Brain Modulation Lab, Department of Neurosurgery, Massachusetts General Hospital, Boston, MA, 02114, USA; Harvard Medical School, Boston, MA, 02114, USA; Center for Brain Circuit Therapeutics Department of Neurology Brigham & Women's Hospital, Harvard Medical School, Boston, MA, USA; Movement Disorder and Neuromodulation Unit, Department of Neurology, Charité -Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Berlin, Germany.
Center for Brain Circuit Therapeutics Department of Neurology Brigham & Women's Hospital, Harvard Medical School, Boston, MA, USA.
Neuroimage. 2023 Mar;268:119862. doi: 10.1016/j.neuroimage.2023.119862. Epub 2023 Jan 5.
Following its introduction in 2014 and with support of a broad international community, the open-source toolbox Lead-DBS has evolved into a comprehensive neuroimaging platform dedicated to localizing, reconstructing, and visualizing electrodes implanted in the human brain, in the context of deep brain stimulation (DBS) and epilepsy monitoring. Expanding clinical indications for DBS, increasing availability of related research tools, and a growing community of clinician-scientist researchers, however, have led to an ongoing need to maintain, update, and standardize the codebase of Lead-DBS. Major development efforts of the platform in recent years have now yielded an end-to-end solution for DBS-based neuroimaging analysis allowing comprehensive image preprocessing, lead localization, stimulation volume modeling, and statistical analysis within a single tool. The aim of the present manuscript is to introduce fundamental additions to the Lead-DBS pipeline including a deformation warpfield editor and novel algorithms for electrode localization. Furthermore, we introduce a total of three comprehensive tools to map DBS effects to local, tract- and brain network-levels. These updates are demonstrated using a single patient example (for subject-level analysis), as well as a retrospective cohort of 51 Parkinson's disease patients who underwent DBS of the subthalamic nucleus (for group-level analysis). Their applicability is further demonstrated by comparing the various methodological choices and the amount of explained variance in clinical outcomes across analysis streams. Finally, based on an increasing need to standardize folder and file naming specifications across research groups in neuroscience, we introduce the brain imaging data structure (BIDS) derivative standard for Lead-DBS. Thus, this multi-institutional collaborative effort represents an important stage in the evolution of a comprehensive, open-source pipeline for DBS imaging and connectomics.
自 2014 年推出以来，在广泛的国际社会的支持下，开源工具包 Lead-DBS 已发展成为一个综合性的神经影像学平台，专门用于定位、重建和可视化植入人脑的电极，适用于深部脑刺激 (DBS) 和癫痫监测。然而，DBS 的临床适应证不断扩大，相关研究工具的可用性不断增加，以及临床科学家研究人员群体的不断壮大，导致需要不断维护、更新和标准化 Lead-DBS 的代码库。近年来，该平台的主要开发工作现在提供了一个基于 DBS 的神经影像学分析的端到端解决方案，允许在单个工具中进行全面的图像预处理、电极定位、刺激体积建模和统计分析。本手稿的目的是介绍 Lead-DBS 管道的基本附加功能，包括变形 warpfield 编辑器和用于电极定位的新算法。此外，我们总共引入了三种全面的工具，将 DBS 效应映射到局部、束和脑网络水平。使用单个患者的示例（用于个体水平分析）以及接受 Subthalamic Nucleus（核下丘）DBS 的 51 名帕金森病患者的回顾性队列（用于组水平分析）来演示这些更新。通过比较分析流中的各种方法选择和临床结果的解释方差量，进一步证明了它们的适用性。最后，基于神经科学研究小组在标准化文件夹和文件命名规范方面的需求不断增加，我们引入了 Lead-DBS 的脑成像数据结构 (BIDS) 衍生标准。因此，这种多机构合作代表了 DBS 成像和连接组学的综合、开源管道发展的重要阶段。
