Scalp recorded spike ripples predict seizure risk in childhood epilepsy better than spikes

Kramer, Mark A.; Ostrowski, Lauren M.; Song, Dan; Thorn, Emily; Stoyell, Sally M.; Parnes, McKenna F.; Chinappen, Dhinakaran M.; Xiao, Guorui; Eden, Uri T.; Staley, Kevin J.; Stufflebeam, Steven M.; Chu, Catherine J.

doi:10.1093/brain/awz059

Cited by 71 publications

(82 citation statements)

References 65 publications

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“…Such a correlation was not found for interictal spikes, ripples, or ripples co-occurring with spikes (IES-ripples). These findings are in line with previous work suggesting that HFOs might identify epileptogenic tissue better than spikes (9,34). It is still subject to debate which of the HFO subgroups is suited best as a biomarker, but a popular notion is that ripples lack specificity, possibly because some of the events are physiological.…”

Section: Association Between Interictal Fast Ripples and Seizure-genesupporting

confidence: 90%

Interictal Fast Ripples Are Associated With the Seizure-Generating Lesion in Patients With Dual Pathology

et al. 2020

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Rationale: Patients with dual pathology have two potentially epileptogenic lesions: One in the hippocampus and one in the neocortex. If epilepsy surgery is considered, stereotactic electroencephalography (SEEG) may reveal which of the lesions is seizure-generating, but frequently, some uncertainty remains. We aimed to investigate whether interictal high-frequency oscillations (HFOs), which are a promising biomarker of epileptogenicity, are associated with the primary focus. Methods: We retrospectively analyzed 16 patients with dual pathology. They were grouped according to their seizure-generating lesion, as suggested by ictal SEEG. An automated detector was applied to identify interictal epileptic spikes, ripples (80-250 Hz), ripples co-occurring with spikes (IES-ripples) and fast ripples (250-500 Hz). We computed a ratio R to obtain an indicator of whether rates were higher in the hippocampal lesion (R close to 1), higher in the neocortical lesion (R close to −1), or more or less similar (R close to 0). Results: Spike and HFO rates were higher in the hippocampal than in the neocortical lesion (p < 0.001), particularly in seizure onset zone channels. Seizures originated exclusively in the hippocampus in 5 patients (group 1), in both lesions in 7 patients (group 2), and exclusively in the neocortex in 4 patients (group 3). We found a significant correlation between the patients' primary focus and the ratio R fast ripples , i.e., the proportion of interictal fast ripples detected in this lesion (p < 0.05). No such correlation was observed for interictal epileptic spikes (p = 0.69), ripples (p = 0.60), and IES-ripples (p = 0.54). In retrospect, interictal fast ripples would have correctly "predicted" the primary focus in 69% of our patients (p < 0.01). Conclusions: We report a correlation between interictal fast ripple rate and the primary focus, which was not found for epileptic spikes. Fast ripple analysis could provide helpful information for generating a hypothesis on seizure-generating networks, especially in cases with few or no recorded seizures.

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Section: Association Between Interictal Fast Ripples and Seizure-genesupporting

confidence: 90%

Interictal Fast Ripples Are Associated With the Seizure-Generating Lesion in Patients With Dual Pathology

et al. 2020

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“…Our external validation shows that the discrimination of the Lamberink 5‐year model is adequate (AUC = 0.68, 95% CI = 0.60‐0.76), but the model significantly overestimated the risk of seizure recurrence (Figure ). To improve the prediction model, additional possibly meaningful predictors should be identified and added to the model, such as scalp‐recorded spike ripples, risk marker alleles, and even individual brain structure . Moreover, in theory, prediction models of epilepsy should be developed into several parts according to different epilepsy syndromes, because epilepsy is a highly heterogeneous disease and different epilepsy syndromes have entirely distinct prognoses .…”

Section: Discussionmentioning

confidence: 99%

External validation and comparison of two prediction models for seizure recurrence after the withdrawal of antiepileptic drugs in adult patients

