Motor Task Detection From Human STN Using Interhemispheric Connectivity

Deep brain stimulation (DBS) provides significant therapeutic benefit for movement disorders, such as Parkinson's disease (PD). Current DBS devices lack real-time feedback (thus are open loop) and stimulation parameters are adjusted during scheduled visits with a clinician. A closed-loop DBS system may reduce power consumption and side effects by adjusting stimulation parameters based on patient's behavior. Subthalamic nucleus (STN) local field potential (LFP) is a great candidate signal for the neural feedback, because it can be recorded from the stimulation lead and does not require additional sensors. In this paper, we introduce a behavior detection method capable of asynchronously detecting the finger movements of PD patients. Our study indicates that there is a motor-modulated inter-hemispheric connectivity between LFP signals recorded bilaterally from the STN. We utilize a non-linear regression method to measure this inter-hemispheric connectivity for detecting finger movement. Our experimental results, using the recordings from 11 patients with PD, demonstrate that this approach is applicable for behavior detection in the majority of subjects (average area under curve of 70±12%).