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International audience; Epileptic seizures, epileptic spikes and high-frequency oscillations (HFOs) are recognized as three electrophysiological markers of epileptogenic neuronal systems. It can be reasonably hypothesized that distinct (hyper)excitability mechanisms underlie these electrophysiological signatures. The question is 'What are these mechanisms?'. Solving this difficult question would considerably help our understanding of epileptogenic processes and would also advance our interpretation of electrophysiological signals. In this paper, we show how computational models of brain epileptic activity can be used to address this issue. With a special emphasis on the hippocampal activity recorded in various experimental models (in vivo and in vitro) as well as in epileptic patients, we confront results and insights we can get from computational models lying at two different levels of description, namely macroscopic (neural mass) and microscopic (detailed network of neurons). At each level, we show how spikes, seizures and HFOs can (or cannot) be generated depending on the model features. The replication of observed signals, the prediction of possible mechanisms as well as their experimental validation are described and discussed; as are the advantages and limitations of the two modelling approaches.

Objective: One cornerstone event during epileptogenesis is the occurrence of the first spontaneous seizure (SZ1). It is therefore important to identify biomarkers of the network alterations leading to SZ1. In experimental models of temporal lobe epilepsy (TLE), interictal-like activity (ILA) precedes SZ1 by several days. The goal of this study was to determine whether ILA dynamics bore electrophysiological features signaling the impeding transition to SZ1. Methods: Experimental TLE was triggered by pilocarpine- or kainic acid-induced status epilepticus (SE). Continuous electroencephalographic recordings were performed 7 days before and up to 40 days after SE. The amplitude and duration of the spike and wave components of interictal spikes were analyzed. Results: Two types of interictal spikes were distinguished: type 1, with a spike followed by a long-lasting wave, and type 2, with a spike without wave. The number, amplitude, and duration of type 1 spikes started to decrease, whereas the number of type 2 spikes increased, several days before SZ1, reaching their minimum/maximum values just before SZ1. Interpretation: The change in ILA pattern could constitute a predictive biomarker of SZ1. The mechanisms underlying these dynamic modifications and their functional impact are discussed in the context of the construction of an epileptogenic network. ANN NEUROL 2012;71:805–814

International audience; Rapid discharges (25-80Hz), a characteristic EEG pattern often recorded at seizure onset in partial epilepsies, are often considered as electrophysiological signatures of the epileptogenic zone. While the recording of rapid discharges from intracranial electrodes has long been established, their observation from the scalp is challenging. The prevailing view is that rapid discharges cannot be seen clearly (or at all) in scalp EEG because they have low signal-to-noise ratio. To date, however, no studies have investigated the 'observability' of rapid discharges, i.e. under what conditions and to what extent they can be visible in recorded EEG signals. Here, we used a model-based approach to examine the impact of several factors (distance to sources, skull conductivity, source area, source synchrony, and background activity) on the observability of rapid discharges in simultaneously simulated depth EEG and scalp EEG signals. In our simulations, the rapid discharge was clearly present in depth EEG signals but mostly almost not visible in scalp EEG signals. We identified some of the factors that may limit the observability of the rapid discharge on the scalp. Notably, surrounding background activity was found to be the most critical factor. The findings are discussed in relation to the presurgical evaluation of epilepsy.

Electroencephalographic (EEG) recordings are often contaminated with muscle artifacts. This disturbing myogenic activity not only strongly affects the visual analysis of EEG, but also most surely impairs the results of EEG signal processing tools such as source localization. This article focuses on the particular context of the contamination epileptic signals (interictal spikes) by muscle artifact, as EEG is a key diagnosis tool for this pathology. In this context, our aim was to compare the ability of two stochastic approaches of blind source separation, namely independent component analysis (ICA) and canonical correlation analysis (CCA), and of two deterministic approaches namely empirical mode decomposition (EMD) and wavelet transform (WT) to remove muscle artifacts from EEG signals. To quantitatively compare the performance of these four algorithms, epileptic spike-like EEG signals were simulated from two different source configurations and artificially contaminated with different levels of real EEG-recorded myogenic activity. The efficiency of CCA, ICA, EMD, and WT to correct the muscular artifact was evaluated both by calculating the normalized mean-squared error between denoised and original signals and by comparing the results of source localization obtained from artifact-free as well as noisy signals, before and after artifact correction. Tests on real data recorded in an epileptic patient are also presented. The results obtained in the context of simulations and real data show that EMD outperformed the three other algorithms for the denoising of data highly contaminated by muscular activity. For less noisy data, and when spikes arose from a single cortical source, the myogenic artifact was best corrected with CCA and ICA. Otherwise when spikes originated from two distinct sources, either EMD or ICA offered the most reliable denoising result for highly noisy data, while WT offered the better denoising result for less noisy data. These results suggest that the performance of muscle artifact correction methods strongly depend on the level of data contamination, and of the source configuration underlying EEG signals. Eventually, some insights into the numerical complexity of these four algorithms are given.

International audience; OBJECTIVE: We propose a new method for automatic detection of fast ripples (FRs) which have been identified as a potential biomarker of epileptogenic processes. METHODS: This method is based on a two-stage procedure: (i) global detection of events of interest (EOIs, defined as transient signals accompanied with an energy increase in the frequency band of interest 250-600Hz) and (ii) local energy vs. frequency analysis of detected EOIs for classification as FRs, interictal epileptic spikes or artifacts. For this second stage, two variants were implemented based either on Fourier or wavelet transform. The method was evaluated on simulated and real depth-EEG signals (human, animal). The performance criterion was based on receiving operator characteristics. RESULTS: The proposed detector showed high performance in terms of sensitivity and specificity. CONCLUSIONS: As designed to specifically detect FRs, the method outperforms any method simply based on the detection of energy changes in high-pass filtered signals and avoids spurious detections caused by sharp transient events often present in raw signals. SIGNIFICANCE: In most of epilepsy surgery units, huge data sets are generated during pre-surgical evaluation. We think that the proposed detection method can dramatically decrease the workload in assessing the presence of FRs in intracranial EEGs.

International audience; In this letter, we propose a general framework for studying neural mass models defined by ordinary differential equations. By studying the bifurcations of the solutions to these equations and their sensitivity to noise, we establish an important relation, similar to a dictionary, between their behaviors and normal and pathological, especially epileptic, cortical patterns of activity. We then apply this framework to the analysis of two models that feature most phenomena of interest, the Jansen and Rit model, and the slightly more complex model recently proposed by Wendling and Chauvel. This model-based approach allows us to test various neurophysiological hypotheses on the origin of pathological cortical behaviors and investigate the effect of medication. We also study the effects of the stochastic nature of the inputs, which gives us clues about the origins of such important phenomena as interictal spikes, interictal bursts, and fast onset activity that are of particular relevance in epilepsy.

