ArticleBrain communications2025
Noninvasive classification of physiological and pathological high frequency oscillations in children.
Article in Brain communications, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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Who cites it
2 citing papers in PubMed.
- Predicting surgical outcome in drug-resistant epilepsy by combining interictal biomarkers within a machine learning framework.Scientific reports · 2026Article
- Predicting Surgical Outcome in Drug-Resistant Epilepsy by Combining Interictal Biomarkers within a Machine Learning Framework.Research square · 2026Article
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Authors and funding
11 authors.
Funding
Abstract
High frequency oscillations have been extensively investigated as interictal biomarkers of epilepsy. Yet, their value is largely debated due to the presence of physiological oscillations, which complicate distinguishing between normal versus abnormal events. So far, this debate has been addressed using intracranial EEG data from patients with drug-resistant epilepsy. Yet, this approach suffers from inability to record control data from healthy subjects and lack of whole brain coverage. Here, we aim to differentiate physiological from pathological high frequency oscillations using non-invasive whole brain electrophysiological recordings from children with drug-resistant epilepsy and typically developing controls. We recorded high-density EEG and magnetoencephalography data from 47 controls (median age: 11 years; 25 females) and 54 children with drug-resistant epilepsy (median age: 14 years, 33 females). We detected high frequency oscillations (in ripple frequency band) semi-automatically and localized their cortical generators through electric or magnetic source imaging. From each ripple, we extracted a set of temporal, morphological, spectral and spatial features. We then compared the features between ripples recorded from the epileptic brain (further distinguished into those from epileptogenic and non-epileptogenic regions) and those recorded from the control group (normal brain). We used these features to cross-validate a Naïve-Bayes algorithm for classifying each ripple recorded from children with epilepsy as coming from an epileptogenic region or not. We observed more high frequency oscillations on EEG than magnetoencephalography recordings (
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