LNODE: Uncovering the Latent Dynamics of Aβ in Alzheimer’s Disease

Abeta Positron Emission Tomography (PET) is often used to manage Alzheimer’s disease (AD). To better understand Abeta progression, we introduce and evaluate a mathematical model that couples Abeta at parcellated gray matter regions. We term this model LNODE for ``latent network ordinary differential equations’’. At each region, we track normal Abeta, abnormal Abeta , and m latent states that intend to capture unobservable mechanisms coupled to Abeta progression. LNODE is parameterized by subject-specific parameters and cohort parameters. We jointly invert for these parameters by fitting the model to Abeta -PET data from 585 subjects from the ADNI dataset. Although underparameterized, our model achieves population R-square > 98% compared to R-square > 60% when fitting without latent states. Furthermore, these preliminary results suggest the existence of different subtypes of Abeta progression.