Detection of seizure onset in childhood absence epilepsy

OBJECTIVE

This study aims to detect the seizure onset, in childhood absence epilepsy, as early as possible. Indeed, interfering with absence seizures with sensory simulation has been shown to be possible on the condition that the stimulation occurs soon enough after the seizure onset.

METHODS

We present four variations (two supervised, two unsupervised) of an algorithm designed to detect the onset of absence seizures from 4 scalp electrodes, and compare their performance with that of a state-of-the-art algorithm. We exploit the characteristic shape of spike-wave discharges to detect the seizure onset. Their performance is assessed on clinical electroencephalograms from 63 patients with confirmed childhood absence epilepsy.

RESULTS

The proposed approaches succeed in early detection of the seizure onset, contrary to the classical detection algorithm. Indeed, the results clearly show the superiority of the proposed methods for small delays of detection, under 750 ms from the onset.

CONCLUSION

The performance of the proposed unsupervised methods is equivalent to that of the supervised ones. The use of only four electrodes makes the pipeline suitable to be embedded in a wearable device.

SIGNIFICANCE

The proposed pipelines perform early detection of absence seizures, which constitutes a prerequisite for a closed-loop system.

Long-term Effect of Multichannel tDCS Protocol in Patients with Central Cortex Epilepsies Associated with Epilepsia Partialis Continua

Epilepsia partialis continua (EPC) is a rare type of focal motor status epilepticus that causes continuous muscle jerking in a specific part of the body. Experiencing this type of seizure, along with other seizure types, such as focal motor seizures and focal to bilateral tonic-clonic seizures, can result in a disabling situation. Non-invasive brain stimulation methods like transcranial direct current stimulation (tDCS) show promise in reducing seizure frequency (SF) when medications are ineffective. However, research on tDCS for EPC and related seizures is limited. We evaluated personalized multichannel tDCS in drug-resistant EPC of diverse etiologies for long-term clinical efficacy We report three EPC patients undergoing a long-term protocol of multichannel tDCS. The patients received several cycles (11, 9, and 3) of five consecutive days of stimulation at 2 mA for 2 × 20 min, targeting the epileptogenic zone (EZ), including the central motor cortex with cathodal electrodes. The primary measurement was SF changes. In three cases, EPC was due to Rasmussen's Encephalitis (case 1), focal cortical dysplasia (case 2), or remained unknown (case 3). tDCS cycles were administered over 6 to 22 months. The outcomes comprised a reduction of at least 75% in seizure frequency for two patients, and in one case, a complete cessation of severe motor seizures. However, tDCS had no substantial impact on the continuous myoclonus characterizing EPC. No serious side effects were reported. Long-term application of tDCS cycles is well tolerated and can lead to a considerable reduction in disabling seizures in patients with various forms of epilepsy with EPC.

Improving Fast Ripples Recording with Model-Guided Design of Microelectrodes

OBJECTIVE

Microelectrodes allow for the recording of neural activities with a high spatial resolution. However, their small sizes result in high impedance causing high thermal noise and poor signal-to-noise ratio. In drug-resistant epilepsy, the accurate detection of Fast Ripples (FRs; 250-600 Hz) can help in the identification of epileptogenic networks and Seizure Onset Zone (SOZ). Consequently, good-quality recordings are instrumental to improve surgical outcome. In this work, we propose a novel model-based approach for the design of microelectrodes optimized for FRs recording.

METHODS

A 3D microscale computational model was developed to simulate FRs generated in the hippocampus (CA1 subfield). It was coupled with a model of the Electrode-Tissue Interface (ETI) that accounts for the biophysical properties of the intracortical microelectrode. This hybrid model was used to analyze the microelectrode geometrical (diameter, position, and direction) and physical (materials, coating) characteristics and their impact on recorded FRs. For model validation, experimental signals (local field potentials, LFPs) were recorded from CA1 using different electrode materials: stainless steel (SS), gold (Au) and Au coated with poly(3,4-ethylene dioxythiophene) /Poly(styrene sulfonate) (Au:PEDOT/PSS).

