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, 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

PhD, University of Cincinnati, 2020, Arts and Sciences: Physics

The presented work in this thesis aims to apply functional MRI (fMRI) to advance understanding of how sleep duration impacts the way the brain responds in key networks for attention, memory, and reward processing in adolescents. Functional MRI is a noninvasive way to measure regional neuronal activity using changes in the blood oxygenation over time. This is different from standard MRI done clinically, which is primarily meant to provide the structural anatomic information. Functional MRI is often acquired to measure response to stimulation, but it can also be used to assess spontaneous activity that can be associated with regional connectivity. The first two chapters provide background of MRI and fMRI. Chapter 1 outlines the basic physics of MRI starting from proton spin and magnetization in an external magnetic field, then moving to radiofrequency excitation and the concept of relaxation time. The Bloch equation and MR signal formation are further described in this chapter. Chapter 2 describes the fundamental techniques of functional brain imaging. The basic principle to understand the human brain in action is called blood-oxygenation-level-dependent (BOLD) contrast. Further, the echo planer imaging (EPI) sequence used to generate fMRI is described. Finally, types of experimental fMRI designs are outlined in this chapter, as well as the corresponding statistical analysis. The subsequent three chapters detail the human subject fMRI research projects with which I have been involved in the course of my PhD studies. Chapter 3 describes a study to investigate the neuronal activation and performance changes in working memory and attention induced by mild chronic sleep restriction (SR) in adolescents. This study utilized a working-memory task with varying levels of difficulty. We observed degeneration of task performance as the level of difficulty increased overall, but without a detectable effect of sleep duration. However, fMRI showed that SR result (open full item for complete abstract)

Committee: Mark DiFrancesco Ph.D. (Committee Chair); Rostislav Serota Ph.D. (Committee Chair); F Paul Esposito Ph.D. (Committee Member); Gregory Lee Ph.D. (Committee Member); Jean Tkach Ph.D. (Committee Member) Subjects: Neurology

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

MS, University of Cincinnati, 2014, Engineering and Applied Science: Biomedical Engineering

Independent Component Analysis (ICA) is a model-free functional connectivity technique which breaks down the blood oxygen level dependent signal into spatial maps (components) representing statistically independent fMRI source signals. Probabilistic ICA (PICA) improves upon the traditional ICA model by adding noise, and assumes for a non-square mixing matrix, enabling more accurate calculations of the spatial components. However, PICA still generates components in a random order requiring additional steps for identifying significant resting state networks (RSNs) across a group of subjects. The state-of-the-art for identifying these RSNs across a group of subjects following PICA either requires an extensive group-wise algorithm that derives individual component maps for each subject, or through correlating the components with a template to determine the goodness-of-fit. This thesis introduces an open-source template-matching algorithm for identifying resting state networks from components following any probabilistic Independent Component Analysis. Component selections are determined objectively through the use of correlations coefficients, and are excluded based on p-values calculated using Fisher's R-to-Z transform. The final output of the algorithm displays both visually appealing and quantitative information enabling researchers to identify significant components without prior knowledge of their spatial distributions.

Committee: Jing-Huei Lee Ph.D. (Committee Chair); Wen-Jang Chu Ph.D. (Committee Member); Marepalli Rao Ph.D. (Committee Member) Subjects: Biomedical Research

