- Open Access
Abnormal binding and disruption in large scale networks involved in human partial seizures
© Bartolomei et al.; licensee Springer on behalf of EPJ. 2013
- Received: 23 March 2013
- Accepted: 11 June 2013
- Published: 24 June 2013
There is a marked increase in the amount of electrophysiological and neuroimaging works dealing with the study of large scale brain connectivity in the epileptic brain. Our view of the epileptogenic process in the brain has largely evolved over the last twenty years from the historical concept of “epileptic focus” to a more complex description of “Epileptogenic networks” involved in the genesis and “propagation” of epileptic activities. In particular, a large number of studies have been dedicated to the analysis of intracerebral EEG signals to characterize the dynamic of interactions between brain areas during temporal lobe seizures. These studies have reported that large scale functional connectivity is dramatically altered during seizures, particularly during temporal lobe seizure genesis and development. Dramatic changes in neural synchrony provoked by epileptic rhythms are also responsible for the production of ictal symptoms or changes in patient’s behaviour such as automatisms, emotional changes or consciousness alteration. Beside these studies dedicated to seizures, large-scale network connectivity during the interictal state has also been investigated not only to define biomarkers of epileptogenicity but also to better understand the cognitive impairments observed between seizures.
- Focal epilepsies
- Non linear correlation
- Functional connectivity
- Intracerebral EEG
Approximately 30% of focal epilepsies are resistant to antiepileptic drugs. In this situation, surgical resection of the epileptogenic zone (EZ) is the only therapeutic option able to suppress seizures. The localisation and the definition of the EZ are therefore crucial issues that can be addressed through detailed analysis of anatomo-functional data acquired in epileptic patients during pre-surgical evaluation. From a theoretical viewpoint, the EZ is a highly illustrative example of complex system exhibiting nonlinear dynamics as well as ruptures (more or less abrupt) between these dynamics (typically during the transition from interictal to ictal activity) as reflected by signals directly recorded from involved brain structures.
Several reviews dealing with “neural networks” and epilepsy or with synchrony and epilepsy [1, 2] are available in the literature. With regard to these reviews, our objectives were more specifically to focus on works studying functional connectivity from stereotactic EEG (SEEG) signals, to propose a general framework (“the epileptogenic network concept”) and to show how this concept may give clues in the comprehension of some clinical manifestations encountered in partial epilepsies.
Therefore we concentrate on studies from our group dealing with the analysis of synchronization processes between distant structures recorded by intracerebral electrodes during seizure genesis and development. These data have been mainly obtained in the context of presurgical evaluation of temporal lobe epilepsy (TLE) and based on the analysis of intracerebral 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 [1, 3, 4]. Other studies based on nonlinear associations in multivariate signals [5, 6] have also reported that large scale functional connectivity is dramatically altered during seizures, or indicated that the topology of networks changes as ictal activity develops [7, 8].
We will also discuss data showing that abnormal changes in neural synchrony provoked by epileptic rhythms may be responsible for (or at least be in relation with) the production of ictal symptoms or changes in patient’s behaviour. Finally, recent works suggest that even at rest during the interictal period these networks are associated with changes in functional connectivity. We will illustrate these findings by discussing results we obtained using both intracerebral EEG recordings and resting-state functional MRI.
The EZ may be considered as a general notion encompassing different conditions.
Regarding the organization of the EZ, two schematically-different situations may be observed when intracerebral recordings and notably when stereotactic EEG (SEEG) is used to record seizures. In some cases, the EZ corresponds to a relatively restricted area of the brain. Seizure genesis takes place in a unique “functional area”, a situation corresponding to the classical notion of an epileptogenic focus, the most popular concept in epileptology (see ).
the ability to generate fast oscillations, in the beta or/and the gamma range, (classically called low voltage rapid discharges, LVRD).
the ability to synchronize their activity at seizure onset and during the course of the seizure.
LVRD constitute a characteristic electrophysiological pattern in focal seizures of human epilepsy characterized by a marked decrease of signal voltage (sometimes preceded by high-amplitude spikes) with a marked increase of signal frequency. They have long been observed in depth-EEG signals. Rapid discharges generally appear in brain regions in keeping with the epileptogenic zone  and are therefore considered as important marker providing substantial information about the EZ spatio-temporal organization [14, 15]. The underlying neuronal mechanisms are not well known. Complex pattern of neuronal firing has been recently described during the initiation of seizures [12, 16]. Computational modelisation  and in vivo studies  support the role of inhibitory neurons firing rather than an hyperactivity of principal cells in these phenomena. In temporal lobe seizures, the LVRD are generally less rapid disclosing a frequency peak in the beta or low gamma range, typically from 20 to 40 Hz [19–22].
