Tracking structural changes in preclinical and prodromal Alzheimer’s disease: insights from amyloid‐beta pathology
Bibliographic record
Abstract
Abstract Background Amyloid‐beta (Aβ) and tau pathology in Alzheimer’s disease (AD) is commonly associated with disruptions in grey matter integrity, including reduced cortical thickness (CT) and increased cortical mean diffusivity (MD). However, some cross‐sectional studies have also reported an increase in CT during the preclinical stage of the disease. Using over 10 years of longitudinal neuroimaging data from the PREVENT‐AD cohort, we examined the association between AD pathology (Aβ and tau) and brain structure, measured by CT, free‐water corrected MD (MD T ), and hippocampal volume. We also estimated the longitudinal trajectories of structural changes along the Aβ positivity timeline across the preclinical and prodromal stages of AD. Method We performed partial least square analyses (PLS) separately for Aβ negative (Aβ−) and Aβ positive (Aβ+) groups to identify key brain regions that contributed to pathological‐structural associations. We then assessed the cross‐sectional and longitudinal associations between AD pathology and structural measures within the PLS‐identified regions across all participants. Using the sampled iterative local approximation algorithm, we estimated the time from Aβ+ onset and calculated years from Aβ+ for each MRI scan. These estimates allowed us to track the structural changes relative to Aβ positivity (Figure 1). Result We found that higher Aβ deposition was associated with decreased MD T and increased CT in the Aβ− group, whereas the Aβ+ group showed opposite associations. Across all participants, associations for MD T followed a U‐shaped pattern, while CT exhibited an inverse U‐shaped relationship with Aβ pathology (Figure 2). These associations were observed in several key AD‐related regions, including the entorhinal cortex, fusiform gyrus, and inferior parietal lobule, and middle temporal regions. Longitudinal analyses revealed similar trajectory patterns along the Aβ+ timeline, with these changes emerging several years before Aβ positivity onset (Figure 3). Conclusion Our study suggests that brain structural changes in response to Aβ pathology start decades before symptoms and may follow highly nonlinear trajectories. The initial increases in CT and decreases in MD T might be related to the space taken by Aβ and/or several other biological processes occurring during the preclinical stage of the disease, such as neuroinflammation, astrocytic and microglia activation, or brain swelling.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".