Holt Abbey B, Wilson Dan, Shinn Max, Moehlis Jeff, Netoff Theoden I
Graduate Program in Neuroscience, University of Minnesota, Minneapolis, Minnesota, United States of America.
Department of Mechanical Engineering, University of California, Santa Barbara, California, United States of America.
PLoS Comput Biol. 2016 Jul 14;12(7):e1005011. doi: 10.1371/journal.pcbi.1005011. eCollection 2016 Jul.
We propose a novel, closed-loop approach to tuning deep brain stimulation (DBS) for Parkinson's disease (PD). The approach, termed Phasic Burst Stimulation (PhaBS), applies a burst of stimulus pulses over a range of phases predicted to disrupt pathological oscillations seen in PD. Stimulation parameters are optimized based on phase response curves (PRCs), which would be measured from each patient. This approach is tested in a computational model of PD with an emergent population oscillation. We show that the stimulus phase can be optimized using the PRC, and that PhaBS is more effective at suppressing the pathological oscillation than a single phasic stimulus pulse. PhaBS provides a closed-loop approach to DBS that can be optimized for each patient.
我们提出了一种用于调整帕金森病（PD）深部脑刺激（DBS）的新型闭环方法。该方法称为相位突发刺激（PhaBS），它在一系列预测会破坏PD中所见病理振荡的相位上施加一阵刺激脉冲。刺激参数基于相位响应曲线（PRC）进行优化，而PRC将从每位患者身上测量得出。此方法在具有突发群体振荡的PD计算模型中进行了测试。我们表明，刺激相位可使用PRC进行优化，并且PhaBS在抑制病理振荡方面比单个相位刺激脉冲更有效。PhaBS为DBS提供了一种可针对每位患者进行优化的闭环方法。
