ArticleNPJ dementia2025
Predicting future amyloid conversion: the role of baseline amyloid distribution, genetic and cognitive resilience.
Article in NPJ dementia, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
2 citing papers in PubMed.
- Concomitant pathologies and their impact on schizophrenia: a narrative overview of current evidence.Journal of neural transmission (Vienna, Austria : 1996) · 2026Review
- Interpretable Alzheimer's Diagnosis via Multimodal Fusion of Regional Brain Experts.Proceedings. IEEE International Conference on Healthcare Informatics · 2026Article
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Authors and funding
3 authors.
Funding
Abstract
Alzheimer's disease (AD) is characterized by early accumulation of amyloid beta (Aβ) prior to cognitive decline. Although Aβ plays a normal role in brain function, excessive buildup is a key early indicator of AD. The transition from Aβ-negative to Aβ-positive reflects disease progression, but patterns predicting this shift remain unclear. Using longitudinal Aβ PET imaging data from the Alzheimer's Disease Neuroimaging Initiative (ADNI), we developed a survival-based machine learning model to estimate the timing of Aβ conversion. The model showed strong predictive accuracy (concordance index = 0.845). SHAP analysis identified higher baseline Aβ burden in frontoparietal and striatal regions, particularly the rostral and caudal middle frontal cortex, as predictors of faster conversion. APOE4 homozygosity and lower baseline cognitive performance were also associated with earlier Aβ positivity. Kaplan-Meier analysis estimated a median transition time of 4.06 years. These findings support machine learning for early AD risk prediction and timely intervention planning.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.