0138 Sleep and Performance on Tests of Pattern Separation and the Cambridge Neuropsychological Test Automated Battery (CANTAB)
Bibliographic record
Abstract
Abstract Introduction Sleep disturbances are considered both a risk factor and symptom of dementia. The present research aimed to identify cognitive tests that are associated with sleep quality or quantity, focusing on cognitive tests designed to evaluate the earliest cognitive changes in dementia. Methods We recruited younger (n=89) and older (n=40) adults 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 Prodromal Alzheimer’s and MCI battery from the Cambridge Neuropsychological Test Automated Battery (CANTAB) and the Mnemonic Similarity Task (MST), which is a test designed to assess pattern separation. The Psychomotor Vigilance Task (PVT) was used as a positive control measure for all participants. The older adults were also assessed with the Montreal Cognitive Assessment (MoCA). Results Multiple linear regression models on the overall sample controlling for gender and age revealed that age was the strongest predictor of performance on MST and CANTAB DMS. Multiple linear regression models in the separate samples showed that sleep (i.e., total sleep time and sleep efficiency), MoCA score, and the interaction between gender and sleep were significant predictors for older adult’s performance on the MST and CANTAB DMS. The regression analyses in the younger cohort revealed only significant effects of sleep efficiency on CANTAB PAL. Conclusion Performance on cognitive tests designed to assess pattern separation are sensitive to sleep patterns and the early cognitive changes associated with dementia, and should be evaluated for potential use as clinical trial outcome measures for sleep-promoting treatments in older adults. Support (if any) Dr. Brianne Kent (supervisor)
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 imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".