Modeling the progression of neuropsychiatric symptoms in Alzheimer’s disease with PET-based Braak staging
Bibliographic record
Abstract
In Alzheimer’s disease (AD), neuropsychiatric symptoms (NPS) correlate with tau deposition in the brain. Here, we investigated the association of PET-based Braak stages with NPS and assessed whether they predict annual changes in NPS. We evaluated 231 individuals in the aging and AD continuum. Participants were assigned a Braak stage at baseline and followed for 1.97 (s.d. 0.62) years. NPS were investigated using the Mild Behavioral Impairment Checklist (MBI-C) and the Neuropsychiatric Inventory Questionnaire severity (NPI-Q-S) and distress (NPI-Q-D) scales. Multiple linear regressions (MLR) assessed the association of Braak stages with baseline NPS and the annual change in NPS scores. At baseline, stages I-II, III-IV, and V-VI were associated with higher MBI-C, NPI-Q-S, and NPI-Q-D scores. Stages V-VI were associated with a significant annual increase in MBI-C scores. These findings suggest that tau accumulation may manifest clinically with an increase in NPS, which seems to be an early event in AD pathophysiology. Moreover, PET-based Braak staging appears to be a good predictor of NPS severity progression. • Neuropsychiatric symptoms are linked to tau pathology from the early disease stages. • Neuropsychiatric symptoms increase as tau pathology becomes more severe. • Tau accumulation may precede the onset of neuropsychiatric symptoms. • PET-based Braak staging predicts future behavioral symptoms in Alzheimer's disease.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".