International audience; Despite the interest in simultaneous EEG-fMRI studies of epileptic spikes, the link between epileptic discharges and their corresponding hemodynamic responses is poorly understood. In this context, biophysical models are promising tools for investigating the mechanisms underlying observed signals. Here, we apply a metabolic-hemodynamic model to simulated epileptic discharges, in part generated by a neural mass model. We analyze the effect of features specific to epileptic neuronal activity on the blood oxygen level dependent (BOLD) response, focusing on the issues of linearity in neurovascular coupling and on the origin of negative BOLD signals. We found both sub- and supra-linearity in simulated BOLD signals, depending on whether one observes the early or the late part of the BOLD response. The size of these non-linear effects is determined by the spike frequency, as well as by the amplitude of the excitatory activity. Our results additionally indicate a minor deviation from linearity at the neuronal level. According to a phase space analysis, the possibility to obtain a negative BOLD response to an epileptic spike depends on the existence of a long and strong excitatory undershoot. Moreover, we strongly suggest that a combined EEG-fMRI modeling approach should include spatial assumptions. The present study is a step towards an increased understanding of the link between epileptic spikes and their BOLD responses, aiming to improve the interpretation of simultaneous EEG-fMRI recordings in epilepsy.

Objective The phenomenon of ‘ deja vu ' is caused by acute disturbance of mnemonic systems of the medial temporal lobe (MTL). In epileptic patients investigated with intracerebral electrodes, deja vu can be more readily induced by stimulation of the rhinal cortices (RCs) than the hippocampus (H). Whether deja vu results from acute dysfunction of the familiarity system alone (sustained by RC) or from more extensive involvement of the MTL region (including H) is debatable. Methods We analysed the synchronisation of intracerebral electroencephalography (EEG) signals recorded from RC, H and amygdala (A) in epileptic patients in whom deja vu was induced by electrical stimulation. EEG signal correlations (between signals from RC, A and H) were evaluated using a nonlinear regression. Results In comparison with RC stimulations that did not lead to deja vu (DV−), stimulations triggering deja vu (DV+) were associated with increased broadband EEG correlation ( p =0.01). Changes in correlations were significantly different in the theta band for RC–A ( p =0.007) and RC–H ( p =0.01) and in the beta band for RC–H ( p =0.001) interactions. Conclusion Deja vu is associated with increased EEG signal correlation between MTL structures. Significance Results are in favour of a mechanism involving transient co-operation between various MTL structures, not limited to RC alone.

International audience; In many physiological or pathological situations, the interpretation of BOLD signals remains elusive as the intimate link between neuronal activity and subsequent flow/metabolic changes is not fully understood. During the past decades, a number of biophysical models of the neurovascular coupling have been proposed. It is now well-admitted that these models may bridge between observations (fMRI data) and underlying biophysical and (patho-)physiological mechanisms (related to flow and metabolism) by providing mechanistic explanations. In this study, three well-established models (Buxton's, Friston's and Sotero's) are investigated. An exhaustive parameter sensitivity analysis (PSA) was conducted to study the marginal and joint influences of model parameters on the three main features of the BOLD response (namely the principal peak, the post-stimulus undershoot and the initial dip). In each model, parameters that have the greatest (and least) influence on the BOLD features as well as on the direction of variation of these features were identified. Among the three studied models, parameters were shown to affect the output features in different manners. Indeed, the main parameters revealed by the PSA were found to strongly depend on the way the flow(CBF)-metabolism(CMRO(2)) relationship is implemented (serial vs. parallel). This study confirmed that the model structure which accounts for the representation of the CBF-CMRO(2) relationship (oxygen supply by the flow vs. oxygen demand from neurons) plays a key role. More generally, this work provides substantial information about the tuning of parameters in the three considered models and about the subsequent interpretation of BOLD signals based on these models.

La question de l’existence d’œuvres ‘autobiographiques’ au Moyen Age a fait l’objet de nombreuses études, qui ne mentionnent jamais le De hominis miseria, mundi et inferni contemptu de Hugues de Miramar. Or, malgré son titre et son contenu (celui d’un traité sur le thème classique du contemptus mundi), cet ouvrage, au-delà de deux passages clairement ‘autobiographiques’, insère dans sa structure d’ensemble, celle d’un traité monastique, un récit rétrospectif à la première personne. Dans son écriture, le De miseria se rattache ainsi, dans une certaine mesure, à la postérité des Confessions de saint Augustin. A cet égard, sa place dans l’histoire littéraire du Moyen Age mériterait d’être reconnue. The question of the existence of ‘autobiographical’ works in the Middle Ages has been dealt with in numerous studies, which never mention the De hominis miseria, mundi and inferni contemptu by Hugues de Miramar. Now, in spite of its title and its contents (that of a treatise on the classical theme of contemptus mundi), this work, beyond two passages clearly ‘autobiographical’, inserts into its general structure, that of a monastic treatise, a retrospective first-person narrative. Thus, in its writing, the De miseria, to some extent, is connected with the posterity of St. Augustine’ s Confessions.In this respect, its place in the Middle Ages’ literary history would deserve to be recognized.

International audience; In this study, we aimed to demonstrate whether spontaneous fluctuations in the blood oxygen level dependent (BOLD) signal derived from resting state functional magnetic resonance imaging (fMRI) reflect spontaneous neuronal activity in pathological brain regions as well as in regions spared by epileptiform discharges. This is a crucial issue as coherent fluctuations of fMRI signals between remote brain areas are now widely used to define functional connectivity in physiology and in pathophysiology. We quantified functional connectivity using non-linear measures of cross-correlation between signals obtained from intracerebral EEG (iEEG) and resting-state functional MRI (fMRI) in 5 patients suffering from intractable temporal lobe epilepsy (TLE). Functional connectivity was quantified with both modalities in areas exhibiting different electrophysiological states (epileptic and non affected regions) during the interictal period. Functional connectivity as measured from the iEEG signal was higher in regions affected by electrical epileptiform abnormalities relative to non-affected areas, whereas an opposite pattern was found for functional connectivity measured from the BOLD signal. Significant negative correlations were found between the functional connectivities of iEEG and BOLD signal when considering all pairs of signals (theta, alpha, beta and broadband) and when considering pairs of signals in regions spared by epileptiform discharges (in broadband signal). This suggests differential effects of epileptic phenomena on electrophysiological and hemodynamic signals and/or an alteration of the neurovascular coupling secondary to pathological plasticity in TLE even in regions spared by epileptiform discharges. In addition, indices of directionality calculated from both modalities were consistent showing that the epileptogenic regions exert a significant influence onto the non epileptic areas during the interictal period. This study shows that functional connectivity measured by iEEG and BOLD signals give complementary but sometimes inconsistent information in TLE.

