Cortical Thickness and Its Association with Clinical Cognitive and Neuroimaging Markers in Cerebral Amyloid Angiopathy
Bibliographic record
Abstract
BACKGROUND: Cerebral amyloid angiopathy (CAA) contributes to brain neurodegeneration and cognitive decline, but the relationship between these two processes is incompletely understood. OBJECTIVE: The purpose of this study is to examine cortical thickness and its association with cognition and neurodegenerative biomarkers in CAA. METHODS: Data were collected from the Functional Assessment of Vascular Reactivity study and the Calgary Normative Study. In total, 48 participants with probable CAA, 72 cognitively normal healthy controls, and 24 participants with mild dementia due to AD were included. Participants underwent an MRI scan, after which global and regional cortical thickness measurements were obtained using FreeSurfer. General linear models, adjusted for age and sex, were used to compare cortical thickness globally and in an AD signature region. RESULTS: Global cortical thickness was lower in CAA compared to healthy controls (mean difference (MD) -0.047 mm, 95% confidence interval (CI) -0.088, -0.005, p = 0.03), and lower in AD compared to CAA (MD -0.104 mm, 95% CI -0.165, -0.043, p = 0.001). In the AD signature region, cortical thickness was lower in CAA compared to healthy controls (MD -0.07 mm, 95% CI -0.13 to -0.01, p = 0.02). Within the CAA group, lower cortical thickness was associated with lower memory scores (R2 = 0.10; p = 0.05) and higher white matter hyperintensity volume (R2 = 0.09, p = 0.04). CONCLUSION: CAA contributes to neurodegeneration in the form of lower cortical thickness, and this could contribute to cognitive decline. Regional overlap with an AD cortical atrophy signature region suggests that co-existing AD pathology may contribute to lower cortical thickness observed 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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".