Visually validated semi-automatic high-frequency oscillation detection aides the delineation of epileptogenic regions during intra-operative electrocorticography

Weiss, Shennan A.; Berry, Brent M.; Chervoneva, Inna; Waldman, Zachary; Guba, Jonathan; Bower, Mark R.; Kucewicz, Michal T.; Brinkmann, Benjamin H.; Kremen, Václav; Khadjevand, Fatemeh; Varatharajah, Yogatheesan; Guragain, H.; Sharan, Ashwini; Wu, Chengyuan; Staba, Richard J.; Engel, Jerome; Sperling, Michael R.; Worrell, Gregory A.

doi:10.1016/j.clinph.2018.06.030

Cited by 48 publications

(80 citation statements)

References 22 publications

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“…Importantly, it has been suggested, that pre-surgically assessed HFO rates might not be key in predicting seizure outcome, but that high rates recorded after resection might be an indicator of seizure reoccurrence (50–54), suggesting the importance to disconnect HFO generating networks (53). Accordingly, Weiss et al (55) found that areas with fast ripples occurring on spikes that were not resected during epilepsy surgery were linked to a poor surgical outcome. This might also explain the high specificities occasionally reported by some studies (42, 50, 52, 56).…”

Section: High-frequency Oscillations In the Intracranial Eegmentioning

confidence: 99%

Localization of the Epileptogenic Zone Using High Frequency Oscillations

2019

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For patients with drug-resistant focal epilepsy, surgery is the therapy of choice in order to achieve seizure freedom. Epilepsy surgery foremost requires the identification of the epileptogenic zone (EZ), defined as the brain area indispensable for seizure generation. The current gold standard for identification of the EZ is the seizure-onset zone (SOZ). The fact, however that surgical outcomes are unfavorable in 40–50% of well-selected patients, suggests that the SOZ is a suboptimal biomarker of the EZ, and that new biomarkers resulting in better postsurgical outcomes are needed. Research of recent years suggested that high-frequency oscillations (HFOs) are a promising biomarker of the EZ, with a potential to improve surgical success in patients with drug-resistant epilepsy without the need to record seizures. Nonetheless, in order to establish HFOs as a clinical biomarker, the following issues need to be addressed. First, evidence on HFOs as a clinically relevant biomarker stems predominantly from retrospective assessments with visual marking, leading to problems of reproducibility and reliability. Prospective assessments of the use of HFOs for surgery planning using automatic detection of HFOs are needed in order to determine their clinical value. Second, disentangling physiologic from pathologic HFOs is still an unsolved issue. Considering the appearance and the topographic location of presumed physiologic HFOs could be immanent for the interpretation of HFO findings in a clinical context. Third, recording HFOs non-invasively via scalp electroencephalography (EEG) and magnetoencephalography (MEG) is highly desirable, as it would provide us with the possibility to translate the use of HFOs to the scalp in a large number of patients. This article reviews the literature regarding these three issues. The first part of the article focuses on the clinical value of invasively recorded HFOs in localizing the EZ, the detection of HFOs, as well as their separation from physiologic HFOs. The second part of the article focuses on the current state of the literature regarding non-invasively recorded HFOs with emphasis on findings and technical considerations regarding their localization.

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Section: High-frequency Oscillations In the Intracranial Eegmentioning

confidence: 99%

Localization of the Epileptogenic Zone Using High Frequency Oscillations

2019

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“…The lack of specificity of spikes, as the irritative zone is usually broader than the SOZ, and the presence of high rates of physiological HFOs weakened their respective performance. In fact, HFOs and spikes are rather complementary because merging the two markers does outperform all other markers at both predicting the SOZ [20] and the outcome [25,26]. Unfortunately, the predictive performance was quite low for some patients (Fig.…”

Section: High-frequency Os-cillations and Spikes Are Complementarymentioning

confidence: 97%

Are high-frequency oscillations better biomarkers of the epileptogenic zone than spikes?

Roehri

Bartolomei

2019

Current Opinion in Neurology

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Purpose of review Precise localization of the epileptogenic zone is imperative for the success of resective surgery of drugresistant epileptic patients. To decrease the number of surgical failures, clinical research has been focusing on finding new biomarkers. For the past decades, high-frequency oscillations (HFOs, 80-500Hz) have ousted interictal spikes-the classical interictal marker-from the research spotlight. Many studies have claimed that HFOs were more linked to epileptogenicity than spikes. This present review aims at refining this statement in light of recent studies. Recent findings Analysis based on single-patient characteristics has not been able to determine which of HFOs or spikes were better marker of epileptogenic tissues. Physiological HFOs are one of the main obstacles to translate HFOs to clinical practice as separating them from pathological HFOs remains a challenge. Fast ripples (a subgroup of HFOs, 250-500Hz) which are mostly pathological are not found in all epileptogenic tissues. Summary Quantified measures of HFOs and spikes give complementary results, but many barriers still persist in applying them in clinical routine. The current way of testing HFO and spike detectors and their performance in delineating the epileptogenic zone is debatable and still lacks practicality. Solutions to handle physiological HFOs have been proposed but are still at a preliminary stage.

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“…Recently, a semi-automatic method has been evaluated to identify epochs with high frequency oscillatory (HFO) activity, a characteristic of epileptic EEG and iEEG Shimamoto et al, 2018;Weiss et al, 2018). The algorithm, run only on depth electrodes for optimal signal-to-noise, annotates an epoch with one of the following labels that are then visually validated: 0) No HFO event detected, 1) Ripple superimposed in an interictal discharge, 2) Sharply contoured epileptiform spike, 3) Ripple on oscillation, 4) Fast ripple superimposed in an interictal discharge, 5) Fast ripple on oscillation.…”

Section: Neurologist-based Epoch Exclusionmentioning

confidence: 99%

Does data cleaning improve brain state classification?

Meisler

Kahana

Ezzyat

2019

Preprint

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Background: Neuroscientists routinely seek to identify and remove noisy or artifactual observations from their data. They do so with the belief that removing such data improves power to detect relations between neural activity and behavior, which are often subtle and can be overwhelmed by noise. Whereas standard methods can exclude certain well-defined noise sources (e.g., 50/60 Hz electrical noise), in many situations there is not a clear difference between noise and signals so it is not obvious how to separate the two. Here we ask whether methods routinely used to "clean" human electrophysiological recordings lead to greater power to detect brain-behavior relations.New Method: This, to the authors' knowledge, is the first large-scale simultaneous evaluation of multiple commonly used methods for removing noise from intracranial EEG recordings.Results: We find that several commonly used data cleaning methods (automated methods based on statistical signal properties and manual methods based on expert review) do not increase the power to detect univariate and multivariate electrophysiological biomarkers of successful episodic memory encoding, a well-characterized broadband pattern of neural activity observed across the brain.Comparison with Existing Methods: Researchers may be more likely to increase statistical power to detect physiological phenomena of interest by allocating resources away from cleaning noisy data and toward collecting more within-patient observations.Conclusions: These findings highlight the challenge of partitioning signal and noise in the analysis of brain-behavior relations, and suggest increasing sample size and numbers of observations, rather than data cleaning, as the best approach to improving statistical power.

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Visually validated semi-automatic high-frequency oscillation detection aides the delineation of epileptogenic regions during intra-operative electrocorticography

Cited by 48 publications

References 22 publications

Localization of the Epileptogenic Zone Using High Frequency Oscillations

Localization of the Epileptogenic Zone Using High Frequency Oscillations

Are high-frequency oscillations better biomarkers of the epileptogenic zone than spikes?

Does data cleaning improve brain state classification?

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