Cognitive Neuroimaging Unit, CNRS ERL 9003, INSERM U992, CEA, Université Paris-Saclay, NeuroSpin Center, Gif/Yvette, France.
Cognitive Neuroimaging Unit, CNRS ERL 9003, INSERM U992, CEA, Université Paris-Saclay, NeuroSpin Center, Gif/Yvette, France.
Cortex. 2021 Sep;142:370-378. doi: 10.1016/j.cortex.2021.05.023. Epub 2021 Jul 7.
Periodic and stable sensory input can result in rhythmic and stable neural responses, a phenomenon commonly referred to as neural entrainment. Although the use of neural entrainment to investigate the regularities the brain tracks has increased in recent years, the methods used for its quantification are not well-defined in the literature. Here we argue that some strategies used in previous papers, are inadequate for the study of steady-state response, and lead to methodological artefacts. The aim of this commentary is to discuss these articles and to propose alternative measures of neural entrainment. Specifically, we applied four possible alternatives and two epoching approaches reported in the literature to quantify neural entrainment on simulated datasets. Our results demonstrate that overlapping epochs, as used in the original Batterink and colleagues articles, inevitably lead to a methodological artefact at the frequency corresponding to the overlap. We therefore strongly discourage this approach and encourage the re-analysis of data based on overlapping epochs. Additionally, we argue that the use of time-frequency decomposition to compute phase coherence at low frequencies to reveal neural entrainment is not optimal.
周期性和稳定的感觉输入可以产生有节奏和稳定的神经反应，这种现象通常被称为神经同步。尽管近年来利用神经同步来研究大脑跟踪的规律的方法有所增加，但文献中并没有很好地定义用于其量化的方法。在这里，我们认为以前的一些论文中使用的策略对于稳态响应的研究是不充分的，并导致了方法学上的人为产物。本评论的目的是讨论这些文章，并提出神经同步的替代测量方法。具体来说，我们应用了文献中报道的四种可能的替代方法和两种分段方法来量化模拟数据集上的神经同步。我们的结果表明，像 Batterink 及其同事的原始文章中使用的重叠分段方法，不可避免地会在与重叠对应的频率上产生方法学上的人为产物。因此，我们强烈反对这种方法，并鼓励对基于重叠分段的数据进行重新分析。此外，我们认为使用时频分解来计算低频的相位相干性以揭示神经同步并不是最优的。