RESULTS

results indicated that a radius between 65 and 120 μm for a wire microelectrode is the most optimal for recording FRs. In addition, in silico and in vivo quantified results showed a possible improvement in FRs observability using PEDOT/PSS coated microelectrodes.

CONCLUSION

the optimization of the design of microelectrodes for FRs recording can improve the observability and detectability of FRs which are a recognized marker of epileptogenicity.

SIGNIFICANCE

This model-based approach can assist in the design of hybrid electrodes (micro, macro) that can be used in the presurgical evaluation of epileptic patients with drug-resistant epilepsy.

HD-EEG for tracking sub-second brain dynamics during cognitive tasks

This work provides the community with high-density Electroencephalography (HD-EEG, 256 channels) datasets collected during task-free and task-related paradigms. It includes forty-three healthy participants performing visual naming and spelling tasks, visual and auditory naming tasks and a visual working memory task in addition to resting state. The HD-EEG data are furnished in the Brain Imaging Data Structure (BIDS) format. These datasets can be used to (i) track brain networks dynamics and their rapid reconfigurations at sub-second time scale in different conditions, (naming/spelling/rest) and modalities, (auditory/visual) and compare them to each other, (ii) validate several parameters involved in the methods used to estimate cortical brain networks through scalp EEG, such as the open question of optimal number of channels and number of regions of interest and (iii) allow the reproducibility of results obtained so far using HD-EEG. We hope that delivering these datasets will lead to the development of new methods that can be used to estimate brain cortical networks and to better understand the general functioning of the brain during rest and task. Data are freely available from https://openneuro.org.

Measurement(s)	brain measurement • cognitive behavior trait
Technology Type(s)	electroencephalography (EEG)
Factor Type(s)	task • age • sex
Sample Characteristic - Organism	Homo sapiens

Machine-accessible metadata file describing the reported data: 10.6084/m9.figshare.13560311

Effect of connectivity measures on the identification of brain functional core network at rest

Magneto/Electro-encephalography (M/EEG) source connectivity is an emergent tool to identify brain networks with high time/space resolution. Here, we aim to identify the brain core network (s-core decomposition) using dense-EEG. We also evaluate the effect of the functional connectivity methods used and more precisely the effect of the correction for the so-called source leakage problem. Two connectivity measures were evaluated: the phase locking value (PLV) and phase lag index (PLI) that supposed to deal with the leakage problem by removing the zero-lag connections. Both methods were evaluated on resting state dense-EEG signals recorded from 19 healthy participants. Core networks obtained by each method was compared to those computed using fMRI from 487 healthy participants at rest (from the Human Connectome Project - HCP). The correlation between networks obtained by EEG and fMRI was used as performance criterion. Results show that PLV networks are closer to fMRI networks with significantly higher correlation values with fMRI networks, than PLI networks. Results suggest caution when selecting the functional connectivity methods and mainly methods that remove the zero-lag connections as it can severely affect the network characteristics. The choice of functional connectivity measure is indeed crucial not only in cognitive neuroscience but also in clinical neuroscience.

Dynamic reshaping of functional brain networks during visual object recognition

OBJECTIVE

Emerging evidence shows that the modular organization of the human brain allows for better and efficient cognitive performance. Many of these cognitive functions are very fast and occur in a sub-second time scale such as the visual object recognition.

APPROACH

Here, we investigate brain network modularity while controlling stimuli meaningfulness and measuring a participant's reaction time. We particularly raised two questions: i) does the dynamic brain network modularity change during the recognition of meaningful and meaningless visual images? And (ii) is there a correlation between network modularity and the reaction time of the participants? To tackle these issues, we collected dense-electroencephalography (EEG, 256 channels) data from 20 healthy human subjects performing a cognitive task consisting of naming meaningful (tools, animals…) and meaningless (scrambled) images. Functional brain networks in both categories were estimated at the sub-second time scale using the EEG source connectivity method. By using multislice modularity algorithms, we tracked the reconfiguration of functional networks during the recognition of both meaningful and meaningless images.

