Objective Sleep Quality and the Underlying Functional Neural Correlates Among Older Adults With Probable MCI
Bibliographic record
Abstract
Abstract Poor sleep is a strong risk factor for dementia and is commonly reported among older adults with mild cognitive impairment (MCI). However, the neural underpinnings of poor sleep among older adults with MCI remains equivocal. The goal of this cross-sectional analysis was to explore the relationship between resting-state functional connectivity in the brain and sleep quality as measured by actigraphy. We hypothesize lower sleep efficiency and higher sleep fragmentation may be associated with aberrant functional connectivity of brain regions involved in somatosensory, somatomotor, and attentional processing. Thirty-six community-dwelling older adults with probable MCI between 65-85 years (mean=71.8 years) were assessed for sleep quality using a motion watch to quantify sleep efficiency and fragmentation over 14 days. All participants completed resting-state functional magnetic resonance imaging (fMRI) within 14 days of sleep monitoring. Independent associations between network connectivity and sleep quality were determined using general linear models. Examined networks included the somatosensory network (SMN), dorsal attention network (DAN), ventral attention network (VAN), frontoparietal network (FPN), and default mode network (DMN). Mean Montreal Cognitive Assessment score was 22.5 (SD=2.7) and Mini-Mental State Examination score was 28.3 (SD=1.5). Mean sleep efficiency and fragmentation index was 80.1% and 31.8 respectively. Higher sleep fragmentation correlated with increased connectivity between the SMN and insula, the SMN and posterior cingulate, as well as FPN and primary motor area (Z=3.1; p<0.05). These results suggest aberrant functional connectivity between brain regions involved in attentional and somatosensory processes may be associated with disrupted sleep mechanisms in older adults with MCI.
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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 | 0.001 |
| 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) | 0.001 | 0.000 |
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.
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".