title: Spectrally resolved fast transient brain states in electrophysiological data.
abstract: 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.
authors.affiliation: Oxford Centre for Human Brain Activity, Department of Psychiatry, University of Oxford, UK. Electronic address: diego.vidaurre@ohba.ox.ac.uk.
authors.forename: Diego
authors.initials: D
authors.lastname: Vidaurre
authors.affiliation: Oxford Centre for Human Brain Activity, Department of Psychiatry, University of Oxford, UK. Electronic address: andrew.quinn@psych.ox.ac.uk.
authors.forename: Andrew J
authors.initials: AJ
authors.lastname: Quinn
authors.affiliation: Oxford Centre for Human Brain Activity, Department of Psychiatry, University of Oxford, UK. Electronic address: adam.baker@ohba.ox.ac.uk.
authors.forename: Adam P
authors.initials: AP
authors.lastname: Baker
authors.affiliation: MRC Brain Network Dynamics Unit, Department of Pharmacology, University of Oxford, UK. Electronic address: david.dupret@pharm.ox.ac.uk.
authors.forename: David
authors.initials: D
authors.lastname: Dupret
authors.affiliation: MRC Brain Network Dynamics Unit, Department of Pharmacology, University of Oxford, UK; Computational Neuroscience, Department Biologie II Ludwig Maximilian, University Munich, Germany. Electronic address: tejero@bio.lmu.de.
authors.forename: Alvaro
authors.initials: A
authors.lastname: Tejero-Cantero
authors.affiliation: Oxford Centre for Human Brain Activity, Department of Psychiatry, University of Oxford, UK. Electronic address: mark.woolrich@ohba.ox.ac.uk.
authors.forename: Mark W
authors.initials: MW
authors.lastname: Woolrich
mesh_terms: D000328:Adult; D058256:Brain Waves / Q000502:physiology*; D003627:Data Interpretation, Statistical; D005260:Female; D059907:Functional Neuroimaging / Q000379:methods*; D006801:Humans; D015225:Magnetoencephalography / Q000379:methods*; D008297:Male; D008962:Models, Theoretical; D009043:Motor Activity / Q000502:physiology; D009044:Motor Cortex / Q000502:physiology*; D055815:Young Adult
keywords: Bayesian modelling; Coherence; MEG; Multitaper; Partial directed coherence; Sign ambiguity; Spectral estimation; Transient connectivity; multivariate autoregressive model