Brain activity comprises both rhythmic (periodic) and arrhythmic (aperiodic) components. These signal elements vary across healthy aging, and disease, and may make distinct contributions to conscious perception. Despite pioneering techniques to parameterize rhythmic and arrhythmic neural components based on power spectra, the methodology for quantifying rhythmic activity remains in its infancy. Variation in analytical choices for isolating brain rhythms from background arrhythmic activity makes interpreting findings across studies difficult. Whether current approaches can accurately recover the independent contribution of these neural signal elements remains to be established. Here, using simulation and parameter recovery approaches, we show that standard analytic methods for quantifying rhythmic activity conflate these two neurophysiological components, yielding spurious correlations between spectral model parameters. We propose an alternative approach to overcome these limitations and demonstrate effective separation of rhythmic and arrhythmic components in simulated neural time series. We validate these methods using resting-state recordings from a large cohort. Our recommendations for spectral parameterization enable the robust independent quantification of rhythmic and arrhythmic signal components for cognitive neuroscience.
Castanheira et al. (Thu,) studied this question.