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| Main Authors: | , , , , , , , , , , , |
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| Format: | Preprint |
| Published: |
2026
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2603.13598 |
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| _version_ | 1866910052743380992 |
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| author | Jiang, Kun Liao, Can Jiang, Sujin Lin, Haidong Hou, Jixin Liu, Tianming Li, Gang Wu, Taotao Mao, Yiqi Kuhl, Ellen Wang, Xianqiao Chen, Xianyan |
| author_facet | Jiang, Kun Liao, Can Jiang, Sujin Lin, Haidong Hou, Jixin Liu, Tianming Li, Gang Wu, Taotao Mao, Yiqi Kuhl, Ellen Wang, Xianqiao Chen, Xianyan |
| contents | Alzheimer's disease involves progressive tau accumulation and spread, leading to regional brain atrophy and disruption of large-scale functional networks. While tau propagation and tissue degeneration have been widely modeled, how atrophy dynamics translate into functional connectivity (FC) degradation remains unclear. Here, we develop a multiphysics framework integrating anisotropic tau reaction-diffusion, finite-deformation biomechanics, and network modeling to link tau-driven atrophy with FC changes. Model fidelity is evaluated by quantitatively comparing simulated atrophy patterns with imaging-derived measurements. Using longitudinal structural and functional MRI, we identify an approximately linear relationship between regional atrophy rates and FC change. We then construct an atrophy-informed structural network degradation matrix from model-predicted region-specific atrophy rates and embed it into a neural oscillation model to predict FC disruption. Our results show that (i) the coupled reaction-diffusion-biomechanical model reproduces observed regional atrophy, (ii) regional atrophy rates parsimoniously predict longitudinal FC changes, and (iii) the atrophy-informed degradation matrix captures the direction and relative magnitude of regional FC disruption. By converting tau-driven atrophy into predictive FC trajectories, the proposed framework offers a clinically interpretable avenue for forecasting disease progression and informing trial design. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2603_13598 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Tau-induced atrophy drives functional connectivity disruption in Alzheimer's disease Jiang, Kun Liao, Can Jiang, Sujin Lin, Haidong Hou, Jixin Liu, Tianming Li, Gang Wu, Taotao Mao, Yiqi Kuhl, Ellen Wang, Xianqiao Chen, Xianyan Medical Physics Numerical Analysis Alzheimer's disease involves progressive tau accumulation and spread, leading to regional brain atrophy and disruption of large-scale functional networks. While tau propagation and tissue degeneration have been widely modeled, how atrophy dynamics translate into functional connectivity (FC) degradation remains unclear. Here, we develop a multiphysics framework integrating anisotropic tau reaction-diffusion, finite-deformation biomechanics, and network modeling to link tau-driven atrophy with FC changes. Model fidelity is evaluated by quantitatively comparing simulated atrophy patterns with imaging-derived measurements. Using longitudinal structural and functional MRI, we identify an approximately linear relationship between regional atrophy rates and FC change. We then construct an atrophy-informed structural network degradation matrix from model-predicted region-specific atrophy rates and embed it into a neural oscillation model to predict FC disruption. Our results show that (i) the coupled reaction-diffusion-biomechanical model reproduces observed regional atrophy, (ii) regional atrophy rates parsimoniously predict longitudinal FC changes, and (iii) the atrophy-informed degradation matrix captures the direction and relative magnitude of regional FC disruption. By converting tau-driven atrophy into predictive FC trajectories, the proposed framework offers a clinically interpretable avenue for forecasting disease progression and informing trial design. |
| title | Tau-induced atrophy drives functional connectivity disruption in Alzheimer's disease |
| topic | Medical Physics Numerical Analysis |
| url | https://arxiv.org/abs/2603.13598 |