A machine learning enrichment strategy for presymptomatic cohorts in Alzheimer’s disease clinical trials
Bibliographic record
Abstract
Abstract Background It is challenging for Alzheimer’s disease trials to enroll presymptomatic individuals who are likely to decline cognitively. We propose using a multimodal machine learning approach to discriminate between cognitively normal individuals who progressed to mild cognitive impairment (MCI) and those who remained stable over 48 months. Method Patterns of gray matter distribution, derived from structural MRI, associated with either healthy aging or Alzheimer’s disease were extracted from AIBL [1] (n=401). We processed images from 823 individuals deemed cognitively normal at baseline from NACC [2] (n=433), ADNI [3] (n=260), and OASIS‐3 [4] (n=130) and generated scores representing the spatial similarities between the gray matter distribution of each individual to the patterns produced from AIBL. Gray matter scores, MMSE, CDR‐SB, FAQ, APOE4 status, education, age, and sex were used to train a machine learning prognostic pipeline using the Foresight platform (Perceiv Research Inc.) to distinguish individuals who received a MCI diagnosis within 48 months from baseline (progressors) from those who remained stable. The model was trained and tested with 5‐fold inner‐loop cross‐validation. Result The model achieved a mean (± std) AUC of 0.752±0.045, accuracy of 70.72±2.44, sensitivity of 62.51±12.12, and specificity of 71.82±3.87. The predicted progressors experienced a steeper cognitive decline, tended to be older, and contained a higher proportion APOE4 carriers and amyloid positive cases, compared to stable individuals. Of the predicted progressors, 33.3% were true progressors. This represents a nearly two‐fold enrichment of progressors over the prevalence of the entire sample where only 17.7% were true progressors. Conclusion An automated algorithm can successfully identify presymptomatic individuals with impending cognitive impairment. This tool can enhance trial enrollment by targeting individuals who are at the highest risk of cognitive decline and potentially reduce trial costs by minimizing the need for prohibitively large sample sizes. [1] Australian Imaging Biomarkers and Lifestyle Study of Ageing (aibl.csiro.au). [2] National Alzheimer’s Coordinating Center (naccdata.org). [3] Alzheimer’s Disease Neuroimaging Initiative (adni.loni.usc.edu). [4] Open Access Series of Imaging Studies (oasis‐brains.org).
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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 | 0.048 | 0.091 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".