High-precision and efficient suppression of pathological brain activity in Parkinsonian rats via a closed-loop deep brain stimulation approach

Abstract

The increase of high-voltage spindles (HVSs) in the basal ganglia network is a hallmark of dopamine depletion in Parkinsonian rats. Emerging evidence highlights the efficacy of deep brain stimulation (DBS) in suppressing HVSs. It is of significant interest to investigate whether suppressing HVSs can mitigate pathological neuron synchrony in the basal ganglia, particularly in early-stage Parkinson's disease. To effectively suppress HVSs using DBS, we developed a closed-loop stimulator triggered by HVS occurrence. Based on autoregressive modeling at intervals, a predictive model was created with parameters trainable offline using the Kalman filter to detect the onset of HVSs, which is suitable for hardware implementation. This model identified all 1131 HVS episodes from four Parkinsonian rats using 144 ms of preceding data, achieving a 94 % mean precision and a mean latency of 72 ms—well below the average HVS duration of 4.3 s. Additionally, it achieves comparable latency while requiring 95 % less computational time than the previous wavelet-based HVS detection model. With the trained model implemented in a microcontroller, we further investigated the effects of closed-loop DBS (cDBS) on HVSs in free-moving Parkinsonian rats with a tethered and wireless system, respectively. In both setups, a stimulation duration as brief as 0.2 s effectively suppressed HVSs. Furthermore, using the wireless system, the inhibition of HVS lasted over 30 min post-cDBS application. These findings underscore the potential of cDBS to suppress HVSs, lower stimulation dosages, and reduce side effects, paving the way for its application in early-stage Parkinson's disease treatment through neuromodulation.