As indicated above, the seizure onset characterized by LVRD involves often distant and functionally distinct brain sites almost simultaneously. Thus it could be hypothesized that a “synchronizing phenomenon” gives rise to the simultaneous start of fast oscillations.
This hypothesis prompted us to study the spatio-temporal dynamics of these phenomena through measuring the interdependencies between generated signals.
Using these methods, it is therefore possible to study functional couplings between several brain regions involved or not at seizure onset. The use of nonlinear approaches is probably well suited as it does not require assumptions on the nature or the relationship [28–30]. In this context, the so-called nonlinear regression analysis provides a parameter, referred to as the nonlinear correlation coefficient h2, which takes values in [0, 1]. Low values of h2 denote that two signals X and Y under analysis are independent. On the other hand, high values of h2 mean that the second signal Y may be explained by a transformation (possibly nonlinear) of the first signal X (i.e. both signals are dependent).
In addition, this method offers the possibility to study the direction of the coupling between neuronal populations which is an important parameter to determine the “leader region” responsible for the “driving” input in the system. In addition to the estimation of h2, a second quantity has been proposed  that brings information on the causal property of the association. This quantity, referred to as the direction index D, takes into account both the estimated time delay τ between signals X and Y (latency) and the asymmetry property of the nonlinear correlation coefficient h2 (values of the h2 coefficient are different if the computation is performed from X to Y or from Y to X). Values of parameter D range from −1.0 (X is driven by Y) to 1.0 (Y is driven by X). These methods in the last ten years have been mainly applied to the study of temporal lobe seizures (TLS) [20, 26, 28, 31, 32]. Finally, results of signal quantification demonstrate that the regions involved in the EZ establish preferential functional links during seizure and militate in favour of the existence of a network organization of the EZ.
In addition, the study of the statistical relations existing between SEEG signals at seizure onset, allowed us to identify 4 sub-types of TLS according to the interactions between mesial (amygdala-hippocampus-entorhinal cortex) and neocortical structures: mesial, mesial-lateral, and lateral-mesial and lateral [26, 28]. Mesial TLS are the most frequent forms of TLS. In this group, functional coupling between several regions belonging to the mesial structures is observed. Absence of coupling between mesial and neocortical structures at seizure onset is also a characteristic feature of these seizures.
This “preictal” synchronization has been particularly quantified in a group of mesial temporal lobe epilepsy (MTLE), by studying the interactions between entorhinal cortex, hippocampus and amygdala . Two main patterns of transition between interictal to ictal state may be observed in these seizures. Some seizures start with a fast discharge and seem to be particularly under the control of the entorhinal cortex while seizure starting by a “pre-ictal” periodic spiking are probably more likely triggered by the hippocampus. In vivo study of human epileptic tissue has shown that the preictal spiking is different from the interictal spiking. These events depend on glutamatergic mechanisms and are preceded by pyramidal cell firing, whereas interneuron firing precedes interictal events that depend on both glutamatergic and depolarizing GABAergic transmission. These pre-ictal discharges are involved in seizure initiation .
The desynchronization between signals observed after a first phase of synchrony, was found to be particularly important when we studied the signal from neocortical sites in neocortical seizures . This desynchronization has been also observed in both neural activity and local field potentials in vitro and in vivo [33, 34]. Finally, the pattern of synchronization/desynchronization between regions forming the EZ appears to be a characteristic property of the EZ and may be observed in many different forms of partial seizures.
Video-SEEG recordings are classically used to make anatomo-electro-clinical correlations and to determine which regions of the brain are responsible for clinical symptoms. One of the most striking phenomena is that semiology generally appears when a large set of different structures are involved in the ictal discharge. In addition, the first clinical signs are observed after the seizure onset (typically several seconds) and are largely related to the propagation of the discharge.
Coordinated interactions between neurons and neuronal populations are an essential feature of brain functioning . In the healthy brain, cognitive and emotional processes are dependent on precise integration of neural activity at specific spatiotemporal scales [36–38].
We made the hypothesis that signs and symptoms during the seizure course could be related to changes and new interactions taking place between brain regions during the development of ictal rhythms. Unfortunately, so far very few studies have attempted to correlate symptoms to changes in brain synchronization. In recent years, we have suggested that some symptoms could be related to the uncontrolled activation of functionally-normal neural networks by epileptic rhythms or on the contrary be related to the disruption of mechanisms underlying normal brain function.