MAIN RESULTS

Results showed a difference in the module's characteristics of both conditions in term of integration (interactions between modules) and occurrence (probability on average of any two brain regions to fall in the same module during the task). Integration and occurrence were greater for meaningless than for meaningful images. Our findings revealed also that the occurrence within the right frontal regions and the left occipito-temporal can help to predict the ability of the brain to rapidly recognize and name visual stimuli.

SIGNIFICANCE

We speculate that these observations are applicable not only to other fast cognitive functions but also to detect fast disconnections that can occur in some brain disorders.

Identification of epileptogenic networks from dense EEG: A model-based study

Epilepsy is a network disease. Identifying the epileptogenic networks from noninvasive recordings is a challenging issue. In this context, M/EEG source connectivity is a promising tool to identify brain networks with high temporal and spatial resolution. In this paper, we analyze the impact of the two main factors that intervene in EEG source connectivity processing: i) the algorithm used to solve the EEG inverse problem and ii) the method used to measure the functional connectivity. We evaluate three inverse solutions algorithms (dSPM, wMNE and cMEM) and three connectivity measures (r(2), h(2) and MI) on data simulated from a combined biophysical/physiological model able to generate realistic interictal epileptic spikes reflected in scalp EEG. The performance criterion used here is the similarity between the network identified by each of the inverse/connectivity combination and the original network used in the source model. Results show that the choice of the combination has a high impact on the identified network. Results suggest also that nonlinear methods (nonlinear correlation coefficient and mutual information) for measuring the connectivity are more efficient than the linear one (the cross correlation coefficient). The dSPM as inverse solution shows the lowest performance compared to cMEM and wMNE.

Computational modeling of high frequency oscillations recorded with clinical intracranial macroelectrodes

High Frequency Oscillations (HFOs) are a potential biomarker of epileptogenic regions. They have been extensively investigated in terms of automatic detection, classification and feature extraction. However, the mechanisms governing the generation of HFOs as well as the observability conditions on clinical intracranial macroelectrodes remain elusive. In this paper, we propose a novel physiologically-relevant macroscopic model for accurate simulation of HFOs as invasively recorded in epileptic patients. This model accounts for both the temporal and spatial properties of the cortical patch at the origin of epileptiform activity. Indeed, neuronal populations are combined with a 3D geometrical representation to simulate an extended epileptic source. Then, by solving the forward problem, the contributions of neuronal population signals are projected onto intracerebral electrode contacts. The obtained signals are qualitatively and quantitatively compared to real HFOs, and a relationship is drawn between macroscopic model parameters such as synchronization and spatial extent on the one hand, and HFO features such as the wave and fast ripple (200-600 Hz) components, on the other hand.

Functional connectivity disruptions correlate with cognitive phenotypes in Parkinson's disease

Cognitive deficits in Parkinson's disease are thought to be related to altered functional brain connectivity. To date, cognitive-related changes in Parkinson's disease have never been explored with dense-EEG with the aim of establishing a relationship between the degree of cognitive impairment, on the one hand, and alterations in the functional connectivity of brain networks, on the other hand.

This study was aimed at identifying altered brain networks associated with cognitive phenotypes in Parkinson's disease using dense-EEG data recorded during rest with eyes closed. Three groups of Parkinson's disease patients (N = 124) with different cognitive phenotypes coming from a data-driven cluster analysis, were studied: G1) cognitively intact patients (63), G2) patients with mild cognitive deficits (46) and G3) patients with severe cognitive deficits (15). Functional brain networks were identified using a dense-EEG source connectivity method. Pairwise functional connectivity was computed for 68 brain regions in different EEG frequency bands. Network statistics were assessed at both global (network topology) and local (inter-regional connections) level.

Results revealed progressive disruptions in functional connectivity between the three patient groups, typically in the alpha band. Differences between G1 and G2 (p < 0.001, corrected using permutation test) were mainly frontotemporal alterations. A statistically significant correlation (ρ = 0.49, p < 0.001) was also obtained between a proposed network-based index and the patients' cognitive score. Global properties of network topology in patients were relatively intact.

These findings indicate that functional connectivity decreases with the worsening of cognitive performance and loss of frontotemporal connectivity may be a promising neuromarker of cognitive impairment in Parkinson's disease.

