The role of transient spectral ‘bursts’ in functional connectivity: a magnetoencephalography study

Zelekha A. Seedat, Andrew J. Quinn, Diego Vidaurre, Lucrezia Liuzzi, Lauren E. Gascoyne, Benjamin A.E. Hunt, George C. O'Neill, Daisie O. Pakenham, Karen J. Mullinger, Peter G. Morris, Mark W. Woolrich, Matthew J. Brookes*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

24 Citations (Scopus)
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Neural oscillations dominate electrophysiological measures of macroscopic brain activity and fluctuations in these rhythms offer an insightful window on cortical excitation, inhibition, and connectivity. However, in recent years the ‘classical’ picture of smoothly varying oscillations has been challenged by the idea that many ‘oscillations’ may actually be formed from the recurrence of punctate high-amplitude bursts in activity, whose spectral composition intersects the traditionally defined frequency ranges (e.g. alpha/beta band). This finding offers a new interpretation of measurable brain activity, however neither the methodological means to detect bursts, nor their link to other findings (e.g. connectivity) have been settled. Here, we use a new approach to detect bursts in magnetoencephalography (MEG) data. We show that a time-delay embedded Hidden Markov Model (HMM) can be used to delineate single-region bursts which are in agreement with existing techniques. However, unlike existing techniques, the HMM looks for specific spectral patterns in timecourse data. We characterise the distribution of burst duration, frequency of occurrence and amplitude across the cortex in resting state MEG data. During a motor task we show how the movement related beta decrease and post movement beta rebound are driven by changes in burst occurrence. Finally, we show that the beta band functional connectome can be derived using a simple measure of burst overlap, and that coincident bursts in separate regions correspond to a period of heightened coherence. In summary, this paper offers a new methodology for burst identification and connectivity analysis which will be important for future investigations of neural oscillations.
Original languageEnglish
Article number116537
Number of pages12
Early online date11 Jan 2020
Publication statusPublished - 1 Apr 2020

Bibliographical note

Funding Information:
Data collection for this paper was funded by a Medical Research Council (MRC) New Investigator Research Grant (MR/M006301/1) and a MRC Partnership Grant (MR/K005464/1). We also acknowledge the UK Quantum Technology Hub for Sensors and Metrology, funded by the Engineering and Physical Sciences Research Council (EPSRC) (EP/M013294/1). Funding from EPSRC and MRC (grant number EP/L016052/1) also provided a PhD studentship for ZS through the Oxford Nottingham Biomedical Imaging Centre for Doctoral Training. This research was also supported by the NIHR Oxford Health Biomedical Research Centre, a Wellcome Trust Strategic Award (Grant 098369/Z/12/Z). ​MWW is supported by a ​Wellcome Investigator Award (106183/Z/14/Z). The Wellcome Centre for Integrative Neuroimaging is supported by core funding from the Wellcome Trust (203139/Z/16/Z). Appendix A

Publisher Copyright:
© 2020

ASJC Scopus subject areas

  • Neurology
  • Cognitive Neuroscience


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