BrainODE: Neural Shape Dynamics for Age- and Disease-aware Brain Trajectories

Wonjung Park, Suhyun Ahn, Maria C. Valdes Hernandez, Susana Muñoz Maniega, Jinah Park

Keywords: Shape analysis, Medical application, Prediagnosis

Abstract: We present BrainODE, a neural ordinary differential equation (ODE)-based framework for modeling continuous longitudinal deformations of brain shapes. BrainODE learns a deformation space over anatomically meaningful brain regions to facilitate early prediction of neurodegenerative disease progression. Addressing inherent challenges of longitudinal neuroimaging data-such as limited sample sizes, irregular temporal sampling, and substantial inter-subject variability-we propose a conditional neural ODE architecture that models shape dynamics with subject-specific age and cognitive status. To enable autoregressive forecasting of brain morphology from a single observation, we propose a pseudo-cognitive status embedding that allows progressive shape prediction across intermediate time points with predicted cognitive decline. Experiments show that BrainODE outperforms time-aware baselines in predicting future brain shapes, demonstrating strong generalization across longitudinal datasets with both regular and irregular time intervals.

Primary Area: Applications (e.g., vision, language, speech and audio, Creative AI)

Submission Number: 3174