Doctor of Philosophy, The Ohio State University, 2009, Biomedical Engineering

Though in widespread clinical and research use as a tool to evaluate brain function, functional magnetic resonance imaging (FMRI) data is severely contaminated by noise, due in large part to physiologic noise caused by respiratory and cardiac variations over time. This dissertation attempts to better characterize several physiologic noise correction techniques applied to pain FMRI data. Three studies are described that collectively work toward determining an optimal physiologic noise correction algorithm to be used in future pain FMRI studies. First, a novel algorithm, RetroSLICE, is described that uses linear regression to correct acquired images for signal intensity fluctuations correlated to measured respiratory, cardiac, and capnometry variations. The impact of this technique was assessed for a 1.5 T pain FMRI experiment. Each physiologic noise regressor used as a part of the RetroSLICE algorithm independently resulted in a decrease in timecourse variance and an improvement in model fit. Combined correction for the instantaneous effects of respiratory and cardiac variations caused a 5.4% decrease in signal variance and increased model fit (mean R2a) by 65%. The addition of ETCO2 correction as part of the general linear model led to 39% further improvement in model fit. Each of these corrections also caused changes in the group activation map. Next, an optimal transfer function between ETCO2 level and BOLD signal changes was empirically determined using FMRI data in which paced breathing forced a 35% decrease in ETCO2. ETCO2 data convolved with this optimized response function was compared to another measure, the respiratory volume over time (RVT) convolved with an optimized respiration response function. When regressed against FMRI data collected during a breathing modulation task, ETCO2 was more strongly and diffusely correlated to the data than RVT. Conversely, when the same comparative analysis was performed on pain FMRI data, RVT was more strongly corre (open full item for complete abstract)

Committee: Petra Schmalbrock PhD (Advisor); Robert Small MD (Committee Chair); Cynthia Roberts PhD (Committee Member); Alan Litsky MD,ScD (Committee Member) Subjects: Engineering; Radiology; Scientific Imaging

Master of Sciences, Case Western Reserve University, 2008, Biomedical Engineering

This study investigated using elevation of the spin-spin relaxation time (T2) in magnetic resonance imaging (MRI) to map recruitment differences in the thigh and calf between two distinct populations during single-leg jumping. Twelve healthy subjects formed two groups based on jumping ability. Subjects took a maximal exercise test (MET) to determine aerobic fitness. Subjects performed 5x10 single-leg jumps at body weight (Post1) and at body weight+33% (Post2) on a force platform while wearing a weighted vest at Post2. Performance was determined as concentric jumping power normalized to the subject's maximum aerobic power. Spin echo MRI at Baseline, Post1 and Post2 determined muscle activation as the percentage of muscle pixels with elevated T2 after exercise. A novel metric based on the performance/activation ratio highlighted recruitment efficiency differences between groups. Results suggest that recruitment efficiency throughout the lower limb (especially suppression of co-activating antagonists) was the dominant factor in enhancing jumping performance.

Committee: Marco Cabrera (Advisor) Subjects:

Doctor of Philosophy, Case Western Reserve University, 2006, Physics

This dissertation reports findings centered principally on comprehensive research related to human bio-signals (EEG, MEG, EMG and fMRI) acquired during repetitive maximal voluntary contractions (MVC) that induced severe fatigue. Fatigue is a common experience that reduces productivity and quality of life and increases chances of injury. Although abundant information has been gained in the last several decades regarding muscular and spinal-level mechanisms of muscle fatigue, very little is known about how cortical centers control and respond to fatigue. The main purpose of this study was to examine the fatigue effects on the central nervous system by analyzing the bio-signals collected in the designed experiments. Healthy human subjects were asked to perform a series of repetitive handgrip MVCs with their dominant hand until exhaustion. Handgrip forces, electrical activity (EMG) from primary and non-primary muscles, and EEG, MEG, or fMRI signals from different locations of the brain were recorded simultaneously. The time series data were segmented into several physiologically meaningful epochs (time phases), from rest to preparation to movement execution/sustaining. A series of studies, including motor-related cortical potential (MRCP) analysis, power spectrum analysis, time-frequency (spectrogram) analysis of EEG, EEG source localization and nonlinear analysis (fractal dimension and largest Lyapunov exponent), and fMRI analysis, was applied to the data. We hypothesized that the fatigue effects would act differently on brain signals of different phases. The MRCP results showed that the negative potential (NP) related to motor task preparation only had minimal changes with fatigue. The power of all EEG frequencies did not alter significantly during the preparation phase but decreased significantly during the sustained phase of the contraction. The fractal dimension and the largest Lyapunov exponent decreased significantly during the sustained phase as fatigue progress (open full item for complete abstract)

Committee: Robert Brown (Advisor) Subjects: