Lena H. Ting, Ph.D. - US grants

DESCRIPTION (provided by applicant): Falls due to a loss of balance are a primary cause of injury and death in the elderly, and a debilitating symptom of a wide range of neurological, musculoskeletal, and cognitive deficits. However, because many neural and musculoskeletal elements can contribute to balance impairments - or equivalently to compensatory strategies - it is often difficult for clinicians to evaluate the severity of balance impairments, or to identify their underlying causes. The objective of this project is to understand the principles governing the modulation of feedforward and feedback neuromechanical elements contributing to postural stability during standing balance control. We have chosen to study the neuromechanical elements that can be rapidly modulated or selected by the nervous system, within the time frame of one session of data collection. We define feedforward neuromechanical elements to be those that adjust the intrinsic mechanical stability of the musculoskeletal system in anticipation of a postural perturbation, such as postural configuration and postural muscle tone. We define feedback neuromechanical elements to be those that activate muscles reactively following postural perturbations, and include task-level feedback gains, muscle synergies, and spinal reflexes. We hypothesize that the feedforward and feedback neuromechanical elements are modulated to achieve implicit performance goals such as stability, maneuverability, energy minimization, or robustness to uncertainty. Differing performance goals could explain variations in movement observed across trials, across individuals, across contexts, or across motor deficits. We predict that various combinations of neuromechanical elements may produce qualitatively similar movements, and yet quantitatively different functional and energetic consequences. We will study interactions between feedforward and feedback neuromechanical elements for standing balance control in normal and neurologically-impaired cats in Aim 1; during short- and long- term postural adaptations in intact animals in Aim 3. In Aim 2, we will identify tradeoffs between functional and energetic costs and constraints that may drive postural adaptations in both health and disease using neuromechanical models of postural control. This proposal continues our development of a general scientific framework toward our long-term goal of understanding, diagnosing, and predicting optimal motor function in individuals with balance deficits, and motor impairments in general.

DESCRIPTION (provided by applicant): Loss of balance leading to falls is a primary cause of injury and accidental death in older adults. This proposal articulates an innovative and quantitative framework for both investigating and understanding temporal and spatial features of muscle activation patterns for human balance control. The objective of the proposal is to develop and validate a quantitative model to predict spatiotemporal muscle activation patterns during postural control. Using engineering tools, we will integrate novel experimental methods and computer simulations to understand how feedback control of posture is executed by the nervous system. Our framework will demonstrate that complex, high-dimensional muscle coordination patterns for postural control can be explained by just a few parameters related to task-level variables. Advancing our ability to quantify and predict muscle coordination patterns is critical to achieving our long-term goal of using paired experimental measures and engineering models to predict the functional consequences of neuromotor impairments and interventional therapies. In Specific Aim 1, we will identify muscle synergies and task variables governing spatial organization of muscle activity during multidirectional perturbations to standing posture. Our approach is to extract muscle synergies from experimental data using optimization and correlate the activation of each muscle synergy to the production of a task-related variable. We will use a musculoskeletal model of the leg to determine the structure of synergies required to produce the muscle activation patterns and task-variables measured experimentally. In Specific Aim 2, we will identify the task- level feedback loops governing temporal organization of muscle activity during sagittal perturbations to standing posture. Our approach is to characterize the feedback relationships between center of mass motion and temporal muscle activation patterns experimentally, and then use a simple inverted pendulum model to test the feasibility of the feedback loops in generating appropriate temporal muscle activation patterns and center of mass kinematics. Our models of postural control will allow us to predict motor dysfunction resulting from changes in motor patterns. Our results will therefore allow us to develop quantitative diagnostic tools for balance and movement disorders and facilitate the design of effective interventional therapies, neural prostheses, and neural repair strategies for motor rehabilitation.

