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SC-TauPath introduces a novel framework that bridges structural connectivity analysis with tau propagation modeling in Alzheimer's disease by leveraging gradient-based attribution to generate interpretable pathway maps. The method successfully validates established Braak staging anatomy using real-world neuroimaging data from 234 participants, offering a biologically grounded alternative to models reliant on heavy biophysical assumptions. This approach provides a significant step forward for researchers seeking neurobiologically interpretable tools to map disease progression without over-reliance on theoretical constraints.
SC-TauPath: A Structural Connectivity Attribution Framework for Mapping Tau Propagation Pathways in Alzheimer's Disease
arXiv:2606.04066v1 Announce Type: cross Abstract: Understanding how structural connections are associated with tau propagation in Alzheimer's disease (AD) remains a central open question, yet existing computational models either rely heavily on biophysical assumptions or lack neurobiologically interpretable pathway maps. We present SC-TauPath, a structural connectivity (SC) attribution framework that maps tau propagation pathways from in vivo neuroimaging data. SC-TauPath combines a Network Diffusion Model (NDM)-augmented multilayer perceptron with gradient $\times$ input attribution to score each SC edge's contribution to tau prediction, then translates these attribution scores into multi-scale pathway maps (backbone edges, high-traffic routes, and hub ROIs), which validates established Braak staging anatomy. Applied to 234 ADNI participants with paired DTI SC and 18F-Flortaucipir PET, SC-TauPath achieves strong cross-validated tau prediction and yields attribution-based pathway maps consistent with established Braak staging anatomy, demonstrating that SC encode spatially specific information about regional tau distribution in AD.
Abstract:Understanding how structural connections are associated with tau propagation in Alzheimer's disease (AD) remains a central open question, yet existing computational models either rely heavily on biophysical assumptions or lack neurobiologically interpretable pathway maps. We present SC-TauPath, a structural connectivity (SC) attribution framework that maps tau propagation pathways from in vivo neuroimaging data. SC-TauPath combines a Network Diffusion Model (NDM)-augmented multilayer perceptron with gradient $\times$ input attribution to score each SC edge's contribution to tau prediction, then translates these attribution scores into multi-scale pathway maps (backbone edges, high-traffic routes, and hub ROIs), which validates established Braak staging anatomy. Applied to 234 ADNI participants with paired DTI SC and 18F-Flortaucipir PET, SC-TauPath achieves strong cross-validated tau prediction and yields attribution-based pathway maps consistent with established Braak staging anatomy, demonstrating that SC encode spatially specific information about regional tau distribution in AD.
Neurons and Cognition (q-bio.NC); Machine Learning (cs.LG)
arXiv:2606.04066 [q-bio.NC]
(or arXiv:2606.04066v1 [q-bio.NC] for this version)
https://doi.org/10.48550/arXiv.2606.04066 Focus to learn more arXiv-issued DOI via DataCite
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