\(\text {A}\beta \) Positron Emission Tomography (PET) is often used to manage Alzheimer’s disease (AD). To better understand \(\text {A}\beta \) progression, we introduce and evaluate a mathematical model  that couples \(\text {A}\beta \) at parcellated gray matter regions. We term this model LNODE for “latent network ordinary differential equations”. At each region, we track normal \(\text {A}\beta \) , abnormal \(\text {A}\beta \) , and m latent states that intend to capture unobservable mechanisms coupled to \(\text {A}\beta \) progression. LNODE is parameterized by subject-specific parameters and cohort parameters. We jointly invert for these parameters by fitting the model to \(\text {A}\beta \) -PET data from 585 subjects from the ADNI dataset. Although underparameterized, our model achieves population \(R^2\ge 98\) % compared to \(R^2\le 60\) % when fitting without latent states. Furthermore, these preliminary results suggest the existence of different subtypes of \(\text {A}\beta \) progression.