Individualized Trajectory Prediction of Early Developing Functional Connectivity

Predicting the development of functional connectivity (FC) derived from resting-state functional MRI is pivotal for elucidating the intrinsic brain functional organization and modeling its dynamic development during infancy. Existing deep learning methods typically predict FC at a target timepoint from each available FC independently, yielding inconsistent predictions and overlooking longitudinal dependencies, which introduce ambiguity in practical applications. Furthermore, the scarcity and irregular distribution of longitudinal rs-fMRI data pose significant challenges in accurately predicting and delineating the trajectories of early brain functional development. To address these issues, we propose a novel Triplet Cycle-Consistent Masked Autoencoder (TC-MAE) for the trajectory prediction of the development of infant FC. Our TC-MAE has the capability to traverse FC over an extended period, extract unique individual characteristics, and predict target FC at any given age in infancy with longitudinal consistency. Extensive experiments on 368 longitudinal infant rs-fMRI scans demonstrate the superior performance of the proposed method in longitudinal FC prediction compared with state-of-the-art approaches.