Neuroinflammation modifies the relationship between stress and perivascular spaces in an elderly population with different levels of cognitive impairment
Bibliographic record
Abstract
Background: Perivascular spaces (PVS) are fluid-filled spaces surrounding the brain parenchymal vasculature. Literature suggests that PVS may play a significant role in aging and neurological disorders, including Alzheimer's disease (AD). The aim of this study is to investigate whether the relationship between MRI-visible PVS and stress is influenced by neuroinflammation in an elderly population with different levels of cognitive impairment. Methods: = 91). PVS volume fraction was calculated in the basal ganglia and centrum semiovale using a semi-automated segmentation approach. Stress was quantified with levels of salivary cortisol. Inflammatory biomarkers measured from plasma included cytokines, matrix metalloproteinases and C-reactive protein. General linear models were used to test the relationship between PVS and cortisol, when interacting with inflammatory markers. This was done on the whole cohort and within each clinical cognitive group. Results: In the centrum semiovale, higher inflammation levels reduced the relationship of cortisol with PVS. In basal ganglia, higher levels of C-reactive protein reduced the negative relationship of cortisol with PVS. All analyses were accounted for age, sex, body mass index (BMI) and total hippocampal volume. There was a significant interaction effect between cortisol and C-reactive protein on PVS volume fraction in the MCI group. Discussion: These findings suggest an influence of neuroinflammation on the PVS structure in Alzheimer's disease spectrum, and offer insight for better understanding physiological processes of cognitive impairment onset.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".