Learning Patient-Specific Spatial Biomarker Dynamics via Operator Learning for Alzheimer's Disease Progression

• URL: http://arxiv.org/abs/2507.16148v1
• Date: Tue, 22 Jul 2025 01:52:28 GMT
• Title: Learning Patient-Specific Spatial Biomarker Dynamics via Operator Learning for Alzheimer's Disease Progression
• Authors: Jindong Wang, Yutong Mao, Xiao Liu, Wenrui Hao,
• Abstract summary: Alzheimers disease (AD) is a complex, multifactorial neurodegenerative disorder.<n>Despite recent therapeutic advances, predictive models capable of accurately forecasting individualized disease trajectories remain limited.<n>Here, we present a machine learning-based operator learning framework for personalized modeling of AD progression.
• Score: 9.499341016835121
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Alzheimer's disease (AD) is a complex, multifactorial neurodegenerative disorder with substantial heterogeneity in progression and treatment response. Despite recent therapeutic advances, predictive models capable of accurately forecasting individualized disease trajectories remain limited. Here, we present a machine learning-based operator learning framework for personalized modeling of AD progression, integrating longitudinal multimodal imaging, biomarker, and clinical data. Unlike conventional models with prespecified dynamics, our approach directly learns patient-specific disease operators governing the spatiotemporal evolution of amyloid, tau, and neurodegeneration biomarkers. Using Laplacian eigenfunction bases, we construct geometry-aware neural operators capable of capturing complex brain dynamics. Embedded within a digital twin paradigm, the framework enables individualized predictions, simulation of therapeutic interventions, and in silico clinical trials. Applied to AD clinical data, our method achieves high prediction accuracy exceeding 90% across multiple biomarkers, substantially outperforming existing approaches. This work offers a scalable, interpretable platform for precision modeling and personalized therapeutic optimization in neurodegenerative diseases.

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