Machine learning improves brain age estimation in Alzheimer's disease research, suggesting better outcomes for diagnostics.
Motivation: The need for precise brain age estimation has grown, especially in differentiating typical from atypical aging patterns in Alzheimer's disease (AD) research. Goal(s): We propose, Quantile-SHAP Gap in Aging Patterns (QS-GAP), which aims to enhance brain aging prediction by using machine learning on MRI data, creating assessments that align with cohort norms. Approach: QS-GAP estimates brain age through quantile-based predictions, using the closest quantile to a person's actual age as a baseline and median quantile for general aging. SHAP values for controls create a reference aging pattern. Results: QS-GAP's quantile-based method slightly outperformed raw features in classifying AD versus cognitively normal (CN) individuals. Impact: QS-GAP offers a refined, personalized approach to assessing brain aging, enhancing classification between AD and CN individuals. By aligning aging patterns with cohort norms, it supports more accurate diagnostics and advances neurodegenerative research, particularly in individualized brain health assessments.
No takes yet. Share an insight, caveat, or question.
Navarro‐González et al. (2025) studied this question.
Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context: