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Record W4398176401 · doi:10.7554/elife.96784.1.sa2

eLife Assessment: Fractal cycles of sleep: a new aperiodic activity-based definition of sleep cycles

2024· peer-review· en· W4398176401 on OpenAlexaff
Adrien Peyrache

Bibliographic record

Venuenot available
Typepeer-review
Languageen
FieldEconomics, Econometrics and Finance
TopicComplex Systems and Time Series Analysis
Canadian institutionsMcGill University
FundersNederlandse Organisatie voor Wetenschappelijk OnderzoekNemzeti Kutatási, Fejlesztési és Innovaciós AlapSchweizerischer Nationalfonds zur Förderung der Wissenschaftlichen ForschungNational Science Foundation
KeywordsAperiodic graphSleep (system call)FractalMathematicsComputer scienceStatistical physicsPsychologyPhysicsCombinatoricsMathematical analysisProgramming language

Abstract

fetched live from OpenAlex

Nocturnal human sleep consists of 4 – 6 ninety-minute cycles defined as episodes of non-rapid eye movement (non-REM) sleep followed by an episode of REM sleep. While sleep cycles are considered fundamental components of sleep, their functional significance largely remains unclear. One of the reasons for a lack of research progress in this field is the absence of a “data-driven” definition of sleep cycles. Here, we proposed to base such a definition on fractal (aperiodic) neural activity, a well-established marker of arousal and sleep stages.We explored temporal dynamics of fractal activity during nocturnal sleep using electroencephalography in 205 healthy adults aged 18 – 75 years. Based on the observed pattern of fractal fluctuations, we introduced a new concept, the “fractal” cycle of sleep, defined as a time interval during which fractal activity descends from its local maximum to its local minimum and then leads back to the next local maximum. Then, we assessed correlations between “fractal” and “classical” (i.e., non-REM – REM) sleep cycle durations. We also studied cycles with skipped REM sleep, i.e., the cycles where the REM phase is replaced by “lightening” of sleep. Finally, we validated the fractal cycle concept in children and adolescents (range: 8 – 17 years, n = 21), the group characterized by deeper sleep and a higher frequency of cycles with skipped REM sleep, as well as in major depressive disorder (n = 111), the condition characterized by altered sleep structure (in addition to its clinical symptoms).We found that “fractal” and “classical” cycle durations (89 ± 34 min vs. 90 ± 25 min) correlated positively (r = 0.5, p < 0.001). Cycle-to-cycle overnight dynamics showed an inverted U-shape of both fractal and classical cycle durations and a gradual decrease in absolute amplitudes of the fractal descents and ascents from early to late cycles.In adults, the “fractal” cycle duration and participant’s age correlated negatively (r = -0.2, p = 0.006). Children and adolescents had shorter “fractal” cycles compared to young adults (76 ± 34 vs. 94 ± 32 min, p < 0.001). The fractal cycle algorithm detected cycles with skipped REM sleep in 53/55 (96%) cases.Medicated patients with depression showed longer “fractal” cycles compared to their own unmedicated state (107 ± 51 min vs. 92 ± 38 min, p < 0.001) and age-matched controls (104 ± 49 vs. 88 ± 31 min, p < 0.001).In conclusion, “fractal” cycles are an objective, quantifiable, continuous and biologically plausible way to display sleep neural activity and its cycling nature. They are useful in healthy, pediatric and clinical populations and should be extensively studied to advance theoretical research on sleep structure.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.851
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0030.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0110.000

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 head0.057
GPT teacher head0.286
Teacher spread0.229 · 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 teacher head, not a consensus.

Study designNot applicable
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
Published2024
Admission routes1
Has abstractyes

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