🤖 AI Summary
This study addresses the challenges of elucidating brain aging drivers and individual trajectory heterogeneity in precision medicine for Alzheimer’s disease by constructing a physics-informed, multiscale digital twin platform. Innovatively integrating fatigue dissipation principles from materials science to quantify brain system stability, the framework employs an open, modular federated architecture to support mechanistic hypothesis testing. The platform enables quantitative assessment of residual compensatory capacity and system stability, facilitating the formal verification, comparison, and refinement of alternative mechanistic hypotheses. Ultimately, this work establishes a novel computational paradigm for dissecting the complex mechanisms underlying brain aging, offering a robust tool for advancing personalized therapeutic strategies in neurodegenerative diseases.
📝 Abstract
Deciphering the drivers of brain ageing and neurodegeneration, and explaining heterogeneity in individual trajectories, remains a central challenge for precision medicine. We propose PIM-BrainTwin, a physics-informed multiscale digital twin for Alzheimer's disease that draws on materials-science principles--fatigue-like depletion, safety margins and critical transitions--to quantify residual compensatory capacity and system stability. Designed as an open, modular and federated platform, it enables alternative mechanistic hypotheses to be formalised, compared and refined.