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Record W4401669761 · doi:10.1101/2024.08.13.607801

The relationship between sleep and cognitive performance on tests of pattern separation in older adults

2024· preprint· en· W4401669761 on OpenAlexaffabout
Aina Roenningen, Devan Gill, Brianne A. Kent

Bibliographic record

VenuebioRxiv (Cold Spring Harbor Laboratory) · 2024
Typepreprint
Languageen
FieldNeuroscience
TopicSleep and Wakefulness Research
Canadian institutionsSimon Fraser University
Fundersnot available
KeywordsCognitionActigraphyEffects of sleep deprivation on cognitive performanceCambridge Neuropsychological Test Automated BatterySleep (system call)PsychologyMontreal Cognitive AssessmentCognitive testSleep deprivationAudiologyDementiaNeuropsychologyEpisodic memoryMedicineInsomniaPsychiatryWorking memoryCognitive impairmentSpatial memoryInternal medicine

Abstract

fetched live from OpenAlex

Abstract Study objectives Sleep disturbances are considered both a risk factor and symptom of dementia. The present research aimed to identify cognitive tests in which performance is associated with objective sleep quality or quantity, focusing on cognitive tests designed to evaluate the earliest cognitive changes in dementia. Methods We recruited older adults (50 years of age or older) and remotely monitored their sleep patterns for 7 consecutive days using wrist actigraphy and sleep diaries. On day 7, participants completed a battery of cognitive tests, which included the Psychomotor Vigilance Task (PVT), the Prodromal Alzheimer’s and Mild Cognitive Impairment battery from the Cambridge Neuropsychological Test Automated Battery (CANTAB), and the Mnemonic Similarity Task (MST), designed to tax pattern separation. The participants were also assessed with the Montreal Cognitive Assessment (MoCA). Results The final sample included 34 participants (mean age: 65.56, SD: 9.57). There were significant correlations between objective total sleep time and PVT and MST performance. MoCA scores were correlated with performance on CANTAB and MST. Objective total sleep time also predicted MST performance when controlling for age and gender. Conclusions Performance on cognitive tests designed to assess pattern separation are sensitive to older adults’ objective sleep duration and the early cognitive changes associated with dementia. MST should be evaluated for potential use as a clinical trial outcome measure for sleep-promoting treatments in older adults. Statement of Significance There is growing emphasis on the importance of sleep as a potential therapeutic target for neurodegenerative diseases (e.g., Alzheimer’s disease). Identifying cognitive measures that are sensitive to both sleep and the earliest cognitive changes associated with dementia are needed for use as outcome measures in clinical trials evaluating the effectiveness of sleep-promoting interventions. Our research addresses this need by investigating the relationship between sleep patterns and performance on cognitive tests designed to assess the earliest cognitive changes in dementia. Our results suggest that cognitive tests designed to assess pattern separation are uniquely sensitive to sleep quantity in older adults.

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)0.001
metaresearch head score (Gemma)0.003
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.297
Teacher spread0.258 · 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
Published2024
Admission routes2
Has abstractyes

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