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
The continuous advancements in AI and neuroimaging techniques are enabling more sophisticated computational models for understanding complex disease mechanisms like Alzheimer's, leading to breakthroughs like SC-TauPath.
This development offers a neurobiologically interpretable method to map disease progression pathways, potentially accelerating the development of targeted therapies and early diagnostic tools for Alzheimer's disease.
Previously, models either relied on heavy biophysical assumptions or lacked clear pathway maps; this framework provides a more accurate and interpretable method for understanding tau propagation in the brain.
- · Neurology researchers
- · Pharmaceutical companies
- · AI healthcare developers
- · Patients with Alzheimer's disease
- · Traditional diagnostic methods
- · Ineffective AD drug candidates
Improved understanding of Alzheimer's disease pathology at a structural level.
Development of more precise and earlier diagnostic tools and targeted therapeutic interventions.
Shift in AD research focus towards network-based interventions and personalized medicine approaches.
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Read at arXiv cs.LG