Understanding Mechanistic Role of Structural and Functional Connectivity in Tau Propagation Through Multi-Layer Modeling

Alzheimer's disease (AD) is a progressive neurodegenerative disorder marked by the pathological accumulation and propagation of tau proteins [1]. Tau pathology, initially localized in the entorhinal cortex, gradually spreads to connected brain regions in a pattern that correlates with cognitive decline and disease severity. Mounting evidence supports the hypothesis that this spread occurs in a prion-like manner, whereby misfolded tau seeds propagate trans-synaptically through neural networks [2]. This insight has shifted the focus from region-specific atrophy to network-based degeneration, assuming the brain's large-scale connectivity architecture as a key determinant of pathological progression. In this regard, a growing body of research has developed connectome-based diffusion models to simulate and predict the spatial and temporal dynamics of tau propagation across the brain [3-5]. These models typically leverage information from structural connectivity (SC), derived from diffusion-weighted imaging (DWI), or functional connectivity (FC), measured via resting-state functional magnetic resonance imaging (fMRI), to model tau spread as a network-driven diffusion process. By modeling tau dynamics within the topology of the brain connectome, these frameworks offer a mechanistic perspective on how pathological burden evolves across regions. Given the connectome-constrained assumption on tau propagation, such models have shown potential in forecasting future tau accumulation [3, 5], identifying vulnerable brain circuits [5], and stratifying individuals based on progression risk [6], thereby informing early diagnosis and therapeutic targeting.