Neuroinflammation is associated with non‐REM sleep reduction in individuals without dementia
Bibliographic record
Abstract
Abstract Background Sleep disturbances and especially reduction of non‐REM (NREM) sleep are common in aging with accumulating evidence showing it might contribute to cognitive decline as well as increase the risk of developing dementia due to Alzheimer’s disease (AD). Lately, sleep disturbances and AD separately have been linked to increase in systemic inflammation. NREM sleep is thought to allow clearance of extracellular Aβ, which led to studies suggesting NREM could be an early biomarker of AD risk. The main objective of this study was to see the relationship between NREM sleep as assessed with polysomnography and neuroinflammation in vivo. Method 25 individuals (18 cognitively unimpaired (CU) and 7 mild cognitive impairment (MCI)) underwent a [11C]PBR28 neuroinflammation‐PET scan, an MRI a full neuropsychological evaluation as well as a polysomnography. [11C]PBR28 standardized uptake value ratios (SUVRs) used the cerebellum grey matter as the reference region and was calculated between 0‐90 min post‐injection. A voxel based regression model evaluated the relationship between neuroinflammation as assessed by PET, and the total minutes of NREM sleep. The model’s covariates were age and diagnosis. Result We found a negative correlation between neuroinflammation and the total minutes of non‐REM sleep measured by a polysomnography. The regions showing neuroinflammation were the reticular formation, the arcuate nucleus of the medulla, the frontal cortex and especially the vmPFC as well as the insula and temporal poles. When we corrected for sex and years of education, results were similar. Conclusion These preliminary results show that less NREM sleep correlates with neuroinflammation, in the brain of CU and MCI individuals. The regions impacted, such as the reticular formation, the vmPFC and the insula are needed for arousal and alertness. The arcuate nucleus of the medulla is required for proper breathing. Finally, temporal poles are part of the first regions affected in AD, and later on in the disease, the medial frontal cortex also starts degrading. Our study adds to previous research showing a link between sleep disturbances and neuroinflammation in vivo and identifies anatomical correlates.
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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) | 0.002 | 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".