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M Le Van Quyen

Publications and source records attributed to M Le Van Quyen.

11 recordsLinked to original sources
PubMed2001

Comparison of Hilbert transform and wavelet methods for the analysis of neuronal synchrony.

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.

Brain↗
PubMed2001

Anticipation of epileptic seizures from standard EEG recordings.

BACKGROUND: New methods derived from non-linear analysis of intracranial recordings permit the anticipation of an epileptic seizure several minutes before the seizure. Nevertheless, anticipation of seizures based on standard scalp electroencephalographical (EEG) signals has not been reported yet. The accessibility to preictal changes from standard EEGs is essential for expanding the clinical applicability of these methods. METHODS: We analysed 26 scalp-EEG/video recordings, from 60 min before a seizure, in 23 patients with temporal-lobe epilepsy. For five patients, simultaneous scalp and intracranial EEG recordings were assessed. Long-term changes before seizure onset were identified by a measure of non-linear similarity, which is very robust in spite of large artifacts and runs in real-time. FINDINGS: In 25 of 26 recordings, measurement of non-linear changes in EEG signals allowed the anticipation of a seizure several minutes before it occurred (mean 7 min). These preictal changes in the scalp EEG correspond well with concurrent changes in depth recordings. INTERPRETATION: Scalp-EEG recordings retain sufficient dynamical information which can be used for the analysis of preictal changes leading to seizures. Seizure anticipation strategies in real-time can now be envisaged for diverse clinical applications, such as devices for patient warning, for efficacy of ictal-single photon emission computed tomography procedures, and eventual treatment interventions for preventing seizures.

Electroencephalography↗
PubMed2001

Nonlinear analysis of orthostatic posture in patients with vertigo or balance disorders.

The stabilometry signals involve irregular and unpredictable components. In order to identify the hidden dynamics that underlie the multi-link networks consisted of the multiple sensory systems, motor components and central integration, we applied a nonlinear analysis to these signals. We evaluated the postural control differences between eyes open and closed by means of the dynamical closeness between two states, known as similarity index, for the patients with vestibular disorders. We were able to demonstrate that some patients (eight of 21) showed a difference between the conventional and nonlinear measures. Especially, the similarity index tended to reflect the clinical course of the vestibular compensation and the findings in the patients with benign paroxysmal positional vertigo (BPPV) demonstrated that its vestibular function may include various pathologies besides canalithiasis. These results suggest that nonlinear analysis can elucidate the complex postural control networks and this procedure may also be able to provide the new findings of the stabilometry examinations.

Adult↗
PubMed2001

Characterizing neurodynamic changes before seizures.

The study of dynamic changes in neural activity preceding epileptic seizure allows the characterization of a preictal state several minutes before seizure onset. This opens up new perspectives for studying the mechanisms of epileptogenesis as well as for possible therapeutic interventions, which represent a major breakthrough. In this review the authors present and discuss the results from their group in this domain using nonlinear analysis of brain signals, as well as the limitations of this topic and current questions.

Animals↗
PubMed2000

Spatio-temporal characterizations of non-linear changes in intracranial activities prior to human temporal lobe seizures.

Recent studies have shown that non-linear analysis of intracranial activities can detect a 'pre-ictal phase' preceding the epileptic seizure. Nevertheless, the dynamical nature of the underlying neuronal process and the spatial extension of this pre-ictal phase still remain unknown. In this paper, we address these aspects using a new non-linear measure of dynamic similarity between different parts of intracranial recordings of nine patients with medial temporal lobe epilepsy recorded during transitions to seizure. Our results confirm that non-linear changes in neuronal dynamics allow, in most cases (16 out of 17), a seizure anticipation several minutes in advance. Furthermore, we show that the spatial distribution of pre-ictal changes often involves an extended network projecting beyond the limits of the epileptogenic region. Finally, the pre-ictal phase could frequently (13 out of 17) be characterized with a marked shift toward slower frequencies in upper delta or theta frequency range.

Cerebral Cortex↗
PubMed1999

Anticipating epileptic seizures in real time by a non-linear analysis of similarity between EEG recordings.

