Heidi Schambra, M.D. - US grants

DESCRIPTION (provided by applicant): This is an application for a K23 award for Dr. Heidi Schambra, a neurologist at Columbia University. Dr. Schambra's long-term career goals are to become a leading clinical investigator in stroke recovery, to improve the well-being of patients with neurologic injury, and to advance the field of neurorehabilitation. This K23 award will provide her with the support necessary to accomplish her short-term career goals, which are: (1) to conduct prospective clinical research in stroke patients; (2) to become expert in quantitative motor recovery and ad- vanced stroke neurophysiology; (3) to implement biostatistician methodology in clinical research; and (4) to de- velop an independent clinical research career that is R01-funded. To achieve these goals, Dr. Schambra has assembled a mentoring team comprised of a primary mentor, Dr. John Krakauer, an internationally recognized authority in motor learning and stroke recovery, and two co-mentors: Dr. Randolph Marshall, a leader in pros- pective clinical research in stroke; and Dr. Pietro Mazzoni, an expert in motor control in neurologically impaired patients. She will also have three scientific advisors: Dr. Pablo Celnik, an authority in stroke neurophysiology; Dr. Todd Ogden, a biostatistician with extensive biomedical research experience; and Dr. Robert Sainburg, a specialist in motor control in stroke. Dr. Schambra has the firm institutional support of the Departments of Neu- urology and Rehabilitation Medicine, and she will receive comprehensive instruction from Columbia's robust scientific and clinical communities. There is a fundamental gap in our mechanistic understanding of motor recovery after stroke. The long- term objective is to use knowledge about biological recovery processes to develop mechanism-based treat- ments for patients after stroke. The specific objective here is to determine how longitudinal changes in corti- cospinal and intracortical physiology relate to changes in motor impairment in either limb after stroke. The ra- tionale for this project is that its successful completion would strongly suggest a mechanistic role for neurophy- siology in motor recovery. This project will leverage the existing infrastructure of a multicenter parent study, lead by Dr. Krakauer, to evaluate 45 stroke patients over 5 time points in the first year following stroke. Dr. Schambra will test the central hypothesi that certain stereotyped recovery behaviors will have distinct neuro- physiological signatures. She will do this by identifying the neurophysiologic correlates of: the recovery of strength and motor control in the paretic arm (Aim 1), the proximal-to-distal progression of motor recovery in the paretic arm (Aim 2), and the recovery of motor control in the nonparetic arm (Aim 3). All aims will utilize correlation analyses. The proposed research is innovative in its use of quantitative physiologic and behavioral measures to study a surprisingly neglected period after stroke. It is expected to be significant by providing a rational basis for targeting specific neurophysiologic components at different times after stroke, to be proposed by Dr. Schambra in a future R01 application.

PROJECT SUMMARY Stroke causes significant disability, and recovery is often incomplete. In animal models of stroke, robust upper extremity (UE) motor recovery can be elicited if high doses of functional training are given early. In humans, however, the optimal training dose is unknown, because no quantitative dose-response trials have been undertaken in the first months after stroke. This deficiency stems from a lack of measurement instruments that can accurately and easily quantify UE functional training dose and recovery. To address this gap, this proposal will generate two new measurement tools to enable quantitative stroke recovery research. The first tool (Aim 1) will quantify the number of functional movements made during stroke rehabilitation, measuring UE training dose. The second tool (Aim 2) will quantify the abnormality of movements, measuring UE recovery and response to interventions. We will combine wireless motion capture and computational methodologies to create objective, precise, and user-friendly tools. Inertial measurement units, worn by individuals with stroke and healthy controls performing various activities, will capture upper body motion. In Aim 1, machine learning algorithms will be trained to identify and count functional movements in activities normally practiced during rehabilitation. In Aim 2, functional principal components analysis will quantify movement impairment and compensation in standardized motions. Validity will be determined by correlating tool outcomes with current gold standards. The proposed study will be conducted at New York University, in collaboration with investigators from the NYU Center for Data Science, Columbia University, and Washington University-St. Louis. Each have complementary expertise in machine learning, functional data analysis, and functional movement identification. This K02 Independent Scientist Award will provide the candidate with skill in advanced motion capture and analytical methodologies needed to study stroke rehabilitation and recovery. The career development plan includes personalized tutorials and coursework combined with longitudinal oversight of data analysis, providing an excellent foundation for launching an independent research career. Ultimately, the developed tools have the potential to immediately impact neurorehabilitation research, facilitating the rigorous dose-response trials so critically needed to change clinical practice and improve stroke outcomes.

