Identification of neurovascular changes associated with cerebral amyloid angiopathy from subject-specific hemodynamic response functions
Bibliographic record
Abstract
Cerebral amyloid angiopathy (CAA) is a small-vessel disease preferentially affecting posterior brain regions. Recent evidence has demonstrated the efficacy of functional MRI in detecting CAA-related neurovascular injury, however, it is unknown whether such perturbations are associated with changes in the hemodynamic response function (HRF). Here we estimated HRFs from two different brain regions from block design activation data, in light of recent findings demonstrating how block designs can accurately reflect HRF parameter estimates while maximizing signal detection. Patients with a diagnosis of probable CAA and healthy controls performed motor and visual stimulation tasks. Time-to-peak (TTP), full-width at half-maximum (FWHM), and area under the curve (AUC) of the estimated HRFs were compared between groups and to MRI features associated with CAA including cerebral microbleed (CMB) count. Motor HRFs in CAA patients showed significantly wider FWHM ( P = 0.006) and delayed TTP ( P = 0.03) compared to controls. In the patient group, visual HRF FWHM was positively associated with CMB count ( P = 0.03). These findings indicate that hemodynamic abnormalities in patients with CAA may be reflected in HRFs estimated from block designs across different brain regions. Moreover, visual FWHM may be linked to structural MR indications associated with 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 | 0.001 | 0.003 |
| 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".