The former situation can be seen in the building of some “elaborated” symptoms mimicking normal behaviours. One of the most remarkable examples is the “dreamy state” that encompasses two kinds of phenomenona: an illusion of familiarity (déjà vu-déjà vécu) or a reminiscence of visual memories. For a long time, the anatomical localization and the mechanisms of these phenomena have been the object of impassioned debates . At the time being, it is admitted that these phenomena are related to epileptic discharges involving the memory systems of the mesial temporal lobe. We recently showed that these “dysmnesic” phenomena were more frequent after stimulation of the rhinal cortices than after stimulation of the hippocampus or the amygdala . We particularly studied one of the patients from this series, in whom the stimulation of perirhinal cortex provoked the recollection of vivid visual scenes. The interdependencies existing between signals generated at the time of the phenomenon of memory recollection were quantified by linear correlation between intracerebral EEG signals filtered into classical frequency sub-bands. We demonstrated that a transient theta range synchronization (studied using linear correlation measure) between the mesial temporal lobe structures as well as a primary visual area was observed at the time of the reminiscence . These results are thus suggestive of the transient activation of a neural network normally involved in the recall of memories.
Another example is the humming/singing automatism. Some patients during temporal seizures exhibit a humming/singing behaviour, highly reproducible from one seizure to another. We studied three patients explored by SEEG and presenting with such an automatism . In comparison with the seizure onset period, we showed that a functional coupling (characterized by the coherence averaged over the frequency band) appears between the temporal superior gyrus and the prefrontal cortex during the humming phenomenon (Figure 4b).
These examples are remarkable in that they suggest that ictal activity, observable in areas remote from the EZ, is able to “reactivate” neural networks involved in some specific (memory for dreamy state, musical function) brain functions.
Excess of synchronization may be in contrast deleterious for brain functioning, particularly for conscious representations. In a recent study, we studied the relationship between neural synchronization and loss of consciousness in MTLE seizures . 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 [46, 47]. Re-entry loops and, in particular, thalamocortical communication would be crucial to functionally bind together different modules. We used intracranial recordings of cortical and subcortical structures in 12 patients with intractable temporal lobe epilepsy as part of their presurgical evaluation to investigate the relationship between states of consciousness and neuronal activity within the brain [6, 48]. The synchronization of EEG signals between distant regions was estimated as a function of time by using non-linear regression analysis. We found that LOC occurring during TLS is characterized by increased long-distance synchronization between structures that are critical in processing awareness, including thalamus and parietal cortices (Figure 4c). The degree of LOC was found to correlate with the amount of synchronization in thalamo-cortical systems. This result suggested that excessive synchronization may overload the structures involved in consciousness processing, preventing them from treating incoming information and thus resulting in LOC. The transition between consciousness and loss of consciousness as a function of synchronization followed a non linear curve, suggesting a bi-stable system: above a certain threshold of synchronization, the involved brain networks are unable to process consciousness representation. This result has been recently extended to extratemporal seizures .
Interestingly the termination of seizures has been proposed to be caused by the large increase in signal synchrony observed at the end of seizures [54, 55]. This report demonstrated that the TH and remote cortical structures synchronize their activity during TLE seizures and suggested that the extension of the epileptogenic network to the TH is a potential important factor determining surgical prognosis.
Despite the classical notion stipulating that epilepsy is associated with abnormally-high synchrony within and between neuronal populations, there are in fact very few works that have proved that during the interictal state, the epileptic brain is the seat of altered synchronization between distant neuronal assemblies. The study reported in  showed a local increase in interictal synchrony using the mean phase coherence in a group of 17 MTLE patients. A recent study estimated the interdependencies between signals recorded by subdural grids in nine patients during presurgical evaluation with neocortical epilepsy . Inter-electrode synchrony was quantified using the mean phase coherence algorithm and revealed areas of elevated local synchrony that may be a marker of epileptogenic cortex.
In a recent study  we characterized the functional coupling between mesial structures during the interictal state by comparing the MTLE group to a “control” group including patients with extra-temporal seizures. Results showed that functional coupling was enhanced in the epileptogenic zone in this group of patients (MTLE). This effect was significant for the theta (4-8Hz), alpha (8-12Hz), beta (12-25Hz) and gamma bands (25–90 Hz) and to a large part independent of the occurrence of interictal spiking. The functional significance of an increase of EEG synchrony between structures forming the EZ is unclear. We can assume that even during the interictal state, abnormal long-range reinforced EEG connectivity between structures forming the EZ network is a fundamental characteristic of this region. Of particular interest, in a recent study using the amygdala kindling model in the rat (an animal model of secondary epileptogenesis), an increase of coherence values between amygdala and frontal cortex was observed. Therefore, enhanced connectivity appears to be a marker of the kindling phenomenon at the EEG level . This result suggests that this particular state can “facilitate” seizure occurrence by “priming” the system to synchronize during seizures.
