We present here ongoing patterns of distributed brain synchronous activity that correlate with the spontaneous flow of perceptual dominance during binocular rivalry. Specific modulation of the magnetoencephalographic (MEG) response evoked during conscious perception of a frequency-tagged stimulus was evidenced throughout rivalry. Estimation of the underlying cortical sources revealed, in addition to strong bilateral striate and extrastriate visual cortex activation, parietal, temporal pole and frontal contributions. Cortical activity was significantly modulated concomitantly to perceptual alternations in visual cortex, medial parietal and left frontal regions. Upon dominance, coactivation of occipital and frontal regions, including anterior cingulate and medial frontal areas, was established. This distributed cortical network, as measured by phase synchrony in the frequency tag band, was dynamically modulated in concert with the perceptual dominance of the tagged stimulus. While the anteroposterior pattern was recurrent through subjects, individual variations in the extension of the network were apparent.

Key words: binocular rivalry, cortical activity, perceptual dominance.

One of the outstanding problems in the cognitive sciences is to understand how ongoing conscious experience is related to the workings of the brain and nervous system. Neurodynamics offers a powerful approach to this problem because it provides a coherent framework for investigating change, variability, complex spatiotemporal patterns of activity, and multiscale processes (among others). In this chapter, we advocate a neurodynamical approach to consciousness that integrates mathematical tools of analysis and modeling, sophisticated physiological data recordings, and detailed phenomenological descriptions. We begin by stating the basic intuition: Consciousness is an intrinsically dynamic phenomenon and must therefore be studied within a framework that is capable of rendering its dynamics intelligible. We then discuss some of the formal, analytical features of dynamical systems theory, with particular reference to neurodynamics. We then review several neuroscientific proposals that make use of dynamical systems theory in characterizing the neurophysiological basis of consciousness. We continue by discussing the relation between spatiotemporal patterns of brain activity and consciousness, with particular attention to processes in the gamma frequency band. We then adopt a critical perspective and highlight a number of issues demanding further treatment. Finally, we close the chapter by discussing how phenomenological data can relate to and ultimately constrain neurodynamical descriptions, with the long-term aim being to go beyond a purely correlational strategy of research.

Key words: Consciousness, neurodynamics, dynamical system theory, complex brain patterns, large-scale integration, gamma band, phase synchrony, phenomenological descriptions.

This paper introduces the use of wavelet analysis to follow the temporal variations in the coupling between oscillatory neural signals. Coherence, based on Fourier analysis, has been commonly used as a first approximation to track such coupling under the assumption that neural signals are stationary. Yet, stationary neural processing may be the exception rather than the rule. In this context, the recent application to physical systems of a wavelet-based coherence, which does not depend on the stationarity of the signals, is highly relevant. This paper fully develops the method of wavelet coherence and its statistical properties so that it can be practically applied to continuous neural signals. In realistic simulations, we show that, in contrast to Fourier coherence, wavelet coherence can detect short, significant episodes of coherence between non-stationary neural signals. This method can be directly applied for an ‘online’ quantification of the instantaneous coherence between two signals.

Key words: wavelet, coherence, synchrony, EEG, LFP

We report here on a first attempt to settle the methodological controversy between advocates of two alternative reconstruction approaches for temporal dynamics in brain signals: the single‐channel method (using data from one recording site and reconstructing by time‐lags), and the multiple‐channel method (using data from a spatially distributed set of recordings sites and reconstructing by means of spatial position). For the purpose of a proper comparison of these two techniques, we computed a series of EEG‐like measures on the basis of well‐known dynamical systems placed inside a spherical model of the head. For each of the simulations, the correlation dimension estimates obtained by both methods were calculated and compared, when possible, with the known (or estimated) dimension of the underlying dynamical system. We show that the single‐channel method fails to reliably quantify spatially extended dynamics, while the multichannel method performs better. It follows that the latter is preferable, given the known spatially distributed nature of brain processes. Hum. Brain Mapping 5:26–47, 1997. © 1997 Wiley‐Liss, Inc.

This paper introduces a new method, single-trial phase locking statistics (S-PLS) to estimate phase locking in single trials of brain signals between two electrodes. The possibility of studying single trials removes an important limitation in the study of long-range synchrony in brain signals. S-PLS is closely related to our previous method, phase locking statistics (PLS) that estimates phase locking over a set of trials. The S-PLS method is described in detail and applied to human surface recordings during the task of face-recognition. We compare these results with those provided by PLS and show that they are qualitatively very similar, although S-PLS provides better discrimination of synchronic episodes.

