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ItemNeuroplasticity and Upper-limb Loss: Towards Predicting and Improving Functional Rehabilitation Outcomes(Georgia Institute of Technology, 2022-07-30) Alterman, Bennett LeonardReaching and grasping is so universally integrated into our daily activities that we can perform it almost without thought. We can grasp a cup of coffee while thinking about what we want for breakfast, listening to the morning weather report, and mentally preparing ourselves for work that day. Unfortunately, reaching and grasping is not so simple for persons living with upper-extremity amputation. We are often unaware of the ability of our brains and bodies to coordinate the movement dynamics and sensory information coming from interacting with the environment to produce efficient and effective actions. A disruption in the peripheral nervous system (e.g., amputation) means the typical neural and kinematic patterns of activation formerly used are no longer up to date. With the loss of part of the hand (partial-hand amputation) or the hand and forearm (transradial amputation), individuals must create new patterns as they learn to control a prosthesis, creating an altered state which presents challenges to interacting with and manipulating objects, often leading to device abandonment. There is great variability in motor behavior using upper-extremity prostheses for different levels of amputation, leading to challenges in interpretation of ideal rehabilitation strategies. Elucidating the underlying neuromotor control mechanisms driving this variability will be beneficial to our understanding of human adaptation after limb loss. The purpose of this thesis is to evaluate the neurobehavioral mechanisms underlying the adaptation to updated motor demands placed on the upper extremity after transradial or partial-hand amputation. In particular, I am examining how training one limb may induce changes in brain organization and behavior corresponding not only to the trained limb, but to the untrained limb as well (“interlimb training”), and how factors like device level and action variability may mediate the efficacy of this training. Aim 1 demonstrated that device level and task complexity mediate grasp posture selection and variability, leading to changes in functional performance outcomes for non-amputated individuals adapting to prosthesis simulator use. Aim 2 validated these findings by showing neural adaptation in the sensorimotor cortices is also mediated by device level and task complexity, exhibiting activation patterns ipsilateral to device use, unlike the predominantly contralateral motor control networks in sound limb individuals. Aim 3 introduced interlimb training, and found that transfer of skill between limbs may occur, and is potentially modulated in a task- and device-dependent manner similar to Aims 1 and 2, providing possible factors to predict the efficacy of interlimb training in rehabilitation. Through the implementation of a multimodal approach, these results provide quantitative insights into functional and neural adaptation processes, which can be used to inform and improve rehabilitation practices for persons with upper-extremity amputation.
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ItemThe neurobehavioral effects of sensorimotor dissonance during observation and execution with upper-extremity prostheses(Georgia Institute of Technology, 2022-07-29) Johnson, John TerryThe human ability to perform powerful, complex, and intricate actions using our hands is made possible by the hand's 27 degrees of freedom, its rich sensory feedback, and our ability to observe and understand the actions of others. In the event of an upper-extremity amputation, not only are articulated biological structures lost, but also their available sensory feedback. While great advances have been made in prosthesis engineering, the rate of prosthesis use in upper-extremity amputees remains low. Those who choose to use a prosthesis only do so an average of 50% of the time they could use them. One proposed strategy to improve prosthesis use and acceptance is the provision of task-salient vibrotactile feedback. Studies have been conducted on the efficacy of vibrotactile feedback, but have largely focused on kinematics, leaving a dearth of knowledge regarding the neural effects of vibrotactile feedback. In addition to changes in carrying out reach-to-grasp tasks, amputation and prosthesis use result in a mismatch between the amputee's end-effector (usually a split-hook), and that of non-amputated persons using their hand. This mismatch may lead to increased difficulty determining the actions of others. Sensorimotor models relating motor actions and their resulting sensory feedback also provide the basis for understanding the intentions of other people as we observe their actions. Brain areas which generate our own movements based on our goals and intentions also relate movements we see performed by others to their probable motor commands. When observing movements, the kinematics we see have probable motor commands, which in turn lead to probable goals, and goals to the probable intention of the action. Thus the lack of sensorimotor models for using a prosthesis can lead to not only difficulty using the prosthesis, but also determining the actions of others. The purpose of the proposed studies is to evaluate the neural and behavioral effects of vibrotactile feedback during prosthesis use, and to evaluate cognitive changes between observing a hand or a prosthesis performing reach-to-grasp actions. In Aim 1, electroencephalography and 3D motion capture were used to examine neural activity while performing reach-to-grasp actions using a prosthesis with and without vibrotactile feedback. In Aim2, functional magnetic resonance imaging was used to measure differences in brain activity when naïve participants unfamiliar with prostheses observed a hand and a prosthesis reaching to grasp everyday objects. In Aim 3, clusters of cortical activity found in Aim 2 were used to assess effective connectivity between clusters, as well as to determine graph-theoretic network properties of those clusters, and their changes when observing either a hand or prosthesis performing reach-to-grasp actions. Overall, findings from these studies reveal anticipation of upcoming vibrotactile feedback in sensorimotor brain areas. When observing actions performed with a prosthesis, findings show a diminished recruitment of brain areas specialized for determining the intention of others when observing their actions. The neural activity anticipating upcoming somatosensory feedback, as well as the changes in neural activity when observing a prosthesis rather than a hand, provide insight into the cognitive processes during prosthesis use, and increases our understanding of the role of sensory feedback in those processes.
