| Literature DB >> 26041930 |
Tanuj Gulati1, Seok Joon Won1, Dhakshin S Ramanathan2, Chelsea C Wong1, Anitha Bodepudi1, Raymond A Swanson1, Karunesh Ganguly3.
Abstract
Intracortical brain-machine interfaces (BMIs) may eventually restore function in those with motor disability after stroke. However, current research into the development of intracortical BMIs has focused on subjects with largely intact cortical structures, such as those with spinal cord injury. Although the stroke perilesional cortex (PLC) has been hypothesized as a potential site for a BMI, it remains unclear whether the injured motor cortical network can support neuroprosthetic control directly. Using chronic electrophysiological recordings in a rat stroke model, we demonstrate here the PLC's capacity for neuroprosthetic control and physiological plasticity. We initially found that the perilesional network demonstrated abnormally increased slow oscillations that also modulated neural firing. Despite these striking abnormalities, neurons in the perilesional network could be modulated volitionally to learn neuroprosthetic control. The rate of learning was surprisingly similar regardless of the electrode distance from the stroke site and was not significantly different from intact animals. Moreover, neurons achieved similar task-related modulation and, as an ensemble, formed cell assemblies with learning. Such control was even achieved in animals with poor motor recovery, suggesting that neuroprosthetic control is possible even in the absence of motor recovery. Interestingly, achieving successful control also reduced locking to abnormal oscillations significantly. Our results thus suggest that, despite the disrupted connectivity in the PLC, it may serve as an effective target for neuroprosthetic control in those with poor motor recovery after stroke.Entities:
Keywords: brain–machine interface; electrophysiology; plasticity; stroke
Mesh:
Year: 2015 PMID: 26041930 PMCID: PMC6605327 DOI: 10.1523/JNEUROSCI.5007-14.2015
Source DB: PubMed Journal: J Neurosci ISSN: 0270-6474 Impact factor: 6.167