Frontal grey matter microstructure is associated with sleep slow waves characteristics in late midlife
Bibliographic record
Abstract
STUDY OBJECTIVES: The ability to generate slow waves (SW) during non-rapid eye movement (NREM) sleep decreases as early as the 5th decade of life, predominantly over frontal regions. This decrease may concern prominently SW characterized by a fast switch from hyperpolarized to depolarized, or down-to-up, state. Yet, the relationship between these fast and slow switcher SW and cerebral microstructure in ageing is not established. METHODS: We recorded habitual sleep under EEG in 99 healthy late midlife individuals (mean age = 59.3 ± 5.3 years; 68 women) and extracted SW parameters (density, amplitude, frequency) for all SW as well as according to their switcher type (slow vs. fast). We further used neurite orientation dispersion and density imaging (NODDI) to assess microstructural integrity over a frontal grey matter region of interest (ROI). RESULTS: In statistical models adjusted for age, sex, and sleep duration, we found that a lower SW density, particularly for fast switcher SW, was associated with a reduced orientation dispersion of neurites in the frontal ROI (p = 0.018, R2β* = 0.06). In addition, overall SW frequency was positively associated with neurite density (p = 0.03, R2β* = 0.05). By contrast, we found no significant relationships between SW amplitude and NODDI metrics. CONCLUSIONS: Our findings suggest that the complexity of neurite organization contributes specifically to the rate of fast switcher SW occurrence in healthy middle-aged individuals, corroborating slow and fast switcher SW as distinct types of SW. They further suggest that the density of frontal neurites plays a key role for neural synchronization during sleep. TRIAL REGISTRATION NUMBER: EudraCT 2016-001436-35.
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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 | 0.001 |
| 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) | 0.001 | 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".