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Biomedical subjects

J Martinerie

Publications and source records attributed to J Martinerie.

17 recordsLinked to original sources
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

Perception's shadow: long-distance synchronization of human brain activity.

Transient periods of synchronization of oscillating neuronal discharges in the frequency range 30-80 Hz (gamma oscillations) have been proposed to act as an integrative mechanism that may bring a widely distributed set of neurons together into a coherent ensemble that underlies a cognitive act. Results of several experiments in animals provide support for this idea. In humans, gamma oscillations have been described both on the scalp (measured by electroencephalography and magnetoencephalography) and in intracortical recordings, but no direct participation of synchrony in a cognitive task has been demonstrated so far. Here we record electrical brain activity from subjects who are viewing ambiguous visual stimuli (perceived either as faces or as meaningless shapes). We show for the first time, to our knowledge, that only face perception induces a long-distance pattern of synchronization, corresponding to the moment of perception itself and to the ensuing motor response. A period of strong desynchronization marks the transition between the moment of perception and the motor response. We suggest that this desynchronization reflects a process of active uncoupling of the underlying neural ensembles that is necessary to proceed from one cognitive state to another.

Adult↗
PubMed1999

Why bother to spatially embed EEG? Comments on Pritchard et al., Psychophysiology, 33, 362-368, 1996.

In a recent paper, Pritchard, Krieble, and Duke (Psychophysiology, 33, 362-368, 1996) studied the validity of spatial embedding of electroencephalographic (EEG) data and rejected this method in favor of time-delay embedding. The present paper describes the nonlinear characterization of brain dynamics using either spatial or time-delay embedding. We discuss the arguments published in Pritchard et al. (1996) and demonstrate that the spatial embedding cannot be rejected on this basis. We also point out the limitations of both spatial and time-delay embeddings related to the spatial extension and the high-dimensional dynamics of brain activity.

Cerebral Cortex↗
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

Entropy maps characterize drug effects on brain dynamics in Alzheimer's disease.

Non-linear quantifiers of brain electrical dynamics (entropy maps computed from the degradation of temporal forecasting of EEG signals) were studied in relation to drug treatment of Alzheimer's disease. A placebo condition was compared to three drug doses (50, 100 and 200 mg). A significant general effect of the drug was found when compared to placebo and specific contrasts between placebo and each of the three drug doses only reveal a significant entropy increase for the highest dose. These effects were localized bilaterally in fronto-temporal areas and support changes in the dynamics of the cerebral structures involved in memory processes.

Acoustic Stimulation↗
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↗
PubMed1998

[Static balance is controlled by a non-linear dynamic system].

Different techniques of stabilometric signal analysis have been used in order to study the adaptation of the fine postural control system to the wearing of corrective glasses with or without prisms. The comparison between the results obtained with conventional techniques and those obtained with non-linear dynamic methods demonstrates the better efficiency of the latter. These results confirm that the postural system behaves as a non-linear dynamical system and may explain the outstanding sensitivity of the fine postural system to small perturbations.

Adult↗
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↗
PubMed1996

Depression as a dynamical disease.

Mathematical models are helpful in the understanding of diseases through the use of dynamical indicators. A previous study has shown that brain activity can be characterized by a decrease of dynamical complexity in depressive subjects. The present paper confirms and extends these conclusions through the use of recent methodological advances: first episode and recurrent patients strongly differ in their dynamical response to therapeutic interventions. These results emphasize the need for clinical follow-ups to avoid recurrence and the necessity of specific therapeutic intervention in the case of recurrent patients.

Adult↗
PubMed1994

Decrease of complexity in EEG as a symptom of depression.

Nonlinear dynamic analysis provides new methods for the processing of the electroencephalogram (EEG). We demonstrate here that the EEG dynamics of major depressive subjects is more predictable, that is less complex, than that of control subjects. Moreover, the consequence of treatment upon the EEG dynamics seems to be dependent on the appearance of the illness. Although the specificity of this dynamic signature for different stages of depression is to be confirmed, the assumption of a strong link between a healthy system and a high level of complexity in dynamics is further supported.

Adult↗
PubMed1994

Non-linear forecasting measurements of multichannel EEG dynamics.

This work presents a new method for studying the underlying dynamics of multichannel EEG on the basis of the mathematical theory of dynamical systems. It computes the local loss of predictability and Kolmogorov entropy of the dynamics reconstructed from brain electrical activity. This reconstruction uses multichannel recordings in order to quantify an equivalent of spatio-temporal mapping. Five experimental conditions have been studied: closed eyes at rest, closed eyes and counting even numbers, staring at a spotlight, passive and active auditive odd-ball tasks. The entropy is positive for all the experimental conditions which proves that the underlying EEG dynamics are chaotic. Moreover, on the basis of the dynamical signature it is possible to differentiate 3 types of EEG activity: the rest closed eyes activity, the task closed eyes activity (counting and odd-ball tasks) and the open eyes activity (staring at a spotlight). It is inferred that this index could characterize task-related changes in brain activity.

Adult↗
PubMed1994

Loss of control of pre-motor activation in anxious agitated and impulsive depressives. A clinical and ERP study.

1. Current research uses a variety of traditional validation methods in order to test the clinical expression of biological models in psychiatry. The application of these methods has resulted in a paradoxical situation which requires the definition of new objectives in biological and pharmacoclinical research: the biological specificity of new psychotropic drugs does not assume any congruence between their pharmacological and their therapeutic effects, but raises the question of the relationship between biological systems and clinical symptomatology. The dimensional description of psychopathological disorders may be more appropriate to biological studies in psychiatry. 2. A study was undertaken on a population of twenty-one in-patients fulfilling the DSM III-R criteria for major depressive episode. They were divided into two groups on the basis of contrasting clinical dimensions: anxious-agitation and impulsiveness versus retardation and affective blunting. 3. Significant clinical differences between the two groups on mood profiles were echoed by contrasts in event-related potentials during a go-nogo task: only anxious agitated and impulsive patients developed an abnormal cortical activity, as measured by contingent negative variation (CNV), in the nogo condition. 4. This paper suggests how a paradigm with control of motor action leads to specify premotor activation abnormalities in the agitated impulsive depression subtype.

Adult↗
PubMed1993

A dynamical analysis of oscillatory responses in the optic tectum.

Multi-unit recordings from the optic tectum of an awake pigeon displaying oscillatory behavior evoked by visual stimulus are highly non-stationary and contain a broad band of frequencies under a time-window analysis. Here we extend these observations by a non-linear dynamical analysis of these oscillatory signals (local fields potentials) in successive epochs during background activity and visual responses. Two numerical estimates have been obtained from the original data every 200 ms: (1) correlation dimension and (2) non-linear forecasting of the trajectories. Results from eight different recording sites analyzed are consistent and indicate, in the average, an increase in complexity of the signal during the oscillatory periods.

Animals↗