Bronte-Stewart Helen M, Petrucci Matthew N, O'Day Johanna J, Afzal Muhammad Furqan, Parker Jordan E, Kehnemouyi Yasmine M, Wilkins Kevin B, Orthlieb Gerrit C, Hoffman Shannon L
Department of Neurology and Neurological Sciences, Stanford University School of Medicine, Stanford, CA, United States.
Department of Neurosurgery, Stanford University School of Medicine, Stanford, CA, United States.
Front Hum Neurosci. 2020 Aug 31;14:353. doi: 10.3389/fnhum.2020.00353. eCollection 2020.
A deep brain stimulation system capable of closed-loop neuromodulation is a type of bidirectional deep brain-computer interface (dBCI), in which neural signals are recorded, decoded, and then used as the input commands for neuromodulation at the same site in the brain. The challenge in assuring successful implementation of bidirectional dBCIs in Parkinson's disease (PD) is to discover and decode stable, robust and reliable neural inputs that can be tracked during stimulation, and to optimize neurostimulation patterns and parameters (control policies) for motor behaviors at the brain interface, which are customized to the individual. In this perspective, we will outline the work done in our lab regarding the evolution of the discovery of neural and behavioral control variables relevant to PD, the development of a novel personalized dual-threshold control policy relevant to the individual's therapeutic window and the application of these to investigations of closed-loop STN DBS driven by neural or kinematic inputs, using the first generation of bidirectional dBCIs.
一种能够进行闭环神经调节的深部脑刺激系统是一种双向深部脑-计算机接口（dBCI），其中神经信号被记录、解码，然后用作大脑同一部位神经调节的输入命令。确保双向dBCI在帕金森病（PD）中成功实施面临的挑战是发现并解码在刺激过程中可追踪的稳定、强健且可靠的神经输入，并优化大脑接口处运动行为的神经刺激模式和参数（控制策略），这些策略是针对个体定制的。从这个角度出发，我们将概述我们实验室在以下方面所做的工作：与PD相关的神经和行为控制变量发现的进展、与个体治疗窗口相关的新型个性化双阈值控制策略的开发，以及利用第一代双向dBCI将这些应用于由神经或运动输入驱动的闭环STN DBS研究。
