Department of Neurology, Center for Neurotechnology and Neurorecovery, Massachusetts General Hospital, Boston, MA, United States of America.
Department of Neurology, Harvard Medical School, Boston, MA, United States of America.
PLoS One. 2023 Jul 7;18(7):e0287921. doi: 10.1371/journal.pone.0287921. eCollection 2023.
Implantation of electrodes in the brain has been used as a clinical tool for decades to stimulate and record brain activity. As this method increasingly becomes the standard of care for several disorders and diseases, there is a growing need to quickly and accurately localize the electrodes once they are placed within the brain. We share here a protocol pipeline for localizing electrodes implanted in the brain, which we have applied to more than 260 patients, that is accessible to multiple skill levels and modular in execution. This pipeline uses multiple software packages to prioritize flexibility by permitting multiple different parallel outputs while minimizing the number of steps for each output. These outputs include co-registered imaging, electrode coordinates, 2D and 3D visualizations of the implants, automatic surface and volumetric localizations of the brain regions per electrode, and anonymization and data sharing tools. We demonstrate here some of the pipeline's visualizations and automatic localization algorithms which we have applied to determine appropriate stimulation targets, to conduct seizure dynamics analysis, and to localize neural activity from cognitive tasks in previous studies. Further, the output facilitates the extraction of information such as the probability of grey matter intersection or the nearest anatomic structure per electrode contact across all data sets that go through the pipeline. We expect that this pipeline will be a useful framework for researchers and clinicians alike to localize implanted electrodes in the human brain.
将电极植入大脑已经被广泛应用于临床,以刺激和记录大脑活动。随着这种方法在越来越多的疾病和障碍的治疗中成为标准,对电极在大脑内的位置进行快速准确的定位的需求也日益增长。我们在此分享一个用于定位植入大脑内电极的协议流程,我们已经在超过 260 名患者身上应用了该流程,该流程易于掌握,执行上具有模块化。该流程使用多个软件包来实现灵活性,允许多种不同的并行输出,同时为每个输出步骤的数量最小化。这些输出包括配准的成像、电极坐标、电极植入的 2D 和 3D 可视化、每个电极的大脑区域的自动表面和容积定位以及匿名化和数据共享工具。我们在此展示了该流程的一些可视化和自动定位算法,我们已经将其应用于确定适当的刺激靶点、进行癫痫发作动力学分析以及在之前的研究中定位认知任务的神经活动。此外,该输出还可以方便地提取信息,例如每个电极接触点与灰质相交的概率或与最近的解剖结构的距离,这些信息可以从通过该流程的所有数据集提取。我们期望该流程将成为研究人员和临床医生在定位人类大脑内植入电极时的有用框架。