Single-trial characterization of neural rhythms: Potential and challenges

• Published in: NeuroImage (Orlando, Fla.) Vol. 206; p. 116331
• Main Authors: Kosciessa, Julian Q., Grandy, Thomas H., Garrett, Douglas D., Werkle-Bergner, Markus
• Format: Journal Article
• Language: English
• Published: United States Elsevier Inc 01.02.2020; Elsevier Limited; Elsevier
• Subjects: Activity patterns; Adult; Algorithms; Alpha Rhythm - physiology; Boundary conditions; Brain - physiology; Decomposition; EEG; Electroencephalography; Electroencephalography - methods; Estimates; Female; Humans; Inter-individual differences; Male; Memory, Short-Term - physiology; Noise; Power; Reproducibility of Results; Rhythm; Rhythm detection; Rhythmic amplitude; Rhythmic duration; Short term memory; Signal Processing, Computer-Assisted; Signal-To-Noise Ratio; Single-trial rhythm estimates; Theta Rhythm - physiology; Theta rhythms; Time series; Young Adult; Rhythmic duration; Inter-individual differences; Rhythm detection; Single-trial rhythm estimates; Rhythmic amplitude
• ISSN: 1053-8119; 1095-9572; 1095-9572
• DOI: 10.1016/j.neuroimage.2019.116331

The 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. [Display omitted] •Traditional narrow-band rhythm estimates conflate the power and duration of rhythmic and arrhythmic temporal episodes. We extend a state-of-the-art detection method (eBOSC) to disambiguate these episodes at the single-trial level.•eBOSC achieves high specificity in simulated and empirical data, albeit with impaired sensitivity when rhythmicity is low.•eBOSC separates sustained and transient rhythmic episodes from arrhythmic content while amplifying power estimates.•Rhythm-specificity increased sensitivity to working memory load, and indicated a frontal theta frequency shift.