Unpacking Transient Event Dynamics in Electrophysiological Power Spectra

Andrew J. Quinn*, Freek van Ede, Matthew J. Brookes, Simone G. Heideman, Magdalena Nowak, Zelekha A. Seedat, Diego Vidaurre, Catharina Zich, Anna C. Nobre, Mark W. Woolrich

*Corresponding author for this work

Research output: Contribution to JournalArticleAcademicpeer-review

Abstract

Electrophysiological recordings of neuronal activity show spontaneous and task-dependent changes in their frequency-domain power spectra. These changes are conventionally interpreted as modulations in the amplitude of underlying oscillations. However, this overlooks the possibility of underlying transient spectral ‘bursts’ or events whose dynamics can map to changes in trial-average spectral power in numerous ways. Under this emerging perspective, a key challenge is to perform burst detection, i.e. to characterise single-trial transient spectral events, in a principled manner. Here, we describe how transient spectral events can be operationalised and estimated using Hidden Markov Models (HMMs). The HMM overcomes a number of the limitations of the standard amplitude-thresholding approach to burst detection; in that it is able to concurrently detect different types of bursts, each with distinct spectral content, without the need to predefine frequency bands of interest, and does so with less dependence on a priori threshold specification. We describe how the HMM can be used for burst detection and illustrate its benefits on simulated data. Finally, we apply this method to empirical data to detect multiple burst types in a task-MEG dataset, and illustrate how we can compute burst metrics, such as the task-evoked timecourse of burst duration.

Original languageEnglish
Pages (from-to)1020-1034
Number of pages15
JournalBrain Topography
Volume32
Issue number6
DOIs
Publication statusPublished - 1 Nov 2019
Externally publishedYes

Funding

This research was supported by the NIHR Oxford Health Biomedical Research Centre, a Wellcome Trust Strategic Award (Grant 098369/Z/12/Z), Wellcome Investigator Awards to MWW (106183/Z/14/Z) and ACN (104571/Z/14/Z) and a James S. McDonnell Foundation Understanding Human Cognition Collaborative Award (220020448). The Wellcome Centre for Integrative Neuroimaging is supported by core funding from the Wellcome Trust (203139/Z/16/Z). MB is supported by the UK Quantum Technology Hub for Sensors and Metrology, funded by the Engineering and Physical Sciences Research Council (EPSRC) (EP/M013294/1). ZAS is supported by a studentship from the Oxford Nottingham Biomedical Imaging Centre for Doctoral Training Centre (EPSRC/MRC - EP/L016052/1).

FundersFunder number
UK Quantum Technology Hub for Sensors
Wellcome Centre for Integrative Neuroimaging
James S. McDonnell Foundation220020448
Center for Outcomes Research and Evaluation, Yale School of Medicine203139/Z/16/Z
Wellcome Trust106183/Z/14/Z, 098369/Z/12/Z
Engineering and Physical Sciences Research CouncilEP/M013294/1
Australian College of Nursing104571/Z/14/Z
NIHR Oxford Biomedical Research Centre
EPSRC Centre for Doctoral Training in Medical ImagingEP/L016052/1

    Keywords

    • Bursting
    • Dynamics
    • Electrophysiology
    • Hidden Markov model
    • Spectrum

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