Lead-DBS v2: Towards a comprehensive pipeline for deep brain stimulation imaging.
Lead-DBS v2: Towards a comprehensive pipeline for deep brain stimulation imaging.
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DOI：
10.1016/j.neuroimage.2018.08.068
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发表时间：
2019-01-01
期刊：
影响因子：
5.7
通讯作者：
Kühn AA
中科院分区：
文献类型：
--
作者：
Horn A;Li N;Dembek TA;Kappel A;Boulay C;Ewert S;Tietze A;Husch A;Perera T;Neumann WJ;Reisert M;Si H;Oostenveld R;Rorden C;Yeh FC;Fang Q;Herrington TM;Vorwerk J;Kühn AA
脑深部电刺激（DBS）是治疗运动障碍的一种非常有效的治疗选择，并且在临床试验中研究了越来越多的其他适应症。为了确保最佳治疗效果，需要精确的电极放置。此外，为了分析电极位置和临床结果之间的关系，需要精确重建电极放置，这对神经成像领域提出了特定的挑战。自2014年以来，开源工具箱Lead-DBS可用，旨在促进这一过程。该工具已成为DBS成像的流行平台。在世界各地研究人员的广泛支持下，方法不断更新，并得到新工具的补充，用于多光谱非线性配准、结构/功能连接分析、脑移位校正、微电极记录重建和分段DBS导线的方向检测等任务。DBS数据分析中这些方法的快速发展和出现要求我们重新审视和修改原始方法出版物中介绍的管道。在本文中，我们使用最先进的高场成像的单个患者示例以及在典型临床环境中以1.5T扫描的回顾性患者队列，展示了电极导线-DBS的更新DBS和连接组管道。使用五种算法对3 T示例患者的成像数据进行配准，并使用十种方法将其非线性扭曲到模板空间中以进行比较。DBS电极重建后（可使用三种方法和特定的优化工具），使用四种不同的模型和各种参数计算两种DBS设置下激活的组织体积。最后，将四种全脑纤维束成像算法应用于患者的术前扩散MRI数据，并使用总共八种方法和数据集估计刺激体积和其他脑区域之间的结构和功能连接。此外，我们证明了选择的预处理策略对51例PD患者的回顾性样本的影响。我们比较了临床改善的方差量，这可以通过计算机模型来解释，这取决于所选择的方法。这项工作代表了多机构的合作努力，为DBS成像和连接组学开发了一个全面的开源管道，这已经授权了几项研究，并可能促进该领域的各种未来研究。
Deep brain stimulation (DBS) is a highly efficacious treatment option for movement disorders and a growing number of other indications are investigated in clinical trials. To ensure optimal treatment outcome, exact electrode placement is required. Moreover, to analyze the relationship between electrode location and clinical results, a precise reconstruction of electrode placement is required, posing specific challenges to the field of neuroimaging. Since 2014 the open source toolbox Lead-DBS is available, which aims at facilitating this process. The tool has since become a popular platform for DBS imaging. With support of a broad community of researchers worldwide, methods have been continuously updated and complemented by new tools for tasks such as multispectral nonlinear registration, structural / functional connectivity analyses, brain shift correction, reconstruction of microelectrode recordings and orientation detection of segmented DBS leads. The rapid development and emergence of these methods in DBS data analysis require us to revisit and revise the pipelines introduced in the original methods publication. Here we demonstrate the updated DBS and connectome pipelines of Lead-DBS using a single patient example with state-of-the-art high-field imaging as well as a retrospective cohort of patients scanned in a typical clinical setting at 1.5T. Imaging data of the 3T example patient is co-registered using five algorithms and nonlinearly warped into template space using ten approaches for comparative purposes. After reconstruction of DBS electrodes (which is possible using three methods and a specific refinement tool), the volume of tissue activated is calculated for two DBS settings using four distinct models and various parameters. Finally, four whole-brain tractography algorithms are applied to the patient’s preoperative diffusion MRI data and structural as well as functional connectivity between the stimulation volume and other brain areas are estimated using a total of eight approaches and datasets. In addition, we demonstrate impact of selected preprocessing strategies on the retrospective sample of 51 PD patients. We compare the amount of variance in clinical improvement that can be explained by the computer model depending on the method of choice. This work represents a multi-institutional collaborative effort to develop a comprehensive, open source pipeline for DBS imaging and connectomics, which has already empowered several studies, and may facilitate a variety of future studies in the field.
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