Hines Kevin, Noecker Angela M, Frankemolle-Gilbert Anneke M, Liang Tsao-Wei, Ratliff Jeffrey, Heiry Melissa, McIntyre Cameron C, Wu Chengyuan
Department of Neurosurgery, Thomas Jefferson University, Philadelphia, Pennsylvania, USA.
Department of Biomedical Engineering, Duke University, Durham, North Carolina, USA.
Mov Disord. 2024 Dec;39(12):2249-2258. doi: 10.1002/mds.30026. Epub 2024 Oct 21.
Efficacy 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.
This 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.
After 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.
Nine 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%).
ACP 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.
脑深部电刺激（DBS）的疗效依赖于电极的精准植入以及刺激参数的优化。尽管目前已有临床软件工具，但编程仍在很大程度上依赖单极评估，这对患者和编程人员来说都是一个繁琐的过程。
本研究调查前瞻性自动连接组学DBS编程（自动连接组学编程[ACP]）的安全性和可行性，重点是特定白质通路的募集。
在植入DBS后，为每位研究参与者建立了患者特异性空间中的详细连接组学DBS模型。使用驱动力模型量化2400种不同DBS设置下的通路募集情况。优化算法在最大限度募集治疗性通路的同时，尽量减少副作用通路的募集。13名受试者参与了两个研究阶段，比较了从ACP得出的DBS设置与标准临床DBS设置。
9例患者接受了ACP重新编程（5例苍白球内侧部[GPi]，4例丘脑底核[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。在重新编程队列中，3例患者更喜欢他们的ACP程序，1例患者觉得它与临床程序相当。初始ACP队列（3例GPi，1例STN）的统一帕金森病评定量表第三部分评分平均提高了43.5%（40.4 - 52.6±5.6%）。
ACP在临床上似乎是安全可行的。它带来了合理的运动功能改善，后续临床调整可进一步优化。需要进一步研究以完善优化算法，并在更大队列中量化ACP的临床益处。© 2024国际帕金森和运动障碍协会。
