BackgroundEfficacy of deep brain stimulation (DBS) relies on accurate lead placement as well as optimization of the stimulation parameters. Although clinical software tools are now available, programming still largely relies on a monopolar review, a tedious process for both patients and programmers.ObjectiveThis study investigates the safety and feasibility of prospective automated connectomic DBS programming (automated connectomic programming [ACP]), focusing on the recruitment of specific white matter pathways.MethodsAfter DBS implantation, a detailed connectomic DBS model in patient‐specific space was developed for each study participant. A driving‐force model was used to quantify pathway recruitment across 2400 different DBS settings. Optimization algorithms maximized recruitment of therapeutic pathways while minimizing recruitment of side‐effect pathways. Thirteen subjects were enrolled in two study phases that compared DBS settings derived from ACP to standard clinical DBS settings.ResultsNine patients underwent reprogramming with ACP (5 globus pallidus interna [GPi], 4 subthalamic nucleus [STN]). Four patients underwent initial programming with ACP (3 GPi, 1 STN). All patients tolerated ACP without persistent side effects. In the reprogramming cohort, 3 patients preferred their ACP program, and 1 patient felt it was comparable to their clinical program. Unified Parkinson's Disease Rating Scale, Part III, scores for the initial ACP cohort (3 GPi, 1 STN) improved by an average of 43.5% (40.4–52.6 ± 5.6%).ConclusionsACP appeared clinically safe and feasible. It provided reasonable motor improvement, which can be further optimized with subsequent clinical adjustment. Additional investigation is required to refine the optimization algorithm and to quantify the clinical benefit of ACP in a larger cohort. © 2024 International Parkinson and Movement Disorder Society.

中文翻译：

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帕金森病的前瞻性基于连接的深部脑刺激计划


背景深部脑刺激 （DBS） 的疗效取决于准确的导联放置以及刺激参数的优化。尽管现在可以使用临床软件工具，但编程仍然在很大程度上依赖于单极审查，这对患者和程序员来说都是一个乏味的过程。目的本研究调查了前瞻性自动连接组 DBS 编程 （automated connectomic programming [ACP]）的安全性和可行性，重点是特定白质通路的募集。方法DBS 植入后，为每个研究参与者在患者特定空间开发详细的连接组 DBS 模型。驱动力模型用于量化 2400 种不同 DBS 设置中的通路募集。优化算法最大限度地提高了治疗通路的募集，同时最大限度地减少了副作用通路的募集。13 名受试者被纳入两个研究阶段，将源自 ACP 的 DBS 设置与标准临床 DBS 设置进行比较。结果9 例患者接受了 ACP 重编程 （5 个苍白球内侧 [GPi]，4 个丘脑底核 [STN]）。4 例患者接受了 ACP 初始编程 （3 例 GPi，1 例 STN）。所有患者均耐受 ACP，无持续副作用。在重编程队列中，3 名患者更喜欢他们的 ACP 计划，1 名患者认为这与他们的临床计划相当。初始 ACP 队列（3 GPi，1 STN）的统一帕金森病评定量表第 III 部分评分平均提高了 43.5%（40.4-52.6 ± 5.6%）。结论ACP 临床上表现为安全可行。它提供了合理的运动改善，可以通过随后的临床调整进一步优化。 需要额外的研究来改进优化算法并量化 ACP 在更大队列中的临床益处。© 2024 年国际帕金森和运动障碍协会。