Axonal Injury, Sleep Disturbances, and Memory following Traumatic Brain Injury
Bibliographic record
Abstract
Abstract Objectives Traumatic brain injury (TBI) is associated with sleep deficits, but it is not clear why some report sleep disturbances and others do not. The objective of this study was to assess the associations between axonal injury, sleep, and memory in chronic and acute TBI. Methods Data were acquired from two independent datasets which included 156 older adult veterans (69.8 years) from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) with prior moderate-severe TBIs and 90 (69.2 years) without a TBI and 374 participants (39.6 years) from Transforming Research and Clinical Knowledge in TBI (TRACK-TBI) with a recent mild TBI (mTBI) and 87 controls (39.6 years), all who completed an MRI, memory assessment, and sleep questionnaire. Results Older adults with a prior TBI had a significant association between axial diffusivity in the left anterior internal capsule (ALIC) and sleep disturbances [95% CI(5.0e+07, 1.7e+08), p ≤ .01]. This association was significantly different [95% CI(6.8e+07,2.2e+08), p=.01] from controls. ALIC predicted changes in memory over one-year in TBI [95% CI(−1.8e+08,-2.7e+07), p=.03]. We externally validated those findings in TRACK-TBI where ALIC axial diffusivity within two-weeks after injury was significantly associated with higher sleep disturbances in the TBI group at two-weeks [95% CI(−7.2e-06, −1.9e-04), p=0.04], six-months [95% CI(−4.2e-06,-1.3e-04), p≤ .01] and 12-months post-injury [95% CI(−5.2e-06, −1.2e-04), p=0.03]. These associations not seen in controls. Interpretations Axonal injury to the ALIC is robustly associated with sleep disturbances in multiple TBI populations. Early assessment of ALIC damage following mTBI could identify those at risk for persistent sleep disturbances following injury.
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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 | 0.001 | 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".