Year: 2023
Type: article
Abstract: Background Alzheimer’s disease (AD) is characterized by the propagation of tau aggregates throughout the brain in a prion‐like manner. Since current pathology imaging technologies only provide a spatial mapping of tau accumulation, computational modeling becomes indispensable in analyzing the spatial‐temporal propagation patterns of widespread tau aggregates. Method Current state‐of‐the‐art works focus on the change of focal patterns, lacking a system‐level understanding of the tau propagation mechanism that can explain and forecast the cascade of tau accumulation. To address this limitation, we conceptualize that the inter‐cellular spreading of tau pathology forms a dynamic system where each node (brain region) is ubiquitously wired with other nodes while interacting with the build‐up of pathological burdens. In this context, we formulate the biological process of tau spreading in a principled potential energy transport model (constrained by brain network topology), which allows us to develop an explainable neural network for uncovering the spatial‐temporal dynamics of tau propagation from the longitudinal tau‐PET scans. Specifically, we first translate the transport equation into a backbone of graph neural network (GNN), where the spreading flows are essentially driven by the potential energy of tau accumulation at each node. Result First, our physics‐informed deep model yields more accurate prediction of future tau aggregates. Second, we generate the population‐wise brain mapping of tau spreading through the brain. Sub‐cortex to cortex flows are the predominant patterns in the early stage of AD while the spreading patterns turn to cortex‐to‐cortex propagation as the disease progresses. Conclusion Our physics‐informed deep model outperforms existing state‐of‐the‐art methods in terms of prediction accuracy and model explainability. Since the region‐to‐region spreading flow provides rich information for understanding the tau propagation mechanism, our learning‐based method has great applicability in current AD studies.
Source: Alzheimer s & Dementia
DOI: 10.1002/alz.070905
Authors: Guorong Wu, Tingting Dan
Institution: University of North Carolina at Chapel Hill
Language: English
Subfield: Physiology
Field: Medicine
Domain: Health Sciences
Open Access status: bronze
OpenAlex ID: W4390194567