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Splitting event‐related potentials: Modeling latent components using regression‐based waveform estimation
European Journal of Neuroscience ( IF 3.698 ) Pub Date : 2020-09-08 , DOI: 10.1111/ejn.14961
Harm Brouwer 1 , Francesca Delogu 1 , Matthew W. Crocker 1
Affiliation  

Event‐related potentials (ERPs) provide a multidimensional and real‐time window into neurocognitive processing. The typical Waveform‐based Component Structure (WCS) approach to ERPs assesses the modulation pattern of components—systematic, reoccurring voltage fluctuations reflecting specific computational operations—by looking at mean amplitude in predetermined time‐windows. This WCS approach, however, often leads to inconsistent results within as well as across studies. It has been argued that at least some inconsistencies may be reconciled by considering spatiotemporal overlap between components; that is, components may overlap in both space and time, and given their additive nature, this means that the WCS may fail to accurately represent its underlying latent component structure (LCS). We employ regression‐based ERP (rERP) estimation to extend traditional approaches with an additional layer of analysis, which enables the explicit modeling of the LCS underlying WCS. To demonstrate its utility, we incrementally derive an rERP analysis of a recent study on language comprehension with seemingly inconsistent WCS‐derived results. Analysis of the resultant regression models allows one to derive an explanation for the WCS in terms of how relevant regression predictors combine in space and time, and crucially, how individual predictors may be mapped onto unique components in LCS, revealing how these spatiotemporally overlap in the WCS. We conclude that rERP estimation allows for investigating how scalp‐recorded voltages derive from the spatiotemporal combination of experimentally manipulated factors. Moreover, when factors can be uniquely mapped onto components, rERPs may offer explanations for seemingly inconsistent ERP waveforms at the level of their underlying latent component structure.

中文翻译:

拆分与事件相关的电位:使用基于回归的波形估计来建模潜在分量

事件相关电位(ERP)为神经认知处理提供了多维实时窗口。针对ERP的典型的基于波形的组件结构(WCS)方法通过查看预定时间窗口中的平均幅度来评估组件的调制模式-反映特定计算操作的系统性反复出现的电压波动。但是,这种WCS方法通常会导致研究内部以及整个研究结果不一致。有人认为,通过考虑组件之间的时空重叠,至少可以解决一些不一致之处。也就是说,组件可能会在空间和时间上重叠,并且考虑到它们的累加性质,这意味着WCS可能无法准确表示其潜在的潜在组件结构(LCS)。我们采用基于回归的ERP(rERP)估算来扩展传统方法,并附加一层分析,从而可以对WCS底层的LCS进行显式建模。为了证明其实用性,我们以渐进的方式对最近一项关于语言理解的研究进行了rERP分析,得出的结果似乎与WCS得出的结果不一致。通过对所得回归模型进行分析,可以得出有关WCS的一种解释,即有关回归预测变量在空间和时间上的组合方式,以及至关重要的是,如何将各个预测变量映射到LCS中的独特组件上,从而揭示这些时空变量在时间上是如何重叠的。 WCS。我们得出的结论是,rERP估计可用于调查头皮记录的电压如何从实验操纵因素的时空组合中得出。而且,
更新日期:2020-09-08
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