Boston Scientific Neuromodulation, 25155 Rye Canyon Loop, Valencia, CA 91355, United States of America.
Department of Neurosurgery, Baylor College of Medicine, One Baylor Plaza, Houston, TX 77030, United States of America.
J Neural Eng. 2022 Jul 20;19(4). doi: 10.1088/1741-2552/ac7e6c.
Therapeutic efficacy of deep brain stimulation (DBS) in both established and emerging indications, is highly dependent on accurate lead placement and optimized clinical programming. The latter relies on clinicians' experience to search among available sets of stimulation parameters and can be limited by the time constraints of clinical practice. Recent innovations in device technology have expanded the number of possible electrode configurations and parameter sets available to clinicians, amplifying the challenge of time constraints. We hypothesize that patient specific neuroimaging data can effectively assist the clinical programming using automated algorithms.This paper introduces the DBS Illumina 3D algorithm as a tool which uses patient-specific imaging to find stimulation settings that optimizes activating a target area while minimizing the stimulation of areas outside the target that could result in unknown or undesired side effects. This approach utilizes preoperative neuroimaging data paired with the postoperative reconstruction of the lead trajectory to search the available stimulation space and identify optimized stimulation parameters. We describe the application of this algorithm in three patients with treatment-resistant depression who underwent bilateral implantation of DBS in subcallosal cingulate cortex and ventral capsule/ventral striatum using tractography optimized targeting with an imaging defined target previously described.Compared to the stimulation settings selected by the clinicians (informed by anatomy), stimulation settings produced by the algorithm that achieved similar or greater target coverage, produced a significantly smaller stimulation area that spilled outside the target (= 0.002).. The DBS Illumina 3D algorithm is seamlessly integrated with the clinician programmer software and effectively and rapidly assists clinicians with the analysis of image based anatomy, and provides a starting point to search the highly complex stimulation parameter space and arrive at the stimulation settings that optimize activating a target area.
脑深部刺激（DBS）在既定和新兴适应症中的治疗效果高度依赖于准确的导联放置和优化的临床编程。后者依赖于临床医生在可用刺激参数集之间进行搜索的经验，并且可能受到临床实践时间限制的限制。设备技术的最新创新扩大了临床医生可用的电极配置和参数集的数量，增加了时间限制的挑战。我们假设患者特定的神经影像学数据可以通过自动化算法有效地辅助临床编程。本文介绍了 DBS Illumina 3D 算法，该算法使用患者特定的影像学数据来找到刺激设置，以优化激活目标区域，同时最小化刺激目标区域外可能导致未知或不良副作用的区域。这种方法利用术前神经影像学数据和术后导联轨迹重建来搜索可用的刺激空间，并确定优化的刺激参数。我们描述了该算法在三名接受双侧扣带回下皮质和腹侧胶囊/腹侧纹状体 DBS 植入治疗难治性抑郁症患者中的应用，该方法使用基于影像学定义的目标进行轨迹优化靶向，该目标之前已被描述。与临床医生选择的刺激设置（基于解剖结构）相比，算法产生的刺激设置实现了相似或更大的目标覆盖，产生了明显更小的刺激区域，刺激区域溢出目标外（= 0.002）。DBS Illumina 3D 算法与临床医生编程器软件无缝集成，有效地、快速地帮助临床医生分析基于图像的解剖结构，并提供一个起点来搜索高度复杂的刺激参数空间，并找到优化激活目标区域的刺激设置。
