Characterizing within-person variance in, and menstrual cycle associations with, event-related potentials associated with positive and negative valence systems: The reward positivity and the error-related negativity

Abstract

Event-related potentials (ERPs) are widely employed as measures of transdiagnostic cognitive processes that are thought to underlie various clinical disorders (Hajcak et al., 2019). Despite their prevalent use as individual difference measures, the effects of within-person processes, such as the human menstrual cycle, on a broad range of ERPs are poorly understood. The present study leveraged a within-subject design to characterize between- and within-person variance in ERPs as well as effects of the menstrual cycle in two frequently studied ERPs associated with positive and negative valence systems underlying psychopathology—the Reward Positivity (RewP) and the Error- Related Negativity (ERN). Seventy-one naturally-cycling participants completed repeated EEG and ecological momentary assessments of positive and negative affect in the menstrual cycle's early follicular, periovulatory, and mid-luteal phases. We examined the mean degree of change between cycle phases in both ERPs, the between-person variability in the degree of change in both ERPs, and whether an individual’s degree of cyclical change in these ERPs show coherence with their degree of cyclical change in positive and negative affect recorded across the cycle. Results revealed no significant changes in positive and negative affect across the cycle and rather small changes in ERP amplitudes. Significant random slopes in our model revealed larger individual differences in trajectories of change in ERP amplitudes and affect, in agreement with prior evidence of heterogeneity in dimensional hormone sensitivity. Additionally, state-variance in these ERPs correlated with positive and negative affect changes across the cycle, suggesting that cycle-mediated ERP changes may have relevance for affect and behavior. Finally, exploratory latent class growth mixture modeling revealed subgroups of individuals that display disparate patterns of change in ERPs that should be further investigated.