During the interictal period, we have also investigated the functional connectivity (FC) within and outside the EZ by using resting-state functional MRI (rsfMRI). Interestingly, in a first study we found a decreased FC within the epileptogenic networks in comparison to a control group contrasting with our results by using SEEG . Conversely we found increased FC in the contralateral MTL structures that correlated to cognitive performances. These results were confirmed in a further study conducted in a larger cohort in which we also demonstrated the potential usefulness of rsfMRI in the lateralization of the EZ at an individual level . Finally, in a last study, we confirmed the apparent paradoxical pattern of FC as measured by SEEG and rsfMRI by co-registering the site of recording and the region of interest chosen for computing FC on rsfMRI data in the same cohort of patients . We proposed different non contradictory hypothesis to interpret this apparent discrepancy between increased FC as measured by SEEG and decreased FC as measured by rsfMRI within the epileptogenic networks. The first is a potential neurovascular decoupling in a pathological network affected by metabolic, perfusion and blood–brain-barrier alterations, especially in the type of partial epilepsies we explored (i.e. temporal lobe epilepsy). The second is that each technique actually quantifies different epilepsy-related phenomena potentially occurring at different time scales and dynamic, affecting correlations between signals in different ways. These results demonstrate the complexity of signal coupling even during the interictal state. Functional connectivity measured between seizures by EEG signal is therefore representative of pathological electrical signal interaction  whereas disrupted functional connectivity measured in the same period by BOLD is rather representative of dysfunction [63–66].
Interestingly, studies dedicated to structural connectivity in epilepsy using either morphometric correlation or diffusion imaging – tractography have also demonstrated a decreased structural connectivity affecting major bundles connected to the epileptogenic regions and even beyond [67–71]. We hypothesize that this anatomical disconnection can also play a role in the decrease of FC as measured by fMRI. We also think that this structural disconnection may have a greater impact on BOLD signal correlation than on SEEG signal interdependencies in pathological regions.
Interestingly, we also quantified the causal relationships (effective connectivity) between three kinds of networks labeled according to SEEG results (i.e. the epileptogenic zone network, the propagation network and network non-involved by electrical abnormalities) . Surprisingly, we found an influence of the epileptogenic network on the “non-involved” network spared by electrical abnormalities but potentially driven by epileptic regions.
- Jiruska P, de Curtis M, Jefferys JG, Schevon CA, Schiff SJ, Schindler K: Synchronization and desynchronization in epilepsy: controversies and hypotheses. J Physiol 2013, 591: 787–797. 10.1113/jphysiol.2012.239590View ArticleGoogle Scholar
- Uhlhaas PJ, Singer W: Neural synchrony in brain disorders: relevance for cognitive dysfunctions and pathophysiology. Neuron 2006, 52: 155–168. 10.1016/j.neuron.2006.09.020View ArticleGoogle Scholar
- Wendling F, Chauvel P, Biraben A, Bartolomei F: From intracerebral EEG signals to brain connectivity: identification of epileptogenic networks in partial epilepsy. Frontiers Syst Neurosci 2010, 4: 154.View ArticleGoogle Scholar
- Bartolomei F, Wendling F: Synchrony in neural networks underlying seizure generation in human partial epilepsies. In Coordinated activity in the brain: measurements and relevance to brain function and behavior. Edited by: Velazquez J, Wennberg R. New York: Springer; 2009:137–147.View ArticleGoogle Scholar
- Guye M, Regis J, Tamura M, Wendling F, McGonigal A, Chauvel P, Bartolomei F: The role of corticothalamic coupling in human temporal lobe epilepsy. Brain 2006, 129: 1917–1928. 10.1093/brain/awl151View ArticleGoogle Scholar
