An adaptive deep brain stimulation for tremor: based on a computational model

Deep brain stimulation (DBS) is an effective treatment for Parkinson’s disease, achieved by suppressing abnormal brain wave oscillations to relieve associated symptoms. Currently, numerous experimental and computational strategies exist for targeting beta (12-35Hz) oscillations in treatment models. These approaches primarily rely on monitoring beta wave activity within the normal range as a biomarker to mitigate abnormalities, alongside utilizing somatic and manual states to address resting tremors. However, within current DBS methodologies, the utilization of theta band activity as a biomarker is limited, with beta band biomarkers predominantly employed. This study introduces a novel approach: instead of solely relying on theta band power thresholds, we propose using the ratio of theta band power to low beta band power as a criterion for adaptive deep brain stimulation. The experimental results demonstrate that using this ratio as the threshold can effectively inhibit resting tremor and reduce the thalamocortex relay (TC) value from 0.48 to 0.08. And the energy consumption is 86% compared with traditional deep brain stimulation. This finding suggests that this value as a biomarker may be very effective for the treatment of resting tremor.