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 JournalReview articleAcademicpeer-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

Keywords

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

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