- Arthuis M, Valton L, Regis J, Chauvel P, Wendling F, Naccache L, Bernard C, Bartolomei F: Impaired consciousness during temporal lobe seizures is related to increased long-distance cortical-subcortical synchronization. Brain 2009, 132: 2091–2101. 10.1093/brain/awp086View ArticleGoogle Scholar
- Ponten S, Bartolomei F, Stam C: Small-world networks and epilepsy: graph theoretical analysis of intracerebrally recorded mesial temporal lobe seizures. Clin Neurophysiol 2007. 10.1016/j.clinph.2006.12.002Google Scholar
- Kramer MA, Kolaczyk ED, Kirsch HE: Emergent network topology at seizure onset in humans. Epilepsy Res 2008, 2–3: 173–186.View ArticleGoogle Scholar
- Rosenow F, Luders H: Presurgical evaluation of epilepsy. Brain 2001, 124: 1683–1700. 10.1093/brain/124.9.1683View ArticleGoogle Scholar
- Bartolomei F, Chauvel P, Wendling F: Spatio-temporal dynamics of neuronal networks in partial epilepsy. Rev Neurol (Paris) 2005, 161: 767–780. 10.1016/S0035-3787(05)85136-7View ArticleGoogle Scholar
- Wendling F, Badier J, Chauvel P, Coatrieux J: A method to quantify invariant information in depth-recorded epileptic seizures. Electroenceph Clin Neurophysiol 1997, 102: 472–485. 10.1016/S0013-4694(96)96633-3View ArticleGoogle Scholar
- Truccolo W, Donoghue JA, Hochberg LR, Eskandar EN, Madsen JR, Anderson WS, Brown EN, Halgren E, Cash SS: Single-neuron dynamics in human focal epilepsy. Nat Neurosci 2011, 14: 635–641. 10.1038/nn.2782View ArticleGoogle Scholar
- Bancaud J, Angelergues R, Bernouilli C, Bonis A, Bordas-Ferrer M, Bresson M, Buser P, Covello L, Morel P, Szikla G, et al.: Functional stereotaxic exploration (SEEG) of epilepsy. Electroencephalogr Clin Neurophysiol 1970, 28: 85–86.View ArticleGoogle Scholar
- Wendling F, Bartolomei F, Bellanger JJ, Bourien J, Chauvel P: Epileptic fast intracerebral EEG activity: evidence for spatial decorrelation at seizure onset. Brain 2003, 126: 1449–1459. 10.1093/brain/awg144View ArticleGoogle Scholar
- Alarcon G, Binnie CD, Elwes RD, Polkey CE: Power spectrum and intracranial EEG patterns at seizure onset in partial epilepsy. Electroencephalogr Clin Neurophysiol 1995, 94: 326–337. 10.1016/0013-4694(94)00286-TView ArticleGoogle Scholar
- Huberfeld G, Menendez de la Prida L, Pallud J, Cohen I, Le Van Quyen M, Adam C, Clemenceau S, Baulac M, Miles R: Glutamatergic pre-ictal discharges emerge at the transition to seizure in human epilepsy. Nat Neurosci 2011, 14: 627–634. 10.1038/nn.2790View ArticleGoogle Scholar
- Wendling F, Hernandez A, Bellanger JJ, Chauvel P, Bartolomei F: Interictal to ictal transition in human temporal lobe epilepsy: insights from a computational model of intracerebral EEG. J Clin Neurophysiol 2005, 22: 343–356.Google Scholar
- Gnatkovsky V, Librizzi L, Trombin F, de Curtis M: Fast activity at seizure onset is mediated by inhibitory circuits in the entorhinal cortex in vitro . Ann Neurol 2008, 64: 674–686. 10.1002/ana.21519View ArticleGoogle Scholar
- Javidan M, Katz A, Tran T, Pacia S, Spencer D, Spencer S: Frequency characteristics of neocortical and hippocampal onset seizures. Epilepsia 1992,33(Suppl 3):58.Google Scholar
- Bartolomei F, Wendling F, Regis J, Gavaret M, Guye M, Chauvel P: Pre-ictal synchronicity in limbic networks of mesial temporal lobe epilepsy. Epilepsy Res 2004, 61: 89–104. 10.1016/j.eplepsyres.2004.06.006View ArticleGoogle Scholar
- Bartolomei F, Chauvel P, Wendling F: Epileptogenicity of brain structures in human temporal lobe epilepsy: a quantified study from intracerebral EEG. Brain 2008, 131: 1818–1830. 10.1093/brain/awn111View ArticleGoogle Scholar
- Aubert S, Wendling F, Regis J, McGonigal A, Figarella-Branger D, Peragut JC, Girard N, Chauvel P, Bartolomei F: Local and remote epileptogenicity in focal cortical dysplasias and neurodevelopmental tumours. Brain 2009, 132: 3072–3086. 10.1093/brain/awp242View ArticleGoogle Scholar
- Brazier MA: Spread of seizure discharges in epilepsy: anatomical and electrophysiological considerations. Exp Neurol 1972, 36: 263–272. 10.1016/0014-4886(72)90022-2View ArticleGoogle Scholar
