Back to the Future: Integrating Event-Based and Network Diffusion Models to Predict Individual Tau Progression in Alzheimer's Disease
Robin Sandell*1, Justin Torok1, Kamalini Ranasinghe1, Srikantan Nagarajan1, Ashish Raj1
1Department of Radiology and Biomedical Imaging, University of California, San Francisco, United States
*Email: robin.sandell@ucsf.edu
Introduction
This paper presents a novel method combining an Event-Based Model (EBM) and a Network Diffusion Model (NDM) to predict individual tau protein progression in Alzheimer's disease. Statistical EBMs can infer longitudinal progression from cross-sectional data but lack mechanistic understanding, while biophysical NDMs provide mechanistic clarity but require data on a longitudinal timescale. Our hybrid approach overcomes these limitations. Using only single-visit data, our model can go back in time to infer initial seeding patterns and predict future progression. Analysis reveals high initial heterogeneity in seeding patterns that converges over time with two main seed archetypes correlating with distinct clinical presentations.
Methods
We analyzed data from 650 patients from the Alzheimer’s Neuroimaging Disease Initiative, including tau-PET, MRI, and cognitive scores. EBM assigned a disease stage to each patient based on their biomarker values, enabling a common timescale across subjects [1,2,]. NDM simulated tau progression on the brain’s structural connectivity with two rate parameters: τ accumulation rate and spread rate[3,4]. We optimized NDM parameters and tau seed pattern to accurately predict each subject’s empirical tau map at their EBM assigned stage. Applications include prediction of individuals’ future tau patterns, analysis of inter-subject heterogeneity over time, and identification of tau seed archetypes through clustering analysis.
Results
Our hybrid model successfully predicted empirical tau using individual tau seed patterns (mean R=0.85) (Fig. 1b). Longitudinal validation confirmed the model's predictive ability (mean R=0.81)(Fig. 1b). Analysis of tau patterns revealed decreasing heterogeneity over disease progression (Fig. 1c). Two primary seed archetypes emerged: focal entorhinal (typical AD) and diffuse temporal (Fig. 1d). The diffuse temporal pattern correlated with earlier disease onset, higher APOE4 carrier frequency, younger age, and faster tau accumulation rates, suggesting a more aggressive disease variant despite similar cognitive impairment levels at diagnosis (Fig. 1e,f).
Discussion
This paper presents a novel hybrid approach combining an Event-Based Model and a Network Diffusion Model to predict individual tau progression in Alzheimer's disease. The method infers initial seeding patterns from single-visit data to forecast future progression. Surprisingly, heterogeneity across subjects was highest at disease onset and decreased over time, suggesting convergence rather than divergence of pathology. Two primary seed archetypes emerged: focal entorhinal (typical AD) and diffuse temporal (associated with earlier onset, higher APOE4 frequency, and faster progression). The hybrid model outperformed prior work while providing mechanistic insights into tau progression that could inform personalized therapeutic strategies[2].
Figure 1. a. Project flow chart. b. Illustration of NDM model fitting and validation for a single patient. c. Distribution of pairwise R correlations between subjects model predicted tau at each stage indicating a process of convergence in tau patterning as disease progresses. d. Emergent tau seed archetypes. e. Demographic variables for each archetypes. f. Total tau over time for each archetypes. Acknowledgements We thank ADNI for making their data available to us.
References
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