The number of Americans ages 55 and older will almost double by 2030 â€“ from 60 million (21 percent of the total US population) to 107.6 million (31 percent of the population) â€“ as Baby Boomers reach retirement age. In 2000, 65+ year olds represented 12.4% of the population, but are expected to grow to be 19% of the population by 2030. Age is one of the most significant risk factors for stroke â€“ this implies that as the population ages, there will be an increasing number of individuals with stroke-related impairments and disabilities. With stroke identified as the third leading cause of death in the world, and the explosion in associated economic costs, there is an urgent need for more effective health care planning and resource allocation. Key to a successful health care plan for stroke is the ability to identify risk factors leading to stroke as well the prognostic factors predicting recovery. The current proposal focuses on identifying prognostic factors associated with good vs. poor functional recovery, knowledge of which can lead to the development of therapeutic interventions catered to an individual patientâ€™s functional recovery goals. Given the wide variability in the severity of stroke and prognosis, it is important to develop models of stroke intervention based on a precise understanding of the neurobiological mechanisms underlying recovery, specifically the time window for intervention, and brain networks that are adaptive and maladaptive towards recovery. Towards this end, the current work will bring together clinicians and scientists engaged in basic and translational science, which is integral to an understanding of the reorganization of the brain after stroke.
The PI of this project was: Vivek Prabhakaran, MD, PhD
This project was funded by: NIH
The term of this project was: July 2015 to June 2018
The number of subjects scanned during this project was: 150