Our vision is to develop caregiver robots that interact fluidly and flexibly with humans during functional
motor activities, while providing motor assistance, enhancement, and communication to facilitate motor
learning. However, we currently lack theories to understand how rehabilitation and movement therapists
provide timely and appropriate physical feedback and assistance to improve mobility in individuals with
motor impairments. To develop devices that could accompany an individual as both assistant and
movement therapist, our goal is to study human motor coordination during cooperative physical
interactions with a humanoid assistive robot. We will use rehabilitative partner dance as a paradigm to
examine a sensory-motor theory of cooperative physical interactions relevant to walking and other
functional motor activities. We will use a "partnered box step", a constrained and defined pattern of
weight shifts and directional changes, as a paradigm for a cooperative physical interaction with welldefined
motor goals.
Objectives: To 1) experimentally verify a hierarchical theory of human sensory-motor control and
learning and 2) develop predictive models of whole-body human movement for cooperative physical
interactions with machines. Over four years, we will test our models by demonstrating the successful
participation of the robot in a box step as leader or follower and adapt its movements to the motor skill
level of a human partner.
Intellectual Merit: Our work will provide transformative experimental, theoretical, and practical
interdisciplinary frameworks that will forge new paths toward autonomous cooperative robots with
physical intelligence to enhance, assist, and improve motor skills in humans with varying motor
capabilities. These advances will aid prosthetic and robotic design and may advance our understanding of
the brain.
Broader Impacts: The expected project outcomes would have long-term impact on the quality of life of
millions of Americans by improving fitness, motor skills, and social engagement. Applications include
healthcare devices or sports robots that entertain and improve fitness. We will provide seminars on
mobility-related issues and rehabilitative dance instruction to older adult living communities and
populations with motor impairments. The broad appeal and social nature of this work will likely garner
media publicity that will increase public interest in science and technology.

DESCRIPTION (provided by applicant): Parkinson's Disease (PD) motor rehabilitation can improve clinical balance and disease severity measures. However, clinical scales cannot identify changes in specific neural pathways, and therefore may not be able to discriminate between remediative and compensatory changes to underlying neural mechanisms during rehabilitation. Our long-term goal is to discriminate between remediation and compensation during effective PD rehabilitation, which would significantly improve our understanding of response to treatment in individuals with PD as well as potentially in other neurological conditions. In contrast to clinical measures, we will study neuromuscular activation measured with electromyography (EMG) as a direct window into the neuromotor activity of individuals with PD before and after 24 weeks of adapted tango rehabilitative dance classes, which is an effective rehabilitative intervention with low attrition. We will quantify neuromotor deficits and recovery before and after rehabilitation using our sensorimotor response model (SRM), which was developed to quantify brainstem-mediated EMG responses evoked during perturbations to standing balance in young individuals as well as in animal models. We will use a novel experimental paradigm to determine whether motor improvements associated with rehabilitation are due to remediative changes in basal ganglia-brainstem pathways or due to compensatory changes in cerebellar-brainstem pathways. Previous studies of perturbation responses during standing balance demonstrate that basal ganglia pathways are used to adapt neural control parameters to new contexts immediately, whereas cerebellar pathways are used to adapt to new contexts slowly, over repeated trials. Therefore, we will examine the timecourse of adaptation of SRM parameters over repeated perturbation trials in novel stance widths. We predict that immediate changes in SRM parameters in novel stance widths will reflect basal ganglia-brainstem remediation, whereas slowly adapting changes will reflect cerebellar compensation. After rehabilitation, we predict that increased magnitudes of immediate changes will reflect basal ganglia-brainstem remediation, whereas increased speed of slowly adapting changes will reflect cerebellar-brainstem compensation. Further, we predict that SRM parameters can serve as biomarkers to identify differences in pathways underlying motor deficits across participants with PD throughout rehabilitation.

NRT: Accessibility, Rehabilitation and Movement Science (ARMS): An Interdisciplinary Traineeship Program in Health-Centered Robotics

The world's demographics are changing. People continue to live longer and the U.S. population is becoming older and more racially and ethnically diverse. There is also an increase in younger individuals living with a life-long disability, such as veterans who sustain catastrophic injuries, persons suffering from neurodegenerative diseases, and children growing up with developmental disorders or delays. With this changing population profile comes an increasing demand for advanced healthcare technologies and a need to train a new generation of engineers able to develop these new technologies. This National Science Foundation Research Traineeship award to the Georgia Institute of Technology will address this demand by training graduate master's and doctoral students in the interdisciplinary field of healthcare robotics. The traineeship anticipates providing a unique and comprehensive training opportunity for one hundred and fifty-five (155) students, including thirty (30) funded trainees, by combining disciplines in robotics, studies in health sciences, interactions with clinical partners, hands-on rehabilitation research, and a culture of innovation and translational research.

