Fabrice Wendling

Epileptic fast activity can be explained by a model of impaired GABAergic dendritic inhibition

This paper focuses on high‐frequency (gamma band) EEG activity, the most characteristic electrophysiological pattern in focal seizures of human epilepsy. It starts with recent hypotheses about: (i) the behaviour of inhibitory interneurons in hippocampal or neocortical networks in the generation of gamma frequency oscillations; (ii) the nonuniform alteration of GABAergic inhibition in experimental epilepsy (reduced dendritic inhibition and increased somatic inhibition); and (iii) the possible depression of GABAA,fast circuit activity by GABAA,slow inhibitory postsynaptic currents. In particular, these hypotheses are introduced in a new computational macroscopic model of EEG activity that includes a physiologically relevant fast inhibitory feedback loop. Results show that strikingly realistic activity is produced by the model when compared to real EEG signals recorded with intracerebral electrodes. They show that, in the model, the transition from interictal to fast ictal activity is explained by the impairment of dendritic inhibition.

Relevance of nonlinear lumped-parameter models in the analysis of depth-EEG epileptic signals

Abstract. In the field of epilepsy, the analysis of stereoelectroencephalographic (SEEG, intra-cerebral recording) signals with signal processing methods can help to better identify the epileptogenic zone, the area of the brain responsible for triggering seizures, and to better understand its organization. In order to evaluate these methods and to physiologically interpret the results they provide, we developed a model able to produce EEG signals from “organized” networks of neural populations. Starting from a neurophysiologically relevant model initially proposed by Lopes Da Silva et al. [Lopes da Silva FH, Hoek A, Smith H, Zetterberg LH (1974) Kybernetic 15: 27–37] and recently re-designed by Jansen et al. [Jansen BH, Zouridakis G, Brandt ME (1993) Biol Cybern 68: 275–283] the present study demonstrates that this model can be extended to generate spontaneous EEG signals from multiple coupled neural populations. Model parameters related to excitation, inhibition and coupling are then altered to produce epileptiform EEG signals. Results show that the qualitative behavior of the model is realistic; simulated signals resemble those recorded from different brain structures for both interictal and ictal activities. Possible exploitation of simulations in signal processing is illustrated through one example; statistical couplings between both simulated signals and real SEEG signals are estimated using nonlinear regression. Results are compared and show that, through the model, real SEEG signals can be interpreted with the aid of signal processing methods.

Mechanisms of physiological and epileptic HFO generation

High frequency oscillations (HFO) have a variety of characteristics: band-limited or broad-band, transient burst-like phenomenon or steady-state. HFOs may be encountered under physiological or under pathological conditions (pHFO). Here we review the underlying mechanisms of oscillations, at the level of cells and networks, investigated in a variety of experimental in vitro and in vivo models. Diverse mechanisms are described, from intrinsic membrane oscillations to network processes involving different types of synaptic interactions, gap junctions and ephaptic coupling. HFOs with similar frequency ranges can differ considerably in their physiological mechanisms. The fact that in most cases the combination of intrinsic neuronal membrane oscillations and synaptic circuits are necessary to sustain network oscillations is emphasized. Evidence for pathological HFOs, particularly fast ripples, in experimental models of epilepsy and in human epileptic patients is scrutinized. The underlying mechanisms of fast ripples are examined both in the light of animal observations, in vivo and in vitro, and in epileptic patients, with emphasis on single cell dynamics. Experimental observations and computational modeling have led to hypotheses for these mechanisms, several of which are considered here, namely the role of out-of-phase firing in neuronal clusters, the importance of strong excitatory AMPA-synaptic currents and recurrent inhibitory connectivity in combination with the fast time scales of IPSPs, ephaptic coupling and the contribution of interneuronal coupling through gap junctions. The statistical behaviour of fast ripple events can provide useful information on the underlying mechanism and can help to further improve classification of the diverse forms of HFOs.

Entorhinal cortex involvement in human mesial temporal lobe epilepsy: an electrophysiologic and volumetric study.