•

We test the use of dense-EEG to identify altered brain networks associated with cognitive phenotypes in Parkinson's disease.

•

The functional connectivity decreases with the worsening of cognitive performance

•

The loss of frontotemporal connectivity may be a promising neuromarker of cognitive impairment in Parkinson's disease.

Brain (Hyper)Excitability Revealed by Optimal Electrical Stimulation of GABAergic Interneurons

BACKGROUND

Neurological disorders are often characterized by an excessive and prolonged imbalance between neural excitatory and inhibitory processes. An ubiquitous finding among these disorders is the disrupted function of inhibitory GABAergic interneurons.

OBJECTIVE

The objective is to propose a novel stimulation procedure able to evaluate the efficacy of inhibition imposed by GABAergic interneurons onto pyramidal cells from evoked responses observed in local field potentials (LFPs).

METHODS

Using a computational modeling approach combined with in vivo and in vitro electrophysiological recordings, we analyzed the impact of electrical extracellular, local, bipolar stimulation (ELBS) on brain tissue. We implemented the ELBS effects in a neuronal population model in which we can tune the excitation-inhibition ratio and we investigated stimulation-related parameters. Computer simulations led to sharp predictions regarding: i) the shape of evoked responses as observed in local field potentials, ii) the type of cells (pyramidal neurons and interneurons) contributing to these field responses and iii) the optimal tuning of stimulation parameters (intensity and frequency) to evoke meaningful responses. These predictions were tested in vivo (mouse). Neurobiological mechanisms were assessed in vitro (hippocampal slices).

RESULTS

Appropriately-tuned ELBS allows for preferential activation of GABAergic interneurons. A quantitative neural network excitability index (NNEI) is proposed. It is computed from stimulation-induced responses as reflected in local field potentials. NNEI was used in four patients with focal epilepsy. Results show that it can readily reveal hyperexcitable brain regions.

CONCLUSION

Well-tuned ELBS and NNEI can be used to locally probe brain regions and quantify the (hyper)excitability of the underlying brain tissue.

Dynamic changes of depolarizing GABA in a computational model of epileptogenic brain: Insight for Dravet syndrome

Abnormal reemergence of depolarizing GABAA current during postnatal brain maturation may play a major role in paediatric epilepsies, Dravet syndrome (DS) being among the most severe. To study the impact of depolarizing GABA onto distinct patterns of EEG activity, we extended a neural mass model as follows: one sub-population of pyramidal cells was added as well as two sub-populations of interacting interneurons, perisomatic-projecting interneurons (basket-like) with fast synaptic kinetics GABAA (fast, I1) and dendritic-projecting interneurons with slow synaptic kinetics GABAA (slow, I2). Basket-like cells were interconnected to reproduce mutual inhibition mechanisms (I1➔I1). The firing rate of interneurons was adapted to mimic the genetic alteration of voltage gated sodium channels found in DS patients, SCN1A(+/-). We implemented the "dynamic depolarizing GABAA" mediated post-synaptic potential in the model, as some studies reported that the chloride reversal potential can switch from negative to more positive value depending on interneuron activity. The "shunting inhibition" promoted by GABAA receptor activation was also implemented. We found that increasing the proportion of depolarizing GABAA mediated IPSP (I1➔I1 and I1➔P) only (i.e., other parameters left unchanged) was sufficient to sequentially switch the EEG activity from background to (1) interictal isolated polymorphic epileptic spikes, (2) fast onset activity, (3) seizure like activity and (4) seizure termination. The interictal and ictal EEG patterns observed in 4 DS patients were reproduced by the model via tuning the amount of depolarizing GABAA postsynaptic potential. Finally, we implemented the modes of action of benzodiazepines and stiripentol, two drugs recommended in DS. Both drugs blocked seizure-like activity, partially and dose-dependently when applied separately, completely and with a synergic effect when combined, as has been observed in DS patients. This computational modeling study constitutes an innovative approach to better define the role of depolarizing GABA in infantile onset epilepsy and opens the way for new therapeutic hypotheses, especially in Dravet syndrome.

