Quinn, AJ;
van Ede, F;
Brookes, MJ;
Heideman, SG;
Nowak, M;
Seedat, ZA;
Vidaurre, D;
... Woolrich, MW; + view all
(2019)
Unpacking Transient Event Dynamics in Electrophysiological Power Spectra.
Brain Topography
10.1007/s10548-019-00745-5.
(In press).
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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.
| Type: | Article |
|---|---|
| Title: | Unpacking Transient Event Dynamics in Electrophysiological Power Spectra |
| Location: | United States |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1007/s10548-019-00745-5 |
| Publisher version: | https://doi.org/10.1007/s10548-019-00745-5 |
| Language: | English |
| Additional information: | This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. |
| Keywords: | Electrophysiology · Spectrum · Dynamics · Bursting · Hidden Markov model |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Brain Sciences UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Brain Sciences > UCL Queen Square Institute of Neurology UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Brain Sciences > UCL Queen Square Institute of Neurology > Clinical and Movement Neurosciences |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10086564 |
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