Sleep Oscillations Across Cortical, Subcortical and Cerebellar Structures in Magnetoencephalography.

Overview

1. Department of Psychology Concordia University Montreal Quebec Canada
2. School of Health Concordia University Montreal Quebec Canada
3. Department of Neurology Max Planck Institute for Human Cognitive and Brain, Sciences Leipzig Germany
4. International Laboratory for Brain, Music, and Sound Research (BRAMS) University of Montreal & McGill University Montreal Quebec Canada
5. Centre for Research on Brain, Language and Music (CRBLM) Montreal Quebec Canada
6. Réseau Sommeil Montreal Quebec Canada
7. Montreal Neurological Institute McGill University Montreal Quebec Canada

Abstract

Sleep involves widespread changes in neural activity, with distinctive oscillatory patterns emerging across frequency bands and brain regions. Characterising these dynamics is essential for understanding their functional roles in health and their disruption in sleep‐related disorders. However, most work on healthy humans has used techniques with limited temporal or spatial resolution, focusing mainly on the cerebral cortex. Growing evidence suggests that subcortical and cerebellar structures contribute to sleep dynamics, yet these regions remain largely unexplored in human neuroimaging due to methodological limitations. Magnetoencephalography (MEG) offers millisecond temporal resolution with spatial precision to localise activity across cortical, subcortical and cerebellar regions. Recent evidence demonstrates that MEG can detect signals from deep brain structures, challenging assumptions about its spatial limitations, but systematic validation and whole‐brain mapping of oscillatory activity during sleep remain lacking. In this study, we provide comprehensive maps of oscillatory power across the whole brain during non‐rapid eye movement (NREM) sleep using source‐localised MEG. We first validated signal differentiability across cortical, subcortical and cerebellar regions using spectral fingerprinting analysis. We then characterised frequency‐specific and stage‐specific changes in oscillatory power across six frequency bands and three NREM sleep stages. Finally, we examined sigma‐band dynamics during spindle‐rich stage 2 sleep to investigate spindle‐related activity across brain regions. Our results reveal structured, region‐specific patterns of sleep modulation that extend beyond traditional cortical‐thalamic circuits, including novel evidence for cerebellar engagement in fast spindle frequencies. These findings expand models of sleep‐related brain activity and demonstrate the utility of whole‐brain MEG for understanding distributed sleep networks.

Reproduced under the paper's license (CC BY-NC), from the paper cited above.

Code

The paper links to its data, not to its authors' code: see the Data section.

Tracing map

A tracing map links a paper to the code its authors published: this paper has none, so it has no map.

Data

Datasets cited

• osf:ewmgc, at OSF; found in “Data Availability Statement”

Data Availability Statement

Anonymised, preprocessed MEG data (in the form of normalised oscillatory power) used in the main analyses are freely available on the Open Science Framework: https://osf.io/ewmgc/?view_only=0c70d62da2924de697ecf30e7be3ee8a. Raw data may be available upon reasonable request, pending ethical approval.

Reproduced under the paper's license (CC BY-NC), from the paper cited above.

Versions

The history of this record: each version stored by the harvester or made by a correction of its authors or of the maintainers of its code, and what changed in its facts. The texts of the paper (its abstract, its availability statements) are not part of it; versions that changed only those are not listed.

Version 2, 28 September 2026

• Publisher: n/a → Wiley

Version 1, 27 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 5 authors, 7 keywords, 12 MeSH terms, 8 funders, 68 references.

Cite

This paper

Greenlaw, K., Calvel, A., Bouhour, C., Steele, C. J., & Coffey, E. B. J. (2026). Sleep Oscillations Across Cortical, Subcortical and Cerebellar Structures in Magnetoencephalography. The European journal of neuroscience, 64(1), e70615. https://doi.org/10.1111/ejn.70615

Similar papers

The papers with a page that share the most with this one: the tools found in their code, their categories, datasets, cited references and authors, the rarest counting most.

[1] doi:10.1002/hbm.70354

Exploring Deep Magnetoencephalography via Thalamo‐Cortical Sleep Spindles

Journal: n/a

In common: MEG, 10 references, author Emily B. J. Coffey

[2] doi:10.1007/s12311-026-02065-4

EEG-ECeG Coherence Mapping of Human Cerebro-cerebellar Projections: An Exploratory Study.

Journal: Cerebellum (London, England)

In common: systems, 3 references

[3] doi:10.7554/elife.100605 [code]

Age-related changes in ‘cortical’ 1/f dynamics are linked to cardiac activity

Journal: n/a

In common: MEG, 3 references

[4] doi:10.1038/s41467-026-74824-0 [code]

Learning regularities in noise engages both neural predictive activity and representational changes.

Journal: Nature communications

In common: MEG, 3 references

[5] doi:10.1162/imag.a.1265

Neurophysiological mechanisms of optimized graphomotor performance in biscriptuals.

Journal: Imaging neuroscience (Cambridge, Mass.)

In common: 3 references

[6] doi:10.1126/sciadv.aee2305 [code]

Prediction of mild cognitive impairment progression using time-sensitive multimodal biomarkers.

Journal: Science advances

In common: MEG, 3 references

[7] doi:10.1126/sciadv.aea3919 [code]

Hierarchical brain dynamics supporting visual perceptual transitions.

Journal: Science advances

In common: MEG, 3 references

[8] doi:10.1002/gps3.70047

Neurophysiological subtyping of schizophrenia reveals a reproducible phenotype with temporoparietal circuit disruptions.

Journal: General psychiatry

In common: 3 references

[9] doi:10.1371/journal.pcbi.1013007 [code]

Traveling waves in the human visual cortex: An MEG-EEG model-based approach

Journal: n/a

In common: MEG, systems, 2 references

[10] doi:10.1162/imag.a.1269 [code]

From early to contemporary normative modeling: Mapping individual differences in neurophysiological signals.

Journal: Imaging neuroscience (Cambridge, Mass.)

In common: MEG, 3 references

Contribute

The authors of this paper can claim it, correct its record and validate its tracing map, and the maintainers of its code (its owner, or a public member of its organization) correct what it says of their repository; anyone signed in can ask for its removal. Every request goes to OSCR's own machine, which answers it; your account page follows them.

Request its removal

To ask OSCR to remove this record, the copies of its authors' scripts or its tracing map, use the removal request page: signed in, you say who you are, what to remove and why, then review and confirm the request. Published rules decide every request (how).

Discussion: questions and error reports about this paper and its code, from signed-in readers and its authors. It opens with sign-in.

Reproductions: reports from readers who ran the authors' code: what they reproduced, with which environment, commit and data. It opens with sign-in.

Activity: what happens around this paper: new versions of its record, its map's validation, discussions and reproductions. It opens with sign-in.