Carolynn Patten - US grants

1159735
Fregly

Stroke is the leading cause of serious long term disability in adults worldwide. Over 795,000 strokes occur in the United States each year. Walking dysfunction is one of the greatest stroke-related physical limitations. While approximately two-thirds of persons who suffer a stroke regain ambulatory function, their gait is slow, asymmetrical, and metabolically inefficient. Despite recognition of the problem, there is limited evidence that rehabilitation produces meaningful changes in walking function. These findings underscore a significant knowledge gap regarding the capacity for locomotor recovery and represent an urgent unmet need obstructing development of interventions to promote recovery and restoration of locomotor function in persons post-stroke.

The long term goal is to improve walking function in persons post-stroke. The objective of this proposal is to develop computational simulation technology that can predict best achievable gait patterns by individuals who have had a stroke. The technology will account for both subject-specific neural control limitations caused by the stroke and remaining neural control capabilities, as well as subject-specific musculoskeletal anatomy. The simulations will target two key aspects of normal walking function - speed and bilateral symmetry - when seeking to predict the gait patterns that a hemiparetic individual is theoretically capable of achieving. Differences between current and predicted muscle excitation patterns, joint kinematics, and joint kinetics will be used in a future project as the basis for selecting, on an individual subject basis, the neurorehabilitation treatment protocol most likely to restore normal gait speed and symmetry.

Intellectual Merit: The intellectual merit of the proposed project is development of novel neuromechanical modeling methods that will permit the prediction of best achievable gait patterns by individuals who have had a stroke. The novel technical aspects are three-fold. First, a new technique called "statistical moment estimation" will be developed that allows the transformation of measured muscle electrical activity signals directly into joint moments. The method uses a statistical over-determined system of equations rather than a geometric under-determined system of equations to calibrate the necessary model parameter values. Second, existing muscle synergy analysis techniques will be extended to quantify subject-specific neural control limitations and constrain gait motion predictions. Third, statistical moment estimation and muscle synergy analysis will be incorporated into an existing computational framework for predictive gait optimization. Whereas existing neuromechanical modeling methods are only descriptive of situations for which experimental data exist, our enhanced computational framework will be predictive of situations for which no experimental data yet exist.

Broader Impact: The broader impact of the proposed project is the development of computational simulation technology that can add objectivity to the design and selection of neurorehabilitation treatments for stroke. Current treatment design paradigms are highly subjective, being based primarily on clinician experience. Thus, there is often no clear rationale for selecting one treatment approach over another or for selecting the specific quantities to target within a selected treatment approach. Objective prediction of the gait patterns that a patient is theoretically capable of achieving could provide clinicians with valuable new information to improve the efficacy of the treatment design process.

Transformative Nature: The proposed research is transformative in two ways. First, it would be the first computational simulation technology capable of predicting gait patterns that an individual is theoretically capable of achieving, given the neural control limitations imposed on the individual by a stroke. Second, it could be a paradigm shift in neurorehabilitation treatment design, since it would transform a subjective, qualitative process into an objective, quantitative one.

? DESCRIPTION (provided by applicant): Stroke is the leading cause of serious, chronic disability in adults worldwide. Over 750,000 new strokes occur in the United States each year accounting for over half of all acute neurological hospital admissions. While two-thirds of persons who suffer a stroke regain ambulatory function, the resulting gait pattern is slow, asymmetrical and metabolically inefficient. Walking dysfunction represents one of the greatest physical limitations post-stroke and improved walking is among the most frequently articulated goals of neurorehabilitation. To date, rehabilitation for walking dysfunction post-stroke has produced highly variable outcomes revealing minimal genuine change in walking function including walking speed or walking pattern. Our long term goal is to improve walking function in persons post-stroke. The objective of this application is to develop a prognostic indicator to determine the physiological potential to improve walking capacity following stroke. The rationale for our proposal stems from our previous work, which has identified distinct patterns of response to therapeutic intervention for hemiparetic walking dysfunction, 'responders' and 'non-responders'. Responders are characterized by significant changes in over ground walking speed and multiple changes in spatio-temporal coordination. Non-responders produced minimal changes in over ground walking speed and few, if any, changes in spatio-temporal coordination. While the presence of responders and non-responders in the post-stroke population is not surprising, at study baseline responders and non-responders could not be differentiated using clinical instruments of motor impairment, activity, or walking speed. Moreover, clinical characteristics of chronicity and severity failed to predict these patterns of response. Taken together our findings suggest the presence of intrinsic, as-yet-unidentified subject-specific characteristics that mediate successful recovery of walking function. Here we propose development of a prognostic indicator to identify: a) neurophysiologic deficits contributing to walking dysfunction and b) an individual's potential to improve walking capacity following stroke. Informed by our prior work, we recognize neurophysiologic function contributes to neuromechanical output during walking. Attainment of a threshold of neurophysiologic functioning will suggest the presence of a neurobiological substrate requisite to improvement in walking capacity. Thus we will be able to differentiate individuals with the capacity to respond to traditional interventions from those who may need more aggressive approaches to remediate neurophysiologic function. Keywords: stroke, locomotion, recovery, rehabilitation, biomechanics