International audience; We propose a new MUSIC-like method, called 2q-ExSo-MUSIC (q≥1). This method is an extension of the 2q-MUSIC (q≥1) approach for solving the EEG/MEG inverse problem, when spatially-extended neocortical sources ("ExSo") are considered. It introduces a novel ExSo-MUSIC principle. The novelty is two-fold: i) the parameterization of the spatial source distribution that leads to an appropriate metric in the context of distributed brain sources and ii) the introduction of an original, efficient and low-cost way of optimizing this metric. In 2q-ExSo-MUSIC, the possible use of higher order statistics (q≥2) offers a better robustness with respect to Gaussian noise of unknown spatial coherence and modeling errors. As a result we reduced the penalizing effects of both the background cerebral activity that can be seen as a Gaussian and spatially correlated noise, and the modeling errors induced by the non-exact resolution of the forward problem. Computer results on simulated EEG signals obtained with physiologically-relevant models of both the sources and the volume conductor show a highly increased performance of our 2q-ExSo-MUSIC method as compared to the classical 2q-MUSIC algorithms.

Epilepsy is a neurological disorder characterized by recurrent seizures which affects about 1% people worldwide. During the past decades, some mechanisms involved in ictogenesis (generation of seizures) have been identified and, to some extent, partially understood. However, regarding epileptogenesis (process by which a neuronal system becomes epileptic), underlying mechanisms remain elusive. This difficulty is mostly related to the fact that epileptogenesis can only be addressed using experimental models. In this study, we have analyzed the shape of a specific electrophysiological pattern, referred to as "epileptic spike", encountered during the epileptogenesis process in an in vivo model of temporal lobe epilepsy (mouse, kainate). Results show that the features of these transient events (duration and amplitude) change as a function of time as the brain evolves towards the chronic epileptic state characterized by the appearance of spontaneous seizures. Using a detailed computational model of the hippocampus (CA1 sub-field), an interpretation of observed modifications is provided, in relationship with possible alterations that take place in underlying neuronal circuits.

International audience; Our objective is to analyze EEG signals recorded with depth electrodes during seizures in patients with drug-resistant epilepsy. Usually, different phases are observed during the seizure process, including a fast onset activity (FOA). We aim to determine how cerebral structures get involved during this FOA, in particular whether some structure can "drive" some other structures. This paper focuses on a linear Granger causality based measure to detect causal relation of interdependence in multivariate signals generated by a physiology-based model of coupled neuronal populations. When coupling between signals exists, statistical analysis supports the relevance of this index for characterizing the information flow and its direction among neuronal populations.

International audience; In this study we have quantified the "epileptogenicity" of several brain regions in seizures originating in the posterior parietal cortex in 17 patients investigated by intracerebral recordings using stereotactic EEG (SEEG). Epileptogenicity of brain structures was quantified according to the "epileptogenicity index" (EI), a way to quantify rapid discharges at seizure onset. Seven patients had maximal epileptogenicity in the superior parietal lobule-BA area 7 (Gr1), 2 patients in the superior parietal lobule-area 5 (Gr2), 4 patients in inferior parietal lobule (Gr3) and 4 in the opercular region (Gr4). A large majority of patients (15/17 (88%)) reported to have at least one aura during the course of their disease. Somato-sensory manifestations were reported in the four groups. Vestibular disturbance was observed mainly in seizures from the superior parietal lobule (Gr1 and 2). Ipsilateral version was the most frequent objective manifestation (64%). Hyperkinetic behaviour (motor agitation) was found to be frequent, observed in 4/17 cases (23%) and observed in seizures from inferior parietal regions. In conclusion, the electrophysiological organization and the clinical manifestations of PLS are various and complex. The subjective manifestations are frequent and often suggestive, therefore must be actively sought.

CE. GEE and P. Benquet : These authors contributed equally to this study.; International audience; Oxygen glucose deprivation (OGD) leads to rapid suppression of synaptic transmission. Here we describe an emergence of rhythmic activity at 8 to 20 Hz in the CA3 subfield of hippocampal slice cultures occurring for a few minutes prior to the OGD-induced cessation of evoked responses. These oscillations, dominated by inhibitory events, represent network activity, as they were abolished by tetrodotoxin. They were also completely blocked by the GABAergic antagonist picrotoxin, and strongly reduced by the glutamatergic antagonist NBQX. Applying CPP to block NMDA receptors had no effect and neither did UBP302, an antagonist of GluK1-containing kainate receptors. The gap junction blocker mefloquine disrupted rhythmicity. Simultaneous whole-cell voltage-clamp recordings from neighboring or distant CA3 pyramidal cells revealed strong cross-correlation of the incoming rhythmic activity. Interneurons in the CA3 area received similar correlated activity. Interestingly, oscillations were much less frequently observed in the CA1 area. These data, together with the observation that the recorded activity consists primarily of inhibitory events, suggest that CA3 interneurons are important for generating these oscillations. This transient increase in inhibitory network activity during OGD may represent a mechanism contributing to the lower vulnerability to ischemic insults of the CA3 area as compared to the CA1 area.

International audience; An extension of the original implementation of JADE, named eJADE((1)) hereafter, was proposed in 2001 to perform independent component analysis for any combination of statistical orders greater than or equal to three. More precisely, eJADE((1)) relies on the joint diagonalization of a set of several cumulant matrices corresponding to different matrix slices of one or several higher order cumulant tensors. An efficient way, without lose of statistical information, of reducing the number of third and fourth order cumulant matrices to be jointly diagonalized is proposed in this paper. The resulting approach, named eJADE(3,4)((2)), can be interpreted as an improvement of the eJADE(3,4)((1)) method. A performance comparison with classical methods is conducted in the context of MRS and EEG signals showing the good behavior of our technique.

