Brain areas with epileptic high frequency oscillations are functionally isolated in MEG virtual electrode networks

Nissen, Ida A.; Klink, Nicole E.C. van; Zijlmans, Maeike; Stam, Cornelis J.; Hillebrand, Arjan

doi:10.1016/j.clinph.2016.04.013

Cited by 42 publications

(56 citation statements)

References 64 publications

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“…Our findings confirm the previously reported functional isolation in the gamma-band network of channels showing epileptiform events (Warren et al, 2010, Ibrahim et al, 2013a, Van Diessen et al, 2013b, Nissen et al, 2016). We found a suggestion of local enhanced connectivity of these channels in the FR-band functional network.…”

Section: Discussionsupporting

confidence: 93%

“…Retrospective network studies on ECoG data of surgical candidates have shown that inclusion of the nodes with the highest (inter)ictal centrality measures in the gamma-band network in the resection, correlated with post-surgical seizure-freedom (Ortega et al, 2008, Wilke et al, 2011, Varotto et al, 2012, Mierlo et al, 2013). This suggests that resection of brain areas generating HFOs, which we confirmed to be functionally isolated in the gamma-band network (Ibrahim et al, 2013a, Van Diessen et al, 2013b, Nissen et al, 2016), and resection of the most central nodes in the gamma-band functional network represent two approaches to achieve seizure freedom. Gamma-band activity is generated by networks of predominantly inhibitory interneurons (Fries et al, 2007, Cardin et al, 2009, Sohal, 2012).…”

Section: Discussionmentioning

confidence: 56%

“…The closely surrounding healthy tissue may seem relatively well connected in the gamma frequency band. This is supported by the increase in gamma-band node centrality with increasing distance from the center of brain areas generating spikes and HFOs (Nissen et al, 2016). Both removal of the epileptogenic, FR-generating tissue and removal of the closely surrounding tissue through which a seizure initially spreads may, in this framework, lead to seizure-freedom.…”

Section: Discussionmentioning

confidence: 91%

“…Interictal network studies have reported higher centrality measures in the epileptogenic zone (Wilke et al, 2011, Varotto et al, 2012) and lower centrality measures in the SOZ (Warren et al, 2010, Van Diessen et al, 2013b). Nissen et al, 2016 found higher average centrality measures in virtual MEG electrodes in the affected compared to the non-affected hemisphere, but a lower node centrality the closer to the center of brain areas generating epileptiform events. The first studies comparing the presence and localization of HFOs with meso-scale hub regions reported that channels displaying HFOs are functionally isolated in the gamma frequency band, while results in the frequency bands below 30 Hz were contradictive (Ibrahim et al, 2013a, Van Diessen et al, 2013b, Nissen et al, 2016).…”

Section: Introductionmentioning

confidence: 81%

“…Nissen et al, 2016 found higher average centrality measures in virtual MEG electrodes in the affected compared to the non-affected hemisphere, but a lower node centrality the closer to the center of brain areas generating epileptiform events. The first studies comparing the presence and localization of HFOs with meso-scale hub regions reported that channels displaying HFOs are functionally isolated in the gamma frequency band, while results in the frequency bands below 30 Hz were contradictive (Ibrahim et al, 2013a, Van Diessen et al, 2013b, Nissen et al, 2016). Based on the presumed enhanced functional connectivity needed to generate FRs, we would expect local functional integration of channels covering these areas, at least in the FR-band.…”

Section: Introductionmentioning

confidence: 81%

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High frequency oscillations and high frequency functional network characteristics in the intraoperative electrocorticogram in epilepsy

Zweiphenning

Klooster

Diessen

et al. 2016

NeuroImage: Clinical

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ObjectiveHigh frequency oscillations (HFOs; > 80 Hz), especially fast ripples (FRs, 250–500 Hz), are novel biomarkers for epileptogenic tissue. The pathophysiology suggests enhanced functional connectivity within FR generating tissue. Our aim was to determine the relation between brain areas showing FRs and ‘baseline’ functional connectivity within EEG networks, especially in the high frequency bands.MethodsWe marked FRs, ripples (80–250 Hz) and spikes in the electrocorticogram of 14 patients with refractory temporal lobe epilepsy. We assessed ‘baseline’ functional connectivity in epochs free of epileptiform events within these recordings, using the phase lag index. We computed the Eigenvector Centrality (EC) per channel in the FR and gamma band network. We compared EC between channels that did or did not show events at other moments in time.ResultsFR-band EC was higher in channels with than without spikes. Gamma-band EC was lower in channels with ripples and FRs.ConclusionsWe confirmed previous findings of functional isolation in the gamma-band and found a first proof of functional integration in the FR-band network of channels covering presumed epileptogenic tissue.Significance‘Baseline’ high-frequency network parameters might help intra-operative recognition of epileptogenic tissue without the need for waiting for events. These findings can increase our understanding of the ‘architecture’ of epileptogenic networks and help unravel the pathophysiology of HFOs.