Trainees will have unique exposure to a variety of approaches developed in Robotics, Physiology, Neuroscience, Rehabilitation, and Psychology. The traineeship will bridge the gap between healthcare and robotics by addressing two major barriers: a) the lack of a formalized framework to enable interdisciplinary collaborations between robotics engineers and health professionals; b) the tendency for students in robotics to be unprepared to address problems in healthcare, including a lack of appreciation for the challenges encountered by clinicians, caregivers, and people with disabilities. Through close interactions with various partners, the traineeship will expand student horizons beyond a technology-first mentality to consider challenges in developing robotic solutions that address the needs of clinicians, caregivers, and people with disabilities. The goal is to develop an interdisciplinary curriculum based upon the concept of participatory design, problem-based learning, and an immersive research experience that blends techniques from multiple disciplines to solve problems posed in healthcare. A second major goal of the traineeship is to increase the participation of women, underrepresented minorities, and students with disabilities in robotics and related engineering fields. The project will develop a new M.S. degree program in healthcare robotics and a new PhD concentration area in healthcare robotics as well as curricular materials and best-practices to allow other institutions to develop similar programs.

The NSF Research Traineeship (NRT) Program is designed to encourage the development and implementation of bold, new, potentially transformative, and scalable models for STEM graduate education training. The Traineeship Track is dedicated to effective training of STEM graduate students in high priority interdisciplinary research areas, through the comprehensive traineeship model that is innovative, evidence-based, and aligned with changing workforce and research needs.

This award is supported, in part, by the EHR Core Research (ECR) program, specifically the ECR Research in Disabilities Education (RDE) area of special interest. ECR emphasizes fundamental STEM education research that generates foundational knowledge in the field. Investments are made in critical areas that are essential, broad and enduring: STEM learning and STEM learning environments, broadening participation in STEM, and STEM workforce development.

Proprioceptive information from muscle spindle sensory afferents plays a critical role in movement, yet we lack mechanistic models to tease apart how physiological and pathological at multiple scales changes alter sensorimotor control. Altered muscle spindle function is implicated in a wide range of sensorimotor impairments including dystonia, hypotonia, ataxia, and Parkinson's disease, as well as in spasticity, which affects those with stroke, cerebral palsy, spinal cord injury, and other neural injuries. But, despite decades of work, the basic mechanisms of muscle spindle sensory encoding are not well understood, and thus their role in sensorimotor disorders have not been clearly identified. Our objective is to develop a novel, mechanistic, multi-scale model of muscle spindle sensory encoding to that will allow us to test hypotheses about the role of molecular, cellular, and circuit level mechanisms on sensorimotor control in healthy and impaired humans and animals. We will build a neuromechanical muscle spindle model incorporating muscle sarcomere cross- bridge dynamics, mechanical properties of the spindle-bearing musculotendon, and biophysical membrane properties of muscle spindle afferent neurons and motor neurons. The model will be a useful platform to integrate classical and new findings of muscle spindle function spanning molecular and behavioral levels. We will identify the source of history-dependent characteristics of muscle spindle firing rates. Specifically, we will identify the mechanisms behind initial bursts, rate relaxation at constant length, and dynamic response modulation to ramps. We will dissociate the relationship of muscle spindle firing rates to kinetic (force) versus kinematic (length) variables using the same set of novel stretch perturbations applied to intact muscle in vivo in cats and rats (Aim 1), single muscle fibers from the same animals in vitro (Aim 2), and a multi-scale neuromechanical model incorporating cross-bridge dynamics, musculotendon viscoelastic properties, and spiking neuron models (Aim 3). \N_e will also use the model to interpret our existing data from animals with sensory loss.

