Sellers Kristin K, Gilron Ro'ee, Anso Juan, Louie Kenneth H, Shirvalkar Prasad R, Chang Edward F, Little Simon J, Starr Philip A
Department of Neurological Surgery, University of California, San Francisco, San Francisco, CA, United States.
Department of Anesthesiology (Pain Management), Neurology, and Neurological Surgery, University of California, San Francisco, San Francisco, CA, United States.
Front Hum Neurosci. 2021 Jul 12;15:714256. doi: 10.3389/fnhum.2021.714256. eCollection 2021.
Closed-loop neurostimulation is a promising therapy being tested and clinically implemented in a growing number of neurological and psychiatric indications. This therapy is enabled by chronically implanted, bidirectional devices including the Medtronic Summit RC+S system. In order to successfully optimize therapy for patients implanted with these devices, analyses must be conducted offline on the recorded neural data, in order to inform optimal sense and stimulation parameters. The file format, volume, and complexity of raw data from these devices necessitate conversion, parsing, and time reconstruction ahead of time-frequency analyses and modeling common to standard neuroscientific analyses. Here, we provide an open-source toolbox written in Matlab which takes raw files from the Summit RC+S and transforms these data into a standardized format amenable to conventional analyses. Furthermore, we provide a plotting tool which can aid in the visualization of multiple data streams and sense, stimulation, and therapy settings. Finally, we describe an analysis module which replicates RC+S on-board power computations, a functionality which can accelerate biomarker discovery. This toolbox aims to accelerate the research and clinical advances made possible by longitudinal neural recordings and adaptive neurostimulation in people with neurological and psychiatric illnesses.
闭环神经刺激是一种很有前景的治疗方法,正在越来越多的神经和精神疾病适应症中进行测试和临床应用。这种治疗方法由长期植入的双向设备实现,包括美敦力Summit RC+S系统。为了成功地为植入这些设备的患者优化治疗,必须对记录的神经数据进行离线分析,以便确定最佳的感知和刺激参数。这些设备原始数据的文件格式、体积和复杂性使得在进行标准神经科学分析中常见的时频分析和建模之前,需要进行数据转换、解析和时间重建。在此,我们提供了一个用Matlab编写的开源工具箱,它可以获取Summit RC+S的原始文件,并将这些数据转换为适合传统分析的标准化格式。此外,我们还提供了一个绘图工具,可帮助可视化多个数据流以及感知、刺激和治疗设置。最后,我们描述了一个分析模块,它可以复制RC+S板载功率计算功能,该功能可加速生物标志物的发现。这个工具箱旨在加速神经和精神疾病患者通过纵向神经记录和自适应神经刺激实现的研究和临床进展。