Biomedical subjects

Jacques Martinerie

Publications and source records attributed to Jacques Martinerie.

14 recordsLinked to original sources

PubMed2006

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

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.

Adult↗

PubMed2005

The many faces of the gamma band response to complex visual stimuli.

While much is known about the functional architecture of the visual system, little is known about its large-scale dynamics during perception. This study describes this dynamics with a high spatial, temporal and spectral resolution. We recorded depth EEG of epileptic patients performing a face detection task and found that the stimuli induced strong modulations in the gamma band (40 Hz to 200 Hz) in selective occipital, parietal and temporal sites, in particular the fusiform gyrus, the lateral occipital gyrus and the intra-parietal sulcus. Occipito-temporal sites were the first to be activated, closely followed by the parietal sites, while portions of the primary visual cortex seemed to deactivate temporarily. Some of those effects were found to be correlated across distant sites, suggesting that a coordinated balance between regional gamma activations and deactivations could be involved during visual perception.

Adult↗

PubMed2005

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

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.

Biophysics↗

PubMed2005

Seizure anticipation: do mathematical measures correlate with video-EEG evaluation?

PURPOSE: Analysis of the EEG signal has recently shown evidence of dynamic changes preceding ictal onset in seizures selected from patients with clear epileptogenic foci. Most of the studies were conducted in highly selected EEG epochs and patients. In addition, these studies relied mostly on mathematical approaches and neglected clinical and visual EEG parameters. We therefore performed a systematic comparison of a nonlinear method (the similarity measure) with classic visual inspection of the EEG and the patient's clinical state. METHODS: We analyzed the dynamics of long epochs of intracranial EEG containing 129 electroclinical and 45 electrographic seizures in 13 successive unselected patients undergoing presurgical evaluation. RESULTS: (a) The similarity measure detected preictal dynamical changes of the EEG signal in two thirds of the seizures whether or not a clear focus was identified, and whether seizures were electroclinical or purely electrographic. The mean duration of preictal changes was 12 min. (b) The preictal changes were correlated with various visually detectable EEG changes in 78.9% of electroclinical seizures. (c) 81.5% of the preictal dynamic changes were correlated with changes of vigilance or behavior. (d) Fluctuations of the dynamics were not necessarily followed by seizures. CONCLUSIONS: Our results indicate that EEG dynamics frequently change before seizures. These preictal changes are most often associated with the EEG changes accompanying transitions between states of vigilance. The preictal dynamic changes may represent physiologic changes acting as facilitating factors or pathologic changes reflecting a network dysfunction.

Adolescent↗

PubMed2004

Preictal state identification by synchronization changes in long-term intracranial EEG recordings.

OBJECTIVE: There is accumulated evidence that mesial temporal lobe seizures are preceded by a preictal transition that evolves over minutes to hours. In the present study, we investigated these possible preictal changes in long-term intracranial recordings of five patients by a measure of phase synchronization. In order to clearly distinguish preictal changes from all the other interictal states, we developed an automatic extraction of representative patterns of interictal synchronization activity. This reference library was used to classify the successive synchronization patterns of long-term recordings into groups of similar patterns. Altered states of brain synchronization were identified as deviating from patterns in the reference library and were evaluated relative to the times of seizure onset in terms of sensitivity and specificity. METHODS: A phase-locking measure was estimated using a sliding window analysis on 15 frequency bands (2Hz steps between 0 and 30Hz), for all pairs of EEG channels in the epileptogenic temporal lobe (14-20 channels), over the entire data sets (total analyzed duration 305h). The preictal identification encompasses three basic stages: (1) a preprocessing stage involving the determination of a reference library of characteristic interictal synchronization patterns using a K-means algorithm, and the identification of discriminant variables differentiating interictal from preictal states, (2) a classification stage of the synchronization pattern via a minimum Mahalanobis distance to the reference patterns, as well as detection of outliers, (3) an evaluation stage of the sensitivity and specificity of the detection by receiver-operating characteristic curves. RESULTS: In most of the cases (36 of 52 seizures, i.e. 70%), a specific state of brain synchronization can be observed several hours before the actual seizure. The changes involved both increases and decreases of the synchronization levels, occurring mostly within the 4-15Hz frequency band, and were often localized near the primary epileptogenic zone. CONCLUSIONS: The analysis of phase synchronization offers a way to distinguish between a preictal state and normal interictal activity. These findings suggest that brain synchronizations are preictally altered in the epileptogenic temporal lobe, inducing a pathological state of higher susceptibility for seizure activity. SIGNIFICANCE: Phase synchronization is capable of extracting information from the EEG that allow the definition of a preictal state. Although the proposed analysis does not constitute genuine seizure anticipation, these changes in neuronal synchronization may provide helpful information for prospective seizure warning.

Brain Mapping↗

PubMed2004

Waves of consciousness: ongoing cortical patterns during binocular rivalry.

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.

Adult↗

PubMed2003

Statistical assessment of nonlinear causality: application to epileptic EEG signals.

In this study an information-theoretic test for general Granger causality is used to identify couplings and information transport between different brain areas during epileptic activities. This method can distinguish information that is actually exchanged between two systems from that due to the response to a common signal or past history. This is achieved by an appropriate conditioning of probabilities. Statistical assessment of causality is made from a nonparametric bootstrap test, whereas nonlinearity is assessed by a comparison with a linearized version of the causality index. The framework proposed here provides a useful and model free test to characterize interactions in intracranial electroencephalography (EEG) signals.

Electroencephalography↗

PubMed2003

Spatio-temporal dynamics prior to neocortical seizures: amplitude versus phase couplings.