Our long-term goal is to advance our scientific knowledge and computational approaches to identify causes of balance impairments leading to falls in Parkinson?s disease (PD) to guide the rational development more effective treatments and rehabilitation for improving balance. Recent insights from our neuromechanical simulation studies in tandem with our ongoing work characterizing changes reactive balance control after rehabilitation in people with PD have implicated our overall hypothesis that the cardinal parkinsonian sign of rigidity is a cause of balance impairments. Rigidity is not typically considered in fall risk, yet our ongoing studies demonstrate a previously-unreported association between leg rigidity and prior falls. We believe that identifying the causes of this association will lead to improved diagnosis and treatment of balance impairments in PD. Our objective is to identify the effects of leg rigidity on postural robustness, defined as the ability to maintain the feet in place in reactive balance. Based on our neuromechanical simulations, we hypothesize that parkinsonian rigidity increases two distinct aspects of postural muscle activity that can each reduce postural robustness: tonic muscle activity, defined as the magnitude of muscle activity in static postures, and dynamic muscle activity is defined as the magnitude and timing of muscle activity generated by sensorimotor feedback in reactive balance. We will use combined experimental and computational approaches to systematically isolate the causal linkages and interactions between rigidity, muscle activity, and postural robustness in PD. In addition to electromyographic (EMG) recordings, we will also establish the reliability of measuring tonic muscle activity during standing using frequency domain near-infrared spectroscopy (FDNIRS) combined with diffuse correlation spectroscopy (DCS). In Aim 1 we will test well-characterized PD participants with confirmed dopamine-responsive rigidity to identify the effects of leg rigidity on tonic muscle activity, dynamic muscle activity, and postural robustness; we will manipulate rigidity using dopamine medication and an activation maneuver. In Aim 2 we will test neurotypical participants to identify causal role of modulating tonic and dynamic muscle activity on postural robustness; we will modulate muscle activity using EMG biofeedback and adaptation. In Aim 3 we will develop neuromechanical simulations to quantitatively demonstrate mechanistic relationships between tonic muscle activity, dynamic muscle activity, and postural robustness. We will augment our neuromechanical models of balance with agonist-antagonist muscle models. If successful, we will 1) identify the causal role of rigidity on impaired balance in PD, 2) validate a novel and clinically-feasible method (NIRS) to measure rigidity in functionally-relevant tasks, and 3) establish a broadly extendable generative neuromechanical model of balance to simulate how multiple mechanisms interact to cause balance impairments. These outcomes will enable us to identify optimal treatment targets for the rational development of rehabilitation and other therapies for balance impairments across many disorders.

Our long-term goal is to advance our scientific knowledge and computational approaches to identify causes of balance impairments leading to falls in Parkinson?s disease (PD) to guide the rational development more effective treatments and rehabilitation for improving balance. Recent insights from our neuromechanical simulation studies in tandem with our ongoing work characterizing changes reactive balance control after rehabilitation in people with PD have implicated our overall hypothesis that the cardinal parkinsonian sign of rigidity is a cause of balance impairments. Rigidity is not typically considered in fall risk, yet our ongoing studies demonstrate a previously-unreported association between leg rigidity and prior falls. We believe that identifying the causes of this association will lead to improved diagnosis and treatment of balance impairments in PD. Our objective is to identify the effects of leg rigidity on postural robustness, defined as the ability to maintain the feet in place in reactive balance. Based on our neuromechanical simulations, we hypothesize that parkinsonian rigidity increases two distinct aspects of postural muscle activity that can each reduce postural robustness: tonic muscle activity, defined as the magnitude of muscle activity in static postures, and dynamic muscle activity is defined as the magnitude and timing of muscle activity generated by sensorimotor feedback in reactive balance. We will use combined experimental and computational approaches to systematically isolate the causal linkages and interactions between rigidity, muscle activity, and postural robustness in PD. In addition to electromyographic (EMG) recordings, we will also establish the reliability of measuring tonic muscle activity during standing using frequency domain near-infrared spectroscopy (FDNIRS) combined with diffuse correlation spectroscopy (DCS). In Aim 1 we will test well-characterized PD participants with confirmed dopamine-responsive rigidity to identify the effects of leg rigidity on tonic muscle activity, dynamic muscle activity, and postural robustness; we will manipulate rigidity using dopamine medication and an activation maneuver. In Aim 2 we will test neurotypical participants to identify causal role of modulating tonic and dynamic muscle activity on postural robustness; we will modulate muscle activity using EMG biofeedback and adaptation. In Aim 3 we will develop neuromechanical simulations to quantitatively demonstrate mechanistic relationships between tonic muscle activity, dynamic muscle activity, and postural robustness. We will augment our neuromechanical models of balance with agonist-antagonist muscle models. If successful, we will 1) identify the causal role of rigidity on impaired balance in PD, 2) validate a novel and clinically-feasible method (NIRS) to measure rigidity in functionally-relevant tasks, and 3) establish a broadly extendable generative neuromechanical model of balance to simulate how multiple mechanisms interact to cause balance impairments. These outcomes will enable us to identify optimal treatment targets for the rational development of rehabilitation and other therapies for balance impairments across many disorders.

