Triplet longitudinal masked autoencoder for predicting individualized functional connectome development during infancy.

Abstract

Brain functional connectivity (FC) constructed from resting-state functional MRI (rs-fMRI) is the predominant method for studying brain functional organization of infants. Predicting the full dynamic developmental trajectory of infant FC from existing incomplete longitudinal data can enrich our understanding of brain function developmental patterns and mechanisms and help identify neurodevelopmental disorders. However, the scarcity of longitudinal infant functional MRI scans with frequent irregular missing data poses significant challenges in accurately predicting and delineating the dynamic trajectory of early normal and abnormal brain development. Moreover, existing deep learning methods typically predict FC at a single target timepoint from each available FC independently, overlooking longitudinal dependencies and yielding temporally inconsistent and inaccurate predictions during infancy. To this end, we propose a novel Triplet Longitudinal Masked Autoencoder (TL-MAE) for the prediction of the full dynamic developmental trajectory of infant FC. Specifically, we adopt the following novel strategies: 1) Creating a longitudinally consistent prediction strategy to ensure the temporal consistency and robustness in the FC generation process; 2) Introducing the FC-specified Masked Autoencoder to capture FC domain features and pre-training this model by leveraging large-scale high-quality data; 3) Developing a dual triplet network alongside an identity conditional module to disentangle entangled identity and age information, enabling individualized predictions at any given age. Experiments on 696 longitudinal infant fMRI scans from two datasets demonstrate that our method not only yields more accurate and temporally consistent predictions of FC developmental trajectories, but also excels at capturing individualized features compared to state-of-the-art techniques.