P1‐024: PREDICTION OF BRAIN AGE USING RESTING‐STATE FUNCTIONAL CONNECTIVITY REVEALS ACCELERATED AGING IN THE PRECLINICAL PHASE OF AUTOSOMAL DOMINANT ALZHEIMER'S DISEASE, IRRESPECTIVE OF AMYLOID PATHOLOGY
Bibliographic record
Abstract
Overlaps exist between the neural systems vulnerable to brain aging and Alzheimer's disease (AD). It is a matter of debate if aging and AD progression are independent phenomenon. Our objective was to assess functional brain aging in the preclinical phase of autosomal dominant Alzheimer's disease (ADAD) and individuals at risk of sporadic AD (sAD), and its relationship with amyloid (Aβ) pathology. Cognitively normal participants from 18 to 94 years old from the DIAN, PREVENT-AD, CamCAN, ADNI, and ICBM cohorts were included (Table). We used graph metrics from resting state fMRI (rsfMRI) to generate a neural net model predicting age (training set n=773; validation set n= 47). Age was then predicted in DIAN and PREVENT-AD (test set; n=158 and n=257, respectively) to assess the effect of AD genetic determinant/risk (ADAD mutation in DIAN and APOE4 in PREVENT-AD) on the discrepancy between functional brain aging and actual age. The effect of Aβ burden on brain prediction was assessed additionally in individuals with Aβ-PET. Age can be predicted from functional topological properties constructed from rsfMRI in a large number of participants selected across multi-site cohorts (Figure1). Importantly, our model estimated that cognitively normal DIAN mutation carriers were in average older than their actual age, and this discrepancy between the estimated and actual age was greater than in non-carriers (Figure2A). Aβ status (or load) however had no impact on the discrepancy between predicted and actual age (Figure2A). In the PREVENT-AD, neither APOE4 nor Aβ burden was related to an overestimation of age (Figure2B).
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".