We present a combined experimental/computational modeling approach aimed at studying the effects of transcranial Direct Current Stimulation (tDCS) on neuronal systems. More particularly, we introduce i) a neural mass model (neuronal population level) of the cerebral cortex and ii) a coupling model between the considered neuronal population and an externally-applied electric field. We then use this computational modeling approach to interpret evoked potentials (EPs) recorded from the somatosensory cortex of the rabbit under tDCS. Results showed that the model could accurately reproduce the time-course of actual EPs (polarity and latency of main peaks) recorded under control (i.e. “no tDCS stimulation”) condition. From real data, we also identified the “major” effects of tDCS on EPs in terms of shape modifications and we studied the necessary and sufficient conditions for which the model could reproduce these effects. We found that pyramidal cells should be depolarized (resp. hyperpolarized) in order to simulate the effects of anodal (resp. cathodal) currents. We also found that some interneurons are sensitive to externally-applied fields, indicating that modelling efforts need to also consider the role of these neurons to fully understand interactions between stimulation currents and underlying neuronal systems.

International audience; PURPOSE: Hyperkinetic seizures are most often considered to originate from prefrontal cortex. Recently however, it has been suggested that hyperkinetic seizures can be found in patients with temporal lobe seizures. The objective of this study was to determine the features of temporal epilepsy with hyperkinetic seizures and the functional anatomy of involved brain networks. METHODS: We retrospectively identified patients investigated by depth electrodes (SEEG) in whom hyperkinetic manifestations were proved to be linked to initial temporal lobe involvement. Seizure organisation was determined according to the "Epileptogenicity Index" (EI), a new way to quantify rapid discharges at seizure onset. RESULTS: We found 7 patients among 130 SEEG investigations that fulfilled the inclusion criteria. Most of the patients presented with hyperkinetic occurring (or predominating) during sleep. SEEG signal analysis demonstrated a common temporo-frontal network in which the temporal pole played a central role. Major involvement of the orbito-frontal cortex and to a lesser extent the cingulate gyrus was also a particular feature of these seizures. DISCUSSION: Seizures originating in the temporal lobe must be recognized as an important cause of hyperkinetic seizures. The temporal pole and its connexions with medio-basal prefrontal cortex represent the main structures involved in epileptogenic networks.

International audience; In the past, considerable effort has been devoted to the development of signal processing techniques aimed at characterizing brain connectivity from signals recorded from spatially-distributed regions during normal or pathological conditions. In this paper, three families of methods (linear and nonlinear regression, phase synchronization, and generalized synchronization) are reviewed. Their performances were evaluated according to a model-based methodology in which a priori knowledge about the underlying relationship between systems that generate output signals is available. This approach allowed us to relate the interdependence measures computed by connectivity methods to the actual values of the coupling parameter explicitly represented in various models of signal generation. Results showed that: (i) some of the methods were insensitive to the coupling parameter; (ii) results were dependent on signal properties (broad band versus narrow band); (iii) there was no "ideal" method, i.e., none of the methods performed better than the other ones in all studied situations. Nevertheless, regression methods showed sensitivity to the coupling parameter in all tested models with average or good performances. Therefore, it is advised to first apply these "robust" methods in order to characterize brain connectivity before using more sophisticated methods that require specific assumptions about the underlying model of relationship. In all cases, it is recommended to compare the results obtained from different connectivity methods to get more reliable interpretation of measured quantities with respect to underlying coupling. In addition, time-frequency methods are also recommended when coupling in specific frequency sub-bands ("frequency-locking") is likely to occur as in epilepsy.

International audience; We propose a new method for localizing intracerebral current sources at the origin of epileptic spikes from non-invasive EEG/MEG data. This method was designed to account for three main constrains. First, most relevant spike generation models assume that sources are extended, i.e. spatially distributed over a focal or muti-focal area. Second, the background activity of the brain also contributes to EEG/MEG signals recorded during epileptic events. In this context, it can be seen as a penalizing Gaussian and spatially correlated noise. Third the array manifold is usually corrupted by errors due to the complexity of the conduction head volume. The proposed method is an adaptation of the well-established MUSIC method, that allows for the localization of Extended Sources (ExSo) assuming that all current dipoles comprised in the extended source are synchronous. In addition, we use Higher Order (HO) statistics, which are asymptotically insensitive to a Gaussian noise of unknown spatial coherence and which offer a greater robustness with respect to modeling errors. The method is called 2q-ExSo-MUSIC (q ges 2) as it combines the ExSo-MUSIC principle with the use of HO statistics. Using computer simulations of EEG signals, it is shown to highly increase the performance of classical MUSIC-like algorithms when physiologically relevant models for current sources and for volume conduction are considered.

International audience; The pathophysiological interpretation of EEG signals recorded with depth electrodes [i.e., local field potentials (LFPs)] during interictal (between seizures) or ictal (during seizures) periods is fundamental in the presurgical evaluation of patients with drug-resistant epilepsy. Our objective was to explain specific shape features of interictal spikes in the hippocampus (observed in LFPs) in terms of cell- and network-related parameters of neuronal circuits that generate these events. We developed a neural network model based on "minimal" but biologically relevant neuron models interconnected through GABAergic and glutamatergic synapses that reproduce the main physiological features of the CA1 subfield. Simulated LFPs were obtained by solving the forward problem (dipole theory) from networks including a large number ( approximately 3000) of cells. Insertion of appropriate parameters allowed the model to simulate events that closely resemble actual epileptic spikes. Moreover, the shape of the early fast component ("spike'') and the late slow component ("negative wave'') was linked to the relative contribution of glutamatergic and GABAergic synaptic currents in pyramidal cells. In addition, the model provides insights about the sensitivity of electrode localization with respect to recorded tissue volume and about the relationship between the LFP and the intracellular activity of principal cells and interneurons represented in the network.

International audience; 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.

International audience; This paper deals with the interpretation of the macroscopic features of epileptic spikes recorded in human hippocampus based on a neural network model of the CA1 subfield. The network consists of principal cells (pyramidal neurons) and local interneurons and uses GABAergic and glutamatergic synapses. For pyramidal cells, this paper introduces a novel two-compartment model that was developed using published data and our own experimental data (intracellular recordings, in vitro). For interneurons, single-compartment models published elsewhere were implemented. The forward problem was solved to calculate the local field potential generated by the network. Our results show that: i) the dasiareducedpsila model approach allows for simulations including a relatively large number of cells, ii) for appropriate changes in model-parameters (related to synaptic transmission), the model can generate ldquospikerdquo events that closely resemble actual epileptic spikes and iii) some features of spike shape (amplitude, duration) can be explained by the degree of excitatory and inhibitory drive to pyramidal cells.

