Aix Marseille Univ, CNRS, LPL, Aix-en-Provence, France; Aix Marseille Univ, CNRS, LPC, Aix-en-Provence, France.
Cortical Systems Laboratory, University of Pittsburgh, Pennsylvania, USA; Aix Marseille Univ, INSERM, INS, Inst Neurosci Syst, Marseille, France.
Neuroimage. 2022 Aug 15;257:119251. doi: 10.1016/j.neuroimage.2022.119251. Epub 2022 May 12.
Intracranial EEG (iEEG) performed during the pre-surgical evaluation of refractory epilepsy provides a great opportunity to investigate the neurophysiology of human cognitive functions with exceptional spatial and temporal precisions. A difficulty of the iEEG approach for cognitive neuroscience, however, is the potential variability across patients in the anatomical location of implantations and in the functional responses therein recorded. In this context, we designed, implemented, and tested a user-friendly and efficient open-source toolbox for Multi-Patient Intracranial data Analysis (MIA), which can be used as standalone program or as a Brainstorm plugin. MIA helps analyzing event related iEEG signals while following good scientific practice recommendations, such as building reproducible analysis pipelines and applying robust statistics. The signals can be analyzed in the temporal and time-frequency domains, and the similarity of time courses across patients or contacts can be assessed within anatomical regions. MIA allows visualizing all these results in a variety of formats at every step of the analysis. Here, we present the toolbox architecture and illustrate the different steps and features of the analysis pipeline using a group dataset collected during a language task.
颅内脑电图（iEEG）在难治性癫痫的术前评估中进行，为研究人类认知功能的神经生理学提供了绝佳的机会，具有出色的空间和时间精度。然而，iEEG 方法在认知神经科学方面的一个难点是，在植入的解剖位置和记录的功能反应方面，患者之间存在潜在的可变性。在这种情况下，我们设计、实现和测试了一个用于多患者颅内数据分析（MIA）的用户友好且高效的开源工具箱，可以作为独立程序或 Brainstorm 插件使用。MIA 有助于分析事件相关的 iEEG 信号，同时遵循良好的科学实践建议，例如构建可重现的分析管道和应用稳健的统计方法。可以在时域和时频域中分析信号，并且可以在解剖区域内评估患者或触点之间的时间过程的相似性。MIA 允许在分析的每个步骤以多种格式可视化所有这些结果。在这里，我们展示了工具箱的架构，并使用在语言任务期间收集的组数据集说明了分析管道的不同步骤和功能。
