title: Frequency flows and the time-frequency dynamics of multivariate phase synchronization in brain signals.
abstract: The quantification of phase synchrony between brain signals is of crucial importance for the study of large-scale interactions in the brain. Current methods are based on the estimation of the stability of the phase difference between pairs of signals over a time window, within successive frequency bands. This paper introduces a new approach to study the dynamics of brain synchronies, Frequency Flows Analysis (FFA). It allows direct tracking and characterization of the nonstationary time-frequency dynamics of phase synchrony among groups of signals. It is based on the use of the one-to-one relationship between frequency locking and phase synchrony, which applies when the concept of phase synchrony is not taken in an extended 'statistical' sense of a bias in the distribution of phase differences, but in the sense of a continuous phase difference conservation during a short period of time. In such a case, phase synchrony implies identical instantaneous frequencies among synchronized signals, with possible time varying frequencies of synchronization. In this framework, synchronous groups of signals or neural assemblies can be identified as belonging to common frequency flows, and the problem of studying synchronization becomes the problem of tracking frequency flows. We use the ridges of the analytic wavelet transforms of the signals of interest in order to estimate maps of instantaneous frequencies and reveal sustained periods of common instantaneous frequency among groups of signal. FFA is shown to track complex dynamics of synchrony in coupled oscillator models, reveal the time-frequency and spatial dynamics of synchrony convergence and divergence in epileptic seizures, and in MEG data the large-scale ongoing dynamics of synchrony correlated with conscious perception during binocular rivalry.
authors.affiliation: LENA, MEG-EEG Center Pitié-Salpêtrière, CNRS UPR 640, Université Pierre et Marie Curie, 47 Bd de l'Hôpital, 75651 Paris Cedex 13, France. david.rudrauf@chups.jussieu.fr
authors.forename: David
authors.initials: D
authors.lastname: Rudrauf
authors.forename: Abdel
authors.initials: A
authors.lastname: Douiri
authors.forename: Christopher
authors.initials: C
authors.lastname: Kovach
authors.forename: Jean-Philippe
authors.initials: JP
authors.lastname: Lachaux
authors.forename: Diego
authors.initials: D
authors.lastname: Cosmelli
authors.forename: Mario
authors.initials: M
authors.lastname: Chavez
authors.forename: Claude
authors.initials: C
authors.lastname: Adam
authors.forename: Bernard
authors.initials: B
authors.lastname: Renault
authors.forename: Jacques
authors.initials: J
authors.lastname: Martinerie
authors.forename: Michel
authors.initials: M
authors.lastname: Le Van Quyen
mesh_terms: D000328:Adult; D000465:Algorithms; D001931:Brain Mapping; D002540:Cerebral Cortex / Q000503:physiopathology*; D003198:Computer Simulation; D003344:Cortical Synchronization / Q000706:statistics & numerical data*; D004292:Dominance, Cerebral / Q000502:physiology; D004569:Electroencephalography / Q000706:statistics & numerical data*; D017034:Epilepsy, Frontal Lobe / Q000503:physiopathology; D005583:Fourier Analysis; D005625:Frontal Lobe / Q000503:physiopathology; D006801:Humans; D015225:Magnetoencephalography / Q000706:statistics & numerical data*; D008297:Male; D008959:Models, Neurological; D015999:Multivariate Analysis; D009415:Nerve Net / Q000503:physiopathology; D009474:Neurons / Q000502:physiology; D017711:Nonlinear Dynamics; D009991:Oscillometry; D010364:Pattern Recognition, Visual / Q000502:physiology; D011999:Recruitment, Neurophysiological / Q000502:physiology; D012815:Signal Processing, Computer-Assisted; D015357:Vision Disparity / Q000502:physiology