International audience; Electroencephalography (EEG) occupies an important place for studying human brain activity in general, and epileptic processes in particular, with appropriate time resolution. Scalp EEG or intracerebral EEG signals recorded in patients with drug-resistant partial epilepsy convey important information about epileptogenic networks that must be localized and understood prior to subsequent therapeutic procedures. However, this information, often subtle, is 'hidden' in the signals. It is precisely the role of signal processing to extract this information and to put it into a 'coherent and interpretable picture' that can participate in the therapeutic strategy. Nowadays, the panel of available methods is very wide depending on the objectives such as, for instance, the detection of transient epileptiform events, the detection and/or prediction of seizures, the recognition and/or the classification of EEG patterns, the localization of epileptic neuronal sources, the characterization of neural synchrony, the determination of functional connectivity, among others. The intent of this paper is to focus on a specific category of methods providing relevant information about epileptogenic networks from the analysis of spatial properties of EEG signals in the time and frequency domain. These methods apply to either interictal or ictal recordings and share the common objective of localizing the subsets of brain structures involved in both types of paroxysmal activity. Most of these methods were developed by our group and are routinely used during pre-surgical evaluation. Examples are detailed. Results, as well as limitations of the methods, are also discussed.

During the past decades, considerable effort has been devoted to the development of signal processing techniques aimed at quantifying the temporal evolution of the cross-correlation (in a wide sense) between signals recorded from spatially-distributed regions in order to characterize brain functional connectivity during normal or pathological (as in epilepsy) conditions. Besides linear methods introduced in the field of EEG analysis fifty years ago, a number of studies have been dedicated to the development of nonlinear methods, mostly because of the nonlinear nature of mechanisms at the origin of EEG signals. Recent studies showed the potential value of methods commonly used in nonlinear physics (see Chap. 15). Three families of methods (linear and nonlinear regression, phase synchronization, and generalized synchronization) are reviewed. Their performances are evaluated on the basis of a simulation model in which a coupling parameter can be tuned between populations of neurons generating bivariate EEG time-series. This evaluation is performed according to quantitative criteria. The main findings of this evaluation are the following. First, some of the methods are insensitive to the coupling parameter. Second, results were found to be dependent on signal properties. In particular, the broadening of the frequency band is a parameter that strongly influences the performances. Third, and generally speaking, there is no 'universal' method for measuring statistical couplings among signals. Indeed, none of the studied methods performs better than the other ones for the two studied situations (background and epileptic activity). Finally, linear and nonlinear regression methods were found to be sensitive to the coupling parameter in all situations and showed either average or good performances. This latter point leads the authors to conclude that these "robust" methods should be applied before using more sophisticated methods.

Focal (or partial) epilepsies are characterized by recurrent seizures generated in an abnormal region of the brain, the epileptogenic zone (EZ). Approximately 30% of cases are resistant to antiepileptic drugs. In this situation, surgical resection of the EZ is the only therapeutic option able to suppress seizures or, at least, to significantly reduce their frequency. The localization and the definition of the EZ are therefore crucial issues in epileptology and are addressed through detailed analysis of anatomo-functional data acquired in epileptic patients during pre-surgical evaluation. Among investigation methods used during this evaluation, intracerebral exploration remains the only way to directly record the electrophysiological activity (depth-EEG) from brain structures and to formulate hypotheses about their potential involvement in epileptogenic processes. In this context, a large number of studies have been dedicated to the analysis of depth-EEG signals. Based on the estimation of interdependences (i.e., statistical coupling) between signals recorded from distinct sites, some reports have demonstrated that the areas involved in the generation of seizures (defining the EZ) are characterized by synchronous oscillations at seizure onset (Bartolomei et al., 2005b; Bartolomei et al., 2001; Bartolomei et al., 2004b; Bartolomei et al., 1999; Duckrow and Spencer, 1992; Gotman and Levtova, 1996; Le Van Quyen et al., 1998; Lieb et al., 1987). The “synchronization” of activities recorded from brain structures is therefore an important phenomenon that may be used for identifying epileptogenic networks (i.e., promoting the initiation of seizures). Other studies based on nonlinear associations in multivariate signals (Guye et al., 2006) have also reported that long distance functional connectivity is dramatically altered during seizures, or indicated that the topology of networks changes as ictal activity develops (Ponten et al., 2007).

International audience; In many applications which make use of EEG to investigate brain functions, a central question is often to relate the recorded signals to the spatio-temporal organization of the underlying neuronal sources of activity. A modeling attempt to quantitatively investigate this imperfectly known relationship is reported. The proposed plausible model of EEG generation relies on an accurate representation of the neuronal sources of activity. It combines both an anatomically realistic description of the spatial features of the sources (convoluted dipole layer) and a physiologically relevant description of their temporal activities (coupled neuronal populations). The model was used in the particular context of epileptiform activity (interictal spikes) to interpret simultaneously generated scalp and intracerebral EEG. Its integrative properties allowed for the bridging between source-related parameters (spatial extent, location, synchronization) and the properties of resulting EEG signals (amplitude of spikes, amplitude gradient along intracerebral electrodes, topography over scalp electrodes). The sensitivity of both recording modalities to source-related parameters was also studied. The model confirmed that the cortical area involved in interictal spikes is rather large. Its relative location with respect to recording electrodes was found to strongly influence the properties of EEG signals as the source geometry is a critical parameter. The influence, on simulated signals, of the synchronization degree between neuronal populations within the epileptic source was also investigated. The model revealed that intracerebral EEG can reflect epileptic activities corresponding to weak synchronization between neuronal populations of the epileptic patch. These results, as well as the limitations of the model, are discussed.

International audience; The identification of brain regions generating seizures ('epileptogenic zone', EZ) in patients with refractory partial epilepsy is crucial prior to surgery. During pre-surgical evaluation, this identification can be performed from the analysis of intracerebral EEG. In particular, the presence of high-frequency oscillations, often referred to as 'rapid discharges', has long been recognized as a characteristic electrophysiological pattern of the EZ. However, to date, there has been no attempt to make use of this specific pattern to quantitatively evaluate the degree of epileptogenicity in recorded structures. A novel quantitative measure that characterizes the epileptogenicity of brain structures recorded with depth electrodes is presented. This measure, called 'Epileptogenicity Index' (EI), is based on both spectral (appearance of fast oscillations replacing the background activity) and temporal (delay of appearance with respect to seizure onset) properties of intracerebral EEG signals. EI values were computed in mesial and lateral structures of the temporal lobe in a group of 17 patients with mesial temporal lobe epilepsy (MTLE). Statistically high EI values corresponded to structures involved early in the ictal process and producing rapid discharges at seizure onset. In all patients, these high values were obtained in more than one structure of the temporal lobe region. In the majority of patients, highest EI values were computed from signals recorded in mesial structures. In addition, when averaged over patients, EI values gradually decreased from structure to structure. For lateral neocortex, higher EI values were found in patients with normal MRI, in contrast with patients with hippocampal sclerosis. In this former sub-group of patients, a greater number of epileptogenic structures was also found. A statistically significant correlation was found between the duration of epilepsy and the number of structures disclosing high epileptogenicity suggesting that MTLE is a gradually evolving process in which the epileptogenicity of the temporal lobe tends to increase with time.

