0404 Sleep Characterization in the APP/NL-F Mouse Model of Alzheimer’s Disease After Repetitive Mild Traumatic Brain Injuries
Bibliographic record
Abstract
Abstract Introduction Sleep disturbances, such as insomnia and hypersomnia, are often reported by people who have experienced mild traumatic brain injuries (mTBIs). TBIs and sleep disturbances are both risk factors for neurodegenerative diseases, such as Alzheimer’s disease (AD). The long-lasting effects of repetitive mTBIs (rmTBIs) on sleep outcomes and whether post-TBI sleep is an effective therapeutic target for reducing the risk of neurodegenerative disease are not yet well understood. Therefore, our study aims to characterize intracranial EEG activity during sleep and wake 3 and 9 months after rmTBIs in APPNLF mice. Methods We used the closed-head impact model of engineered rotational acceleration (CHIMERA) method to deliver 3 rmTBIs 48hr apart to female and male APPNL-F mice (n=42), at 6 and 12 months of age. At 15 months old, we implanted EEG cortical electrodes and recorded 72 continuous hours of EEG. Using the Sirenia Sleep Pro software and an in-house R code, we quantified sleep-wake patterns. Unpaired Welch t-tests with Holm-Šídák correction for multiple comparisons was used to compare the sleep-wake patterns between the rmTBI and sham groups. Additionally, the partial least squares regression method was used to analyze the spectral power data. Results Our results show no differences in the time spent in each vigilance state between the TBI and sham groups at either 3- or 9-months post-injury. There were no differences in the number of transitions between states, bout counts, or bout lengths between the experimental groups. However, in the power spectra analysis, we observed a statistically significant decrease in power at the lower frequencies (3-8Hz) and an increase in the power at the higher frequencies (18-30Hz) in the TBI group during wake at 9 months post-injury. There were no significant changes in the power spectra after 3 months post-injury, although there was a trend towards higher power in the TBI group in the lower frequencies during NREM. Conclusion This is the first study, to our knowledge, presenting the chronic effects of repetitive closed-head mTBIs on sleep and EEG power spectra. Support (if any)
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".