Amyloid Beta-Weighted Cortical Thickness: A New Imaging Biomarker in Alzheimer's Disease
Bibliographic record
Abstract
Alzheimer's disease (AD) is the most common neurodegenerative disorder pathologically characterized by amyloid-beta (Aβ) plaques and neurofibrillary tangles. The aggregation of Aβ precedes tau pathologies in AD; however, the causal relation between the two pathologies and the mechanisms by which aggregated forms of Aβ contribute to cortical thinning are not fully understood. We proposed quantitative Aβ-weighted cortical thickness analysis to investigate the regional relationship between cortical thinning and amyloid plaque deposition using magnetic resonance (MR) and Pittsburg Compound B (PiB) positron emission tomography (PET) images in patients with AD, mild cognitive impairment (MCI), and subjects with normal cognition. We hypothesized that there are cortical areas that have prominent changes associated with Aβ deposition and there are areas that are relatively independent from Aβ deposition where pathologies other than Aβ (such as tau) are predominant. The study was performed using MRI and PiB PET data from the Alzheimer's Disease Neuroimaging Initiative. We measured accuracy of classification models in three different pairs of groups comparing AD, MCI, and normal cognition. Classification models that used Aβ-weighted cortical thickness were not inferior to classification models that used only cortical thickness or amyloid deposition. In addition, based on timing of changes in cortical thinning and Aβ deposition such as Aβ deposition after cortical thinning; cortical thinning after Aβ deposition, or concurrent Aβ deposition and cortical thinning, we identified three types of relationships between cortical thinning and Aβ deposition: (1) Aβ-associated cortical thinning; (2) Aβ-independent cortical thinning; and (3) Aβ deposition only without cortical thinning. Taken together, these findings suggest that Aβ-weighted cortical thickness values can be used as an objective biomarker of structural changes caused by amyloid pathology in the brain.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".