Summary:  Purpose: Several studies have demonstrated diminution in the volume of entorhinal cortex (EC) ipsilateral to the pathologic side in patients with temporal lobe epilepsy (TLE). The relation between the degree of EC atrophy and the epileptogenicity of this structure has never been directly studied. The purpose of the study was to determine whether atrophy of the EC evaluated by the quantitative magnetic resonance imaging (MRI) method is correlated with the epileptogenicity of this structure in TLE.

Interpretation of interdependencies in epileptic signals using a macroscopic physiological model of the EEG

This paper presents a neurophysiologically relevant model in which vectorial epileptiform electroencephalographic (EEG) signals are produced from multiple coupled neural populations. This model is used to evaluate the performances of non-linear regression analysis as a method to characterize couplings between neural populations from EEG signals they produce. Two quantities, estimated on generated signals, namely the non-linear correlation coefficient and the direction index, are related to the degree and direction of coupling parameters of the model. Their statistical behavior is first studied on a set of signals simulated for relevant configurations of the model. They are then measured on real stereoelectroencephalographic (SEEG) signals. Results obtained in three patients suffering from temporal lobe epilepsy (TLE) show that abnormal functional couplings between cerebral structures, that establish during seizures, can be interpreted in terms of causality. Perspectives are oriented to the identification of epileptogenic networks in TLE.

Impaired consciousness during temporal lobe seizures is related to increased long-distance cortical-subcortical synchronization.

Loss of consciousness (LOC) is a dramatic clinical manifestation of temporal lobe seizures. Its underlying mechanism could involve altered coordinated neuronal activity between the brain regions that support conscious information processing. The consciousness access hypothesis assumes the existence of a global workspace in which information becomes available via synchronized activity within neuronal modules, often widely distributed throughout the brain. Re-entry loops and, in particular, thalamo-cortical communication would be crucial to functionally bind different modules together. In the present investigation, we used intracranial recordings of cortical and subcortical structures in 12 patients, with intractable temporal lobe epilepsy (TLE), as part of their presurgical evaluation to investigate the relationship between states of consciousness and neuronal activity within the brain. The synchronization of electroencephalography signals between distant regions was estimated as a function of time by using non-linear regression analysis. We report that LOC occurring during temporal lobe seizures is characterized by increased long-distance synchronization between structures that are critical in processing awareness, including thalamus (Th) and parietal cortices. The degree of LOC was found to correlate with the amount of synchronization in thalamo-cortical systems. We suggest that excessive synchronization overloads the structures involved in consciousness processing, preventing them from treating incoming information, thus resulting in LOC.

Transcranial direct-current stimulation modulates synaptic mechanisms involved in associative learning in behaving rabbits

Transcranial direct-current stimulation (tDCS) is a noninvasive brain stimulation technique that has been successfully applied for modulation of cortical excitability. tDCS is capable of inducing changes in neuronal membrane potentials in a polarity-dependent manner. When tDCS is of sufficient length, synaptically driven after-effects are induced. The mechanisms underlying these after-effects are largely unknown, and there is a compelling need for animal models to test the immediate effects and after-effects induced by tDCS in different cortical areas and evaluate the implications in complex cerebral processes. Here we show in behaving rabbits that tDCS applied over the somatosensory cortex modulates cortical processes consequent to localized stimulation of the whisker pad or of the corresponding area of the ventroposterior medial (VPM) thalamic nucleus. With longer stimulation periods, poststimulation effects were observed in the somatosensory cortex only after cathodal tDCS. Consistent with the polarity-specific effects, the acquisition of classical eyeblink conditioning was potentiated or depressed by the simultaneous application of anodal or cathodal tDCS, respectively, when stimulation of the whisker pad was used as conditioned stimulus, suggesting that tDCS modulates the sensory perception process necessary for associative learning. We also studied the putative mechanisms underlying immediate effects and after-effects of tDCS observed in the somatosensory cortex. Results when pairs of pulses applied to the thalamic VPM nucleus (mediating sensory input) during anodal and cathodal tDCS suggest that tDCS modifies thalamocortical synapses at presynaptic sites. Finally, we show that blocking the activation of adenosine A1 receptors prevents the long-term depression (LTD) evoked in the somatosensory cortex after cathodal tDCS.

