Slow wave activity moderates the association between new learning and traumatic brain injury severity
Bibliographic record
Abstract
STUDY OBJECTIVES: Sleep-wake complaints and difficulties in making new learning are among the most persistent and challenging long-term sequelea following moderate to severe traumatic brain injury (TBI). Yet, it is unclear whether, and to what extent, sleep characteristics during the chronic stage of TBI contribute to sleep-wake and cognitive complaints. We aimed to characterize sleep architecture in chronic moderate to severe TBI adults and assess whether non-rapid eye movement slow wave activity (SWA) is associated to next day performance in episodic memory tasks according to TBI severity. METHODS: Forty-two moderate to severe TBI participants, 12-47 months post-injury, and 38 healthy controls were tested with one night of in-laboratory polysomnography, followed the next morning by questionnaires (sleep quality, fatigue, and sleepiness) and neuropsychological assessment. We used multiple regression analyses to assess the moderator effect of SWA power on TBI severity and next-day memory performance. RESULTS: We found that TBI participants reported worse sleep quality and fatigue, and had worse cognitive performance than controls. No between group differences were found on macro- and micro-architecture of sleep. However, SWA significantly interacted with TBI severity to explain next-day memory performance: higher SWA was more strongly associated to better memory performance in more severe TBI compared to milder TBI. CONCLUSIONS: This study provides evidence that the injured brain is able to produce macro- and micro-architecture of sleep comparable to what is seen in healthy controls. However, with increasing TBI severity, lower non-rapid eye movement SWA power is associated with reduced ability to learn and memorise new information the following day.
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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 | 0.000 | 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) | 0.003 | 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".