Utility of linear mixed effects models for event-related potential research with infants and children.
Utility of linear mixed effects models for event-related potential research with infants and children.
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DOI：
10.1016/j.dcn.2022.101070
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发表时间：
2022-04
影响因子：
4.7
通讯作者：
Bowman LC
中科院分区：
文献类型：
--
作者：
Heise MJ;Mon SK;Bowman LC
事件相关电位（ERP）有利于研究认知发展。然而，它们在婴儿/儿童中的应用是具有挑战性的，因为儿童很难坐着完成ERP任务中所需的多次试验。因此，发展ERP研究中的一个大问题是由于可分析的试验太少而导致的高受试者排除。常见的分析方法（包括对受试者内的试验进行平均，并排除试验次数过少的受试者，如方差分析和线性回归）可以解决这个问题，但不能缓解这个问题。此外，这些做法可能导致测量神经信号的不准确性。排除的对象越多，不准确的问题就越多。我们回顾了最近的发展ERP研究，以说明这些问题的普遍性。重要的是，我们展示了ERP分析的另一种方法-线性混合效应（LME）建模，它在发展ERP研究中提供了独特的实用程序。我们证明了模拟和真实的ERP数据，从学龄前儿童，通常采用方差分析产生偏差的结果，成为更有偏见的主题排除增加。相比之下，LME模型即使在受试者具有低试验计数的情况下也产生准确、无偏的结果，并且能够更好地检测真实的条件差异。我们包括教程和示例代码，以促进LME分析在未来的ERP研究。线性混合效应模型（LME）在事件相关电位分析中具有优势。特别是对于婴幼儿事件相关电位，LME模型优于传统的方差分析模型，具有上级优势。在模拟的ERP数据中，ANOVA返回有偏的结果，但LME是无偏的。在真实的儿童ERP数据中，LME检测到条件差异，而ANOVA没有。为指导今后研究使用大型海洋生态系统而提供的参考资料和样本代码。
Event-related potentials (ERPs) are advantageous for investigating cognitive development. However, their application in infants/children is challenging given children’s difficulty in sitting through the multiple trials required in an ERP task. Thus, a large problem in developmental ERP research is high subject exclusion due to too few analyzable trials. Common analytic approaches (that involve averaging trials within subjects and excluding subjects with too few trials, as in ANOVA and linear regression) work around this problem, but do not mitigate it. Moreover, these practices can lead to inaccuracies in measuring neural signals. The greater the subject exclusion, the more problematic inaccuracies can be. We review recent developmental ERP studies to illustrate the prevalence of these issues. Critically, we demonstrate an alternative approach to ERP analysis—linear mixed effects (LME) modeling—which offers unique utility in developmental ERP research. We demonstrate with simulated and real ERP data from preschool children that commonly employed ANOVAs yield biased results that become more biased as subject exclusion increases. In contrast, LME models yield accurate, unbiased results even when subjects have low trial-counts, and are better able to detect real condition differences. We include tutorials and example code to facilitate LME analyses in future ERP research. Linear Mixed Effects models (LMEs) have advantages for event-related potential analyses. For infant/child ERPs especially, LME is superior to traditional ANOVA models. In simulated ERP data, ANOVAs returned biased results, but LME was unbiased. In real, child ERP data, LME detected condition differences where ANOVA did not. Tutorial and sample codes given to guide use of LMEs in future research.
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