EEG extended source localization: tensor-based vs. conventional methods

The localization of brain sources based on EEG measurements is a topic that has attracted a lot of attention in the last decades and many different source localization algorithms have been proposed. However, their performance is limited in the case of several simultaneously active brain regions and low signal-to-noise ratios. To overcome these problems, tensor-based preprocessing can be applied, which consists in constructing a space-time-frequency (STF) or space-time-wave-vector (STWV) tensor and decomposing it using the Canonical Polyadic (CP) decomposition. In this paper, we present a new algorithm for the accurate localization of extended sources based on the results of the tensor decomposition. Furthermore, we conduct a detailed study of the tensor-based preprocessing methods, including an analysis of their theoretical foundation, their computational complexity, and their performance for realistic simulated data in comparison to conventional source localization algorithms such as sLORETA, cortical LORETA (cLORETA), and 4-ExSo-MUSIC. Our objective consists, on the one hand, in demonstrating the gain in performance that can be achieved by tensor-based preprocessing, and, on the other hand, in pointing out the limits and drawbacks of this method. Finally, we validate the STF and STWV techniques on real measurements to demonstrate their usefulness for practical applications.

Energy deprivation transiently enhances rhythmic inhibitory events in the CA3 hippocampal network in vitro

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.

Pitfalls of high-pass filtering for detecting epileptic oscillations: a technical note on "false" ripples

OBJECTIVES

To analyze interictal High frequency oscillations (HFOs) as observed in the medial temporal lobe of epileptic patients and animals (ripples, 80-200Hz and fast ripples, 250-600Hz). To show that the identification of interictal HFOs raises some methodological issues, as the filtering of sharp transients (e.g., epileptic spikes or artefacts) or signals with harmonics can result in "false" ripples. To illustrate and quantify the occurrence of false ripples on filtered EEG traces.

METHODS

We have performed high-pass filtering on both simulated and real data. We have also used two alternate methods: time-frequency analysis and matching pursuit.

RESULTS

Two types of events were shown to produce oscillations after filtering that could be confounded with actual oscillatory activity: sharp transients and harmonics of non-sinusoidal signals.

CONCLUSIONS

High-pass filtering of EEG traces for detection of oscillatory activity should be performed with great care. Filtered traces should be compared to original traces for verification of presence of transients. Additional techniques such as time-frequency transforms or sparse decompositions are highly beneficial.

SIGNIFICANCE

Our study draws the attention on an issue of great importance in the marking of HFOs on EEG traces. We illustrate complementary methods that can help both researchers and clinicians.

[The concept of an epileptogenic network in human partial epilepsies]

An anatomical and functional model of drug-resistant partial seizures is presented and discussed based on research conducted by our team over the last decade. This research is based on the study of intracerebral stereoelectroencephalography (SEEG) recordings in an attempt to identify the neural networks involved in generating paroxystic activities so as to understand their dynamics in space and time, and to propose targeted therapies that could "control" these networks. Today, the classical notion of epileptic focus should be replaced by a more complex model that takes into account the potential interactions within the neuronal networks involved in seizures. During partial epileptic seizures, the cerebral structures involved are the seat of characteristic oscillations that may be synchronized or, on the contrary, that can desynchronize in a transitory manner. These epileptic rhythms disturb the physiological rhythms that underlie the cognitive and emotional processes, which can thus be altered in partial epilepsy, even if located far from the original discharge site. We suggest that seizures originate in a group of structures that are highly epileptogenic (epileptogenic zone network, [EZN]) whose activity is synchronized before the appearance of fast oscillations that are transitorily desynchronized. Later, other cortical and subcortical structures are the seat of slower, synchronized rhythmic modifications (propagation network, [PN]). The emergence of clinical signs in the seizure depend on these phenomena, which in some cases can mimic a normal cognitive process or, on the contrary, lead to a deep rupture in normal cerebral functioning.

Modeling and interpretation of scalp-EEG and depth-EEG signals during interictal activity

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.

A realistic spatiotemporal source model for EEG activity: application to the reconstruction of epileptic depth-EEG signals

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.

Detection of synchronized firings in multivariate neural spike trains during motor tasks

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.

Model-based measurement of epileptic tissue excitability

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.