In a previous publication we showed that non-linear analysis can extract spatio-temporal changes of brain electrical activity prior to epileptic seizures. Here we describe a new method to analyze this long-term non-stationarity in the EEG by a measure of dynamical similarity between different parts of the time series. We apply this method to the study of a group of patients with temporal lobe epilepsy recorded intracranially during transitions to seizure. We show that the method, which can be implemented on a personal computer, can track in real time spatio-temporal changes in brain dynamics several minutes prior to seizure.

Algorithms↗
PubMed1999

[Interactions between the epileptic network and brain function: an approach by nonlinear analysis of intracranial EEG].

Recent advances in the non-linear dynamics analysis have made it possible to identify hidden recurrences in EEG signals that could be missed by more traditional linear techniques such as power spectrum or coherence analysis. This is particularly true for epileptic EEG recordings either in animals or in humans as epileptic phenomena are usually concomitant with the emergence a strong non-linear EEG behavior. Non-linear dynamical analysis techniques quantify the relations between EEG signals. The literature concerning the spatio-temporal characteristics of the epileptic processes during seizures and interictal periods is reviewed. Our attention has been mainly focused on the interdependences between brain structures or on the dynamical changes of one particular brain region during intracranial recordings. These data could explain in part the dysfunctioning of the cerebral cortex induced by epileptic activities and provide an insight into the spatio-temporal organization of the epileptic network. Futhermore, by tracking the time variation of non-linear indices, one can anticipate the occurrence of seizures in temporal lobe epilepsies. All this information could contribute to improve definitions of the epileptogenic zone in partial epilepsy and also open the way to preventive interventions.

Brain↗
PubMed1998

Nonlinear interdependencies of EEG signals in human intracranially recorded temporal lobe seizures.

The degree of interdependence between intracranial EEG channels was investigated in four epileptic patients with complex partial seizures of mesial temporal lobe origin. With a new method to characterize nonlinear dynamical interdependence-the mutual nonlinear prediction-we demonstrated here a possibility to quantify, during epileptic seizures, the relationships between EEG signals of electrode contacts in the epileptogenic area. During the interictal period, the degree of nonlinear interdependences were very low or absent. In contrast, it was found that transient patterns of nonlinear interdependences emerge at the initial spread of the seizure, during essential parts of its development, and at seizure end, but the maintenance of these interactions are not observed throughout the seizure activity. These results suggest that the nonlinear associations plays an important role in epileptogenesis, and that the process of neuronal entrainment during seizure onset involves a transient interaction between a distributed network of neuronal aggregates, but the maintenance of this interaction is not required for sustained seizure activity. Furthermore, this technique can describe properly the spatio-temporal organisation of the seizures of medio-temporal lobe origin and could become a very useful tool to aid the localization of the epileptogenic regions at the origin of epileptic seizures and their pathways of propagation.

Algorithms↗
PubMed1998

Epileptic seizures can be anticipated by non-linear analysis.

Epileptic seizures are a principal brain dysfunction with important public health implications, as they affect 0.8% of humans. Many of these patients (20%) are resistant to treatment with drugs. The ability to anticipate the onset of seizures in such cases would permit clinical interventions. The view of chronic focal epilepsy now is that abnormally discharging neurons act as pacemakers to recruit and entrain other normal neurons by loss of inhibition and synchronization into a critical mass. Thus, preictal changes should be detectable during the stages of recruitment. Traditional signal analyses, such as the count of focal spike density, the frequency coherence or spectral analyses are not reliable predictors. Non-linear indicators may undergo consistent changes around seizure onset. Our objective was to follow the transition into seizure by reconstructing intracranial recordings in implanted patients as trajectories in a phase space and then introduce non-linear indicators to characterize them. These indicators take into account the extended spatio-temporal nature of the epileptic recruitment processes and the corresponding physiological events governed by short-term causalities in the time series. We demonstrate that in most cases (17 of 19), seizure onset could be anticipated well in advance (between 2-6 minutes beforehand), and that all subjects seemed to share a similar 'route' towards seizure.

Electrophysiology↗
PubMed1997

Temporal patterns in human epileptic activity are modulated by perceptual discriminations.

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.

Adult↗