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Neuroimage. 2020 Feb 1;206:116331. doi: 10.1016/j.neuroimage.2019.116331. Epub 2019 Nov 8.

Single-trial characterization of neural rhythms: Potential and challenges.

Author information

1
Max Planck UCL Centre for Computational Psychiatry and Ageing Research, Lentzeallee 94, 14195, Berlin, Germany; Center for Lifespan Psychology, Max Planck Institute for Human Development, Lentzeallee 94, 14195, Berlin, Germany; Department of Psychology, Humboldt-Universität zu Berlin, Rudower Chaussee 18, 12489, Berlin, Germany. Electronic address: kosciessa@mpib-berlin.mpg.de.
2
Center for Lifespan Psychology, Max Planck Institute for Human Development, Lentzeallee 94, 14195, Berlin, Germany.
3
Max Planck UCL Centre for Computational Psychiatry and Ageing Research, Lentzeallee 94, 14195, Berlin, Germany; Center for Lifespan Psychology, Max Planck Institute for Human Development, Lentzeallee 94, 14195, Berlin, Germany.
4
Center for Lifespan Psychology, Max Planck Institute for Human Development, Lentzeallee 94, 14195, Berlin, Germany. Electronic address: werkle@mpib-berlin.mpg.de.

Abstract

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.

KEYWORDS:

Inter-individual differences; Rhythm detection; Rhythmic amplitude; Rhythmic duration; Single-trial rhythm estimates

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