在帕金森氏病的深部脑刺激手术中,商用微电极记录算法对丘脑底核边界的表征和轨迹的建议。

François D Roy, Babak Afsharipour, Aleksandra King, Michelle Waldron, Fang Ba, Aakash Shetty, Tejas Sankar
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引用次数: 0

摘要

背景和目的:在帕金森病的深部脑刺激(DBS)手术中,丘脑下核(STN)内的微电极记录(MER)是常规的。已经开发出商用算法来检测STN边界,并根据MER数据推荐最佳DBS导联轨迹。我们的目标是描述一个广泛使用的算法的STN边界估计和轨迹建议的方差。方法:21例患者的37个STN-DBS植入物的MER数据使用半自动化算法离线分析,利用MER数据的振荡活动(HaGuide, Alpha Omega)。软件推荐使用默认的STN设置在3个不同的“站点大小”和2个“等待时间”中计算。对于6个试验中的每一个,分析了STN入口、STN背侧振荡区出口、STN出口、STN长度、背侧振荡区比率(%)、刺激深度和轨迹建议的值。结果:即使使用不同的输入参数,该算法对所选轨迹内STN出口和STN入口的估计具有较低的受试者内部变异性,并且与临床团队选择的最终DBS导联深度高度相关(STN出口:r = 0.86和STN入口:r = 0.70;P < 0.001)。然而,该算法的轨迹推荐对输入参数更为敏感,该算法在42%的植入物中推荐了1个以上的轨迹。结论:常用算法半自动识别STN边界对算法输入参数的敏感性相对较低,与专家外科团队最终确定的STN- dbs导联深度相关性较好。然而,算法生成的最佳轨迹建议受输入参数的影响更大,在DBS植入过程中应该更加谨慎地解释。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Characterization of Subthalamic Nucleus Boundary and Trajectory Recommendations From a Commercially Available Microelectrode Recording Algorithm During Deep Brain Stimulation Surgery for Parkinson Disease.

Background and objectives: Microelectrode recordings (MER) within the subthalamic nucleus (STN) are routinely performed during deep brain stimulation (DBS) surgery for Parkinson disease. Commercially available algorithms have been developed to detect STN boundaries and recommend an optimal DBS lead trajectory based on MER data. We aimed to characterize the variance of a broadly used algorithm's STN border estimates and trajectory recommendations.

Methods: MER data from 37 STN-DBS implants in 21 patients were analyzed offline using a semiautomated algorithm making use of oscillatory activity in MER data (HaGuide, Alpha Omega). Software recommendations were computed using the default STN settings across 3 different 'Site Sizes' and 2 'Waiting Times'. For each of the 6 trials, values for the STN Entrance, STN dorsolateral oscillatory region Exit, STN Exit, STN Length, dorsolateral oscillatory region ratio (%), Stimulation Depth, and trajectory recommendations were analyzed.

Results: Even with different input parameters, the algorithm's estimates of STN Exit and STN Entrance within the chosen trajectory had low intrasubject variability and were highly correlated with the depth of the final DBS lead as chosen by the clinical team (STN Exit: r = 0.86 and STN Entrance: r = 0.70; both P < .001). However, the algorithm's trajectory recommendations were more sensitive to input parameters, with the algorithm recommending more than 1 trajectory in 42% of implants.

Conclusion: Semiautomated identification of STN boundaries by a commonly used algorithm is relatively less sensitive to algorithm input parameters and well-correlated with final STN-DBS lead depth as determined by an expert surgical team. However, algorithm-generated optimal trajectory recommendations are more strongly influenced by input parameters and should be interpreted with more caution during DBS implantation.

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