary, or no exercise. Changes in brain connectivity were then analyzed as a function of pedaling rate. Changes in task-related FC (trFC) from the most affected motor cortex (*M1) to the ipsilateral thalamus were significantly positively correlated to pedaling rate. Changes in trFC between *M1 and the supplementary motor area were significantly negatively correlated to pedaling rate. This relationship persisted after 4 weeks of follow up. This indicates that patients who pedal faster tend to show stronger increases in cortico-subcortical trFC, and stronger decreases in cortico-cortical trFC.

Committee: Jay Alberts Ph.D. (Advisor); Kenneth Gustafson Ph.D. (Committee Chair); Micheal Phillips M.D. (Committee Member); Hubert Fernandez M.D. (Committee Member); Mark Lowe Ph.D. (Other); Erik Beall Ph.D. (Other) Subjects: Biomedical Engineering; Medical Imaging; Neurosciences