Single-trial characterization of neural rhythms: potential and challenges

Abstract

AbstractThe average power of rhythmic neural responses as captured by MEG/EEG/LFP recordings is a prevalent index of human brain function. Increasing evidence questions the utility of trial-/group averaged power estimates, as seemingly sustained activity patterns may be brought about by time-varying transient signals in each single trial. Hence, it is crucial to accurately describe the duration and power of rhythmic and arrhythmic neural responses on the single trial-level. However, it is less clear how well this can be achieved in empirical MEG/EEG/LFP recordings. Here, we extend an existing rhythm detection algorithm (extended Better OSCillation detection: “eBOSC”; cf. Whitten et al., 2011) to systematically investigate boundary conditions for estimating neural rhythms at the single-trial level. Using simulations as well as resting and task-based EEG recordings from a micro-longitudinal assessment, we show that alpha rhythms can be successfully captured in single trials with high specificity, but that the quality of single-trial estimates varies greatly between subjects. Importantly, our analyses suggest that rhythmic estimates are reliable within-subject markers, but may not be consistently valid descriptors of the individual rhythmic process. Finally, we highlight the utility and potential of rhythm detection with multiple proof-of-concept examples, and discuss various implications for single-trial analyses of neural rhythms in electrophysiological recordings.HighlightsTraditional narrow-band rhythm metrics conflate the power and duration of rhythmic and arrhythmic periods.We extend a state-of-the-art rhythm detection method (eBOSC) to derive rhythmic episodes in single trials that can disambiguate rhythmic and arrhythmic periods.Simulations indicate that this can be done with high specificity given sufficient rhythmic power, but with strongly impaired sensitivity when rhythmic power is low.Empirically, surface EEG recordings exhibit stable inter-individual differences in α-rhythmicity in ranges where simulations suggest a gradual bias, leading to high collinearity between narrow-band and rhythm-specific estimates.Beyond these limitations, we highlight multiple empirical proof-of-concept benefits of characterizing rhythmic episodes in single trials.