Skill Consolidation - The Role of Sleep.
Cardinal features of motor learning involve enhanced speed and automaticity with improvements in accuracy. We are particularly interested in the contribution of “online” (i.e. during task practice) and “offline” processes (e.g. sleep) (e.g. Ramanathan et al., PLoS Biology, 2015; Gulati et al., Nat Neurosci, 2014; Gulati et al., Nat Neurosci, 2017).
Distributed Control of Skill Learning/Motor Recovery.
We aim to develop a systems levels model of network dynamics during motor/ neuroprosthetic learning and motor recovery. Neural activity is known to dynamically represent multiple temporal and spatial scales; the time varying interactions between neurons, ensembles and motor areas can coordinate information from the micro- to the mesoscale. Our approach involves monitoring of neural activity with high temporal and spatial resolution at the micro- and mesoscale level (Ganguly et al., 2009; Wong et al., J Neurosci Meth, 2015).
Closed-Loop Interfaces for Stroke.
We are also investigating the integration of artificial systems with injured motor networks. We specifically conduct research to understand how neural interfaces can enhance motor function in the injured nervous system (e.g. Ganguly et al., J Neurosci 2009; Gulati et al., J Neurosci 2015).
The incidence of stroke in the US is ~700,000 per year. While current rehabilitation approaches have helped individuals, a substantial number of patients continue to experience chronic upper limb dysfunction. We are currently clinically testing novel neuromodulation approaches to improve hand and finger function after stroke (Tsu et al, under revision). ClinicalTrials@UCSF Site.
Recent pilot clinical studies have impressively demonstrated control of upper-limb prosthetics. We are pursuing clinical translation of ECoG based technology in patients with severe upper-limb motor impairments (e.g. Ganguly et al., J Neurosci, 2009; Godlove et al., 2016).