Quantifying the Interdependence of the Motor Network Using Intracranial Electrodes - PROJECT SUMMARY/ABSTRACT To survive, human behavior must adapt to ever-changing contexts. This includes movement, which requires constant coordination and adjustment of a motor network made of distributed brain regions. These regions can be categorized into two classes: the primary motor nodes (M1) in which activation of distinct regions leads to distinct movement types, and non-primary nodes (e.g. PMv, PMd, SMA) which inform M1 prior to movement execution. Among the 800,000 strokes in the U.S. each year, the most common deficits are related to disruption of this motor network. Current post-stroke rehab tools, such as neurofeedback therapy, assume that these motor areas act together, but if true, this interdependence severely limits treatment of patients as damage to the network can’t be overcome. Recent findings challenge this assumption by showing that non-primary regions, like M1, send direct projections to the spinal cord and the classical primary motor area contains non-primary cortex. Thus, the functions of M1 and non-primary nodes may overlap more than previously thought. This raises the possibility that non-primary areas could substitute for M1, allowing damaged networks to use new motor patterns to bypass dysfunctional nodes. Thus, there is a critical need to better understand the interdependence of the motor network. This can be experimentally carried out by measuring the ability of non-primary nodes to act independently of M1. Our hypothesis is that non-primary motor nodes can indeed be modulated independently of M1 when provided custom feedback, resulting in altered network connectivity. To test this, we will use stereoelectroencephalography, a method of intracranial monitoring for patients with epilepsy that records from the entire brain to 1) identify the innate human motor network using motor BCI, 2) reinforce M1 decoupling from the rest of the network using custom imagery brain computer interfaces and 3) measure the connectivity changes resulting from this BCI-driven adaptation using electrical stimulation. In addition to the scientific discoveries afforded by this proposal, it will also serve to assess the clinical validity of sEEG-based neurofeedback as a potential post-stroke motor therapy.
