Sleep spindle density and morphology are resilient to post-traumatic gray matter volume loss
Bibliographic record
Abstract
Moderate to severe traumatic brain injury (TBI) leads to gray matter volume (GMV) loss, cognitive dysfunction, and persistent sleep-wake complaints. Given the link between GMV and sleep spindles in healthy adults and the role of spindles in neural plasticity and protecting sleep against disturbances, we investigated GMV-spindle associations following TBI. In this cross-sectional study, 27 adults with chronic moderate to severe TBI (32.0 ± 12.2 years old) and 32 healthy controls (29.2 ± 11.5 years old) underwent full-night polysomnography and 3-Tesla MRI. Spindle density, amplitude, frequency, duration, and sigma spectral power (11-16 Hz) were computed. We tested GMV-spindle associations in 1) clusters with GMV loss following TBI (right and left frontotemporal and left temporal) and 2) regions previously linked to spindles in healthy adults (hippocampus, insula, cingulate, supplementary motor area, cerebellum, Heschl's gyri, thalamus, medial prefrontal cortex, putamen, and pallidum). Multiple regression analyses were performed with Group as a moderator, controlled for age. Across all participants, higher spindle amplitude and sigma power were associated with larger GMVs in the left frontotemporal, left temporal, thalamic, and medial prefrontal regions. Faster spindle frequency was associated with larger GMV in most regions, though for the left and right frontotemporal regions and hippocampus, these associations were observed only in controls. No Group effects were found for spindle characteristics. The lack of stronger GMV-spindle associations following TBI and the absence of Group effects for spindle characteristics suggest spindles' resilience to post-traumatic GMV loss.
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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".