Department of Computer Science, University of Freiburg, Freiburg, Germany; Medical AI Lab, Department of Neurosurgery, Medical Center - University of Freiburg, Freiburg, Germany; BrainLinks-BrainTools Cluster of Excellence, University of Freiburg, Freiburg, Germany.
Medical AI Lab, Department of Neurosurgery, Medical Center - University of Freiburg, Freiburg, Germany; BrainLinks-BrainTools Cluster of Excellence, University of Freiburg, Freiburg, Germany.
J Neurosci Methods. 2019 Nov 1;327:108396. doi: 10.1016/j.jneumeth.2019.108396. Epub 2019 Aug 19.
Intracranial electroencephalography (iEEG) is increasingly used in neuroscientific research. However, the position of the implanted electrodes varies greatly between patients, which makes group analyses particularly difficult. Therefore, an assignment procedure is needed that enables the neuroanatomical information to be obtained for each individual electrode contact.
Here, we present a MATLAB-based electrode assignment approach for iEEG electrode contacts, implemented in the open-source toolbox ELAS, that allows a hierarchical probabilistic assignment of individual electrode contacts to cytoarchitectonically-defined brain areas. The here presented ELAS consists of two major steps: (I) a pre-assignment to the cerebral lobes and (II) a following probabilistic assignment based on lobe-specific probability maps of the SPM Anatomy Toolbox.
We analyzed iEEG data obtained in 14 epilepsy patients with a total of 783 intracranial electrode contacts. The neuroanatomical assignment to cortical brain areas was possible in 72.5% of the electrode contacts that were located on the lateral cortical convexity.
This assignment procedure is to our knowledge the first approach that combines both individual macro-anatomical and cytoarchitectonic probabilistic information. Due to the integration of information about individual anatomical landmarks, incorrect assignments could be avoided in approx. 7% of electrode contacts.
The present study demonstrates how probabilistic assignment procedures developed for the analysis of neuroimaging data can be adapted to iEEG, which is especially helpful for group analyses. The presented assignment approach is freely available via the open-source toolbox ELAS, including a 3D visualization and a file export for virtual reality setups.
颅内脑电图（iEEG）在神经科学研究中越来越多地被使用。然而，植入电极在患者之间差异很大，这使得组分析特别困难。因此，需要一种分配程序，以便为每个单独的电极接触获取神经解剖信息。
在这里，我们提出了一种基于 MATLAB 的 iEEG 电极接触的电极分配方法，该方法在开源工具包 ELAS 中实现，允许对单个电极接触进行分层概率分配，以确定与细胞构筑定义的大脑区域相对应的位置。这里提出的 ELAS 由两个主要步骤组成：（I）对大脑叶进行预分配，（II）根据 SPM 解剖工具箱的叶特异性概率图进行后续概率分配。
我们分析了 14 名癫痫患者的 iEEG 数据，共涉及 783 个颅内电极接触。位于外侧皮质凸面的电极接触中，有 72.5%的电极接触可以进行神经解剖分配到皮质脑区。
这种分配程序是我们所知的第一个将个体宏观解剖和细胞构筑概率信息结合起来的方法。由于整合了关于个体解剖标志的信息，可以避免大约 7%的电极接触出现错误分配。
本研究展示了如何将为神经影像学数据分析开发的概率分配程序适应于 iEEG，这对于组分析特别有帮助。所提出的分配方法可通过开源工具包 ELAS 免费获得，包括 3D 可视化和虚拟现实设置的文件导出。
