Statements in which the resource exists as a subject.
PredicateObject
rdf:type
lifeskim:mentions
pubmed:issue
1
pubmed:dateCreated
2006-5-23
pubmed:abstractText
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.
pubmed:language
eng
pubmed:journal
pubmed:citationSubset
IM
pubmed:status
MEDLINE
pubmed:month
May
pubmed:issn
1053-8119
pubmed:author
pubmed:issnType
Print
pubmed:day
15
pubmed:volume
31
pubmed:owner
NLM
pubmed:authorsComplete
Y
pubmed:pagination
209-27
pubmed:dateRevised
2006-11-15
pubmed:meshHeading
pubmed-meshheading:16413209-Adult, pubmed-meshheading:16413209-Algorithms, pubmed-meshheading:16413209-Brain Mapping, pubmed-meshheading:16413209-Cerebral Cortex, pubmed-meshheading:16413209-Computer Simulation, pubmed-meshheading:16413209-Cortical Synchronization, pubmed-meshheading:16413209-Dominance, Cerebral, pubmed-meshheading:16413209-Electroencephalography, pubmed-meshheading:16413209-Epilepsy, Frontal Lobe, pubmed-meshheading:16413209-Fourier Analysis, pubmed-meshheading:16413209-Frontal Lobe, pubmed-meshheading:16413209-Humans, pubmed-meshheading:16413209-Magnetoencephalography, pubmed-meshheading:16413209-Male, pubmed-meshheading:16413209-Models, Neurological, pubmed-meshheading:16413209-Multivariate Analysis, pubmed-meshheading:16413209-Nerve Net, pubmed-meshheading:16413209-Neurons, pubmed-meshheading:16413209-Nonlinear Dynamics, pubmed-meshheading:16413209-Oscillometry, pubmed-meshheading:16413209-Pattern Recognition, Visual, pubmed-meshheading:16413209-Recruitment, Neurophysiological, pubmed-meshheading:16413209-Signal Processing, Computer-Assisted, pubmed-meshheading:16413209-Vision Disparity
pubmed:year
2006
pubmed:articleTitle
Frequency flows and the time-frequency dynamics of multivariate phase synchronization in brain signals.
pubmed: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
pubmed:publicationType
Journal Article