Lin

Ding

et al. 2019

Epilepsia

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Objective:The models currently available for predicting the risk of seizure recurrence after antiepileptic drug (AED) withdrawal in adult epilepsy patients include the prediction model developed by Lamberink et al (Lamberink model, 2017) and the Medical Research Council prediction model (MRC model, 1993). However, there was no external validation for the two models. The purpose of this study was to perform an independent external validation and a comparison of the Lamberink model and the MRC model in adult patients. Methods: The study population was recruited from the Wenzhou Epilepsy Follow-up Registry Database (WEFURD). All the predictors of the Lamberink and MRC models and the occurrence of seizure recurrence in the participants were collected based on the WEFURD. Participants' predicted probabilities of seizure recurrence were obtained by a Web-based tool and the prognostic index formula. The external validation of the Lamberink model and the MRC model were quantified by discrimination, calibration, and decision curve analysis (DCA). Results: Of 212 patients, 126 (59.4%) had seizure recurrence after AED withdrawal. The Lamberink 2-year model, the Lamberink 5-year model, the MRC 1-year model, and the MRC 2-year model had areas under the curve of 0.71 (95% confidence interval [CI] = 0.64-0.78), 0.68 (95% CI = 0.60-0.76), 0.60 (95% CI = 0.50-0.69), and 0.58 (95% CI = 0.50-0.66), respectively. Additionally, the Lamberink 2-year model had a significantly better integrated discrimination improvement than the MRC 2-year model (P < .001). Regarding calibration, the Lamberink 2-year model (P = .121) and the MRC 1-year model (P = .264) were well calibrated, but the Lamberink 5-year model (P = .022) and the MRC 2-year model (P = .008) were not. In the DCA, the Lamberink 2-year model performed well at threshold probabilities of 30%-65%. Significance: This external validation shows that the Lamberink 2-year model might be more accurate and has greater clinical benefit than others for guiding drug withdrawal in adult epilepsy clinics. 116 | LIN et aL. K E Y W O R D S adult epilepsy, drug withdrawal, external validation, prediction model, seizure recurrence Key Points • The Lamberink 2-year model had the highest AUC (0.71) among the models in the external validation • The Lamberink 2-year model (P = .121) and the MRC 1-year model (P = .264) were well calibrated, but the other prediction models were not • In the DCA, the Lamberink 2-year model performed well at threshold probabilities of 30%-65%

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“…Increasing evidence showed that HFOs below 250 Hz (ripples) can be seen on scalp EEG [10][11][12][13][14][15][16][17] and magnetoencephalography [18][19][20][21][22][23][24][25][26][27] (MEG) recordings, despite the small extent of their cortical generators. 11,16,28 Yet, their translation to clinical practice is still limited.…”

Section: Introductionmentioning

confidence: 99%

Scalp ripples as prognostic biomarkers of epileptogenicity in pediatric surgery

Tamilia

Dirodi

Alhilani

et al. 2020

Ann Clin Transl Neurol

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Objective To assess the ability of high‐density Electroencephalography (HD‐EEG) and magnetoencephalography (MEG) to localize interictal ripples, distinguish between ripples co‐occurring with spikes (ripples‐on‐spike) and independent from spikes (ripples‐alone), and evaluate their localizing value as biomarkers of epileptogenicity in children with medically refractory epilepsy. Methods We retrospectively studied 20 children who underwent epilepsy surgery. We identified ripples on HD‐EEG and MEG data, localized their generators, and compared them with intracranial EEG (icEEG) ripples. When ripples and spikes co‐occurred, we performed source imaging distinctly on the data above 80 Hz (to localize ripples) and below 70 Hz (to localize spikes). We assessed whether missed resection of ripple sources predicted poor outcome, separately for ripples‐on‐spikes and ripples‐alone. Similarly, predictive value of spikes was calculated. Results We observed scalp ripples in 16 patients (10 good outcome). Ripple sources were highly concordant to the icEEG ripples (HD‐EEG concordance: 79%; MEG: 83%). When ripples and spikes co‐occurred, their sources were spatially distinct in 83‐84% of the cases. Removing the sources of ripples‐on‐spikes predicted good outcome with 90% accuracy for HD‐EEG (P = 0.008) and 86% for MEG (P = 0.044). Conversely, removing ripples‐alone did not predict outcome. Resection of spike sources (generated at the same time as ripples) predicted good outcome for HD‐EEG (P = 0.036; accuracy = 87%), while did not reach significance for MEG (P = 0.1; accuracy = 80%). Interpretation HD‐EEG and MEG localize interictal ripples with high precision in children with refractory epilepsy. Scalp ripples‐on‐spikes are prognostic, noninvasive biomarkers of epileptogenicity, since removing their cortical generators predicts good outcome. Conversely, scalp ripples‐alone are most likely generated by non‐epileptogenic areas.

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Scalp recorded spike ripples predict seizure risk in childhood epilepsy better than spikes

Cited by 71 publications

References 65 publications

Interictal Fast Ripples Are Associated With the Seizure-Generating Lesion in Patients With Dual Pathology

Interictal Fast Ripples Are Associated With the Seizure-Generating Lesion in Patients With Dual Pathology

External validation and comparison of two prediction models for seizure recurrence after the withdrawal of antiepileptic drugs in adult patients

Scalp ripples as prognostic biomarkers of epileptogenicity in pediatric surgery

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