SUMMARYEpileptogenic networks are defined by the brain regions involved in the production and propagation of epileptic activities. In this review we describe the historical, methodologic, and conceptual bases of this model in the analysis of electrophysiologic intracerebral recordings. In the context of epilepsy surgery, the determination of cerebral regions producing seizures (i.e., the "epileptogenic zone") is a crucial objective. In contrast with a traditional focal vision of focal drug-resistant epilepsies, the concept of epileptogenic networks has been progressively introduced as a model better able to describe the complexity of seizure dynamics and realistically describe the distribution of epileptogenic anomalies in the brain. The concept of epileptogenic networks is historically linked to the development of the stereoelectroencephalography (SEEG) method and subsequent introduction of means of quantifying the recorded signals. Seizures, and preictal and interictal discharges produce clear patterns on SEEG. These patterns can be analyzed utilizing signal analysis methods that quantify high-frequency oscillations or changes in functional connectivity. Dramatic changes in SEEG brain connectivity can be described during seizure genesis and propagation within cortical and subcortical regions, associated with the production of different patterns of seizure semiology. The interictal state is characterized by networks generating abnormal activities (interictal spikes) and also by modified functional properties. The introduction of novel approaches to large-scale modeling of these networks offers new methods in the goal of better predicting the effects of epilepsy surgery. The epileptogenic network concept is a key factor in identifying the anatomic distribution of the epileptogenic process, which is particularly important in the context of epilepsy surgery.

A better understanding of interstructure relationship sustaining drug-resistant epileptogenic networks is crucial for surgical perspective and to better understand the consequences of epileptic processes on cognitive functions. We used resting-state fMRI to study basal functional connectivity within temporal lobes in medial temporal lobe epilepsy (MTLE) during interictal period. Two hundred consecutive single-shot GE-EPI acquisitions were acquired in 37 right-handed subjects (26 controls, eight patients presenting with left and three patients with right MTLE). For each hemisphere, normalized correlation coefficients were computed between pairs of time-course signals extracted from five regions involved in MTLE epileptogenic networks (Brodmann area 38, amygdala, entorhinal cortex (EC), anterior hippocampus (AntHip), and posterior hippocampus (PostHip)). In controls, an asymmetry was present with a global higher connectivity in the left temporal lobe. Relative to controls, the left MTLE group showed disruption of the left EC-AntHip link, and a trend of decreased connectivity of the left AntHip-PostHip link. In contrast, a trend of increased connectivity of the right AntHip-PostHip link was observed and was positively correlated to memory performance. At the individual level, seven out of the eight left MTLE patients showed decreased or disrupted functional connectivity. In this group, four patients with left TLE showed increased basal functional connectivity restricted to the right temporal lobe spared by seizures onset. A reverse pattern was observed at the individual level for patients with right TLE. This is the first demonstration of decreased basal functional connectivity within epileptogenic networks with concomitant contralateral increased connectivity possibly reflecting compensatory mechanisms.

Individual variability has clear effects upon the outcome of therapies and treatment approaches. The customization of healthcare options to the individual patient should accordingly improve treatment results. We propose a novel approach to brain interventions based on personalized brain network models derived from non-invasive structural data of individual patients. Along the example of a patient with bitemporal epilepsy, we show step by step how to develop a Virtual Epileptic Patient (VEP) brain model and integrate patient-specific information such as brain connectivity, epileptogenic zone and MRI lesions. Using high-performance computing, we systematically carry out parameter space explorations, fit and validate the brain model against the patient's empirical stereotactic EEG (SEEG) data and demonstrate how to develop novel personalized strategies towards therapy and intervention.

