Ammanuel Simon G, Kleen Jonathan K, Leonard Matthew K, Chang Edward F
Department of Neurological Surgery, University of California, San Francisco, San Francisco, CA, United States.
Department of Neurology, Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA, United States.
Front Hum Neurosci. 2020 Mar 3;14:44. doi: 10.3389/fnhum.2020.00044. eCollection 2020.
Intracranial electroencephalography (IEEG) involves recording from electrodes placed directly onto the cortical surface or deep brain locations. It is performed on patients with medically refractory epilepsy, undergoing pre-surgical seizure localization. IEEG recordings, combined with advancements in computational capacity and analysis tools, have accelerated cognitive neuroscience. This Perspective describes a potential pitfall latent in many of these recordings by virtue of the subject population-namely interictal epileptiform discharges (IEDs), which can cause spurious results due to the contamination of normal neurophysiological signals by pathological waveforms related to epilepsy. We first discuss the nature of IED hazards, and why they deserve the attention of neurophysiology researchers. We then describe four general strategies used when handling IEDs (manual identification, automated identification, manual-automated hybrids, and ignoring by leaving them in the data), and discuss their pros, cons, and contextual factors. Finally, we describe current practices of human neurophysiology researchers worldwide based on a cross-sectional literature review and a voluntary survey. We put these results in the context of the listed strategies and make suggestions on improving awareness and clarity of reporting to enrich both data quality and communication in the field.
颅内脑电图（IEEG）涉及直接将电极放置在皮质表面或脑深部位置进行记录。它用于患有药物难治性癫痫且正在接受术前癫痫发作定位的患者。IEEG记录与计算能力和分析工具的进步相结合，加速了认知神经科学的发展。这篇观点文章描述了由于研究对象群体的原因，在许多此类记录中潜在的一个陷阱——即发作间期癫痫样放电（IEDs），由于与癫痫相关的病理波形对正常神经生理信号的污染，可能会导致虚假结果。我们首先讨论IED危害的本质，以及它们为何值得神经生理学研究人员关注。然后我们描述处理IEDs时使用的四种一般策略（手动识别、自动识别、手动 - 自动混合以及在数据中保留它们而忽略），并讨论它们的优缺点和相关背景因素。最后，我们基于横断面文献综述和一项自愿调查描述了全球人类神经生理学研究人员的当前做法。我们将这些结果置于所列出的策略背景下，并就提高认识和报告清晰度提出建议，以丰富该领域的数据质量和交流。
