Cerebrovascular reactivity in cerebral amyloid angiopathy, Alzheimer disease, and mild cognitive impairment
Bibliographic record
Abstract
Objective To assess cerebrovascular reactivity in response to a visual task in participants with cerebral amyloid angiopathy (CAA), Alzheimer disease (AD), and mild cognitive impairment (MCI) using fMRI. Methods This prospective cohort study included 40 patients with CAA, 22 with AD, 27 with MCI, and 25 healthy controls. Each participant underwent a visual fMRI task using a contrast-reversing checkerboard stimulus. Visual evoked potentials (VEPs) were used to compare visual cortex neuronal activity in 83 participants. General linear models using least-squares means, adjusted for multiple comparisons with the Tukey test, were used to estimate mean blood oxygen level–dependent (BOLD) signal change during the task and VEP differences between groups. Results After adjustment for age and hypertension, estimated mean BOLD response amplitude was as follows: CAA 1.88% (95% confidence interval [CI] 1.60%–2.15%), AD 2.26% (1.91%–2.61%), MCI 2.15% (1.84%–2.46%), and control 2.65% (2.29%–3.00%). Only patients with CAA differed from controls (p = 0.01). In the subset with VEPs, group was not associated with prolonged latencies or lower amplitudes. Lower BOLD amplitude response was associated with higher white matter hyperintensity (WMH) volumes in CAA (for each 0.1% lower BOLD response amplitude, the WMH volume was 9.2% higher, 95% CI 6.0%–12.4%) but not other groups (p = 0.002 for interaction) when controlling for age and hypertension. Conclusions Mean visual BOLD response amplitude was lowest in participants with CAA compared to controls, without differences in VEP latencies and amplitudes. This suggests that the impaired visual BOLD response is due to reduced vascular reactivity in CAA. In contrast to participants with CAA, the visual BOLD response amplitude did not differ between those with AD or MCI and controls.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.001 | 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".