The overall research objective of this collaborative project is to create an embodied, intelligent robotic system that can induce meaningful long-term change in human motor function by providing personalized, adaptive feedback and noninvasive neural stimulation designed to induce desirable neuromotor plasticity. The motor behavior targeted for enhancement is plantarflexor power during the push-off phase of gait; stroke survivors often produce diminished plantarflexor power and rely instead on an inappropriate hip flexion "pull-off" compensation, thereby limiting the quality of their gait and quality of life. Personalized learning methods will be employed to model and optimize behavioral responses to changes in performance feedback provided by an intelligent mobile robotic coach, which will guide gait training. The project will lay the foundation to determine whether training based solely on principles of motor learning suffice to induce meaningful increases in plantarflexor power that are retained over time, or whether simultaneous targeted changes in brain excitability are required. This project advances the NSF mission to promote the progress of science and advance the national health by developing an adaptive motor learning algorithm embedded within an interactive mobile robot to induce meaningful long-term changes in human motor function through human-robot interaction. Broader impacts of the project include efforts to enhance research reproducibility and rigor, and to broaden participation in STEM for women, minorities, and persons with disabilities.

The overall objective of this research is to create an embodied, intelligent system that provides personalized, adaptive feedback to induce neuromotor plasticity, mediate motor adaptation, and promote meaningful, lasting increases in plantarflexor power, which is diminished during walking in many stroke survivors. Three sets of human subject experiments are researched. The first will identify critical parameters of performance feedback that facilitate the desired behavioral change. The second will use a novel learning paradigm to model and optimize behavioral responses to changes in performance feedback provided by an intelligent robotic coach. The third will use single-pulse transcranial magnetic stimulation (TMS) and paired associative stimulation (PAS) to harness neuroplastic effects in humans such that desired behavioral changes induced by optimized feedback training are made persistent through Hebbian learning mechanisms. The envisioned system will involve bi-directional learning between the human and machine intelligences to determine how to control important, but subject-specific, variables critical for maintaining and promoting motor function across the life and health span. Understanding these bi-directional relationships within the context of neurorehabilitation may provide insights that can further advance human-robot teaming in a range of application domains, including healthcare, manufacturing, and personal transportation.

This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

Abstract Osteoarthritis, stroke, spinal cord injury, traumatic brain injury, and amputation affect roughly 19% of the U.S. adult population, with osteoarthritis and stroke being leading causes of serious long-term disability in adults worldwide. Along with other conditions such as cerebral palsy, Parkinson's disease, and orthopedic cancer, these conditions often significantly impair movement, resulting in substantial societal costs, an increased risk of other serious health conditions (e.g., heart disease and diabetes), a reduction or even loss of independence, and a decreased quality of life. Despite the significance of the problem and the uniqueness of each patient, treatment design for movement impairments has not progressed substantially beyond off-the-shelf interventions selected based on subjective clinical judgment. If affected individuals are to recover the most function possible, a paradigm shift is needed toward personalized interventions designed using objective evidence-based methods. This project seeks to develop innovative software technology that will allow engineers working in collaboration with clinicians to design effective personalized interventions for movement impairments using objective physics-based computer models. The software technology will employ the same computer modeling and simulation methods that have revolutionized the design of airplanes and automobiles over the past 25 years. The proposed software will create a virtual representation of the patient and then apply virtual treatments to the virtual patient to identify the treatment design that is most likely to maximize recovery of lost function. Virtual patient models will obey laws of physics and principles of physiology to reflect how the patient moves before treatment and predict how the patient will move after treatment. To enable fast and easy construction of patient models and optimization of patient functional outcomes, the software technology will be incorporated into the NIH-funded OpenSim software for modeling and simulation of human movement. To support development and adoption of the proposed software, the project will also use the software to design personalized interventions for three individuals post-stroke with impaired, asymmetric walking function. The research team will organize a three-year ?Stroke Grand Challenge Competition,? held each year at the same professional conference, to engage the research community in model-based personalized treatment design. An extensive human movement data set will be collected from each subject to be used for constructing a virtual model of the subject. Competing research teams will use the software and the subject's virtual model to design personalized treatments that improve the subject's walking symmetry. In addition, the research team will use the new software to develop its own personalized intervention designs for the same subjects. Any clinically promising interventions identified by either competition participants or the research team will be implemented on the same subjects in a follow-on project to evaluate their efficacy.