MétaCan
Menu
← Back to cohort
Record W4414370737 · doi:10.1101/2025.09.14.676090

Beyond Circadian: A Yearlong Electroencephalography Study Reveals Hidden Ultralong-term Sleep Cycles

2025· preprint· en· W4414370737 on OpenAlexafffund
Tamir Avigdor, Jonas Duun‐Henriksen, Esben Ahrens, Alyssa Ho, Matthew Moye, Birgit Frauscher, Sándor Beniczky

Bibliographic record

VenuebioRxiv (Cold Spring Harbor Laboratory) · 2025
Typepreprint
Languageen
FieldNeuroscience
TopicSleep and Wakefulness Research
Canadian institutionsMcGill UniversityMontreal Neurological Institute and Hospital
FundersCanadian Institutes of Health ResearchSchool of Medicine, Duke University
KeywordsSleep (system call)Circadian rhythmElectroencephalographySleep StagesNon-rapid eye movement sleepFree-running sleepSleep patternsSleep spindleActigraphy

Abstract

fetched live from OpenAlex

Abstract Sleep is essential for brain function and overall health. While circadian rhythms and sleep stages across the night have been well-characterized, long-term variations in sleep remain poorly understood. We used a novel technology, subcutaneous electroencephalography, to collect yearlong sleep recordings from 20 healthy individuals. We investigated intrinsic and extrinsic drivers of sleep variability and identified recurring multi-day cycles of 8-60 days in sleep duration, latency, architecture, and stability. Additionally, sleep was modulated by external factors including season, weather, weekends and holidays. This reveals that while sleep processes are governed by internal dynamics, they remain sensitive to environmental influences. These results uncover a previously unrecognized temporal variation in human sleep and suggest new directions for understanding sleep patterns and their relevance to health and disease. Graphical abstract

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.010
Threshold uncertainty score0.033

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0100.001

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.023
GPT teacher head0.271
Teacher spread0.249 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations0
Published2025
Admission routes2
Has abstractyes

Explore more

Same venuebioRxiv (Cold Spring Harbor Laboratory)→Same topicSleep and Wakefulness Research→French-language works237,207→