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Record W4412069745 · doi:10.1093/sleep/zsaf184

Association of sleep spindle activity with executive functioning and intellectual ability in children and adolescents

2025· article· en· W4412069745 on OpenAlexaff
Melany Morales-Ghinaglia, Fan He, Susan L. Calhoun, Anthony Rahawi, Jidong Fang, Alexandros N. Vgontzas, Duanping Liao, Edward O. Bixler, Magdy Younes, Anna Ricci, Julio Fernández‐Mendoza

Bibliographic record

VenueSLEEP · 2025
Typearticle
Languageen
FieldNeuroscience
TopicSleep and Wakefulness Research
Canadian institutionsUniversity of Manitoba
FundersNational Center for Advancing Translational SciencesNational Institute of Mental HealthNational Heart, Lung, and Blood InstituteNational Institutes of Health
KeywordsWorking memoryPsychologyCognitionExecutive functionsAssociation (psychology)PolysomnographySleep spindleWechsler Adult Intelligence ScaleIntelligence quotientPopulationPsychiatryAudiologyClinical psychologyMedicineElectroencephalographyNon-rapid eye movement sleep

Abstract

fetched live from OpenAlex

STUDY OBJECTIVES: Sleep spindles have been studied as an underlying mechanism of cognition. Prior research primarily relied on experimental studies of selective samples of healthy youth. We tested the relationship between spindle activity and cognition in youth from the general population. METHODS: Eight hundred and ninety-two sleep electroencephalographies (EEGs) from 9-hour polysomnography were leveraged from 456 typically developing children (median 8 years), and 258 typically developing adolescents (median 16 years) and youth with unmedicated psychiatric/behavioral disorders (89 children; 89 adolescents). Multivariable-adjusted linear regression models examined associations between sleep spindle density (SSD; number/minute) and peak spindle frequency (PSF; 10-16 Hz range) during N2 with Wechsler indices of processing speed, working memory, verbal intelligence, and nonverbal intelligence. We first analyzed typically developing and unmedicated psychiatric/behavioral youth, followed by an analysis of the 47 unmedicated attention deficit/hyperactivity disorder (ADHD) subgroup. RESULTS: In typically developing children, higher SSD and PSF were associated with better working memory and verbal intelligence. In adolescents, higher SSD was associated with better working memory and nonverbal intelligence, while slower PSF was associated with better nonverbal intelligence. Longitudinally, higher childhood SSD was associated with better adolescent nonverbal intelligence among typically developing youth. In youth with unmedicated psychiatric/behavioral disorders, spindle-cognition associations were lost, except in ADHD, where higher childhood SSD and slower adolescent PSF supported working memory. CONCLUSION: Sleep spindles may serve as a biomarker for neural and cognitive maturation, with developmental differences reflecting key brain maturational changes from childhood to adolescence. While altered in unmedicated psychiatric/behavioral disorders, lower-frequency spindles may provide a protective mechanism for working memory in adolescents with ADHD. Statement of Significance Sleep spindles occur as bursts of activity in the sigma-frequency range during non-rapid eye movement sleep. Due to their thalamocortical origin, spindles have been linked to cognitive functioning. We examined spindle activity in typically developing youth and unmedicated youth with psychiatric/behavioral disorders. In typically developing children, higher spindle density was associated with better verbal intelligence and, as they transitioned to adolescence, with better nonverbal intelligence. In unmedicated children with attention deficit/hyperactivity disorder (ADHD), lower-frequency spindles were associated with better working memory in adolescence. Sleep spindles promote neural plasticity for cognitive growth, with reduced impact in later developmental stages reflecting matured neural pathways. In youth with ADHD, sleep spindles may serve a protective role in mitigating cognitive deficits over time.

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)0.000
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.067
Threshold uncertainty score0.311

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.011
GPT teacher head0.257
Teacher spread0.247 · 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 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

Citations3
Published2025
Admission routes1
Has abstractyes

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