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ItemEarly Predictors of Post-stroke Motor Recovery(Georgia Institute of Technology, 2021-12-06) Saltao da Silva, Mary AliceStroke is a leading cause of long-term disability worldwide. Despite robust spontaneous biological recovery mechanisms and provision of intensive rehabilitation therapies, most stroke survivors experience persistent loss of upper extremity function which is directly related to reduced independence in activities of daily living and diminished quality of life. Identification of clinical, anatomical, or neurophysiologic indices that accurately predict the capacity for recovery post-stroke is crucial to facilitate precision-based medicine approaches for clinical management, including targeted therapeutic interventions. The Predict Recovery Potential (PREP2) prediction tool uses a combination of clinical measurements and neurological biomarkers to predict paretic upper extremity (PUE) motor outcomes but has yet to be externally validated in the US healthcare system. The primary study objectives were to: 1) evaluate external validation feasibility of PREP2 in the US; 2) retrospectively assess current care practices to determine the routinely collected measures that are most predictive of PUE functional outcome post-stroke; 3) evaluate the prognostic merit of biomarkers isolated from clinical neuroimaging. I hypothesized that our data would demonstrate that PREP2 will be feasible for external validation in healthcare settings in the US and that a combination of clinical measures and biomarkers extracted from clinical magnetic resonance imaging (MRI) would accurately predict level of PUE motor recovery post-stroke in patients who underwent acute inpatient rehabilitation (AR) post-stroke. The studies were conducted via retrospective chart review for two cohorts of stroke patients over fiscal years 2016-2018. In Aim 1, I assessed prospective validation feasibility of the PREP2 prediction tool in acute care settings in the US using a cohort of all stroke admissions to Emory University and Grady Memorial Hospitals. In Aims 2 and 3, I assessed the ability of currently collected clinical measures and neurologic biomarkers isolated from clinical imaging to predict PUE motor outcomes post-stroke using a cohort of patients who remained within the Emory University Hospital system for acute hospitalization, AR, and outpatient care, allowing longitudinal assessment to track recovery and to estimate the level of PUE motor function return. Institutional electronic medical record systems were utilized to extract metrics including demographic data, stroke characteristics, longitudinal documentation of post-stroke motor function, and metrics of stroke care management along the post-stroke care continuum. Clinically diagnostic MRI was used to create lesion masks which were spatially normalized and processed to obtain corticospinal tract (CST) lesion overlap in both primary motor (M1) and non-M1 CST projections. Metric associations were investigated with correlation and cluster analyses, Kruskal-Wallis tests, classification and regression tree (CART) analyses. In Aim 1, we found that current stroke management allows for shoulder abduction finger extension manual muscle tests (SAFE score) to be obtained at therapy evaluations and for the National Institutes of Stroke Scale score to be extracted from the patient chart. On average, patients appropriate for CST integrity assessment remain in the acute care hospital setting at a time when CST function should be evaluated for PREP2 validation. In Aims 2 and 3, estimations of PUE strength extracted from the patient chart (E-SAFE) and clinical MRI-derived CST lesion overlap were associated with PUE functional outcome. Cluster analysis produced three distinct outcome groups and aligned closely to previous outcome categories. Outcome groups significantly differed in E-SAFE scores and lesion overlap on cortical projections within the CST, in particular those emanating from non-M1 cortical areas. Exploratory predictive models using clinical MRI metrics, either alone or in combination with clinical measures, were able to accurately identify recovery outcome category for patients using assessments made during both the acute and early subacute phases of post-stroke recovery. Results suggest that (1) prospective PREP2 validation studies are feasible in a US healthcare setting, (2) SAFE is an easy-to-acquire, readily implementable screening metric with high clinical utility for patients who undergo AR post-stroke, and (3) clinical MRI-derived biomarkers of both M1 and non-M1 contributions to CST integrity may offer unique insight into PUE motor outcome potential.
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ItemAnticipating Explicit Motor Learning by Assessing Arousal Levels using HRV and GSR(Georgia Institute of Technology, 2021-05) Cox, OliviaBiometrics, including heart rate variability (HRV) and galvanic skin response (GSR), are already used to gauge autonomic regulation, emotional reactivity, attention, and flow, a concentration state. Given the role of arousal seen in motor learning factors such as optimal stress, anxiety, and task engagement, this study investigates whether HRV and GSR show distinguished patterns in those who explicitly learn a hidden sequence in a motor task as compared to those who only learn implicitly. This is done using a serial reaction time task (SRTT) and the collection of electrocardiogram (ECG) and GSR data throughout the task then comparing qualitative data across subjects. HRV decrease and GSR increase are noted at serval instances of explicit motor learning emergence, and even in instances when the shift is not exaggerated, it is never found varying in the opposite direction as the hypothesized pattern. Despite a low participant sample size and a low sampling frequency for ECG and GSR, the results tentatively support the concept of using HRV and GSR to gauge whether or not a person’s current state is conducive to explicit motor learning. This biometric monitoring holds the potential for real-time biofeedback and could be useful in physical rehabilitation settings due to the relative ease of implementation.