- Gotman J, Levtova V: Amygdala-hippocampus relationships in temporal lobe seizures: a phase coherence study. Epilepsy Res 1996, 25: 51–57. 10.1016/0920-1211(96)00021-6View ArticleADSGoogle Scholar
- Le Van Quyen M, Adam C, Baulac M, Martinerie J, Varela F: Nonlinear interdependencies of EEG signals in human intracranially recorded temporal lobe seizures. Brain Res 1998, 792: 24–40. 10.1016/S0006-8993(98)00102-4View ArticleGoogle Scholar
- Bartolomei F, Wendling F, Vignal J, Kochen S, Bellanger J, Badier J, Le Bouquin-Jeannes R, Chauvel P: Seizures of temporal lobe epilepsy: identification of subtypes by coherence analysis using stereo-electro-encephalography. Clin Neurophysiol 1999, 110: 1741–1754. 10.1016/S1388-2457(99)00107-8View ArticleGoogle Scholar
- Wendling F, Ansari-Asl K, Bartolomei F, Senhadji L: From EEG signals to brain connectivity: a model-based evaluation of interdependence measures. J Neurosci Methods 2009,30(183):9–18.View ArticleGoogle Scholar
- Bartolomei F, Wendling F, Bellanger J, Regis J, Chauvel P: Neural networks involved in temporal lobe seizures: a nonlinear regression analysis of SEEG signals interdependencies. Clin Neurophysiol 2001, 112: 1746–1760. 10.1016/S1388-2457(01)00591-0View ArticleGoogle Scholar
- Wendling F, Bartolomei F: Modeling EEG signals and interpreting measures of relationship during temporal-lobe seizures: an approach to the study of epileptogenic networks. Epileptic Disord 2001, Special Issue: 67–78.Google Scholar
- Meeren HK, Pijn JP, Van Luijtelaar EL, Coenen AM, Lopes da Silva FH: Cortical focus drives widespread corticothalamic networks during spontaneous absence seizures in rats. J Neurosci 2002, 22: 1480–1495.Google Scholar
- Bartolomei F, Khalil M, Wendling F, Sontheimer A, Regis J, Ranjeva JP, Guye M, Chauvel P: Entorhinal cortex involvement in human mesial temporal lobe epilepsy: an electrophysiologic and volumetric study. Epilepsia 2005, 46: 677–687. 10.1111/j.1528-1167.2005.43804.xView ArticleGoogle Scholar
- Wendling F, Bartolomei F, Bellanger JJ, Chauvel P: Interpretation of interdependencies in epileptic signals using a macroscopic physiological model of the EEG. Clin Neurophysiol 2001, 112: 1201–1218. 10.1016/S1388-2457(01)00547-8View ArticleGoogle Scholar
- Netoff TI, Schiff SJ: Decreased neuronal synchronization during experimental seizures. J Neurosci 2002, 22: 7297–7307.Google Scholar
- Cymerblit-Sabba A, Schiller Y: Development of hypersynchrony in the cortical network during chemoconvulsant-induced epileptic seizures in vivo. J Neurophysiol 2012, 107: 1718–1730. 10.1152/jn.00327.2011View ArticleGoogle Scholar
- Velazquez J, Guevara Erra R, Wennberg R, Dominguez L: Correlations of cellular activities in the nervous system: physiological and methodological considerations. In Coordinated activity in the brain: measurements and relevance to brain function and behavior. Edited by: Velazquez J, Wennberg R. New York: Springer; 2009:1–24.View ArticleGoogle Scholar
- Varela F, Lachaux JP, Rodriguez E, Martinerie J: The brainweb: phase synchronization and large-scale integration. Nat Rev Neurosci 2001, 2: 229–239.View ArticleGoogle Scholar
- Stam CJ, van Straaten EC: The organization of physiological brain networks. Clin Neurophysiol 2012, 123: 1067–1087. 10.1016/j.clinph.2012.01.011View ArticleGoogle Scholar
- Bassett DS, Bullmore ET, Meyer-Lindenberg A, Apud JA, Weinberger DR, Coppola R: Cognitive fitness of cost-efficient brain functional networks. Proc Natl Acad Sci U S A 2009, 106: 11747–11752. 10.1073/pnas.0903641106View ArticleADSGoogle Scholar
- Bancaud J, Brunet-Bourgin F, Chauvel P, Halgren E: Anatomical origin of déjà vu and vivid ‘memories’ in human temporal lobe epilepsy. Brain 1994, 117: 71–90. 10.1093/brain/117.1.71View ArticleGoogle Scholar
- Bartolomei F, Barbeau E, Gavaret M, Guye M, McGonigal A, Regis J, Chauvel P: Cortical stimulation study of the role of rhinal cortex in deja vu and reminiscence of memories. Neurology 2004, 63: 858–864. 10.1212/01.WNL.0000137037.56916.3FView ArticleGoogle Scholar