The mechanisms underlying the transition of brain activity toward epileptic seizures remain unclear. Based on nonlinear analysis of both intracranial and scalp electroencephalographic (EEG) recordings, different research groups have recently reported dynamical smooth changes in epileptic brain activity several minutes before seizure onset. Such preictal states have been detected in populations of patients with mesial temporal lobe epilepsy (MTLE) and, more recently, with different neocortical partial epilepsies (NPEs). In this paper, we are particularly interested in the spatio-temporal organization of epileptogenic networks prior to seizures in neocortical epilepsies. For this, we characterize the network of two patients with NPE by means of two nonlinear measures of interdependencies. Since the synchronization of neuronal activity is an essential feature of the generation and propagation of epileptic activity, we have analyzed changes in phase synchrony between EEG time series. In order to compare the phase and amplitude dynamics, we have also studied the degree of association between pairs of signals by means of a nonlinear correlation coefficient. Recent findings have suggested changes prior to seizures in a wideband frequency range. Instead, for the examples of this study, we report a significant decrease of synchrony in the focal area several minutes before seizures (>>30 min in both patients) in the frequency band of 10-25 Hz mainly. Furthermore, the spatio-temporal organization of this preictal activity seems to be specifically related to this frequency band. Measures of both amplitude and phase coupling yielded similar results in narrow-band analysis. These results may open new perspectives on the mechanisms of seizure emergence as well as the organization of neocortical epileptogenic networks. The possibility of forecasting the onset of seizures has important implications for a better understanding, diagnosis and a potential treatment of the epilepsy.

Adult↗

PubMed2003

Toward a neurodynamical understanding of ictogenesis.

Although considerable information on cellular and network mechanisms of epilepsy exists, it is still not understood why, how, and when the transition from interictal to ictal state takes place. The authors review their work on nonlinear EEG analysis and provide consistent evidences that dynamical changes in the neural activity allows the characterization of a preictal state several minutes before seizure onset. This new neurodynamical approach of ictogenesis opens new perspectives for studying the basic mechanisms in epilepsy as well as for possible therapeutic interventions.

Brain↗

PubMed2002

Role of visual input in nonlinear postural control system.

Stabilometry signals involve irregular and unpredictable components. The purpose of the present study was to investigate these signals with a nonlinear technique to examine how the complexity of the postural control system breaks down under altered visual conditions. We evaluated the dynamical similarities of the postural control system when the eyes were open or closed, or when there was optokinetic stimulation (OKS). A similarity index was calculated by the cross-correlation integral between the two dynamics: eyes open and eyes closed, or eyes open with OKS. Using this technique, dynamical changes were not observed between eyes-open and eyes-closed conditions. This result suggests that the nonvision condition does not produce any striking effect on the postural control system; instead, the eyes-open condition causes a decrease in the stochastic activity of the postural control system, which may originate mainly from the stiffness of the musculoskeletal systems. In contrast, the visual input of OKS affected the dynamics of the postural control system in nearly half of the subjects (group 2) despite showing no significant differences between the eyes-open condition and the other conditions for area as the conventional parameter. However, the other half of the subjects (group 1) did not experience any influence of OKS on their postural dynamics, despite showing significant differences between eyes-open and the other conditions for all traditional parameters. From the results for group 2, we hypothesize that OKS may induce the striking effect on dynamics properties of the multilink network system involving visual and vestibular cortex related to self-motion perception, which acts to decrease the stochastic activity in order to correct disturbed posture.

Adult↗

PubMed2002

Guiding the study of brain dynamics by using first-person data: synchrony patterns correlate with ongoing conscious states during a simple visual task.

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.

Adult↗

PubMed2002

Estimating the time-course of coherence between single-trial brain signals: an introduction to wavelet coherence.

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.

Algorithms↗

PubMed2002

Seizure anticipation in human neocortical partial epilepsy.

The transition of brain activity towards an epileptic seizure is still a poorly understood phenomenon. Dynamic changes in brain activity have been detected several minutes before seizure emergence in populations of patients with mesial temporal lobe epilepsy (MTLE), using non-linear analysis of intracranial EEG recordings. Similar detection of a pre-ictal state has been obtained with standard scalp EEG recordings using a modified non-linear method. Here we applied this strategy to the seizures of patients with neocortical partial epilepsy. Results obtained by non-linear similarity analysis of 41 seizures from 11 patients with refractory partial epilepsy originating from various sites of the neocortex showed that (i) a pre-ictal state was detected in 90% of the patients and in 83% of the seizures whatever their location, with a mean anticipation time of 7.5 min; (ii) similar pre-ictal dynamic changes were detected when non-linear analysis methods were applied to either intracranial or scalp EEG recordings; (iii) the recording sites exhibiting these pre-ictal changes were distributed both within the epileptogenic focus and at remote locations; (iv) most pre-ictal dynamic changes were not correlated with linear changes in the frequency spectrum or with changes in the visually inspected EEG and the patients' behaviour. Hypotheses on the neuronal mechanisms underlying the pre-ictal period are discussed. The present results, together with those recently obtained in an MTLE population, suggest that changes in pre-ictal dynamics are a general phenomenon associated with seizure emergence in a wide population of patients with partial epilepsy, wherever the epileptogenic focus is located. The possibility of anticipating the onset of seizures has considerable therapeutic implications.

Adolescent↗

PubMedDate not supplied

Exploring the nonlinear dynamics of the brain.

The growing need for a better understanding of large-scale brain dynamics has stimulated in the last decade the development of new and more advanced data analysis techniques. Progress in this domain has greatly benefited from developments in nonlinear time series analysis. This review gives a short overview of some of the nonlinear properties one may wish to infer from brain recordings and presents some examples and recent applications.

Brain↗