The proposal initiates a new, innovative training program in Computational Neural Engineering at Georgia Tech and Emory University to train the next generation of researchers at the intersection of computational neuroscience, data science, and clinical neurophysiology. It addresses the opportunities provided by the explosion of tools for measurement and manipulation of nervous system function and the challenges posed by the growing threat of neurological diseases and disorders on an expanding senior population. The program leverages past successes in federally-funded training efforts that have helped to catalyze rapid and recent growth in research and education in Computational Neural Engineering across Emory and Georgia. Early exposure to the intersection of fields is critical to the program mission. Interdisciplinary training in the first two years of the PhD program will provide trainees with unique opportunities for training across axes that span basic to clinical neuroscience, and from neural engineering to computational neuroscience, data science, and machine learning. Two graduate students per will be recruited from the applicant pools for the Biomedical Engineering (GT and Emory), Bioengineering (GT), Electrical and Computer Engineering (GT), and Machine Learning PhD programs, which collectively enroll over 200 PhD students per year. None of the participating programs offer research rotations or funding in the first year of graduate school. We will support a total of four students per year over a five-year period, providing two years of support for two entering students per year. funding such support will help attract the highest quality students to the program, and offer trainees the unique opportunity to rotate through research labs and establish new collaborative research projects. In our prior training experience, such interactions have led to new collaborations funded through fellowships and new research grants. Didactic training will complement core training in each PhD program with existing and new courses in computational neuroscience, neuropathology and neuroengineering, and a new course providing students with an immersive clinical experience at the Emory Brain Health Center. Extracurricular training includes Innovation Forums for clinician/engineering interactions, and a wide variety of seminars, methods clinics, and journal clubs. Trainees will also be provided with professional development for this new generation of researchers, including training in leadership, mentorship, neuroethics, and public scholarship. Trainees will also learn the growing industry in neural engineering, and will have opportunities for internships. Importantly, with solid preliminary evidence for the success in all of these ventures, this program targets an imperative area for growth.

PROJECT SUMMARY Our long-term goal is to identify neural mechanisms and the functional roles of sensorimotor signals in health and disease as needed to guide mechanistically targeted diagnoses, assessments, and treatments for neurological movement disorders. Here we address the scientific barriers to understanding and treating a broad class of movement disorder symptoms recently defined as joint hyper-resistance, which encompass spasticity in stroke, spinal cord injury, or cerebral palsy; parkinsonian rigidity, and hypertonia. The objective of this collaborative, interdisciplinary proposal is to identify neural mechanisms of hyper-resistance and dissociate their relative roles in abnormal movement. We will focus on the neural mechanisms underlying two clinically- defined neural contributions to hyper-resistance: non-velocity dependent involuntary background activation and velocity-dependent stretch hyper-reflexia. We hypothesize that increased spinal excitability in many neurological disorders causes involuntary background activation and velocity-dependent stretch hyper-reflexia via three dissociable neural mechanisms: 1) alpha-drive to extrafusal muscle fibers increasing background muscle tension, 2) gamma-drive to specialized intrafusal muscle fibers in muscle spindles sensory organs, increasing their sensitivity to muscle stretch, and 3) sensorimotor gain of the spinal transformation of monosynaptic sensory input into motor output. Our proposed tests of this hypothesis will advance understanding of the important, yet still unresolved relative contributions made by these neural mechanisms to hyper-resistance. Based on our neuromechanical and multiscale modeling advances in the prior funding period, in Aim 1 we will develop a multiscale in silico neuromuscular circuit model to predict how independent changes in alpha- drive, gamma-drive, and sensorimotor gain differentially affect clinically-relevant movements such as the tendon tap and pendulum test. In Aim 2, we will characterize the relative increases in alpha-drive, gamma-drive, and sensorimotor gain across clinically-relevant spinal excitability levels in a living biological neuromuscular circuit in vivo using a decerebrate rat preparation. In Aim 3 we will identify clinically-relevant movement abnormalities across spinal excitability levels in a novel biohybrid robotic system coupling the living neuromuscular circuit (in vivo) to a virtual biomechanical limb (in silico). A robotic controller will enforce the physics of dynamically changing inertial and gravitational forces, allowing movement to emerge from the causal interaction between the in vivo neuromuscular circuit and the virtual limb. Through the close coordination of these Aims, we will establish a computational and experimental framework to address clinical barriers (1) to determine how changes in neural mechanisms and the inertial properties of the limb could correct movement abnormalities, (2) to provide insight into how these mechanisms could be identified through different clinical assessment scenarios, and (3) to compare the relative effects of different treatment targets. The proposed work will likely impact both clinically-relevant human sensorimotor research and basic sensorimotor neuroscience.