Cell domain-dependent changes in the glutamatergic and GABAergic drives during epileptogenesis in the rat CA1 region

An increased ratio of the glutamatergic drive to the overall glutamatergic/GABAergic drive characterizes the chronic stage of temporal lobe epilepsy (TLE), but it is unclear whether this modification is present during the latent period that often precedes the epileptic stage. Using the pilocarpine model of TLE in rats, we report that this ratio is decreased in hippocampal CA1 pyramidal cells during the early phase of the latent period (3–5 days post pilocarpine). It is, however, increased during the late phase of the latent period (7–10 days post pilocarpine), via cell domain‐dependent alterations in synaptic current properties, concomitant with the occurrence of interictal‐like activity in vivo. During the late latent period, the glutamatergic drive was increased in somata via an enhancement in EPSC decay time constant and in dendrites via an increase in EPSC frequency and amplitude. The GABAergic drive remained unchanged in the soma but was decreased in dendrites, since the drop off in IPSC frequency was more marked than the increase in IPSC kinetics. Theoretical considerations suggest that these modifications are sufficient to produce interictal‐like activity. In epileptic animals, the ratio of the glutamatergic drive to the overall synaptic drive was not further modified, despite additional changes in synaptic current frequency and kinetics. These results show that the global changes to more glutamatergic and less GABAergic activities in the CA1 region precede the chronic stage of epilepsy, possibly facilitating the occurrence and/or the propagation of interictal activity.

From Intracerebral EEG Signals to Brain Connectivity: Identification of Epileptogenic Networks in Partial Epilepsy

Epilepsy is a complex neurological disorder characterized by recurring seizures. In 30% of patients, seizures are insufficiently reduced by anti-epileptic drugs. In the case where seizures originate from a relatively circumscribed region of the brain, epilepsy is said to be partial and surgery can be indicated. The success of epilepsy surgery depends on the accurate localization and delineation of the epileptogenic zone (which often involves several structures), responsible for seizures. It requires a comprehensive pre-surgical evaluation of patients that includes not only imaging data but also long-term monitoring of electrophysiological signals (scalp and intracerebral EEG). During the past decades, considerable effort has been devoted to the development of signal analysis techniques aimed at characterizing the functional connectivity among spatially distributed regions over interictal (outside seizures) or ictal (during seizures) periods from EEG data. Most of these methods rely on the measurement of statistical couplings among signals recorded from distinct brain sites. However, methods differ with respect to underlying theoretical principles (mostly coming from the field of statistics or the field of non-linear physics). The objectives of this paper are: (i) to provide an brief overview of methods aimed at characterizing functional brain connectivity from electrophysiological data, (ii) to provide concrete application examples in the context of drug-refractory partial epilepsies, and iii) to highlight some key points emerging from results obtained both on real intracerebral EEG signals and on signals simulated from physiologically plausible models in which the underlying connectivity patterns are known a priori (ground truth).

EEG source connectivity analysis: from dense array recordings to brain networks.

The recent past years have seen a noticeable increase of interest for electroencephalography (EEG) to analyze functional connectivity through brain sources reconstructed from scalp signals. Although considerable advances have been done both on the recording and analysis of EEG signals, a number of methodological questions are still open regarding the optimal way to process the data in order to identify brain networks. In this paper, we analyze the impact of three factors that intervene in this processing: i) the number of scalp electrodes, ii) the combination between the algorithm used to solve the EEG inverse problem and the algorithm used to measure the functional connectivity and iii) the frequency bands retained to estimate the functional connectivity among neocortical sources. Using High-Resolution (hr) EEG recordings in healthy volunteers, we evaluated these factors on evoked responses during picture recognition and naming task. The main reason for selection this task is that a solid literature background is available about involved brain networks (ground truth). From this a priori information, we propose a performance criterion based on the number of connections identified in the regions of interest (ROI) that belong to potentially activated networks. Our results show that the three studied factors have a dramatic impact on the final result (the identified network in the source space) as strong discrepancies were evidenced depending on the methods used. They also suggest that the combination of weighted Minimum Norm Estimator (wMNE) and the Phase Synchronization (PS) methods applied on High-Resolution EEG in beta/gamma bands provides the best performance in term of topological distance between the identified network and the expected network in the above-mentioned cognitive task.