Cerebral Gray Matter May Not Explain Sleep Slow-Wave Characteristics after Severe Brain Injury
Bibliographic record
Abstract
Sleep slow waves are the hallmark of deeper non-rapid eye movement sleep. It is generally assumed that gray matter properties predict slow-wave density, morphology, and spectral power in healthy adults. Here, we tested the association between gray matter volume (GMV) and slow-wave characteristics in 27 patients with moderate-to-severe traumatic brain injury (TBI, 32.0 ± 12.2 years old, eight women) and compared that with 32 healthy controls (29.2 ± 11.5 years old, nine women). Participants underwent overnight polysomnography and cerebral MRI with a 3 Tesla scanner. A whole-brain voxel-wise analysis was performed to compare GMV between groups. Slow-wave density, morphology, and spectral power (0.4-6 Hz) were computed, and GMV was extracted from the thalamus, cingulate, insula, precuneus, and orbitofrontal cortex to test the relationship between slow waves and gray matter in regions implicated in the generation and/or propagation of slow waves. Compared with controls, TBI patients had significantly lower frontal and temporal GMV and exhibited a subtle decrease in slow-wave frequency. Moreover, higher GMV in the orbitofrontal cortex, insula, cingulate cortex, and precuneus was associated with higher slow-wave frequency and slope, but only in healthy controls. Higher orbitofrontal GMV was also associated with higher slow-wave density in healthy participants. While we observed the expected associations between GMV and slow-wave characteristics in healthy controls, no such associations were observed in the TBI group despite lower GMV. This finding challenges the presumed role of GMV in slow-wave generation and morphology.
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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".