Department of Biostatistics, University of California, Los Angeles, USA.
Departments of Statistics and Political Science, University of California, Los Angeles, USA.
Neuroimage. 2020 May 15;212:116630. doi: 10.1016/j.neuroimage.2020.116630. Epub 2020 Feb 20.
Event-related potentials (ERP) waveforms are the summation of many overlapping signals. Changes in the peak or mean amplitude of a waveform over a given time period, therefore, cannot reliably be attributed to a particular ERP component of ex ante interest, as is the standard approach to ERP analysis. Though this problem is widely recognized, it is not well addressed in practice. Our approach begins by presuming that any observed ERP waveform - at any electrode, for any trial type, and for any participant - is approximately a weighted combination of signals from an underlying set of what we refer to as principle ERPs, or pERPs. We propose an accessible approach to analyzing complete ERP waveforms in terms of their underlying pERPs. First, we propose the principle ERP reduction (pERP-RED) algorithm for investigators to estimate a suitable set of pERPs from their data, which may span multiple tasks. Next, we provide tools and illustrations of pERP-space analysis, whereby observed ERPs are decomposed into the amplitudes of the contributing pERPs, which can be contrasted across conditions or groups to reveal which pERPs differ (substantively and/or significantly) between conditions/groups. Differences on all pERPs can be reported together rather than selectively, providing complete information on all components in the waveform, thereby avoiding selective reporting or user discretion regarding the choice of which components or windows to use. The scalp distribution of each pERP can also be plotted for any group/condition. We demonstrate this suite of tools through simulations and on real data collected from multiple experiments on participants diagnosed with Autism Spectrum Disorder and Attention Deficit Hyperactivity Disorder. Software for conducting these analyses is provided in the pERPred package for R.
事件相关电位（ERP）波形是许多重叠信号的总和。因此，在给定时间段内，波形的峰值或均值幅度的变化不能可靠地归因于特定的 ERP 成分，这是 ERP 分析的标准方法。尽管这个问题已经被广泛认识，但在实践中并没有得到很好的解决。我们的方法首先假设任何观察到的 ERP 波形——在任何电极、任何试验类型和任何参与者——都是来自我们称之为基本 ERP（pERP）或 pERPs 的潜在信号的加权组合。我们提出了一种易于使用的方法，可根据其潜在的 pERPs 来分析完整的 ERP 波形。首先，我们提出了基本 ERP 减少（pERP-RED）算法，供研究人员从其数据中估计合适的 pERP 集，这些数据可能跨越多个任务。接下来，我们提供了 pERP 空间分析的工具和说明，通过该分析，观察到的 ERP 被分解为贡献 pERP 的幅度，可以在条件或组之间进行对比，以揭示条件/组之间哪些 pERP 存在差异（实质性和/或显著）。可以一起报告所有 pERP 的差异，而不是选择性地报告，从而提供有关波形中所有成分的完整信息，从而避免了选择性报告或用户自行决定选择使用哪些成分或窗口的问题。每个 pERP 的头皮分布也可以为任何组/条件绘制。我们通过模拟和从多个患有自闭症谱系障碍和注意力缺陷多动障碍的参与者的实验中收集的真实数据来展示这些工具套件。用于执行这些分析的软件在 R 中的 pERPred 包中提供。
