How vigilance states influence source imaging of physiological brain oscillations: Evidence from intracranial EEG

Cortical oscillations across sleep-wake cycles are essential for coordinating functional brain dynamics. High-density electroencephalography (HDEEG) combined with electrical source imaging (ESI) provides a noninvasive approach to map cortical dynamics; however, its ability to capture spatial ongoing oscillations across different vigilance states remains uncertain. Here, we directly compared HDEEG source imaging by comparing it to a normative intracranial EEG (iEEG) atlas from 110 epilepsy patients with electrodes in healthy brain regions (https://mni-open-ieegatlas.research.mcgill.ca/). Wavelet-based Maximum Entropy on the Mean (wMEM) was applied to localize oscillatory patterns using overnight HDEEG recordings from 35 healthy adults (16 females, mean age 31.1 ± 6.3 years). Virtual iEEG (ViEEG) signals were estimated by applying an iEEG forward model to wMEM sources to examine oscillatory patterns across 5 frequency bands, 38 regions, and 4 vigilance states. We found that HDEEG source imaging exhibited comparable spectral patterns of iEEG in low frequencies but overestimated oscillatory activities at high frequencies. Lateral cortical regions exhibited more accurate source estimation than medial regions (p < 0.05). After removing the aperiodic components, the spectral alignment between ViEEG and iEEG significantly improved except for N3 sleep (p < 0.05). Oscillatory peak patterns in ViEEG reflect state-dependent dynamics that are broadly consistent with iEEG peaks (p < 0.05). HDEEG-derived ViEEG and magnetoencephalography-derived ViEEG approximated iEEG spectral features, showing complementary correspondence. These findings reveal that vigilance states significantly shape cortical oscillations by altering their spectral and spatial profiles. Our results establish HDEEG as a powerful tool for large-scale, noninvasive investigations of human sleep neurophysiology and brain network dynamics.

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Journal
NeuroImage
Published
2026-02-12
DOI
https://doi.org/10.1016/j.neuroimage.2026.121803
Primary Topic
EEG and Brain-Computer Interfaces
Type
article
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article

How vigilance states influence source imaging of physiological brain oscillations: Evidence from intracranial EEG

Alyssa Ho, Kassem Jaber, Katharina Schiller, Édouard Delaire et al.
NeuroImage
EEG and Brain-Computer Interfaces
article

How vigilance states influence source imaging of physiological brain oscillations: Evidence from intracranial EEG

Alyssa Ho, Kassem Jaber, Katharina Schiller, Édouard Delaire, Jawata Afnan, Xiaoyan Wei, Nicolás von Ellenrieder, Jessica Royer, Erica Minato, Christophe Grova, Boris C Bernhardt, Birgit Frauscher, Tamir Avigdor, Yingqi Laetitia Wang, Jean-Marc Lina, Matt Moye
article en

Abstract

Cortical oscillations across sleep-wake cycles are essential for coordinating functional brain dynamics. High-density electroencephalography (HDEEG) combined with electrical source imaging (ESI) provides a noninvasive approach to map cortical dynamics; however, its ability to capture spatial ongoing oscillations across different vigilance states remains uncertain. Here, we directly compared HDEEG source imaging by comparing it to a normative intracranial EEG (iEEG) atlas from 110 epilepsy patients with electrodes in healthy brain regions (https://mni-open-ieegatlas.research.mcgill.ca/). Wavelet-based Maximum Entropy on the Mean (wMEM) was applied to localize oscillatory patterns using overnight HDEEG recordings from 35 healthy adults (16 females, mean age 31.1 ± 6.3 years). Virtual iEEG (ViEEG) signals were estimated by applying an iEEG forward model to wMEM sources to examine oscillatory patterns across 5 frequency bands, 38 regions, and 4 vigilance states. We found that HDEEG source imaging exhibited comparable spectral patterns of iEEG in low frequencies but overestimated oscillatory activities at high frequencies. Lateral cortical regions exhibited more accurate source estimation than medial regions (p < 0.05). After removing the aperiodic components, the spectral alignment between ViEEG and iEEG significantly improved except for N3 sleep (p < 0.05). Oscillatory peak patterns in ViEEG reflect state-dependent dynamics that are broadly consistent with iEEG peaks (p < 0.05). HDEEG-derived ViEEG and magnetoencephalography-derived ViEEG approximated iEEG spectral features, showing complementary correspondence. These findings reveal that vigilance states significantly shape cortical oscillations by altering their spectral and spatial profiles. Our results establish HDEEG as a powerful tool for large-scale, noninvasive investigations of human sleep neurophysiology and brain network dynamics.

NeuroImageVol. 329
Western University (CA), Montreal Neurological Institute and Hospital (CA), Duke University (US), Hôpital du Sacré-Cœur de Montréal (CA), Duke University Hospital (US), McGill University (CA), Concordia University (CA), École de Technologie Supérieure (CA)
Openalex Percentile: Top 59%
EEG and Brain-Computer Interfaces
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