Brain Network Connectivity Measures in Early Stroke Rehabilitation

点击翻译按钮获取中文摘要

The research and training plans proposed in this Pathway to Independence Award will accelerate the candidate's transition to independent research scientist and tenure-track faculty member at a primary research institution. This award will support the candidate's investigation of functional neuroimaging measures of brain network connectivity in inpatient stroke rehabilitation. The candidate will acquire training in advanced resting- state functional magnetic resonance imaging (rsfMRI) and electroencephalography (EEG) analysis and clinical research methodology. The training plan involves customized mentoring, coursework in signal analysis and statistics, hands-on laboratory experiences, and professional career development activities targeting grant writing and grantsmanship, public speaking, and transitioning to academia. This award will complement the candidate's clinical background in physical therapy and prior stroke rehabilitation research. The K99 phase of the award will occur at the University of California, Irvine under the mentorship of Steven C. Cramer, MD, an exceptional neurologist and leader in stroke rehabilitation, and co-mentorship of Ramesh Srinivasan, PhD, a renowned specialist in EEG neurophysiology and data analysis. The University of California, Irvine will provide the candidate with an excellent research environment and an assortment of resources and opportunities to achieve her scientific and professional goals. Specifically, the University's Institute for Clinical & Translational Science and Medical Center will serve pivotal roles in the candidate's hospital-based research project. Co-mentors Alex Carter, MD, PhD (Washington University) and Carolee Winstein, PhD, PT, FAPTA (University of Southern California) will strengthen the candidate's independence in rsfMRI and clinical research methodology, respectively. Neuroimaging has the potential to greatly inform clinical decision-making in the context of stroke rehabilitation by offering unique insight beyond current behavioral-based measures. The candidate will assess sensorimotor network connectivity in patients with subacute stroke residing in an inpatient rehabilitation facility using advanced rsfMRI and EEG brain mapping techniques to determine if these functional measures predict motor recovery and, if so, how well these measurements predict motor recovery in comparison to behavioral and structural injury measures (Aim 1). The candidate will generate an ideal predictive model of motor recovery and move this model forward to Aim 2 (R00 phase) where it will be validated in a larger, independent sample, applied to a longer recovery timeframe, and examined in the presence of several key clinical covariates. The proposed training and projects under this award will propel the candidate to independence and facilitate a precision medicine approach to rehabilitation.