Comparison of two estimators of time-frequency interdependencies between nonstationary signals: application to epileptic EEG

Numerous works have been dedicated to the development of signal processing methods aimed at measuring the degree of association between EEG signals. This interdependency parameter is often used to characterize the 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 interdependencies between nonstationary signals. Particularly, we propose a novel estimator based on the cross correlation of narrow band filtered signals. In a simulation framework, results show that this estimator may exhibit higher statistical performances (bias and variance) compared to a more classical estimator based on the coherence function. On real data (intracerebral EEG signals), they show that this estimator enhances the readability of the time-frequency representation of the relationship and can thus 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).

Automatic lateralization of temporal lobe epilepsy based on scalp EEG

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.

Dynamique des réseaux neuraux dans les épilepsies partielles humaines

INTRODUCTION

The anatomo-functional organization of partial drug-resistant epilepsies is the subject of much current research aiming at better understanding these pathologies and improving their treatment. The work carried out by our team on the study of intracerebral recording falls within this category of research. The objectives are to identify the neural networks involved in the generation of paroxysmal activity and to understand their spatio-temporal dynamics, in order to be able in the long term to propose targeted therapeutic approaches likely to "control" these networks.

STATE OF ART

The traditional concept of epileptic "focus" must nowadays be replaced by a more complex model taking into account potential interactions within the neural networks involved in the seizure. Indeed, during partial seizures, involved cerebral structures are the site of characteristic oscillations which may be synchronized or on the contrary transiently desynchronized. These epileptic rhythms may disturb the physiological rhythms underlying normal cognitive processes; these cognitive processes may thus be impaired in partial epilepsy, even those remote from the site of origin of the discharge. In this article we describe a model of organization of human partial seizures, through characterization of the relationships ("synchrony") between intracerebral signals recorded in the involved structures. We propose that seizures are generated in an initial network of highly epileptogenic brain structures (epileptogenic zone network, EZN) whose activity is synchronized; this activity is then transiently desynchronized with the appearance of fast oscillations. During a second ictal phase, other cortical and subcortical structures are the seat of slower rhythmic modifications that are synchronized (propagation network, PN). The emergence of a particular clinical semiology in the course of the seizure depends on these phenomena which can in certain cases "mimic" a normal cerebral process or on the contrary provoke a major rupture in normal cerebral functioning.

CONCLUSIONS

These studies contribute to improvement in our knowledge of the neural networks involved in partial epilepsies. In the future, this type of research may contribute to the development of specific treatments that target certain pathophysiological mechanisms involved in seizure generation.

A method to identify reproducible subsets of co-activated structures during interictal spikes. Application to intracerebral EEG in temporal lobe epilepsy

OBJECTIVE

We present a novel quantitative method to statistically analyze the distribution of multichannel intracerebral interictal spikes (multi-IIS) in stereoelectroencephalographic (SEEG) recordings. The method automatically extracts groups of brain structures conjointly and frequently involved in the generation of interictal activity. These groups are referred to as 'subsets of co-activated structures' (SCAS). We applied the method to long duration interictal recordings in patients with mesial temporal lobe epilepsy (MTLE) and analyzed the reproducibility of subsets of structures involved in the generation of multi-IIS for each patient and among patients.

METHODS

Fifteen patients underwent long-term intracerebral EEG recording (SEEG technique) using depth electrodes. A 1 h period of continuous interictal EEG recording was selected for each patient with precautions regarding the time after anesthesia pre-SEEG, the temporal distance with respect to seizures, the vigilance state of the patient, and the anti-epileptic drug withdrawal. A research of SCAS was conducted on each recording using the developed method that includes 3 steps: (i) automatic detection of monochannel intracerebral interictal spikes (mono-IIS), (ii) formation of multi-IIS using a temporal sliding window, and (iii) extraction of SCAS. In the third step, statistical tests are used to evaluate the frequency of multi-IIS as well as their significance (with respect to the 'random distribution of mono-IIS' case).