This article presents, for the first time, a practical method for the direct quantification of frequency-specific synchronization (i.e., transient phase-locking) between two neuroelectric signals. The motivation for its development is to be able to examine the role of neural synchronies as a putative mechanism for long-range neural integration during cognitive tasks. The method, called phase-locking statistics (PLS), measures the significance of the phase covariance between two signals with a reasonable time-resolution (<100 ms). Unlike the more traditional method of spectral coherence, PLS separates the phase and amplitude components and can be directly interpreted in the framework of neural integration. To validate synchrony values against background fluctuations, PLS uses surrogate data and thus makes no a priori assumptions on the nature of the experimental data. We also apply PLS to investigate intracortical recordings from an epileptic patient performing a visual discrimination task. We find large-scale synchronies in the gamma band (45 Hz), e.g., between hippocampus and frontal gyrus, and local synchronies, within a limbic region, a few cm apart. We argue that whereas long-scale effects do reflect cognitive processing, short-scale synchronies are likely to be due to volume conduction. We discuss ways to separate such conduction effects from true signal synchrony.

Key words: neural synchrony, phase-locking, coherence, EEG, EcoG, epilepsy, gamma-band, deblurring

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.

Key words: human, gamma-band activity, intracortical recordings, phase synchronization, surrogate data, time-frequency analysis, visual discrimination

The quantification of phase synchrony between neuronal signals is of crucial importance for the study of large-scale interactions in the brain. Two methods have been used to date in neuroscience, based on two distinct approaches which permit a direct estimation of the instantaneous phase of a signal [Phys. Rev. Lett. 81 (1998) 3291; Human Brain Mapping 8 (1999) 194]. The phase is either estimated by using the analytic concept of Hilbert transform or, alternatively, by convolution with a complex wavelet. In both methods the stability of the instantaneous phase over a window of time requires quantification by means of various statistical dependence parameters (standard deviation, Shannon entropy or mutual information). The purpose of this paper is to conduct a direct comparison between these two methods on three signal sets: (1) neural models; (2) intracranial signals from epileptic patients; and (3) scalp EEG recordings. Levels of synchrony that can be considered as reliable are estimated by using the technique of surrogate data. Our results demonstrate that the differences between the methods are minor, and we conclude that they are fundamentally equivalent for the study of neuroelectrical signals. This offers a common language and framework that can be used for future research in the area of synchronization.

Key words: phase synchrony, neural synchrony, Hilbert transform, wavelet transform, surrogate data

We studied subdural recordings from a patient with an unusually focal and stable occipito-temporal epileptic discharge under four experimental conditions. The series of time intervals between successive spike discharges displayed a few (3–5) clusters of periodic values representing statistically significant short-term periodicities when tested against surrogate data. This short-term predictability was modulated during the different experimental conditions by periodicity shifts of the order of 15–30 ms. Correspondingly, there was an increased gamma-band (30–70 Hz) coherence between the epileptic focus and surrounding recording sites. We conclude that the focal epileptic activity is part of an extended network of neural activities which exert a fast modulation reflected in changes of transiently periodic activities.

Even during well-calibrated cognitive tasks, successive brain responses to repeated identical stimulations are highly variable. The source of this variability is believed to reside mainly in fluctuations of the subject’s cognitive ‘‘context’’ defined by his????her attentive state, spontaneous thought process, strategy to carry out the task, and so on… As these factors are hard to manipulate precisely, they are usually not controlled, and the variability is discarded by averaging techniques. We combined first-person data and the analysis of neural processes to reduce such noise. We presented the subjects with a three-dimensional illusion and recorded their electrical brain activity and their own report about their cognitive context. Trials were clustered according to these first-person data, and separate dynamical analyses were conducted for each cluster. We found that (i) characteristic patterns of endogenous synchrony appeared in frontal electrodes before stimulation. These patterns depended on the degree of preparation and the immediacy of perception as verbally reported. (ii) These patterns were stable for several recordings. (iii) Preparatory states modulate both the behavioral performance and the evoked and induced synchronous patterns that follow. (iv) These results indicated that first-person data can be used to detect and interpret neural processes.