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Record W2608581059 · doi:10.1101/130500

Neural Correlates of human cognitive abilities during sleep

2017· preprint· en· W2608581059 on OpenAlexafffund
Zhuo Fang, L. Bryan Ray, Adrian M. Owen, Stuart Fogel

Bibliographic record

VenuebioRxiv (Cold Spring Harbor Laboratory) · 2017
Typepreprint
Languageen
FieldNeuroscience
TopicSleep and Wakefulness Research
Canadian institutionsUniversity of OttawaWestern University
FundersCanada Excellence Research Chairs, Government of Canada
KeywordsSleep spindleFunctional magnetic resonance imagingNeuroscienceCognitionElectroencephalographyPsychologySleep (system call)Basal gangliaWorking memoryCognitive psychologyNon-rapid eye movement sleepComputer scienceCentral nervous system

Abstract

fetched live from OpenAlex

ABSTRACT Inter-individual differences in sleep spindles are highly correlated with “Reasoning” abilities (problem solving skills; i.e., the ability to employ logic, identify complex patterns), but not Short Term Memory or Verbal abilities. Simultaneous electroencephalography and functional magnetic resonance imaging (EEG-fMRI) have revealed brain activations time-locked to spindles (e.g., thalamic, paralimbic, and motor cortical areas)–yet the functional significance of inter-individual differences in spindle-related brain activation remains to be investigated. Using EEG-fMRI during sleep, we identified, for the first time, the neural activation patterns time-locked to spindles that are correlated with cognitive abilities. Similar to previous studies, activations time-locked to spindles were observed in thalamocortical circuitry and basal ganglia regions. Importantly, spindle-related activation in a subset of these regions were specifically related to inter-individual differences in Reasoning, but not STM or Verbal abilities. These results may help elucidate the physiological mechanisms which support the function of sleep for the capacity for reasoning.

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)0.001
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 score0.002
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

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)0.0020.000

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 head0.039
GPT teacher head0.283
Teacher spread0.243 · 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

Citations4
Published2017
Admission routes2
Has abstractyes

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