Roediger Jan, Dembek Till A, Wenzel Gregor, Butenko Konstantin, Kühn Andrea A, Horn Andreas
Movement Disorder and Neuromodulation Unit, Department of Neurology, Charité University Medicine Berlin, Charitéplatz 1, Berlin, 10117, Germany.
Einstein Center for Neurosciences Berlin, Charité University Medicine Berlin, Charitéplatz 1, Berlin, 10117, Germany.
Mov Disord. 2022 Mar;37(3):574-584. doi: 10.1002/mds.28878. Epub 2021 Nov 27.
Finding the optimal deep brain stimulation (DBS) parameters from a multitude of possible combinations by trial and error is time consuming and requires highly trained medical personnel.
We developed an automated algorithm to identify optimal stimulation settings in Parkinson's disease (PD) patients treated with subthalamic nucleus (STN) DBS based on imaging-derived metrics.
Electrode locations and monopolar review data of 612 stimulation settings acquired from 31 PD patients were used to train a predictive model for therapeutic and adverse stimulation effects. Model performance was then evaluated within the training cohort using cross-validation and on an independent cohort of 19 patients. We inverted the model by applying a brute-force approach to determine the optimal stimulation sites in the target region. Finally, an optimization algorithm was established to identify optimal stimulation parameters. Suggested stimulation parameters were compared to the ones applied in clinical practice.
Predicted motor outcome correlated with observed outcome (R = 0.57, P < 10 ) across patients within the training cohort. In the test cohort, the model explained 28% of the variance in motor outcome differences between settings. The stimulation site for maximum motor improvement was located at the dorsolateral border of the STN. When compared to two empirical settings, model-based suggestions more closely matched the setting with superior motor improvement.
We developed and validated a data-driven model that can suggest stimulation parameters leading to optimal motor improvement while minimizing the risk of stimulation-induced side effects. This approach might provide guidance for DBS programming in the future. © 2021 The Authors. Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society.
通过反复试验从众多可能的组合中找到最佳的脑深部电刺激（DBS）参数既耗时，又需要训练有素的医务人员。
我们开发了一种自动化算法，以基于影像学指标识别接受丘脑底核（STN）DBS治疗的帕金森病（PD）患者的最佳刺激设置。
使用从31例PD患者获得的612种刺激设置的电极位置和单极回顾数据来训练治疗性和不良刺激效果的预测模型。然后在训练队列中使用交叉验证对模型性能进行评估，并在一个由19例患者组成的独立队列中进行评估。我们通过应用蛮力法反转模型，以确定目标区域中的最佳刺激部位。最后，建立了一种优化算法来识别最佳刺激参数。将建议的刺激参数与临床实践中应用的参数进行比较。
在训练队列中的患者中，预测的运动结果与观察到的结果相关（R = 0.57，P < 10）。在测试队列中，该模型解释了不同设置之间运动结果差异中28%的方差。最大运动改善的刺激部位位于STN的背外侧边界。与两种经验性设置相比，基于模型的建议与具有更好运动改善的设置更接近匹配。
我们开发并验证了一种数据驱动的模型，该模型可以建议导致最佳运动改善同时将刺激引起的副作用风险降至最低的刺激参数。这种方法可能为未来的DBS编程提供指导。© 2021作者。由Wiley Periodicals LLC代表国际帕金森和运动障碍协会出版的《运动障碍》。
