Single-trial characterization of neural rhythms: potentials 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 however, 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. Despite those signal-to-noise-based limitations, we highlight the utility and potential of rhythm detection with multiple proof-of-concept examples, and discuss implications for single-trial analyses of neural rhythms in electrophysiological recordings. Using an applied example of working memory retention, rhythm detection indicated load-related increases in the duration of frontal theta and posterior alpha rhythms, in addition to a frequency decrease of frontal theta rhythms that was observed exclusively through amplification of rhythmic amplitudes. HighlightsO_LITraditional 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. C_LIO_LISimulations indicate that this can be done with high specificity given sufficient rhythmic power, but with strongly impaired sensitivity when rhythmic SNR is low. Empirically, surface EEG recordings exhibit stable inter-individual differences in -rhythmicity in ranges where simulations suggest a gradual bias, leading to high collinearity between narrow-band and rhythm-specific estimates. C_LIO_LIBeyond these limitations, we highlight multiple empirical benefits of characterizing rhythmic episodes in single trials, such as (a) a principled separation of rhythmic and arrhythmic content, (b) an amplification of rhythmic amplitudes, and (c) a specific characterization of sustained and transient events. C_LIO_LIIn an exemplary application, rhythm-specific estimates increase sensitivity to working memory load effects, in addition to indicating a frequency modulation of frontal theta rhythms through the amplification of rhythmic power. C_LI