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Record W4414775589 · doi:10.1111/jsr.70212

Tone‐Evoked Sleep Electroencephalographic Slow Oscillations as a Function of Peripheral Rhythms: New Insights Into the Brain–Heart Integration

2025· article· en· W4414775589 on OpenAlexafffund
Mohamad Forouzanfar, Sepehr Sardooeinasab, Fiona C. Baker, Ian M. Colrain, Massimiliano de Zambotti

Bibliographic record

VenueJournal of Sleep Research · 2025
Typearticle
Languageen
FieldNeuroscience
TopicSleep and Wakefulness Research
Canadian institutionsÉcole de Technologie SupérieureInstitut Universitaire de Gériatrie de MontréalUniversité du Québec à Montréal
FundersNatural Sciences and Engineering Research Council of CanadaNational Institutes of Health
KeywordsElectroencephalographyStimulus (psychology)NeuromodulationNon-rapid eye movement sleepPeripheralPolysomnographyTranscranial magnetic stimulationElectrophysiologyStimulation

Abstract

fetched live from OpenAlex

Recent studies have shown that acoustic stimulation, a common neuromodulation technique, can enhance slow-wave activity (SWA), which is associated with immune, autonomic nervous system activity and cognitive health benefits. Despite some disagreement, many studies suggest that maximising tone-evoked SWA depends on the timing of the acoustic stimulus in relation to ongoing cortical slow oscillations. Given the close connection between the central and peripheral systems during sleep, particularly at the cortico-cardiac level, we here aimed to examine the overlooked relationship between the timing of stimulation and the dominant cortical and cardiac rhythms. We evaluated the effect of acoustic stimulation in different phases of the EEG slow oscillation (SO; ~0.8 Hz) component of SWA (0.5-4 Hz) and heart rate (HR) low-frequency (LF) (0.04-0.15 Hz) and high-frequency (HF) (0.15-0.4 Hz) oscillations on tone-evoked EEG slow activity and HR profiles. One hundred thirty-three adolescents underwent overnight polysomnography where acoustic tones (80 dB at 1000 Hz for 50 msec) were played with a random 15-30 s interstimulus interval. The analysis was limited to artefact and arousal-free episodes of NREM sleep. Playing acoustic tones in the upstate phases of EEG SOs, upstate phases of HR LF oscillations and downstate phases of HR HF oscillations induced significantly higher peak-to-peak amplitude EEG SOs (110%, 16% and 7%, respectively) (p < 0.001) and HR oscillations (16%, 56% and 25%, respectively) (p < 0.001), produced a greater number of EEG SOs (22%, 12% and 5%, respectively) and increased the SWA (3%, 14% and 3%, respectively) (p < 0.05) in contrast to playing tones in the other phase (downstate phases of EEG SOs, downstate phases of LF oscillations and upstate phases of HR HF oscillations). Our findings reveal complex interactions between the central and peripheral nervous systems in processing external stimuli, leading to significant variations in postcortical and cardiac oscillations. These results have potential implications for developing deep sleep enhancement technologies using adaptive interventions based on multidimensional oscillations.

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)0.003
metaresearch head score (Gemma)0.006
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.208
Threshold uncertainty score0.760

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.005
Science and technology studies0.0010.001
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.002
Insufficient payload (model declined to judge)0.0000.000

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 head0.045
GPT teacher head0.373
Teacher spread0.328 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
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

Citations1
Published2025
Admission routes2
Has abstractyes

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