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Spectrally resolved fast transient brain states in electrophysiological data

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  • Diego Vidaurre
  • Andrew J. Quinn, University of Oxford
  • ,
  • Adam P. Baker, University of Oxford
  • ,
  • David Dupret, University of Oxford
  • ,
  • Alvaro Tejero-Cantero, University of Oxford, Ludwig Maximilian University of Munich
  • ,
  • Mark W. Woolrich, University of Oxford

The brain is capable of producing coordinated fast changing neural dynamics across multiple brain regions in order to adapt to rapidly changing environments. However, it is non-trivial to identify multiregion dynamics at fast sub-second time-scales in electrophysiological data. We propose a method that, with no knowledge of any task timings, can simultaneously identify and describe fast transient multiregion dynamics in terms of their temporal, spectral and spatial properties. The approach models brain activity using a discrete set of sequential states, with each state distinguished by its own multiregion spectral properties. This can identify potentially very short-lived visits to a brain state, at the same time as inferring the state's properties, by pooling over many repeated visits to that state. We show how this can be used to compute state-specific measures such as power spectra and coherence. We demonstrate that this can be used to identify short-lived transient brain states with distinct power and functional connectivity (e.g., coherence) properties in an MEG data set collected during a volitional motor task.

Original languageEnglish
Pages (from-to)81-95
Number of pages15
Publication statusPublished - 1 Feb 2016
Externally publishedYes

    Research areas

  • Bayesian modelling, Coherence, MEG, Multitaper, Multivariate autoregressive model, Partial directed coherence, Sign ambiguity, Spectral estimation, Transient connectivity

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