Physiological and pathological high frequency oscillations in focal epilepsy

Cimbálník, Jan; Brinkmann, Benjamin H.; Kremen, Václav; Jurák, Pavel; Berry, Brent M.; Gompel, Jamie Van; Stead, Matt; Worrell, Greg

doi:10.1002/acn3.618

Cited by 89 publications

(97 citation statements)

References 48 publications

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“…Other machine learning techniques have been applied to the detection and analysis of HFOs. Support vector machines have been used to distinguish HFOs from false positives due to the filtering of sharp transients, 42 to distinguish between pathological and physiological HFOs, 23 to classify individual channels based on HFO features, 43 and to classify high‐frequency events as ictal or nonictal. Pearce et al 44 also utilized logistic regression and k‐nearest neighbors clustering.…”

Section: Discussionmentioning

confidence: 99%

Detection of anomalous high‐frequency events in human intracranial EEG

et al. 2020

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This is an open access article under the terms of the Creative Commons Attribution-NonCommercial-NoDerivs License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non-commercial and no modifications or adaptations are made. AbstractObjective: High-frequency oscillations (HFOs) are a promising biomarker for the epileptogenic zone. However, no physiological definition of an HFO has been established, so detection relies on the empirical definition of an HFO derived from visual observation. This can bias estimates of HFO features such as amplitude and duration, thereby hindering their utility as biomarkers. Therefore, we set out to develop an algorithm that detects high-frequency events in the intracranial EEG that are morphologically distinct from background without requiring assumptions about event amplitude or shape. Method: We propose the anomaly detection algorithm (ADA), which uses unsupervised machine learning to identify segments of data that are distinct from the background. We apply ADA and a standard HFO detector using a root mean square amplitude threshold to intracranial EEG from 11 patients undergoing evaluation for epilepsy surgery. The rate, amplitude, and duration of the detected events and the percent overlap between the two detectors are compared. Result: In the seizure onset zone (SOZ), ADA detected a subset of conventional HFOs. In non-SOZ channels, ADA detected at least twice as many events as the standard approach, including some conventional HFOs; however, ADA also identified many low and intermediate amplitude events missed by the standard amplitudebased method. The rate of ADA events was similar across all channels; however, the amplitude of ADA events was significantly higher in SOZ channels (P < .0045), and the amplitude measurement was more stable over time than the HFO rate, as indicated by a lower coefficient of variation (P < .0125). Significance: ADA does not require human supervision, parameter optimization, or prior assumptions about event shape, amplitude, or duration. Our results suggest that the algorithm's estimate of event amplitude may differentiate SOZ and non-SOZ channels. Further studies will examine the utility of HFO amplitude as a biomarker for epilepsy surgical outcome. |

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Section: Discussionmentioning

confidence: 99%

Detection of anomalous high‐frequency events in human intracranial EEG

et al. 2020

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“…All patients showed icEEG ripples. The icEEG-ripplezone included 33 (22)(23)(24)(25)(26)(27)(28)(29)(30)(31)(32)(33)(34)(35)(36)(37)(38)(39)(40)(41) contacts with mean rate of 9.8 (6.3-14.3) ripples/min. Of all the ripple sources within icEEG coverage, 79% for ESI and 83% for MSI were concordant to the icEEG-ripple-zone, without difference between lesional and nonlesional patients (ESI: P = 0.8; MSI: P = 0.5).…”

Section: Concordance Of Ripple Sources With Iceegmentioning

confidence: 99%

“…Furthermore, very few studies investigated the localizing value of scalp‐recorded ripples as epilepsy biomarkers for surgery. This may have significant clinical impact since several icEEG studies showed that ripples are not always generated by epileptogenic areas, but can also represent physiological events generated by non‐epileptogenic tissues …”

Section: Introductionmentioning

confidence: 99%

“…This may have significant clinical impact since several icEEG studies showed that ripples are not always generated by epileptogenic areas, but can also represent physiological events generated by non-epileptogenic tissues. 3,[29][30][31] Previous studies showed that noninvasively localized ripple generators correlate with regions generating interictal spikes 22,27 or with different approximations of the EZ defined by clinical information. 14,19,22,23,[25][26][27] However, most of these studies included patients who did not necessarily undergo surgery, with the exception of two MEG studies on patients with focal MRI lesions 25 or insular epilepsy.…”

Section: Introductionmentioning

confidence: 99%

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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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“…Since their first description, HFOs are believed to be associated with epileptogenesis (Bragin et al, 1999). Findings that (a) the appearance of HFOs is strongly associated with seizure onset, and (b) removing areas with higher HFO rates leads to better surgical outcomes (Jacobs et al, 2010;Zijlmans et al, 2011Zijlmans et al, , 2012van't Klooster et al, 2017;Cimbalnik et al, 2018), are particularly interesting and hold clinical relevance. Despite this and other evidence for the diagnostic utility of HFOs, only a few studies have investigated HFO incidence during ictogenesis.…”

Section: Introductionmentioning

confidence: 99%

Seizure onset location shapes dynamics of initiation

Salami

Peled

Nadalin

et al. 2020

Preprint

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ObjectiveIctal electrographic patterns are widely thought to reflect underlying neural mechanisms of seizures. Here we studied the degree to which seizure patterns are consistent in a given patient, relate to particular brain regions and if two candidate biomarkers (high-frequency oscillations, HFOs; infraslow activity, ISA) and network activity, as assessed with cross-frequency interactions, can discriminate between seizure types.MethodsWe analyzed temporal changes in low and high frequency oscillations recorded during seizures, as well as phase-amplitude coupling (PAC) to monitor the interactions between delta/theta and ripple/fast ripple frequency bands at seizure onset.ResultsSeizures of multiple pattern types were observed in a given patient and brain region. While there was an increase in HFO rate across different electrographic patterns, there are specific relationships between types of HFO activity and onset region. Similarly, changes in PAC dynamics were more closely related to seizure onset region than they were to electrographic patterns while ISA was a poor indicator for seizure onset.ConclusionsOur findings suggest that the onset region sculpts neurodynamics at seizure initiation and that unique features of the cytoarchitecture and/or connectivity of that region play a significant role in determining seizure mechanism.SignificanceClinicians should consider more than just overt electrographic patterns when considering seizure mechanisms and regions of onset. Examination of onset pattern in conjunction with the interactions between different oscillatory frequencies in the context of different brain regions might be more informative and lead to more reliable clinical inference as well as novel therapeutic approaches.

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Physiological and pathological high frequency oscillations in focal epilepsy

Cited by 89 publications

References 48 publications

Detection of anomalous high‐frequency events in human intracranial EEG

Detection of anomalous high‐frequency events in human intracranial EEG

Scalp ripples as prognostic biomarkers of epileptogenicity in pediatric surgery

Seizure onset location shapes dynamics of initiation

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