It has been shown that the analysis of electroencephalographic (EEG) signals submitted to an appropriate external stimulation (active paradigm) is efficient with respect to anticipating epileptic seizures [S. Kalitzin et al., Clin. Neurophysiol. 116, 718 (2005)]. To better understand how an active paradigm is able to detect properties of EEG signals by means of which proictal states can be identified, we performed a simulation study using a computational model of seizure generation of a hippocampal network. Applying the active stimulation methodology, we investigated (i) how changes in model parameters that lead to a transition from the normal ongoing EEG to an ictal pattern are reflected in the properties of the simulated EEG output signals and (ii) how the evolution of neuronal excitability towards seizures can be reconstructed from EEG data using an active paradigm, rather than passively, using only ongoing EEG signals. The simulations indicate that a stimulation paradigm combined with appropriate analytical tools, as proposed here, may yield information about the change in excitability that precedes the transition to a seizure. Such information is apparently not fully reflected in the ongoing EEG activity. These findings give strong support to the development and application of active paradigms with the aim of predicting the occurrence of a transition to an epileptic seizure.

Publisher Summary Temporal lobe epilepsy (TLE) is one of the most common forms of partial epilepsy, which is characterized by recurrent seizures originating in one or several anatomic divisions of the temporal lobe and propagating through interconnected neuronal networks within or outside the temporal region. This chapter presents some insights into transition mechanisms to ictal activity in human TLE that can be drawn from this modeling approach. It begins with a brief historical background on neuronal population models and presents some general features. Following this, it describes electroencephalographic signals recorded during transition from interictal to ictal activity along with the recording techniques namely stereoelectroencephalography (SEEG). These signals constitute input data to models, which are presented in this chapter. The first model is a generic model of neuronal population used to interpret transition from normal background activity to spiking activity. Thereafter, the chapter presents a second model adapted to the organization of neurons and interneurons in the hippocampus and emphasizes on the capacity of this model to explain typical activities recorded from this structure in patients. Finally, it highlights some perspectives related to modeling approaches aimed at improving the understanding of basic processes underlying epileptic behavior.

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International audience; In this paper, a high-resolution method for solving potentially ill-posed inverse problems is proposed. This method named FO-D-MUSIC allows for localization of brain current sources with unconstrained orientations from surface electroencephalographic (EEG) or magnetoencephalographic (MEG) data using spherical or realistic head geometries. The FO-D-MUSIC method is based on the following: 1) the separability of the data transfer matrix as a function of location and orientation parameters, 2) the fourth-order (FO) virtual array theory, and 3) the deflation concept extended to FO statistics accounting for the presence of potentially but not completely statistically dependent sources. Computer results display the superiority of the FO-D-MUSIC approach in different situations (very closed sources, small number of electrodes, additive Gaussian noise with unknown spatial covariance, etc.) compared to classical algorithms.

International audience; This paper describes and compares two classical methods for the detection of neuron groups which exhibit synchronized firings in multivariate spike trains. These methods were compared on experimental and randomized data corresponding to the firing activity of 104 neurons located in motor, premotor, and parietal cortices in a monkey during movement tasks. Both methods exhibited high false positive rates in randomized data, but results showed that this rate can be advantageously reduced with a simple postprocessing. Otherwise, one method permitted to detect a significant number of synchronized groups of neurons related to the behavioral task.

This paper describes a macroscopic neurophysiologically-relevant model of the entorhinal cortex (EC), a brain structure largely involved in human mesio-temporal lobe epilepsy. This model is intervalidated in the experimental framework of ictogenesis animal model (isolated guinea-pig brain perfused with bicuculline). Using sensitivity and stability analysis, an investigation of model parameters related to GABA neurotransmission (recognized to be involved in epileptic activity generation) was performed. Based on spectral and statistical features, simulated signals generated from the model for multiple GABAergic inhibition related parameter values were classified into eight classes of activity. Simulated activities showed striking agreement (in terms of realism) with typical epileptic activities identified in field potential recordings performed in the experimental model. From this combined computational/experimental approach, hypotheses are suggested about the role of the different types of GABAergic neurotransmission in the generation of epileptic activities in EC.

International audience; OBJECTIVE: Our aim was to investigate relationships between heterotopic and remote cortical structures at seizure initiation, in a patient with bilateral periventricular nodular heterotopias (BPNH) explored by intracerebral electrodes. METHODS: Stereoelectroencephalography (SEEG) was performed in a man with BPNH and refractory epilepsy to investigate the hypothesis of right temporal lobe epilepsy and the possible involvement of heterotopic structures during seizures. SEEG signals were analyzed with quantification of functional coupling between different brain structures during seizures, using nonlinear regression. We have used Z-score transformation of correlation values to reflect the change from the preictal period. Relationships between BPNH and cortical structures were investigated using analysis of stimulation-induced potentials. RESULTS: Three spontaneous seizures were recorded and analyzed. Signal analysis of interdependencies in two seizures demonstrated a large initial network involving both heterotopia and cortical structures. Stimulations of heterotopia induced responses in remote cortical structures. CONCLUSIONS: Distinct epileptogenic networks were identified, in which leader structures were either the heterotopic or the mesial temporal structures, with functional connections between heterotopic and cortical areas. SIGNIFICANCE: These results confirm that a vast epileptogenic network, including heterotopic and cortical neurons, may be responsible for seizure generation in BPNH. This may explain certain surgical failures in this group.

