  Brain network analysis is vital for understanding the neural interactions
regarding brain structures and functions, and identifying potential biomarkers
for clinical phenotypes. However, widely used brain signals such as Blood
Oxygen Level Dependent (BOLD) time series generated from functional Magnetic
Resonance Imaging (fMRI) often manifest three challenges: (1) missing values,
(2) irregular samples, and (3) sampling misalignment, due to instrumental
limitations, impacting downstream brain network analysis and clinical outcome
predictions. In this work, we propose a novel model called BrainODE to achieve
continuous modeling of dynamic brain signals using Ordinary Differential
Equations (ODE). By learning latent initial values and neural ODE functions
from irregular time series, BrainODE effectively reconstructs brain signals at
any time point, mitigating the aforementioned three data challenges of brain
signals altogether. Comprehensive experimental results on real-world
neuroimaging datasets demonstrate the superior performance of BrainODE and its
capability of addressing the three data challenges.
