BrainODE: Neural Shape Dynamics for Age- and Disease-aware Brain Trajectories
Wonjung Park, Suhyun Ahn, Maria Hernandez, Susana Maniega, Jinah Park
We propose BrainODE, a neural ordinary differential equations (ODEs)-based framework for modeling continuous longitudinal deformations of brain shapes. BrainODE learns a deformation space over anatomically meaningful brain regions to enable early prediction of neurodegenerative disease progression. Addressing key challenges in longitudinal neuroimaging— including limited data, irregular sampling intervals, and inter-subject variability—we design a conditional neural ODE architecture that modulates shape dynamics with subject-specific age and cognitive status. To enable autoregressive forecasting from a single observation, we introduce a pseudo-cognitive status embedding that jointly encodes age and estimated cognitive status. We demonstrate that BrainODE outperforms time-aware baselines in predicting future brain shapes, demonstrating strong generalization across longitudinal datasets with both regular and irregular time intervals.