Longitudinal Infant Functional Connectivity Prediction Via Conditional Intensive Triplet Network
Abstract
Longitudinal infant brain functional connectivity (FC) constructed from resting-state functional MRI (rs-fMRI) has increasingly become a pivotal tool in studying the dynamics of early brain development. However, due to various reasons including high acquisition cost, strong motion artifact, and subject dropout, there has been an extreme shortage of usable longitudinal infant rs-fMRI scans to construct longitudinal FCs, which hinders comprehensive understanding and modeling of brain functional development at early ages. To address this issue, in this paper, we propose a novel conditional intensive triplet network (CITN) for longitudinal prediction of the dynamic development of infant FC, which can traverse FCs within a long duration and predict the target FC at any specific age during infancy. Targeting at accurately modeling of the progression pattern of FC, while maintaining the individual functional uniqueness, our model effectively disentangles the intrinsically mixed age-related and identity-related information from the source FC and predicts the target FC by fusing well-disentangled identity-related information with the specific age-related information. Specifically, we introduce an intensive triplet auto-encoder for effective disentanglement of age-related and identity-related information and an identity conditional module to mix identity-related information with designated age-related information. We train the proposed model in a self-supervised way and design downstream tasks to help robustly disentangle age-related and identity-related features. Experiments on 464 longitudinal infant fMRI scans show the superior performance of the proposed method in longitudinal FC prediction in comparison with state-of-the-art approaches.
Recommended Citation
X. Yu and D. Hu and L. Zhang and Y. Huang and Z. Wu and T. Liu and L. Wang and W. Lin and D. Zhu and G. Li, "Longitudinal Infant Functional Connectivity Prediction Via Conditional Intensive Triplet Network," Lecture Notes in Computer Science Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics, vol. 13438 LNCS, pp. 255 - 264, Springer, Jan 2022.
The definitive version is available at https://doi.org/10.1007/978-3-031-16452-1_25
Department(s)
Computer Science
Keywords and Phrases
Autoencoder; Functional connectivity; Longitudinal prediction
International Standard Book Number (ISBN)
978-303116451-4
International Standard Serial Number (ISSN)
1611-3349; 0302-9743
Document Type
Article - Conference proceedings
Document Version
Citation
File Type
text
Language(s)
English
Rights
© 2025 Springer, All rights reserved.
Publication Date
01 Jan 2022
Comments
National Institutes of Health, Grant 1U01MH110274