- Barbeau E, Wendling F, Regis J, Duncan R, Poncet M, Chauvel P, Bartolomei F: Recollection of vivid memories after perirhinal region stimulations: synchronization in the theta range of spatially distributed brain areas. Neuropsychologia 2005, 43: 1329–1337. 10.1016/j.neuropsychologia.2004.11.025View ArticleGoogle Scholar
- Bartolomei F, Barbeau EJ, Nguyen T, McGonigal A, Regis J, Chauvel P, Wendling F: Rhinal-hippocampal interactions during deja vu. Clin Neurophysiol 2012, 123: 489–495. 10.1016/j.clinph.2011.08.012View ArticleGoogle Scholar
- Bartolomei F, Wendling F, Vignal JP, Chauvel P, Liegeois-Chauvel C: Neural networks underlying epileptic humming. Epilepsia 2002, 43: 1001–1012. 10.1046/j.1528-1157.2002.48501.xView ArticleGoogle Scholar
- Arthuis M, Valton L, Regis J, Chauvel P, Wendling F, Naccache L, Bernard C, Bartolomei F: Impaired consciousness during temporal lobe seizures is related to increased long-distance cortical-subcortical synchronization. Brain 2009, 132: 2091–2101. 10.1093/brain/awp086View ArticleGoogle Scholar
- Dehaene S, Naccache L: Towards a cognitive neuroscience of consciousness: basic evidence and a workspace framework. Cognition 2001, 79: 1–37. 10.1016/S0010-0277(00)00123-2View ArticleGoogle Scholar
- Bartolomei F, Naccache L: The global workspace (GW) theory of consciousness and epilepsy. Behav Neurol 2011, 24: 67–74.View ArticleGoogle Scholar
- Sergent C, Naccache L: Imaging neural signatures of consciousness: ‘What’, ‘When’, ‘Where’ and ‘How’ does it work? Archives italiennes de biologie 2012, 150: 91–106.Google Scholar
- Bartolomei F: Coherent neural activity and brain synchronization during. Archives italiennes de biologie 2012, 150: 164–171.Google Scholar
- Lambert I, Arthuis M, McGonigal A, Wendling F, Bartolomei F: Alteration of global workspace during loss of consciousness: a study of parietal seizures. Epilepsia 2012, 53: 2104–2110. 10.1111/j.1528-1167.2012.03690.xView ArticleGoogle Scholar
- Bartolomei F, Trebuchon A, Gavaret M, Regis J, Wendling F, Chauvel P: Acute alteration of emotional behaviour in epileptic seizures is related to transient desynchrony in emotion-regulation networks. Clin Neurophysiol 2005, 116: 2473–2479. 10.1016/j.clinph.2005.05.013View ArticleGoogle Scholar
- Vaugier L, Aubert S, McGonigal A, Trebuchon A, Guye M, Gavaret M, Regis J, Chauvel P, Wendling F, Bartolomei F: Neural networks underlying hyperkinetic seizures of “temporal lobe” origin. Epilepsy Res 2009, 86: 200–208. 10.1016/j.eplepsyres.2009.06.007View ArticleGoogle Scholar
- Bertram EH, Mangan PS, Zhang D, Scott CA, Williamson JM: The midline thalamus: alterations and a potential role in limbic epilepsy. Epilepsia 2001, 42: 967–978. 10.1046/j.1528-1157.2001.042008967.xView ArticleGoogle Scholar
- Rosenberg DS, Mauguiere F, Demarquay G, Ryvlin P, Isnard J, Fischer C, Guenot M, Magnin M: Involvement of medial pulvinar thalamic nucleus in human temporal lobe seizures. Epilepsia 2006, 47: 98–107. 10.1111/j.1528-1167.2006.00375.xView ArticleGoogle Scholar
- Topolnik L, Steriade M, Timofeev I: Partial cortical deafferentation promotes development of paroxysmal activity. Cereb Cortex 2003, 13: 883–893. 10.1093/cercor/13.8.883View ArticleGoogle Scholar
- Schindler K, Leung H, Elger CE, Lehnertz K: Assessing seizure dynamics by analysing the correlation structure of multichannel intracranial EEG. Brain 2007, 130: 65–77. 10.1093/brain/awl321View ArticleGoogle Scholar
- Mormann F, Lehnertz K, David P, Elger CE: Mean phase coherence as a measure for phase synchronization and its application to the EEG of epilepsy patients. Physica D 2000, 144: 358–369. 10.1016/S0167-2789(00)00087-7View ArticleADSGoogle Scholar
- Schevon CA, Cappell J, Emerson R, Isler J, Grieve P, Goodman R, McKhann G Jr, Weiner H, Doyle W, Kuzniecky R, et al.: Cortical abnormalities in epilepsy revealed by local EEG synchrony. Neuroimage 2007, 35: 140–148. 10.1016/j.neuroimage.2006.11.009View ArticleGoogle Scholar
