ArticleMedical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention2020
Disentangled Intensive Triplet Autoencoder for Infant Functional Connectome Fingerprinting.
Article in Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- Flexible Individualized Developmental Prediction of Infant Cortical Surface Maps via Intensive Triplet Autoencoder.IEEE transactions on medical imaging · 2025Article
- INDIVIDUALIZED TRAJECTORY PREDICTION OF EARLY DEVELOPING FUNCTIONAL CONNECTIVITY.Proceedings. IEEE International Symposium on Biomedical Imaging · 2025Article
- Longitudinally Consistent Individualized Prediction of Infant Cortical Morphological Development.Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention · 2024Article
- Disentangled Hybrid Transformer for Identification of Infants with Prenatal Drug Exposure.Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention · 2024Article
- Multi-task Joint Prediction of Infant Cortical Morphological and Cognitive Development.Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention · 2023Article
- Longitudinal Infant Functional Connectivity Prediction via Conditional Intensive Triplet Network.Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention · 2022Article
- Reference-Relation Guided Autoencoder with Deep CCA Restriction for Awake-to-Sleep Brain Functional Connectome Prediction.Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted InterventionArticle
Corrections and comments
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Authors and funding
8 authors.
Funding
Abstract
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.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.