Stereoelectroencephalography (depth-EEG signals) is a presurgical investigation technique of drug-resistant partial epilepsy, in which multiple sensor intracerebral electrodes are used to directly record brain electrical activity. In order to interpret depth-EEG signals, we developed an extended source model which connects two levels of representation: (1) a distributed current dipole model which describes the spatial distribution of neuronal sources; (2) a model of coupled neuronal populations which describes their temporal dynamics. From this extended source model, depth-EEG signals were simulated from the forward solution at each electrode sensor located inside the brain. Results showed that realistic transient epileptiform activities (spikes) are obtained under specific conditions in the model in terms of degree of coupling between neuronal populations and spatial extent of the source. In particular, the cortical area involved in the generation of epileptic spikes was estimated to vary from 18 to 25 cm2, for brain conductivity values ranging from 30 to 35 x 10(-5) S/mm, for high coupling degree between neuronal populations and for a volume conductor model that accounts for the three main tissues of the head (brain, skull, and scalp). This study provides insight into the relationship between spatio-temporal properties of cortical neuronal sources and depth-EEG signals.

International audience; In epileptic patients candidate to surgery, the interpretation of electrophysiological signals recorded invasively (depth-EEG) and non-invasively (scalp-EEG) is a crucial issue to determine epileptogenic network and to define subsequent therapeutic strategy. This issue is addressed in this work through realistic modeling of both scalp-EEG and depth-EEG signals. The model allows for studying the influence, on signals, of source-related parameters leading to the generation of epileptic transient activity (interictal spikes). This parametric study is based on a variety of scenarios in which either spatial or temporal features of the sources of activity are modified. Statistical quantities measured on simulated signals allow for better understanding of the influence of source-related parameters on the information conveyed by these signals, collected from scalp or depth electrodes.

International audience; Magnetoencephalography (MEG) sensors are capable of recording the tiny magnetic activity from the brain. They can be constituted of either magnetometers or gradiometers that respectively record the magnetic field or its gradient. In this paper, we present a framework for constructing realistic MEG signals. This framework can be used to test different probe configurations and source localization algorithms. The methodology of generation of synthetic signals is presented, and synthetic signals are compared to real signals. Paroxysmal activity generated with this model and originating from a deep cerebral source is determined with two different localization algorithms. Preliminary results show that gradiometers even with a short baseline perform close to magnetometer and that the use of hybrid systems should be further investigated.

International audience; In the context of pre-surgical evaluation of epileptic patients, depth-EEG signals constitute a valuable source of information to characterize the spatiotemporal organization of paroxysmal interictal and ictal activities, prior to surgery. However, interpretation of these very complex data remains a formidable task. Indeed, interpretation is currently mostly qualitative and efforts are still to be produced in order to quantitatively assess pathophysiological information conveyed by signals. The proposed EEG model-based approach is a contribution to this effort. It introduces both a physiological parameter set which represents excitation and inhibition levels in recorded neuronal tissue and a methodology to estimate this set of parameters. It includes Sequential Monte Carlo nonlinear filtering to estimate hidden state trajectory from EEG and Particle Swarm Optimization to maximize a likelihood function deduced from Monte Carlo computations. Simulation results illustrate what it can be expected from this methodology.

International audience; Objective: The objective of this work is the determination of the lateralization of the epileptic seizure onset zone using the scalp EEG signal processing. Methods: A comprehensive method based on the evaluation of the evolution of the correlation coefficients computed between bipolar channels (longitudinal montage) was applied to 43 patients (87 seizures). The correlation coefficients are estimated by a nonlinear regression analysis. The methodology that leads to the lateralization is based on several processing steps: segmentation, seizure onset determination and then lateralization. Results: Results show that the mean level of the nonlinear correlation values computed between EEG channels at the seizure onset time is significantly higher on the side of the beginning of a seizure. Conclusions: The side of the seizure onset was determined for about 80–90% of the seizures studied with a satisfactory high reproducibility level. Significance: Comparison of nonlinear correlation coefficients between both sides of the brain leads to the determination of the side of seizure onset.

International audience; The objective of this work is to characterize evolution of the similarities in the dynamic of epileptic seizures, using non invasive EEG. A comprehensive method is presented and applied to temporal lobe epileptic seizures. It is based on the degree of correlation between scalp electrodes at seizure onset, determined by a nonlinear regression analysis. Results show that reproducible patterns may be extracted from different seizure of the same patient and confirm the existence of different subtypes of temporal lobe epilepsy.

Brain functional connectivity can be characterized by the temporal evolution of correlation between signals recorded from spatially-distributed regions. It is aimed at explaining how different brain areas interact within networks involved during normal (as in cognitive tasks) or pathological (as in epilepsy) situations. Numerous techniques were introduced for assessing this connectivity. Recently, some efforts were made to compare methods performances but mainly qualitatively and for a special application. In this paper, we go further and propose a comprehensive comparison of different classes of methods (linear and nonlinear regressions, phase synchronization (PS), and generalized synchronization (GS)) based on various simulation models. For this purpose, quantitative criteria are used: in addition to mean square error (MSE) under null hypothesis (independence between two signals) and mean variance (MV) computed over all values of coupling degree in each model, we introduce a new criterion for comparing performances. Results show that the performances of the compared methods are highly depending on the hypothesis regarding the underlying model for the generation of the signals. Moreover, none of them outperforms the others in all cases and the performance hierarchy is model-dependent.

International audience; Two high resolution methods solving inverse problems potentially ill-posed, named 4-MUSIC and 4-RapMUSIC, are proposed. They allow for localization of brain current sources with unconstrained orientations from surface electro-or magneto-encephalographic data using spherical or realistic head geometries. The 4-MUSIC and 4-RapMUSIC methods are based on i) the separability of the data transfer matrix as a function of location and orientation parameters and ii) the fourth order (FO) virtual array theory. In addition, 4-RapMUSIC uses the deflation concept extended to FO statistics accounting for the presence of potentially but not totally coherent sources. Computer results display the superiority of the 4-RapMUSIC approach in different situations (two closed sources, additive Gaussian noise with unknown spatial covariance, ...) especially over classical algorithms.

International audience; The context of this work is the interpretation of depth-EEG signals recorded in epileptic patients. This study focuses on the relationship between spatial and temporal properties of neuronal sources and depth-EEG signals observed along intracerebral electrodes (source/sensor relationship). We developed an extended source model which connects two levels of representation: a model of coupled neuronal populations and a distributed current dipole model. This model was used to simulate epileptic spiking depth-EEG signals from the forward solution at each intracerebral sensor location. Results showed that realistic spikes were obtained in the model under two specific conditions: a sufficiently large spatial extension of the neocortical source and a high degree of coupling between activated neuronal populations composing this extended source.

