Nordenström Simon, Petermann Katrin, Debove Ines, Nowacki Andreas, Krack Paul, Pollo Claudio, Nguyen T A Khoa
Department of Neurosurgery, University Hospital Bern, Bern, Switzerland.
Department of Neurology, University Hospital Bern, Bern, Switzerland.
Front Hum Neurosci. 2022 Nov 1;16:925283. doi: 10.3389/fnhum.2022.925283. eCollection 2022.
Deep Brain Stimulation (DBS) is an effective treatment for advanced Parkinson's disease. However, identifying stimulation parameters, such as contact and current amplitudes, is time-consuming based on trial and error. Directional leads add more stimulation options and render this process more challenging with a higher workload for neurologists and more discomfort for patients. In this study, a sweet spot-guided algorithm was developed that automatically suggested stimulation parameters. These suggestions were retrospectively compared to clinical monopolar reviews. A cohort of 24 Parkinson's disease patients underwent bilateral DBS implantation in the subthalamic nucleus at our center. First, the DBS' leads were reconstructed with the open-source toolbox Lead-DBS. Second, a sweet spot for rigidity reduction was set as the desired stimulation target for programming. This sweet spot and estimations of the volume of tissue activated were used to suggest (i) the best lead level, (ii) the best contact, and (iii) the effect thresholds for full therapeutic effect for each contact. To assess these sweet spot-guided suggestions, the clinical monopolar reviews were considered as ground truth. In addition, the sweet spot-guided suggestions for best lead level and best contact were compared against reconstruction-guided suggestions, which considered the lead location with respect to the subthalamic nucleus. Finally, a graphical user interface was developed as an add-on to Lead-DBS and is publicly available. With the interface, suggestions for all contacts of a lead can be generated in a few seconds. The accuracy for suggesting the best out of four lead levels was 56%. These sweet spot-guided suggestions were not significantly better than reconstruction-guided suggestions ( = 0.3). The accuracy for suggesting the best out of eight contacts was 41%. These sweet spot-guided suggestions were significantly better than reconstruction-guided suggestions ( < 0.001). The sweet spot-guided suggestions of each contact's effect threshold had a mean error of 1.2 mA. On an individual lead level, the suggestions can vary more with mean errors ranging from 0.3 to 4.8 mA. Further analysis is warranted to improve the sweet spot-guided suggestions and to account for more symptoms and stimulation-induced side effects.
深部脑刺激(DBS)是晚期帕金森病的一种有效治疗方法。然而,基于反复试验来确定刺激参数,如触点和电流幅度,是耗时的。定向电极增加了更多的刺激选择,使这个过程更具挑战性,给神经科医生带来更高的工作量,也给患者带来更多不适。在本研究中,开发了一种基于最佳刺激点引导的算法,该算法能自动建议刺激参数。这些建议与临床单极评估进行了回顾性比较。我们中心的24名帕金森病患者队列接受了双侧丘脑底核DBS植入。首先,使用开源工具箱Lead-DBS重建DBS电极。其次,将用于减轻僵硬的最佳刺激点设定为编程的期望刺激目标。这个最佳刺激点和组织激活体积的估计值被用来建议:(i)最佳电极水平,(ii)最佳触点,以及(iii)每个触点达到完全治疗效果的效应阈值。为了评估这些基于最佳刺激点引导的建议,将临床单极评估视为金标准。此外,将基于最佳刺激点引导的最佳电极水平和最佳触点建议与基于重建引导的建议进行了比较,后者考虑了电极相对于丘脑底核的位置。最后,开发了一个图形用户界面作为Lead-DBS的附加组件并公开可用。通过该界面,可以在几秒钟内生成电极所有触点的建议。从四个电极水平中建议出最佳水平的准确率为56%。这些基于最佳刺激点引导的建议并不比基于重建引导的建议显著更好(P = 0.3)。从八个触点中建议出最佳触点的准确率为41%。这些基于最佳刺激点引导的建议显著优于基于重建引导的建议(P < 0.001)。每个触点效应阈值的基于最佳刺激点引导的建议平均误差为1.2 mA。在单个电极水平上,建议的变化可能更大,平均误差范围为0.3至4.8 mA。有必要进行进一步分析以改进基于最佳刺激点引导的建议,并考虑更多症状和刺激引起的副作用。