Stroke is a major cause of disability in veterans. Despite significant advances in stroke rehabilitation methods there continue to be substantial long-term disability. Additional research is required to develop new methods to facilitate recovery. We propose to delineate and modulate the neurophysiological offline processing and performance gains that appear to occur during the process of recovery (i.e. systems level neural processing and enhancements of performance that occur after the actual training has stopped). Based on our preliminary data, we will focus on the role of offline processing during sleep. More specifically, using multiscale electrophysiological techniques that monitor task-related activity at multiple resolutions, we propose to conduct studies that will both delineate how sleep enhances motor recovery after stroke as well as test how best to structure/modify sleep during rehabilitation. Our underlying hypothesis is that `offline neural processing' during sleep facilitates motor recovery. The main goals of this proposal are to: (1) delineate the stages of sleep that can promote motor recovery after training, (2) elucidate the neural processes that promote motor learning during sleep, and (3) test if optimization of sleep-dependent processing can be used to enhance recovery. Using long-term multi- scale electrophysiological recordings in rats, our preliminary data illustrates tht sleep in general, and non-rapid eye movement (NREM) sleep in specific, plays an important role in the consolidation of motor skills in both the non-injured and the injured motor system. Further, we found a close link between `replay' of neural patterns formed during task-specific training and offline gains in performance. The specific underlying hypothesis of this proposal is that offline `replay' of neurons during NREM sleep is important for sleep-dependent long-term improvements in motor recovery after stroke. We propose the following aims: (1) Determine the optimal amount and type of sleep to induce short-term offline gains in motor performance after stroke; (2) Determine the electrophysiological correlates of NREM sleep- dependent offline processing in the stroke perilesional cortex after motor rehabilitation; (3) Determine if sleep-dependent processing can be used to actively enhance long-term motor recovery. Our proposed research has the possibility of discovering important knowledge about the network and the neurophysiological basis of motor recovery and can offer novel approaches for neuromodulation to enhance motor recovery after stroke.
Few neurological conditions are as complex and devastating as stroke. It is a major cause of disability in the United States and the aging population of veterans. While there have been important strides taken toward optimizing functional recovery, a substantial proportion of stroke survivors continue to experience long-term disability. The goal of this proposal is to develop a better neurophysiological model of the `offline' recovery process and to use this to test new therapeutic approaches. We hope to use this knowledge to create novel therapies to promote motor recovery. 1
| Ramanathan, Dhakshin S; Guo, Ling; Gulati, Tanuj et al. (2018) Low-frequency cortical activity is a neuromodulatory target that tracks recovery after stroke. Nat Med 24:1257-1267 |
| Gulati, Tanuj; Guo, Ling; Ramanathan, Dhakshin S et al. (2017) Neural reactivations during sleep determine network credit assignment. Nat Neurosci 20:1277-1284 |