International audience; For the past decades, numerous works have been dedicated to the development of signal processing methods aimed at measuring the degree of association between electroencephalographic (EEG) signals. This interdependency parameter, which may be defined in various ways, is often used to characterize a functional coupling between different brain structures or regions during either normal or pathological processes. In this paper, we focus on the time-frequency characterization of the interdependency between signals. Particularly, we propose a novel estimator of the linear relationship between nonstationary signals based on the cross correlation of narrow band filtered signals. This estimator is compared to a more classical estimator based on the coherence function. In a simulation framework, results show that it may exhibit better statistical performances (bias and variance or mean square error) when a priori knowledge about time delay between signals is available. On real data (intracerebral EEG signals), results show that this estimator may also enhance the readability of the time-frequency representation of relationship and, thus, can improve the interpretation of nonstationary interdependencies in EEG signals. Finally, we illustrate the importance of characterizing the relationship in both time and frequency domains by comparing with frequency-independent methods (linear and nonlinear).

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. Methods: Intracerebral recordings (SEEG method) of seizures from 11 patients with mesial TLE were analyzed. Seizures were classified according to patterns of onset: pattern 1 was the emergence of a low-frequency, high-amplitude rhythmic spiking followed by a tonic discharge, and pattern 2 was the emergence of a tonic discharge in the mesial structures. A nonlinear measure of SEEG signal interdependencies was used to evaluate the functional couplings occurring between hippocampus (Hip) and EC at seizure onset. MRI volumetric analysis was performed by using a T1-weighted three-dimensional gradient-echo sequence in TLE patients and 12 healthy subjects. Results: Significant interactions between Hip and Ec were quantified at seizure onset. The EC was found to be the leader structure in most of the pattern 2 seizures. Volumetric measurements of EC demonstrated an atrophy in 63% of patients ipsilateral to the epileptic side. A significant correlation between the strength of EC–Hip coupling and the degree of atrophy was found. In addition, in those patients that had a normal EC volume, the EC was never the leader structure in Ec–Hip coupling. Conclusions: These results validate the potential role of volumetry to predict the epileptogenesis of the EC in patients with hippocampal sclerosis and MTLE.

Electrical stimulation of the temporal cortex in patients with epilepsy sometimes elicits experiential phenomena such as recollection of vivid memories. The neurophysiological substrate of such phenomena is poorly understood. Furthermore, the relation between the site of stimulation and the type of memory elicited has only recently started to be investigated. We investigated these issues in patient FGA who had intracerebral electrodes stereotaxically implanted in the right temporal lobe for investigation of drug-resistant epilepsy. We report the results of electrical stimulations of the perirhinal region. Two stimulations elicited experiential phenomena consisting of visual memories that belonged to FGA's past, but which were not related to any particular episode. These visual memories consisted of objects or of details of objects. These two stimulations were contrasted with other stimulations in the same subhippocampal region. Cross-correlation analysis of the depth-EEG signals filtered in frequency sub-bands revealed that experiential phenomena occurred only when the various brain structures involved in the after-discharge were synchronized in the theta range. These structures included the perirhinal region, the hippocampus, other limbic structures as well as a primary visual area. Our results suggest that recollection of vivid memory after electric stimulation of the cortex may rely on wide networks of brain areas that transiently synchronize. These results also highlight the role of the perirhinal region in human memory. Experiential phenomena are rarely obtained after brain stimulation. Replication of these results is thus required due to the small number of observations reported.

Introduction The mission of the European HIVE (Hyperinteraction viability experiments) project is to develop new stimulation paradigms and to create a new generation of non-invasive brain stimulation technologies. The transcranial current stimulation (tCS) is a non-invasive brain stimulation technique that has been successfully applied in both basic and clinical researches. Whereas transcranial direct-current stimulation (tDCS) is capable of inducing changes in neuronal membrane potentials in a polarity-dependent way, transcranial alternating-current stimulation (tACS) has been proposed to interact with ongoing cortical oscillations enhancing or diminishing specific frequencies of cortical neural activities. Nevertheless, little is known about tCS effects on cortical circuits and its implications in sensory perception processes. Objectives The aim of the HIVE project is to investigate the biophysics of non-invasive brain stimulation at the theoretical, computational and experimental level – both in humans and animals. Specifically, the objective of this study was to investigate tDCS effects on the plastic properties of the somatosensory cortex, to provide a new animal model to study the effects of tCS in learning, and to test for the viability of hyperinteraction experiments, implying the direct generation of perceptions and, hence, transmission of information, by transcranial stimulation of the cerebral cortex. Materials and methods Rabbits were prepared for the chronic recording of local field potentials (LFP) in the somatosensory cortex (SS) in response to whisker and/or ventroposterior medial (VPM) thalamic nucleus stimulations in the presence of tDCS and tACS. A second group of animals was prepared for classical eyeblink conditioning and simultaneous tCS. Results tDCS and tACS applied over the SS modulated cerebral cortical processes subsequent to the localized stimulation of the whisker pad or of the corresponding area of the VPM nucleus. Longer stimulation periods indicate that post-stimulation effects were only observed in the SS after cathodal tDCS. Consistently with this polarity-specific effects, the acquisition of a classical eyeblink conditioning was potentiated or depressed by the application of anodal or cathodal tDCS respectively, when stimulation of whisker pad was used as a conditioned stimulus (CS). In addition, we noticed that tACS (100 ms, 30 Hz) can successfully substitute for whisker CS during an associative learning task. tACS-CS induced conditioned responses similar to those observed when a direct stimulation of the whisker pad was carried out. We also studied the putative mechanisms underlying immediate and after-effect of tDCS observed in the SS. Pairs of pulses applied to the thalamic VPM nucleus suggested that tDCS modifies thalamocortical synapses at presynaptic sites. In addition, blocking activation of adenosine A1 receptors prevented the long-term depression evoked in the SS following cathodal tDCS. Conclusion Results reported here confirm earlier studies in humans regarding the effects of tDCS and tACS on the cerebral cortex, highlighting the potential of this technique for modulating associative learning. In addition, it is shown the participation of adenosine A1 receptors in tDCS actions on cortical circuits. We also show that peripheral whisker stimulation can be substituted by tACS as CS when the proper stimulation frequencies are applied. Acknowledgements Supported by grants BFU2011-29089, P07-CVI-02487 to J.M.D.-G., and BFU2011-29286, P07-CVI-02686 to A.G., from Spain. In addition, this work was funded by the EU FP7 FET-Open HIVE (222079) Project.