Sleep–wake states change the interictal localization of candidate epileptic source generators

Sleep–wake states change the interictal localization of candidate epileptic source generators

Abstract Study Objectives To compare estimated epileptic source localizations from 5 sleep–wake states (SWS): wakefulness (W), rapid eye movement sleep (REM), and non-REM 1-3. Methods Electrical source localization (sLORETA) of interictal spikes from different SWS on surface EEG from the epilepsy mo...

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Bibliographic Details
Published in:Sleep (New York, N.Y.) Vol. 45; no. 6; p. 1
Main Authors: McLeod, Graham A, Abbasian, Parandoush, Toutant, Darion, Ghassemi, Amirhossein, Duke, Tyler, Rycyk, Conrad, Serletis, Demitre, Moussavi, Zahra, Ng, Marcus C
Format: Journal Article
Language:English
Published: US Oxford University Press 01-06-2022
Subjects:
Electroencephalography
Epilepsy
Humans
Localization
Neurological Disorders
Seizures (Medicine)
Sleep
Sleep, REM
Wakefulness
Canada
epilepsy
epileptic generator
source generator
seizures
source localization
sLORETA
sleep activation
rapid eye movement sleep
sleep wake states
concordance
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Summary:Abstract Study Objectives To compare estimated epileptic source localizations from 5 sleep–wake states (SWS): wakefulness (W), rapid eye movement sleep (REM), and non-REM 1-3. Methods Electrical source localization (sLORETA) of interictal spikes from different SWS on surface EEG from the epilepsy monitoring unit at spike peak and take-off, with results mapped to individual brain models for 75% of patients. Concordance was defined as source localization voxels shared between 2 and 5 SWS, and discordance as those unique to 1 SWS against 1–4 other SWS. Results 563 spikes from 16 prospectively recruited focal epilepsy patients across 161 day-nights. SWS exerted significant differences at spike peak but not take-off. Source localization size did not vary between SWS. REM localizations were smaller in multifocal than unifocal patients (28.8% vs. 54.4%, p = .0091). All five SWS contributed about 45% of their localizations to converge onto 17.0 ± 15.5% voxels. Against any one other SWS, REM was least concordant (54.4% vs. 66.9%, p = .0006) and most discordant (39.3% vs. 29.6%, p = .0008). REM also yielded the most unique localizations (20.0% vs. 8.6%, p = .0059). Conclusions REM was best suited to identify candidate epileptic sources. sLORETA proposes a model in which an “omni-concordant core” of source localizations shared by all five SWS is surrounded by a “penumbra” of source localizations shared by some but not all SWS. Uniquely, REM spares this core to “move” source voxels from the penumbra to unique cortex not localized by other SWS. This may reflect differential intra-spike propagation in REM, which may account for its reported superior localizing abilities.
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ISSN:0161-8105
1550-9109
DOI:10.1093/sleep/zsac062