Disentangled Intensive Triplet Autoencoder for Infant Functional Connectome Fingerprinting

Functional connectome “fingerprint” is a highly characterized brain pattern that distinguishes one individual from others. Although its existence has been demonstrated in adults, an unanswered but fundamental question is whether such individualized pattern emerges since infancy. This problem is barely investigated despites its importance in identifying the origin of the intrinsic connectome patterns that mirror distinct behavioral phenotypes. However, addressing this knowledge gap is challenging because the conventional methods are only applicable to developed brains with subtle longitudinal changes and typically fail on the dramatically developing infant brains. To tackle this challenge, we invent a novel model, namely, disentangled intensive triplet autoencoder (DI-TAE). First, we introduce the triplet autoencoder to embed the original connectivity into a latent space with higher discriminative capability among infant individuals. Then, a disentanglement strategy is proposed to separate the latent variables into identity-code, age-code, and noise-code, which not only restrains the interference from age-related developmental variance, but also captures the identity-related invariance. Next, a cross-reconstruction loss and an intensive triplet loss are designed to guarantee the effectiveness of the disentanglement and enhance the inter-subject dissimilarity for better discrimination. Finally, a variance-guided bootstrap aggregating is developed for DI-TAE to further improve the performance of identification. DI-TAE is validated on three longitudinal resting-state fMRI datasets with 394 infant scans aged 16 to 874 days. Our proposed model outperforms other state-of-the-art methods by increasing the identification rate by more than 50%, and for the first time suggests the plausible existence of brain functional connectome “fingerprint” since early infancy.