CiteULike: xlli Adam

Frequency flows and the time-frequency dynamics of multivariate phase synchronization in brain signals.

David Rudrauf — 2006-03-17T11:35:21-00:00

Neuroimage (10 January 2006)

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.

Towards a proper estimation of phase synchronization from time series.

M Chavez — 2006-03-17T11:32:37-00:00

J Neurosci Methods (27 January 2006)

In experimental synchronization studies a continuous phase variable is commonly estimated from a scalar time series by means of its representation on the complex plane. The aim is to obtain a pair of functions A(t), phi(t) defining its instantaneous amplitude and phase, respectively. However, any arbitrary pair of functions cannot be considered as the amplitude and the phase of the real observable. Here, we point out some criteria that the pair A(t), phi(t) must observe to unambiguously define the instantaneous amplitude and phase of the observed signal. In this work, we illustrate how the complex representation may fail if the signal possesses a multi-component or a broadband spectra. We also point out a practical procedure to test whether a signal, not displaying a single oscillation at a unique frequency, has a narrow-band behavior. Implications for the study of phase interdependencies are illustrated and discussed. Phase dynamics estimated from electric brain activities recorded from an epileptic patient are also discussed.

On the intrinsic time scales involved in synchronization: A data-driven approach

Mario Chavez — 2006-03-17T11:16:59-00:00

Chaos: An Interdisciplinary Journal of Nonlinear Science, Vol. 15, No. 2. (2005)

We address the problem of detecting, from scalar observations, the time scales involved in synchronization of complex oscillators with several spectral components. Using a recent data-driven procedure for analyzing nonlinear and nonstationary signals [Huang, Proc. R. Soc. London A 454, 903 (1998)], we decompose a time series in distinct oscillation modes which may display a time varying spectrum. When applied to coupled oscillators with multiple time scales, we found that motions are captured in a finite number of phase-locked oscillations. Further, in the synchronized state distinct phenomena as phase slips, anti-phase or perfect phase locking can be simultaneously observed at specific time scales. This fully data-driven approach (without a priori choice of filters or basis functions) is tested on numerical examples and illustrated on electric intracranial signals recorded from an epileptic patient. Implications for the study of the build-up of synchronized states in nonstationary and noisy systems are pointed out. ©2005 American Institute of Physics

A quantitative study of gamma-band activity in human intracranial recordings triggered by visual stimuli.

JP Lachaux — 2005-07-14T16:04:43-00:00

Eur J Neurosci, Vol. 12, No. 7. (July 2000), pp. 2608-2622.

This paper studies gamma-band responses from two implanted epileptic patients during a simple visual discrimination task. Our main aim was to ascertain, in a reliable manner, whether evoked (stimulus-locked) and induced (triggered by, but not locked to, stimuli) responses are present in intracranial recordings. For this purpose, we introduce new methods adapted to detect the presence of gamma responses at this level of recording, intermediary between EEG-scalp and unicellular responses. The analysis relies on a trial-by-trial time-frequency analysis and on the use of surrogate data for statistical testing. We report that visual stimulation reliably elicits evoked and induced responses in human intracranial recordings. Induced intracranial gamma activity is significantly present in short oscillatory bursts (a few cycles) following visual stimulation. These responses are highly variable from trial to trial, beginning after 200 ms and lasting up to 500 ms. In contrast, intracranial-evoked gamma responses concentrate around 100 ms latencies corresponding to evoked responses observed on the scalp. We discuss our results in relation to scalp gamma response in a similar protocol [Tallon-Baudry et al. (1996) J. Neurosci., 16, 4240-4249] and draw some conclusions for bridging the gap between gamma oscillations observed on the scalp surface and their possible cortical sources.