Event-related Potential Additivity as an Index of Neural Independence

Abstract

Background Scalp-recorded event-related potentials (ERPs) are poorly suited for certain types of source analysis. For example, it is often difficult to precisely assess whether two ERP waveforms were produced by similar neural sources, especially when the waveforms share the same polarity and a similar scalp topography and temporal dynamics. We report here an alternative method to establishing independence of neural sources grounded in the principle of superposition, which stipulates that electrical fields summate where they intersect in time and space. We assessed the independence of two frequently reported positive waves in the ERP literature, the P300 (elicited by unexpected stimuli) and P600 (elicited by syntactic anomalies). Subjects read sentences that contained a word that was either non-anomalous, unexpected in one feature (capitalized, different font, different font color, or ungrammatical), or unexpected in two features (capitalized and different font style, capitalized and different font color, or capitalized and ungrammatical). Thus, in the double anomaly condition, the similarity between a shared feature (i.e., capitalization) and a second feature was systematically manipulated across conditions from larger degree (i.e., font style) to lesser degree (i.e., ungrammatical) of feature similarity. Results We quantified the degree of source independence for the features of interest by applying a novel Additivity Index, which compares ERPs elicited by the doubly anomalous words to composite waveforms formed by mathematically summing the ERP response to singly anomalous words. The degree of source independence is reflected by the degree of summation, with Additivity scores ranging from 0 (completely non-independent) to 1 (completely independent). The computed Additivity Index values varied with feature similarity in the predicted direction: similar features demonstrated lower Additivity Index values, or lower degrees of independence. On the other hand, dissimilar features manifested robust additivity, resulting in larger AI values. Conclusion We quantified the degree to which the P600 and P300 effects are neurally distinct across stimulus features with varying degrees of similarity by computing a continuous measure of independence via the Additivity Index. These findings indicate that the Additivity Index provides a valid and general method for quantifying the neural independence of scalp-recorded brain potentials.