PROJECT SUMMARY The neural substrates underlying motor recovery after stroke are poorly understood. Despite decades of research, strategies for optimizing recovery remain lacking, possibly because of the failure to consider non- corticospinal pathways as potential recovery substrates. The corticoreticulospinal tract (CReST) is a bilaterally descending motor pathway in humans. In animals that have recovered from corticospinal tract injury, the contralesional CReST shows functional and structural upregulation. In humans that have recovered from corticospinal stroke, contralesional CReST neurophysiology shows increased excitability. These findings suggest that the contralesional CReST has the capacity to reorganize after stroke, but it is not known if these changes directly relate to motor improvement. The overall objective of this application is to identify the role of the contralesional CReST in motor recovery. Our central hypothesis is that functional and structural changes in the contralesional CReST will causally relate to motor recovery in the upper extremity. The rationale underlying the proposed research is that, once a recovery role is identified, the CReST could be manipulated to accelerate recovery. In the first six months following ischemic stroke, we will longitudinally measure strength, motor control, and motor synergies to characterize motor recovery. We will use transcranial magnetic stimulation (Aim 1) and structural MRI (Aim 2) to precisely detail contralesional CReST neurophysiology and microstructure. Pathway-behavior relationships will be assessed within a causal inference framework. We expect to show that contralesional CReST reorganization, manifesting as increased excitability and tissue complexity, will relate to increasing strength, motor control, and synergy expression after stroke. The proposed work is significant, because it is expected to provide strong scientific justification for targeting the contralesional CReST to potentiate recovery. The proposed work is innovative, because it combines advanced complementary approaches to focus on a surprisingly understudied pathway in motor recovery. Our study is likely to have a positive impact on the field of neurorehabilitation, because it will vertically advance our understanding of motor recovery mechanisms, leading to the development of rationally designed therapies to improve stroke outcomes.

Advances in wearable electronics, personal mobile devices, and sensor technology are opening the door to many promising applications in medical care and biomedical research. However, the resulting datasets are often challenging to process due to variability caused by extraneous effects unrelated to the tasks of interest, such as changes in environmental conditions, heteroscedasticity in measurement noise, or patient idiosyncrasies. These effects produce systematic differences between the data used to train machine- learning algorithms and the data on which they are applied in practice, impairing real-world performance. The proposed research will address the fundamental problem of factoring out extraneous effects associated with known nuisance variables. We will develop a novel methodology for extracting features that ar.e invariant to nuisance variables-and hence also to the associated extraneous effects-but that are still useful for classification or regression. The methodology is based on nonparametric deep-network models that perform automatic normalization of the data, and further enforce invariance via adversarial learning. We will apply the approach to an important problem in stroke rehabilitation, the quantitated dosing of motor training. Using a dataset of sensor-based motion data, we will train the model to identify and count functional movements in stroke patients performing rehabilitation activities. We expect to show that our approach can surmount patient variability to enable rigorous movement classification and quantitation. The proposed work is significant, because it will empower investigators to undertake the dosing trials critically needed in stroke rehabilitation. The proposed work is innovative, because it departs from traditional data preprocessing techniques by combining advanced data normalization and model calibration procedures. Our work is likely to have a positive impact on stroke rehabilitation by facilitating the research required to change clinical practice and improve stroke outcomes. Our quantitative approach is broadly generalizable to applications hindered by nuisance variables, such as medical diagnostics and genomics.