Prediction of mild cognitive impairment progression using time-sensitive multimodal biomarkers
Bibliographic record
Abstract
Alzheimer's disease (AD) develops over a prolonged asymptomatic phase marked by silent pathology. Identifying cognitively unimpaired individuals likely to progress to mild cognitive impairment (MCI) is essential for early intervention. We investigated whether multimodal combinations of biomarkers, including frequency-specific neurophysiological activity, enhance prediction beyond demographic and genetic factors in older adults (n = 102; 31 progressors; mean follow-up = 5.9 years). Biomarkers included MEG-derived alpha power, MRI-derived hippocampal volume, plasma Aβ42/40 ratio and p-tau217, and neocortical Aβ and entorhinal tau PET. Cox regression models estimated progression risk and tested time-varying prognostic effects. Neurophysiological and proteinopathy biomarkers improved prediction beyond clinical and genetic factors. Elevated alpha power predicted short-term risk, but its predictive value weakened over time, whereas high neocortical Aβ became increasingly predictive with longer follow-up. Plasma Aβ42/40, p-tau217, and tau PET each conferred higher risk, while hippocampal volume did not. Findings support a multimodal, time-sensitive framework for individualized risk prediction in preclinical AD.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| 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".