Abstract TP417: Cortical Microinfarcts on 3T MRI in Cerebral Amyloid Angiopathy: Associations With MRI Burden and Cognitive Dysfunction
Bibliographic record
Abstract
Background: Cerebral amyloid angiopathy (CAA) causes vascular cognitive impairment, possibly due to ischemic lesions that are caused by impaired cerebral blood flow. Cerebral microinfarcts (CMIs) are small ischemic lesions that are found in CAA patients at autopsy. Hypothesis: Cortical CMIs can be detected on in vivo 3T MR images in CAA and will correlate with markers of CAA-related vascular brain injury and cognitive function. Methods: We analysed data from CAA cases and neurologically healthy controls participating in the Functional Assessment of Vascular Reactivity (FAVR) study. All participants underwent a standardized clinical, neuropsychological and 3T MR assessment. Cortical CMIs were rated according to standardized criteria, by a single rater blinded to clinical information. Results: There were 36 CAA patients (mean age 73.0±9.0 years) and 22 healthy controls (69.0±8.3 years). Cortical CMIs were found in significantly more patients with CAA (50%) (median number: 1, range: 1-9) than in healthy controls (18%) ( p =0.02). In CAA, patients with cortical CMIs had higher white matter hyperintensity volumes (median 29.5 mL vs 13.0 mL, p =0.04) and cerebral microbleed counts (median 28 vs 5.5, p =0.049). CAA patients with cortical CMIs also showed lower occipital fMRI activation (median BOLD change 1.97% vs 2.57%, r =0.24, p =0.15) and worse memory performance (mean z -score -0.60 vs -0.05, p =0.12), although both effects were non-significant. No significant association of cortical CMIs with age, sex or vascular risk factors was found. Conclusions: Cortical CMIs are frequently detected on 3T MRI in CAA. They relate to well-established ischemic and hemorrhagic MRI markers of CAA. Therefore in vivo cortical CMIs can be regarded as a new marker of CAA disease severity. Additional, larger studies are needed to determine the relationship between CMI and clinical outcomes in 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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.004 | 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".