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Interictal functional connectivity of human epileptic networks assessed by intracerebral EEG and BOLD signal fluctuations.

ABSTRACT: 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.

SUBMITTER: Bettus G

PROVIDER: S-EPMC3098283 | biostudies-literature | 2011

REPOSITORIES: biostudies-literature

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Interictal functional connectivity of human epileptic networks assessed by intracerebral EEG and BOLD signal fluctuations.

Bettus Gaelle G Ranjeva Jean-Philippe JP Wendling Fabrice F Bénar Christian G CG Confort-Gouny Sylviane S Régis Jean J Chauvel Patrick P Cozzone Patrick J PJ Lemieux Louis L Bartolomei Fabrice F Guye Maxime M

PloS one 20110519 5

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 pathophysiolog ...[more]

PMID: 21625517

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Project description:Epileptic networks, defined as brain regions involved in epileptic brain activity, have been mapped by functional connectivity in simultaneous electroencephalography and functional magnetic resonance imaging (EEG-fMRI) recordings. This technique allows to define brain hemodynamic changes, measured by the Blood Oxygen Level Dependent (BOLD) signal, associated to the interictal epileptic discharges (IED), which together with ictal events constitute a signature of epileptic disease. Given the highly time-varying nature of epileptic activity, a dynamic functional connectivity (dFC) analysis of EEG-fMRI data appears particularly suitable, having the potential to identify transitory features of specific connections in epileptic networks. In the present study, we propose a novel method, defined dFC-EEG, that integrates dFC assessed by fMRI with the information recorded by simultaneous scalp EEG, in order to identify the connections characterised by a dynamic profile correlated with the occurrence of IED, forming the dynamic epileptic subnetwork. Ten patients with drug-resistant focal epilepsy were included, with different aetiology and showing a widespread (or multilobar) BOLD activation, defined as involving at least two distinct clusters, located in two different lobes and/or extended to the hemisphere contralateral to the epileptic focus. The epileptic focus was defined from the IED-related BOLD map. Regions involved in the occurrence of interictal epileptic activity; i.e., forming the epileptic network, were identified by a general linear model considering the timecourse of the fMRI-defined focus as main regressor. dFC between these regions was assessed with a sliding-window approach. dFC timecourses were then correlated with the sliding-window variance of the IED signal (VarIED), to identify connections whose dynamics related to the epileptic activity; i.e., the dynamic epileptic subnetwork. As expected, given the very different clinical picture of each individual, the extent of this subnetwork was highly variable across patients, but was but was reduced of at least 30% with respect to the initially identified epileptic network in 9/10 patients. The connections of the dynamic subnetwork were most commonly close to the epileptic focus, as reflected by the laterality index of the subnetwork connections, reported higher than the one within the original epileptic network. Moreover, the correlation between dFC timecourses and VarIED was predominantly positive, suggesting a strengthening of the dynamic subnetwork associated to the occurrence of IED. The integration of dFC and scalp IED offers a more specific description of the epileptic network, identifying connections strongly influenced by IED. These findings could be relevant in the pre-surgical evaluation for the resection or disconnection of the epileptogenic zone and help in reaching a better post-surgical outcome. This would be particularly important for patients characterised by a widespread pathological brain activity which challenges the surgical intervention.

Project description:Over the last decade, several methods for analysis of epileptiform signals in electroencephalography (EEG) have been proposed. These methods mainly use EEG signal features in either the time or the frequency domain to separate regular, interictal, and ictal brain activity. The aim of this work was to evaluate the feasibility of using functional connectivity (FC) based feature extraction methods for the analysis of epileptiform discharges in EEG signals. These signals were obtained from EEG-fMRI sessions of 10 patients with mesial temporal lobe epilepsy (MTLE) with unilateral hippocampal atrophy. The connectivity functions investigated were motif synchronization, imaginary coherence, and magnitude squared coherence in the alpha, beta, and gamma bands of the EEG. EEG signals were sectioned into 1-s epochs and classified according to (using neurologist markers): activity far from interictal epileptiform discharges (IED), activity immediately before an IED and, finally, mid-IED activity. Connectivity matrices for each epoch for each FC function were built, and graph theory was used to obtain the following metrics: strength, cluster coefficient, betweenness centrality, eigenvector centrality (both local and global), and global efficiency. The statistical distributions of these metrics were compared among the three classes, using ANOVA, for each FC function. We found significant differences in all global (p < 0.001) and local (p < 0.00002) graph metrics of the far class compared with before and mid for motif synchronization on the beta band; local betweenness centrality also pointed to a degree of lateralization on the frontotemporal structures. This analysis demonstrates the potential of FC measures, computed using motif synchronization, for the characterization of epileptiform activity of MTLE patients. This methodology may be helpful in the analysis of EEG-fMRI data applied to epileptic foci localization. Nonetheless, the methods must be tested with a larger sample and with other epileptic phenotypes.

Project description:ObjectiveRapid eye movement (REM) sleep has a suppressing effect on epileptic activity. This effect might be directly related to neuronal desynchronization mediated by cholinergic neurotransmission. We investigated whether interictal epileptiform discharges (IEDs) and high frequency oscillations-a biomarker of the epileptogenic zone-are evenly distributed across phasic and tonic REM sleep. We hypothesized that IEDs are more suppressed during phasic REM sleep because of additional cholinergic drive.MethodsTwelve patients underwent polysomnography during long-term combined scalp-intracerebral electroencephalography (EEG) recording. After sleep staging in the scalp EEG, we identified segments of REM sleep with rapid eye movements (phasic REM) and segments of REM sleep without rapid eye movements (tonic REM). In the intracerebral EEG, we computed the power in frequencies <30 Hz and from 30 to 500 Hz, and marked IEDs, ripples (>80 Hz) and fast ripples (>250 Hz). We grouped the intracerebral channels into channels in the seizure-onset zone (SOZ), the exclusively irritative zone (EIZ), and the normal zone (NoZ).ResultsPower in frequencies <30 Hz was lower during phasic than tonic REM sleep (p < 0.001), most likely reflecting increased desynchronization. IEDs, ripples and fast ripples, were less frequent during phasic than tonic REM sleep (phasic REM sleep: 39% of spikes, 35% of ripples, 18% of fast ripples, tonic REM sleep: 61% of spikes, 65% of ripples, 82% of fast ripples; p < 0.001). In contrast to ripples in the epileptogenic zone, physiologic ripples were more abundant during phasic REM sleep (phasic REM sleep: 73% in NoZ, 30% in EIZ, 28% in SOZ, tonic REM sleep: 27% in NoZ, 70% in EIZ, 72% in SOZ; p < 0.001).SignificancePhasic REM sleep has an enhanced suppressive effect on IEDs, corroborating the role of EEG desynchronization in the suppression of interictal epileptic activity. In contrast, physiologic ripples were increased during phasic REM sleep, possibly reflecting REM-related memory consolidation and dreaming.

Dataset Information

Interictal functional connectivity of human epileptic networks assessed by intracerebral EEG and BOLD signal fluctuations.

Publications

Interictal functional connectivity of human epileptic networks assessed by intracerebral EEG and BOLD signal fluctuations.

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