RESULTS

In each patient, several thousands of multi-IIS (mean+/-SD, 3322+/-2190) were formed and several SCAS (mean+/-SD, 3.80+/-1.47) were automatically extracted. Results show that reproducible subsets of brain structures are involved in the generation of interictal activity. Although SCAS were found to be variable from one patient to another, some invariant information was pointed up. In all patients, multi-IIS distribute over two distinct groups of structures: mesial structures (15/15) and lateral structures (7/15). Moreover, two particular structures, the internal temporal pole and the temporo-basal cortex, may be conjointly involved with either the first or the second group. Finally, some extracted SCAS seem to match well-defined anatomo-functional circuits of the temporal lobe.

CONCLUSIONS AND SIGNIFICANCE

During interictal activity in MTLE, similar subsets of temporal lobe structures are involved in the generation of spikes. This paper brings statistical evidence for the existence of these subsets and presents a method to automatically extract them from SEEG recordings. Interictal activity is spatially organized in the temporal lobe and preferentially involves two functional systems of the temporal lobe (either mesial or lateral).

Pre-ictal synchronicity in limbic networks of mesial temporal lobe epilepsy

PURPOSE

We recorded with intracerebral electrodes the onset of limbic seizures in patients with mesial temporal lobe epilepsy (MTLE) to identify the dynamic interactions between the hippocampus (HIP), amygdala (AMY) and entorhinal cortex (EC).

METHODS

Interactions were quantified by analyzing the interdependencies between stereo-electroencephalographic (SEEG) signals using a nonlinear cross-correlation method. Seizures from 12 patients were analyzed by identifying three periods of interest: (i) the rapid discharge that occurs at seizure onset ("during rapid discharge", DRD period); (ii) the time interval that precedes this rapid discharge ("before rapid discharge", BRD period); and the time that follows the rapid discharge ("after rapid discharge", ARD period). The transition from interictal to ictal discharge was classified into: (i) "type 1 transition" in which the emergence of pre-ictal spiking was followed by a rapid discharge; and (ii) "type 2 transition" that was associated with rapid discharge onset without prior spiking.

RESULTS

In both types of transition the BRD period was characterized by significant cross-correlation values indicating strong interactions among mesial temporal structures as compared to those seen during background activity. Interactions between HIP and EC were predominant in 10 of 12 patients (83%). Interactions between EC and AMY were observed in 6 of 12 cases (50%) and between AMY and HIP in 7 of 12 cases (58%). Analysis of coupling directionality indicated that most of the couplings were driven either by HIP (six patients) or by the EC (four patients). The DRD period was characterized by a significant decrease of cross-correlation values. In addition, type 1 transition was characterized by interactions that uniformly involved the three structures, while type 2 transition was associated with interactions between EC and HIP. Finally, analysis of coupling direction demonstrated that the HIP was always the leader in type 1 transition whereas in type 2 the EC was most often the leading structure.

CONCLUSIONS

This study demonstrates that pre-ictal synchronization between mesial structures is the initial event for seizures starting in the mesial temporal region.

Epileptic fast intracerebral EEG activity: evidence for spatial decorrelation at seizure onset

Low-voltage rapid discharges (or fast EEG ictal activity) constitute a characteristic electrophysiological pattern in focal seizures of human epilepsy. They are characterized by a decrease of signal voltage with a marked increase of signal frequency (typically beyond 25 Hz). They have long been observed in stereoelectroencephalographic (SEEG) signals recorded with intra-cerebral electrodes, generally occurring at seizure onset and simultaneously involving distinct brain regions. Spectral properties of rapid ictal discharges as well as spatial correlations measured between SEEG signals generated from distant sites before, during and after these discharges were studied. Cross-correlation estimates within typical EEG sub-bands and statistical tests performed in 10 patients suffering from partial epilepsy (frontal, temporal or fronto-temporal) reveal that SEEG signals are significantly de-correlated during the discharge period compared with periods that precede and follow this discharge. These results can be interpreted as a functional decoupling of distant brain sites at seizure onset followed by an abnormally high re-coupling when the seizure develops. They lead to the concept of 'disruption' that is complementary of that of 'activation' (revealed by significantly high correlations between signals recorded during seizures), both giving insights into our understanding of pathophysiological processes involved in human partial epilepsies as well as in the interpretation of clinical semiology.