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帕金森病脑深部电刺激手术中,基于市售微电极记录算法对丘脑底核边界的特征描述及轨迹推荐

Characterization of Subthalamic Nucleus Boundary and Trajectory Recommendations From a Commercially Available Microelectrode Recording Algorithm During Deep Brain Stimulation Surgery for Parkinson Disease.

作者信息

Roy François D, Afsharipour Babak, King Aleksandra, Waldron Michelle, Ba Fang, Shetty Aakash, Sankar Tejas

机构信息

Department of Surgery, University of Alberta, Edmonton, Alberta, Canada.

Alberta Health Services, Edmonton, Alberta, Canada.

出版信息

Oper Neurosurg. 2025 Jul 10. doi: 10.1227/ons.0000000000001708.

Abstract

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.

摘要

背景与目的

在帕金森病的脑深部电刺激(DBS)手术中,常规会在丘脑底核(STN)内进行微电极记录(MER)。已经开发出了商用算法来检测STN边界,并根据MER数据推荐最佳的DBS电极轨迹。我们旨在描述一种广泛使用的算法对STN边界估计和轨迹推荐的差异。

方法

对21例患者的37次STN-DBS植入手术的MER数据进行离线分析,使用一种利用MER数据中的振荡活动的半自动算法(HaGuide,Alpha Omega)。在3种不同的“位点大小”和2种“等待时间”下,使用默认的STN设置计算软件推荐。对6次试验中的每一次,分析STN入口、STN背外侧振荡区域出口、STN出口、STN长度、背外侧振荡区域比例(%)、刺激深度和轨迹推荐的值。

结果

即使输入参数不同,该算法在所选轨迹内对STN出口和STN入口的估计在个体内的变异性较低,并且与临床团队选择的最终DBS电极深度高度相关(STN出口:r = 0.86,STN入口:r = 0.70;P均<0.001)。然而,该算法的轨迹推荐对输入参数更敏感,在42%的植入手术中该算法推荐了不止一条轨迹。

结论

常用算法对STN边界的半自动识别对算法输入参数相对不太敏感,并且与专家手术团队确定的最终STN-DBS电极深度相关性良好。然而,算法生成的最佳轨迹推荐受输入参数的影响更大,在DBS植入过程中应更谨慎地解释。

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