Clinical NeurophysiologyNeurosciencePeer-reviewed journal article
Fast oscillations >40Hz localize the epileptogenic zone: an electrical source imaging study using high-density electroencephalography
Tamir AvigdoriD, Chifaou AbdallahiD, Nicolás von EllenriederiD, Tanguy HedrichiD, Annalisa Rubino, Giorgio Lo RussoiD, Boris C. BernhardtiD, Lino NobiliiD, Christophe GrovaiD, Birgit FrauscheriD
Abstract
ObjectiveFast Oscillations (FO) >40 Hz are a promising biomarker of the epileptogenic zone (EZ). Evidence using scalp electroencephalography (EEG) remains scarce. We assessed if electrical source imaging of FO using 256-channel high-density EEG (HD-EEG) is useful for EZ identification. MethodsWe analyzed HD-EEG recordings of 10 focal drug-resistant epilepsy patients with seizure-free postsurgical outcome. We marked FO candidate events at the time of epileptic spikes and verified them by screening for an isolated peak in the time-frequency plot. We performed electrical source imaging of spikes and FO within the Maximum Entropy of the Mean framework. Source localization maps were validated against the surgical cavity. ResultsWe identified FO in five out of 10 patients who had a superficial or intermediate deep generator. The maximum of the FO maps was localized inside the cavity in all patients (100%). Analysis with a reduced electrode coverage using the 10-10 and 10-20 system showed a decreased localization accuracy of 60% and 40% respectively. ConclusionsFO recorded with HD-EEG localize the EZ. HD-EEG is better suited to detect and localize FO than conventional EEG approaches. SignificanceThis study acts as proof-of-concept that FO localization using 256-channel HD-EEG is a viable marker of the EZ. Highlights- Fast oscillations > 40Hz are able to correctly localize the epileptogenic zone. - HD-EEG is superior in detection and localization of fast oscillations compared to conventional EEG approaches. - Presence of fast oscillations on the scalp might point to a superficial epileptic generator.
- organism: human
- assay: electronic health records
- research focus: cell type annotation
- approach: statistical model
- Citations
- 36
- FWCI
- 3.28
- Field percentile
- top 7%
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