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Section: Discussionsupporting

confidence: 93%

Section: Discussionmentioning

confidence: 56%

Section: Discussionmentioning

confidence: 91%

Section: Introductionmentioning

confidence: 81%

Section: Introductionmentioning

confidence: 81%

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High frequency oscillations and high frequency functional network characteristics in the intraoperative electrocorticogram in epilepsy

Zweiphenning

Klooster

Diessen

et al. 2016

NeuroImage: Clinical

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Noninvasive Mapping of Ripple Onset Predicts Outcome in Epilepsy Surgery

Tamilia

Matarrese

Ntolkeras

et al. 2021

Annals of Neurology

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Objective: Intracranial electroencephalographic (icEEG) studies show that interictal ripples propagate across the brain of children with medically refractory epilepsy (MRE), and the onset of this propagation (ripple onset zone [ROZ]) estimates the epileptogenic zone. It is still unknown whether we can map this propagation noninvasively. The goal of this study is to map ripples (ripple zone [RZ]) and their propagation onset (ROZ) using high-density EEG (HD-EEG) and magnetoencephalography (MEG), and to estimate their prognostic value in pediatric epilepsy surgery. Methods: We retrospectively analyzed simultaneous HD-EEG and MEG data from 28 children with MRE who underwent icEEG and epilepsy surgery. Using electric and magnetic source imaging, we estimated virtual sensors (VSs) at brain locations that matched the icEEG implantation. We detected ripples on VSs, defined the virtual RZ and virtual ROZ, and estimated their distance from icEEG. We assessed the predictive value of resecting virtual RZ and virtual ROZ for postsurgical outcome. Interictal spike localization on HD-EEG and MEG was also performed and compared with ripples. Results: We mapped ripple propagation in all patients with HD-EEG and in 27 (96%) patients with MEG. The distance from icEEG did not differ between HD-EEG and MEG when mapping the RZ (26-27mm, p = 0.6) or ROZ (22-24mm, p = 0.4). Resecting the virtual ROZ, but not virtual RZ or the sources of spikes, was associated with good outcome for HD-EEG (p = 0.016) and MEG (p = 0.047). Interpretation: HD-EEG and MEG can map interictal ripples and their propagation onset (virtual ROZ). Noninvasively mapping the ripple onset may augment epilepsy surgery planning and improve surgical outcome of children with MRE.

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Novel noninvasive identification of patient‐specific epileptic networks in focal epilepsies: Linking single‐photon emission computed tomography perfusion during seizures with resting‐state magnetoencephalography dynamics

Krishnan

Tousseyn

Wang

et al. 2022

Human Brain Mapping

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Single‐photon emission computed tomography (SPECT) during seizures and magnetoencephalography (MEG) during the interictal state are noninvasive modalities employed in the localization of the epileptogenic zone in patients with drug‐resistant focal epilepsy (DRFE). The present study aims to investigate whether there exists a preferentially high MEG functional connectivity (FC) among those regions of the brain that exhibit hyperperfusion or hypoperfusion during seizures. We studied MEG and SPECT data in 30 consecutive DRFE patients who had resective epilepsy surgery. We parcellated each ictal perfusion map into 200 regions of interest (ROIs) and generated ROI time series using source modeling of MEG data. FC between ROIs was quantified using coherence and phase‐locking value. We defined a generalized linear model to relate the connectivity of each ROI, ictal perfusion z score, and distance between ROIs. We compared the coefficients relating perfusion z score to FC of each ROI and estimated the connectivity within and between resected and unresected ROIs. We found that perfusion z scores were strongly correlated with the FC of hyper‐, and separately, hypoperfused ROIs across patients. High interictal connectivity was observed between hyperperfused brain regions inside and outside the resected area. High connectivity was also observed between regions of ictal hypoperfusion. Importantly, the ictally hypoperfused regions had a low interictal connectivity to regions that became hyperperfused during seizures. We conclude that brain regions exhibiting hyperperfusion during seizures highlight a preferentially connected interictal network, whereas regions of ictal hypoperfusion highlight a separate, discrete and interconnected, interictal network.

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Brain areas with epileptic high frequency oscillations are functionally isolated in MEG virtual electrode networks

Cited by 42 publications

References 64 publications

High frequency oscillations and high frequency functional network characteristics in the intraoperative electrocorticogram in epilepsy

High frequency oscillations and high frequency functional network characteristics in the intraoperative electrocorticogram in epilepsy

Noninvasive Mapping of Ripple Onset Predicts Outcome in Epilepsy Surgery

Novel noninvasive identification of patient‐specific epileptic networks in focal epilepsies: Linking single‐photon emission computed tomography perfusion during seizures with resting‐state magnetoencephalography dynamics

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