INSERM, INS, Institut de Neurosciences des Systèmes, Aix-Marseille University, Marseille, France.
CNRS, LPL, Aix-Marseille University, Aix-en-Provence, France.
Neuroimage. 2022 Oct 15;260:119438. doi: 10.1016/j.neuroimage.2022.119438. Epub 2022 Jul 2.
Since the second-half of the twentieth century, intracranial electroencephalography (iEEG), including both electrocorticography (ECoG) and stereo-electroencephalography (sEEG), has provided an intimate view into the human brain. At the interface between fundamental research and the clinic, iEEG provides both high temporal resolution and high spatial specificity but comes with constraints, such as the individual's tailored sparsity of electrode sampling. Over the years, researchers in neuroscience developed their practices to make the most of the iEEG approach. Here we offer a critical review of iEEG research practices in a didactic framework for newcomers, as well addressing issues encountered by proficient researchers. The scope is threefold: (i) review common practices in iEEG research, (ii) suggest potential guidelines for working with iEEG data and answer frequently asked questions based on the most widespread practices, and (iii) based on current neurophysiological knowledge and methodologies, pave the way to good practice standards in iEEG research. The organization of this paper follows the steps of iEEG data processing. The first section contextualizes iEEG data collection. The second section focuses on localization of intracranial electrodes. The third section highlights the main pre-processing steps. The fourth section presents iEEG signal analysis methods. The fifth section discusses statistical approaches. The sixth section draws some unique perspectives on iEEG research. Finally, to ensure a consistent nomenclature throughout the manuscript and to align with other guidelines, e.g., Brain Imaging Data Structure (BIDS) and the OHBM Committee on Best Practices in Data Analysis and Sharing (COBIDAS), we provide a glossary to disambiguate terms related to iEEG research.
自 20 世纪下半叶以来,颅内脑电图(iEEG),包括皮质电图(ECoG)和立体脑电图(sEEG),为深入了解人类大脑提供了一种直观的方法。在基础研究和临床之间的界面上,iEEG 提供了高时间分辨率和高空间特异性,但也存在限制,例如个体电极采样的稀疏性。多年来,神经科学研究人员发展了他们的实践,以充分利用 iEEG 方法。在这里,我们在入门级的教学框架中批判性地回顾了 iEEG 研究实践,同时也解决了熟练研究人员遇到的问题。范围有三方面:(i)回顾 iEEG 研究中的常见实践,(ii)建议处理 iEEG 数据的潜在指南,并根据最广泛的实践回答常见问题,(iii)基于当前的神经生理知识和方法,为 iEEG 研究中的良好实践标准铺平道路。本文的组织遵循 iEEG 数据处理的步骤。第一节将 iEEG 数据采集置于背景下。第二节侧重于颅内电极的定位。第三节强调主要的预处理步骤。第四节介绍 iEEG 信号分析方法。第五节讨论统计方法。第六节从独特的角度探讨 iEEG 研究。最后,为了确保整篇文章中术语的一致性,并与其他指南(例如,脑成像数据结构(BIDS)和 OHBM 委员会最佳数据分析和共享实践(COBIDAS))保持一致,我们提供了一个词汇表来区分与 iEEG 研究相关的术语。