The EEG activity of the thalamus and temporal lobe structures (hippocampus, entorhinal cortex and neocortex) was obtained using intracerebral recordings (stereoelectroencephalography, SEEG) performed in patients with TLE seizures undergoing pre-surgical evaluation. Synchrony was studied using a statistical measure of SEEG signal interdependencies (non-linear correlation). The results demonstrated an overall increase of synchrony between the thalamus and temporal lobe structures during seizures. Moreover, although there was great inter-individual variability, we found that values from seizure onset period were significantly higher than values from the background period (P = 0.001). Values at the end of seizure were significantly higher than values from the seizure onset (P < 0.0001). Several indices were also defined in order to correlate some clinical features to the degree of coupling between cortical structures and the thalamus. In patients with mesial TLE seizures, a correlation was found between the degree of thalamocortical synchrony and the presence of an early loss of consciousness but not with other clinical parameters. In addition, surgical prognosis seemed better in patients with low values of thalamocortical couplings at the seizure onset. This report demonstrates that the thalamus and remote cortical structures synchronize their activity during TLE seizures and suggest that the extension of the epileptogenic network to the thalamus is a potential important factor determining surgical prognosis.

Summary:Purpose: The International League Against Epilepsy (ILAE) classification distinguishes medial and neocortical temporal lobe epilepsies. Among other criteria, this classification relies on the identification of two different electroclinical patterns, those of medial (limbic) and lateral (neocortical) temporal lobe seizures, depending on the structure initially involved in the seizure activity. Recent electrophysiologic studies have now identified seizures in which medial and neocortical structures are both involved at seizure onset. The purpose of the study was therefore to study the correlations of ictal semiology with the spatiotemporal pattern of discharge in temporal lobe seizures.Methods: The 187 stereoelectroencephalography-recorded seizures from 55 patients were analyzed. Patients were classified into three groups according to electrophysiologic findings: medial (M; seizure onset limited to medial structures, n = 24), lateral (L; seizure onset limited to lateral structures, n = 13), and medial-lateral (ML; seizure onset involving both medial and lateral structures, n = 18). Clinical findings were compared between groups.Results: Initial epigastric sensation, initial fear, delayed oroalimentary and elementary upper limb automatisms, delayed loss of contact, long seizure duration, and absent or rare secondary generalizations were associated with M seizures. Initial auditory illusion or hallucination, initial loss of contact, shorter duration of seizures, and more frequent generalizations were associated with L seizures. Initial epigastric sensation, initial loss of contact, early oroalimentary and verbal automatisms, and long duration of seizures were associated with ML seizures.Conclusions: Although the syndrome of mesial temporal epilepsy is now relatively well defined, our findings support the idea that the organization of temporal lobe seizures may be complex and that different patterns exist. We demonstrate three distinct patterns, characterized by both semiologic and electrophysiologic features. This distinction may help to define better the epileptogenic zone and the subsequent surgical procedure.

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Graph theoretical analysis of structural and functional connectivity MRI data (ie. diffusion tractography or cortical volume correlation and resting-state or task-related (effective) fMRI, respectively) has provided new measures of human brain organization in vivo. The most striking discovery is that the whole-brain network exhibits "small-world" properties shared with many other complex systems (social, technological, information, biological). This topology allows a high efficiency at different spatial and temporal scale with a very low wiring and energy cost. Its modular organization also allows for a high level of adaptation. In addition, degree distribution of brain networks demonstrates highly connected hubs that are crucial for the whole-network functioning. Many of these hubs have been identified in regions previously defined as belonging to the default-mode network (potentially explaining the high basal metabolism of this network) and the attentional networks. This could explain the crucial role of these hub regions in physiology (task-related fMRI data) as well as in pathophysiology. Indeed, such topological definition provides a reliable framework for predicting behavioral consequences of focal or multifocal lesions such as stroke, tumors or multiple sclerosis. It also brings new insights into a better understanding of pathophysiology of many neurological or psychiatric diseases affecting specific local or global brain networks such as epilepsy, Alzheimer's disease or schizophrenia. Graph theoretical analysis of connectivity MRI data provides an outstanding framework to merge anatomical and functional data in order to better understand brain pathologies.