Abstract
Aβ Positron Emission Tomography (PET) is often used to manage Alzheimer's disease (AD). To better understand Aβ progression, we introduce and evaluate a mathematical model that couples Aβ at parcellated gray matter regions. We term this model LNODE for "latent network ordinary differential equations". At each region, we track normal Aβ , abnormal Aβ , and m latent states that intend to capture unobservable mechanisms coupled to Aβ progression. LNODE is parameterized by subject-specific parameters and cohort parameters. We jointly invert for these parameters by fitting the model to Aβ -PET data from 585 subjects from the ADNI dataset. Although underparameterized, our model achieves population R2 ≥ 98% compared to R2 ≤ 60% when fitting without latent states. Furthermore, these preliminary results suggest the existence of different subtypes of Aβ progression.