Relationships between interictal epileptic spikes and ripples in surface EEG

Klink, Nicole van; Frauscher, Birgit; Zijlmans, Maeike; Gotman, Jean

doi:10.1016/j.clinph.2015.04.059

Cited by 43 publications

(50 citation statements)

References 27 publications

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“…The presence of spikes was not required to perform the analysis, but it increased the chance of finding ripples (Melani et al, 2013). We found ripples in 64% of the patients with spikes in the MEG, which is in line with 61–88% of focal epilepsy patients with ripples that are reported in scalp EEG (Andrade-Valenca et al, 2011, Melani et al, 2013, Van Klink et al, 2016b). Our results also suggest that the chance of good localization is higher when the number of identified ripples is higher.…”

Section: Discussionsupporting

confidence: 89%

“…The rate of true ripples was 1.3/minute in patients with ripples, which is low, but comparable to visually marked ripples (Van Klink et al, 2016b). One other study that automatically detected ripples in the time domain found ripples in 8 out of 17 patients (47%), without using beamformer virtual sensors, and found similar ripple rates (Von Ellenrieder et al, 2016).…”

Section: Discussionmentioning

confidence: 50%

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Automatic detection and visualisation of MEG ripple oscillations in epilepsy

Klink

Rosmalen

Nenonen

et al. 2017

NeuroImage: Clinical

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High frequency oscillations (HFOs, 80–500 Hz) in invasive EEG are a biomarker for the epileptic focus. Ripples (80–250 Hz) have also been identified in non-invasive MEG, yet detection is impeded by noise, their low occurrence rates, and the workload of visual analysis. We propose a method that identifies ripples in MEG through noise reduction, beamforming and automatic detection with minimal user effort. We analysed 15 min of presurgical resting-state interictal MEG data of 25 patients with epilepsy. The MEG signal-to-noise was improved by using a cross-validation signal space separation method, and by calculating ~ 2400 beamformer-based virtual sensors in the grey matter. Ripples in these sensors were automatically detected by an algorithm optimized for MEG. A small subset of the identified ripples was visually checked. Ripple locations were compared with MEG spike dipole locations and the resection area if available. Running the automatic detection algorithm resulted in on average 905 ripples per patient, of which on average 148 ripples were visually reviewed. Reviewing took approximately 5 min per patient, and identified ripples in 16 out of 25 patients. In 14 patients the ripple locations showed good or moderate concordance with the MEG spikes. For six out of eight patients who had surgery, the ripple locations showed concordance with the resection area: 4/5 with good outcome and 2/3 with poor outcome. Automatic ripple detection in beamformer-based virtual sensors is a feasible non-invasive tool for the identification of ripples in MEG. Our method requires minimal user effort and is easily applicable in a clinical setting.

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Section: Discussionsupporting

confidence: 89%

Section: Discussionmentioning

confidence: 50%

Automatic detection and visualisation of MEG ripple oscillations in epilepsy

Klink

Rosmalen

Nenonen

et al. 2017

NeuroImage: Clinical

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“…The onset of ripple 28 and gamma 29 oscillations can precede inter-ictal epileptiform discharges, while gamma can occur after inter-ictal discharges as well as fast ripples, i.e. fast ripple tail gamma 7 .…”

Section: Discussionmentioning

confidence: 99%

Ripples on spikes show increased phase‐amplitude coupling in mesial temporal lobe epilepsy seizure‐onset zones

et al. 2016

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Objective Ripples (80–150 Hz) recorded from clinical macroelectrodes have been shown to be an accurate biomarker of epileptogenic brain tissue. We investigated coupling between epileptiform spike phase and ripple amplitude to better understand the mechanisms that generate this type of pathological ripple (pRipple) event. Methods We quantified phase amplitude coupling (PAC) between epileptiform EEG spike phase and ripple amplitude recorded from intracranial depth macroelectrodes during episodes of sleep in 12 patients with mesial temporal lobe epilepsy. PAC was determined by 1) a phasor transform that corresponds to the strength and rate of ripples coupled with spikes, and a 2) ripple-triggered average to measure the strength, morphology, and spectral frequency of the modulating and modulated signals. Coupling strength was evaluated in relation to recording sites within and outside the seizure onset zone (SOZ). Results Both the phasor transform and ripple-triggered averaging methods showed ripple amplitude was often robustly coupled with epileptiform EEG spike phase. Coupling was more regularly found inside than outside the SOZ, and coupling strength correlated with the likelihood a macroelectrode’s location was within the SOZ (p<0.01). The ratio of the rate of ripples coupled with EEG spikes inside the SOZ to rates of coupled ripples in non-SOZ was greater than the ratio of rates of ripples on spikes detected irrespective of coupling (p<0.05). Coupling strength correlated with an increase in mean normalized ripple amplitude (p<0.01), and a decrease in mean ripple spectral frequency (p<0.05). Significance Generation of low-frequency (80–150 Hz) pRipples in the SOZ involves coupling between epileptiform spike phase and ripple amplitude. The changes in excitability reflected as epileptiform spikes may also cause clusters of pathologically interconnected bursting neurons to grow and synchronize into aberrantly large neuronal assemblies.

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“…Interictal scalp gamma or ripple oscillations have been reported in patients with focal epilepsy and correlated with the location of the SOZ [58,59]. Interictal HFOs have also been found in generalized epilepsy.…”

Section: High-frequency Oscillationsmentioning

confidence: 95%

Advances of Intracranial Electroencephalography in Localizing the Epileptogenic Zone

Jin

Wang

2016

Neurosci. Bull.

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Intracranial electroencephalography (iEEG) provides the best precision in estimating the location and boundary of an epileptogenic zone. Analysis of iEEG in the routine EEG frequency range (0.5-70 Hz) remains the basis in clinical practice. Low-voltage fast activity is the most commonly reported ictal onset pattern in neocortical epilepsy, and low-frequency high-amplitude repetitive spiking is the most commonly reported ictal onset pattern in mesial temporal lobe epilepsy. Recent studies using wideband EEG recording have demonstrated that examining higher (80-1000 Hz) and lower (0.016-0.5 Hz) EEG frequencies can provide additional diagnostic information and help to improve the surgical outcome. In addition, novel computational techniques of iEEG signal analysis have provided new insights into the epileptic network. Here, we review some of these recent advances. Although these sophisticated and advanced techniques of iEEG analysis show promise in localizing the epileptogenic zone, their utility needs to be further validated in larger studies.

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Relationships between interictal epileptic spikes and ripples in surface EEG

Cited by 43 publications

References 27 publications

Automatic detection and visualisation of MEG ripple oscillations in epilepsy

Automatic detection and visualisation of MEG ripple oscillations in epilepsy

Ripples on spikes show increased phase‐amplitude coupling in mesial temporal lobe epilepsy seizure‐onset zones

Advances of Intracranial Electroencephalography in Localizing the Epileptogenic Zone

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