Relationship between cerebrospinal fluid circulation markers, brain degeneration, and cognitive impairment in cerebral amyloid angiopathy
Bibliographic record
Abstract
Objectives To investigate whether cerebrospinal fluid (CSF) circulation markers alter in patients with probable cerebral amyloid angiopathy (pCAA) and whether they are associated with brain degeneration and cognitive impairment. Methods We screened pCAA patients from the ADNI3 database according to the Boston 2.0 Criteria. Fifty-two patients with cognitive impairment (26 pCAA; 26 age-sex-matched non-pCAA) and 26 age-sex-matched cognitively normal control (NC) were included in this study. All participants underwent neurological MRI and cognitive assessments. Choroid plexus (ChP) was segmented using a deep learning-based method and its volume was extracted. Diffusion tensor imaging analysis along the perivascular space (DTI-ALPS) was used to assess perivenous fluid mobility. AD pathological markers (Aβ and tau) were assessed using positron emission tomography. Brain parenchymal damage markers included white matter hyperintensities (WMH) volume and brain atrophy ratio. All markers were compared among the three groups. Correlations among the ChP volume, DTI-ALPS index, parenchymal damage markers, and cognitive scales were analyzed in the pCAA group. Results The three groups exhibited significant differences in cognitive scores, AD biomarkers, and imaging markers. Post hoc analyses showed that patients with pCAA had significantly higher WMH volume, higher Aβ and tau deposition, and lower DTI-ALPS compared to NC. However, no difference in ChPs volume was found among the groups. Controlling for age, sex, and vascular risk factors, partial correlation analyses showed a significant negative correlation between the DTI-ALPS and WMH volume fraction (r = −0.606, p = 0.002). ChP volume was significantly associated with the Montreal cognitive assessment score (r = −0.492, p = 0.028). Conclusion CSF circulation markers were associated with elevated WMH burden and cognitive impairments in probable CAA.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".