- Bettus G, Wendling F, Guye M, Valton L, Regis J, Chauvel P, Bartolomei F: Enhanced EEG functional connectivity in mesial temporal lobe epilepsy. Epilepsy Res 2008, 81: 58–68. 10.1016/j.eplepsyres.2008.04.020View ArticleGoogle Scholar
- Blumenfeld H, Rivera M, Vasquez JG, Shah A, Ismail D, Enev M, Zaveri HP: Neocortical and thalamic spread of amygdala kindled seizures. Epilepsia 2007, 48: 254–262. 10.1111/j.1528-1167.2006.00934.xView ArticleGoogle Scholar
- Bettus G, Guedj E, Joyeux F, Confort-Gouny S, Soulier E, Laguitton V, Cozzone PJ, Chauvel P, Ranjeva JP, Bartolomei F, Guye M: Decreased basal fMRI functional connectivity in epileptogenic networks and contralateral compensatory mechanisms. Hum Brain Mapp 2009, 30: 1580–1591. 10.1002/hbm.20625View ArticleGoogle Scholar
- Bettus G, Bartolomei F, Confort-Gouny S, Guedj E, Chauvel P, Cozzone PJ, Ranjeva JP, Guye M: Role of resting state functional connectivity MRI in presurgical investigation of mesial temporal lobe epilepsy. J Neurol Neurosurg Psychiatry 2010, 81: 1147–1154. 10.1136/jnnp.2009.191460View ArticleGoogle Scholar
- Bettus G, Ranjeva JP, Wendling F, Benar CG, Confort-Gouny S, Regis J, Chauvel P, Cozzone PJ, Lemieux L, Bartolomei F, Guye M: Interictal functional connectivity of human epileptic networks assessed by intracerebral EEG and BOLD signal fluctuations. PLoS One 2011, 6: e20071. 10.1371/journal.pone.0020071View ArticleADSGoogle Scholar
- Waites AB, Briellmann RS, Saling MM, Abbott DF, Jackson GD: Functional connectivity networks are disrupted in left temporal lobe epilepsy. Ann Neurol 2006, 59: 335–343. 10.1002/ana.20733View ArticleGoogle Scholar
- Liao W, Zhang Z, Pan Z, Mantini D, Ding J, Duan X, Luo C, Lu G, Chen H: Altered functional connectivity and small-world in mesial temporal lobe epilepsy. PLoS One 2010, 5: e8525. 10.1371/journal.pone.0008525View ArticleADSGoogle Scholar
- Zhang Z, Lu G, Zhong Y, Tan Q, Liao W, Wang Z, Wang Z, Li K, Chen H, Liu Y: Altered spontaneous neuronal activity of the default-mode network in mesial temporal lobe epilepsy. Brain Res 2010, 1323: 152–160.View ArticleGoogle Scholar
- Vlooswijk MC, Jansen JF, Majoie HJ, Hofman PA, de Krom MC, Aldenkamp AP, Backes WH: Functional connectivity and language impairment in cryptogenic localization-related epilepsy. Neurology 2010, 75: 395–402. 10.1212/WNL.0b013e3181ebdd3eView ArticleGoogle Scholar
- Bonilha L, Edwards JC, Kinsman SL, Morgan PS, Fridriksson J, Rorden C, Rumboldt Z, Roberts DR, Eckert MA, Halford JJ: Extrahippocampal gray matter loss and hippocampal deafferentation in patients with temporal lobe epilepsy. Epilepsia 2010, 51: 519–528. 10.1111/j.1528-1167.2009.02506.xView ArticleGoogle Scholar
- Powell HW, Parker GJ, Alexander DC, Symms MR, Boulby PA, Wheeler-Kingshott CA, Barker GJ, Koepp MJ, Duncan JS: Abnormalities of language networks in temporal lobe epilepsy. Neuroimage 2007, 36: 209–221. 10.1016/j.neuroimage.2007.02.028View ArticleGoogle Scholar
- Yogarajah M, Powell HW, Parker GJ, Alexander DC, Thompson PJ, Symms MR, Boulby P, Wheeler-Kingshott CA, Barker GJ, Koepp MJ, Duncan JS: Tractography of the parahippocampal gyrus and material specific memory impairment in unilateral temporal lobe epilepsy. Neuroimage 2008, 40: 1755–1764. 10.1016/j.neuroimage.2007.12.046View ArticleGoogle Scholar
- McDonald CR, Ahmadi ME, Hagler DJ, Tecoma ES, Iragui VJ, Gharapetian L, Dale AM, Halgren E: Diffusion tensor imaging correlates of memory and language impairments in temporal lobe epilepsy. Neurology 2008, 71: 1869–1876. 10.1212/01.wnl.0000327824.05348.3bView ArticleGoogle Scholar
- Bernhardt BC, Worsley KJ, Besson P, Concha L, Lerch JP, Evans AC, Bernasconi N: Mapping limbic network organization in temporal lobe epilepsy using morphometric correlations: insights on the relation between mesiotemporal connectivity and cortical atrophy. Neuroimage 2008, 42: 515–524. 10.1016/j.neuroimage.2008.04.261View ArticleGoogle Scholar
- Bartolomei F, Wendling F, Chauvel P: The concept of an epileptogenic network in human partial epilepsies. Neurochirurgie 2008, 54: 174–184. 10.1016/j.neuchi.2008.02.013View ArticleGoogle Scholar
This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.