Differentiation of specific ripple patterns helps to identify epileptogenic areas for surgical procedures

Kerber, Karolin; Dümpelmann, Matthias; Schelter, Björn; Van, Pierre Le; Korinthenberg, Rudolf; Schulze‐Bonhage, Andreas; Jacobs, Julia

doi:10.1016/j.clinph.2013.11.030

Cited by 127 publications

(130 citation statements)

References 35 publications

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“…One important question of immediate practical interest is the spatial relationship between the temporally asymmetric signals we found as markers of the epileptogenic zone and other signal characteristics such as ictal (Modur et al, 2011, Malinowska et al, 2015 and interictal fast oscillations (Cho et al, 2014, Kerber et al, 2014, Okanishi et al, 2014, Gliske et al, 2016 . Clarifying this relationship will be relevant for assessing how much additional information can be provided financial time series into HVG and then constructed a multilayer ("multiplex") network, in which each layer consisted of one HVG.…”

Section: Discussionmentioning

confidence: 99%

Ictal time-irreversible intracranial EEG signals as markers of the epileptogenic zone

Schindler

Rummel

Andrzejak

et al. 2016

Clinical Neurophysiology

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Section: Discussionmentioning

confidence: 99%

Ictal time-irreversible intracranial EEG signals as markers of the epileptogenic zone

Schindler

Rummel

Andrzejak

et al. 2016

Clinical Neurophysiology

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“…Analyzing the EEG characteristics associated with ripples may be useful for identifying pathological ripples. Interictal ripples occurring in an oscillatory background activity may be suggestive of physiological activity, while ripples on a flat background probably reflect epileptic activity [57]. Alternatively, neocortical ripples nested in an interictal epileptiform discharge, so-called 'Type I ripples', have been found to be specifically distributed in the SOZ or primary propagation areas [30].…”

Section: High-frequency Oscillationsmentioning

confidence: 99%

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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“…The approach we presented here is novel and different from the existing ones (Kerber et al, 2014;Wang et al, 2013) as it is based exclusively on the morphology of HFOs, and not based on relation to spikes or background activity. For more accurate comparison and delineation of pathological and physiological HFOs, one should compare rates of different HFO types of cortical tissue without epilepsy.…”

Section: Other Approaches To Discriminate Physiological and Pathologimentioning

confidence: 99%

“…Another approach based on the background activity was presented in Kerber et al (2014) . The authors showed that resection of areas with ripples occurring in a flat background activity correlates with a good postsurgical outcome, whereas resection of areas generating ripples in a continuously oscillating background did not show an association with the surgical outcome.…”

Section: Introductionmentioning

confidence: 99%

The morphology of high frequency oscillations (HFO) does not improve delineating the epileptogenic zone

Burnos

Frauscher

Zelmann

et al. 2016

Clinical Neurophysiology

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OBJECTIVE: We hypothesized that high frequency oscillations (HFOs) with irregular amplitude and frequency more specifically reflect epileptogenicity than HFOs with stable amplitude and frequency. METHODS: We developed a fully automatic algorithm to detect HFOs and classify them based on their morphology, with types defined according to regularity in amplitude and frequency: type 1 with regular amplitude and frequency; type 2 with irregular amplitude, which could result from filtering of sharp spikes; type 3 with irregular frequency; and type 4 with irregular amplitude and frequency. We investigated the association of different HFO types with the seizure onset zone (SOZ), resected area and surgical outcome. RESULTS: HFO rates of all types were significantly higher inside the SOZ than outside. HFO types 1 and 2 were strongly correlated to each other and showed the highest rates among all HFOs. Their occurrence was highly associated with the SOZ, resected area and surgical outcome. The automatic detection emulated visual markings with 93% true positives and 57% false detections. CONCLUSIONS: HFO types 1 and 2 similarly reflect epileptogenicity. SIGNIFICANCE: For clinical application, it may not be necessary to separate real HFOs from "false oscillations" produced by the filter effect of sharp spikes. Also for automatically detected HFOs, surgical outcome is better when locations with higher HFO rates are included in the resection. We hypothesized that high frequency oscillations (HFOs) with irregular amplitude and frequency more specifically reflect epileptogenicity than HFOs with stable amplitude and frequency. Methods:We developed a fully automatic algorithm to detect HFOs and classify them based on their morphology, with types defined according to regularity in amplitude and frequency: type 1 with regular amplitude and frequency; type 2 with irregular amplitude, which could result from filtering of sharp spikes; type 3 with irregular frequency; and type 4 with irregular amplitude and frequency. We investigated the association of different HFO types with the seizure onset zone (SOZ), resected area and surgical outcome. Results:HFO rates of all types were significantly higher inside the SOZ than outside. HFO types 1 and 2 were strongly correlated to each other and showed the highest rates among all HFOs. Their occurrence was highly associated with the SOZ, resected area and surgical outcome. The automatic detection emulated visual markings with 93% true positives and 57% false detections. Conclusions:HFO types 1 and 2 similarly reflect epileptogenicity. Significance:For clinical application, it may not be necessary to separate real HFOs from "false oscillations" produced by the filter effect of sharp spikes. Also for automatically detected HFOs, surgical outcome is better when locations with higher HFO rates are included in the resection.

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Differentiation of specific ripple patterns helps to identify epileptogenic areas for surgical procedures

Cited by 127 publications

References 35 publications

Ictal time-irreversible intracranial EEG signals as markers of the epileptogenic zone

Ictal time-irreversible intracranial EEG signals as markers of the epileptogenic zone

Advances of Intracranial Electroencephalography in Localizing the Epileptogenic Zone

The morphology of high frequency oscillations (HFO) does not improve delineating the epileptogenic zone

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