Effects of Cerebral Blood Flow and White Matter Integrity on Cognition in CADASIL Patients
Bibliographic record
Abstract
Background: It remains unclear whether the degree of white matter tract damage or cerebral hypoperfusion could better predict the global cognitive impairment in CADASIL. We sought to determine the independent effects of cerebral perfusion status and white matter integrity on cognition. Methods: We reviewed prospectively collected clinical and imaging data from genetically-confirmed CADASIL patients who underwent both arterial spin labeling (ASL) perfusion MRI and diffusion tensor imaging (DTI). We analyzed the cerebral blood flow (CBF), mean diffusion (MD) and fractional anisotropy (FA) by dividing the brain tissue into white matter hyperintensity (WMH) and normal appearing white matter (NAWM). Global cognitive function was evaluated by using Mini-Mental State Examination (MMSE) and Montreal Cognitive Assessment (MoCA). Results: Of the included 29 CADASIL patients, the age was 48.4 ± 7.9 years, and 17 (58.6%) were women. MD was significantly correlated with CBF in both WMH (r = -0.407, P = 0.035) and NAWM (r = -0.437, P = 0.023), after adjusting for age and WMH volume. MoCA score was obtained in thirteen patients, and was significantly correlated with CBF in both WMH (r = 0.742, P = 0.004) and NAWM (r = 0.659, P = 0.014). Both CBF in WMH (area under the curve, 0.767; 95% CI, 0.586-0.947, P = 0.015) and MD in WMH (area under the curve, 0.740; 95% CI, 0.557-0.924, P = 0.028) were good predictor for cognitive impairment (MMSE score < 27). However, multiple linear regression analysis revealed that global cognitive function was independently associated with CBF in WMH only (standardized β = 0.485, P = 0.015), after adjusting for age, gender, WMH volume, the presence of subcortical infarcts and DTI metrics. Conclusions: Our findings suggested that cerebral hypoperfusion was more strongly associated with global cognitive dysfunction than the severity of brain microstructural damage, supporting that CBF assessed by ASL could serve as a candidate imaging indicator for monitoring alterations of global cognitive function in CADASIL.
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 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 | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 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".