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.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Liu, J., Liao, X., Xia, M., et. al.: Chronnectome fingerprinting: Identifying individuals and predicting higher cognitive functions using dynamic brain connectivity patterns. Hum. Brain Mapp. 39(2), 902–915 (2018)
Miranda-Dominguez, O., Feczko, E., Grayson, D.S., et. al.: Heritability of the human connectome: a connectotyping study. Netw. Neurosci. 2(02), 175–199 (2018)
Finn, E.S., Shen, X., Scheinost, D., et. al.: Functional connectome fingerprinting: identifying individuals using patterns of brain connectivity. Nat. Neurosci. 18(11), 1664–1674 (2015)
Horien, C., Shen, X., Scheinost, D., et. al.: The individual functional connectome is unique and stable over months to years. Neuroimage 189, 676–687 (2019)
Kaufmann, T., Alnæs, D., Doan, N.T., et. al.: Delayed stabilization and individualization in connectome development are related to psychiatric disorders. Nat. Neurosci. 20(4), 513–515 (2017)
Gilmore, J.H., Knickmeyer, R.C., Gao, W.: Imaging structural and functional brain development in early childhood. Nat. Rev. Neurosci. 19(3), 123 (2018)
Zhang, H., Shen, D., Lin, W.: Resting-state functional MRI studies on infant brains: a decade of gap-filling efforts. NeuroImage 185, 664–684 (2019)
Hoffer, E., Ailon, N.: Deep metric learning using triplet network. In: Feragen, A., Pelillo, M., Loog, M. (eds.) SIMBAD 2015. LNCS, vol. 9370, pp. 84–92. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-24261-3_7
Yang, Y., Chen, H., Shao, J.: Triplet enhanced autoencoder: model-free discriminative network embedding. In: Proceedings of the 28th International Joint Conference on Artificial Intelligence, pp. 5363–5369. AAAI Press (2019)
Bishop, C.M.: Pattern Recognition and Machine Learning (Information Science and Statistics). Springer-Verlag, New York Inc, Secaucus, NJ, USA (2006)
Amico, E., Goñi, J.: The quest for identifiability in human functional connectomes. Sci. Rep. 8(1), 1–14 (2018)
Byrge, L., Kennedy, D.P.: High-accuracy individual identification using a “thin slice” of the functional connectome. Netw. Neurosci. 3(2), 363–383 (2019)
Thomas Yeo, B.T., Krienen, F.M., Sepulcre, J., et. al.: The organization of the human cerebral cortex estimated by intrinsic functional connectivity. J. Neurophysiol. 106(3), 1125–1165 (2011)
Demeter, D.V., Engelhardt, L.E., Mallett, R., et. al.: Functional connectivity fingerprints at rest are similar across youths and adults and vary with genetic similarity. iScience 23(1), 100801 (2020)
Vanderwal, T., Eilbott, J., Finn, E.S., et. al.: Individual differences in functional connectivity during naturalistic viewing conditions. Neuroimage 157, 521–530 (2017)
Makhzani, A., Shlens, J., Jaitly, N., Goodfellow, I., Frey, B.: Adversarial autoencoders (2015). arXiv preprint arXiv:1511.05644
Glasser, M.F., Sotiropoulos, S.N., Wilson, J.A., et. al.: The minimal preprocessing pipelines for the Human Connectome Project. Neuroimage 80, 105–124 (2013)
Zhang, H., Stanley, N., Mucha, Peter J., Yin, W., Lin, W., Shen, D.: Multi-layer large-scale functional connectome reveals infant brain developmental patterns. In: Frangi, Alejandro F., Schnabel, Julia A., Davatzikos, C., Alberola-López, C., Fichtinger, G. (eds.) MICCAI 2018. LNCS, vol. 11072, pp. 136–144. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-00931-1_16
Stoecklein, S., Hilgendorff, A., Li, M., et. al.: Variable functional connectivity architecture of the preterm human brain: Impact of developmental cortical expansion and maturation. Proc. Natl. Acad. Sci. 117(2), 1201–1206 (2020)
Li, G., Wang, L., Yap, P.-T., et al.: Computational neuroanatomy of baby brains: a review. Neuroimage 185, 906–925 (2019)
Duan D., Xia S., Rekik I., et al.: Individual identification and individual variability analysis based on cortical folding features in developing infant singletons and twins. Hum. Brain Mapp. 41(8), 1985–2003 (2020)
Howell, B.R., Styner, M.A., Gao, W., et al.: The UNC/UMN baby connectome project (BCP): an overview of the study design and protocol development. NeuroImage 185, 891–905 (2019)
Wang, L., et al.:Volume-based analysis of 6-month-old infant brain MRI for autism biomarker identification and early diagnosis. In: Frangi, A., Schnabel, J., Davatzikos, C., Alberola-López, C., Fichtinger, G. (eds.) MICCAI 2018. LNCS, vol. 11072, pp. 411–419. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-00931-1_47
Li, G., Nie, J., Wang, L., Shi, F., et al.: Measuring the dynamic longitudinal cortex development in infants by reconstruction of temporally consistent cortical surfaces. NeuroImage 90, 266–279 (2014)
Li, G., Wang, L., Shi, F., Lin, W., Shen, D.: Simultaneous and consistent labeling of longitudinal dynamic developing cortical surfaces in infants. Med. Image Anal. 18(8), 1274–1289 (2014)
Sun L., Zhang D., Lian C., Wang L., et al.: Topological correction of infant white matter surfaces using anatomically constrained convolutional neural network. NeuroImage 198, 114–124 (2019)
Li, G., Wang, L., Shi, F., Gilmore, J.H., Lin, W., Shen, D.: Construction of 4D high-definition cortical surface atlases of infants: methods and applications. Med. Image Anal. 25(1), 22–36 (2015)
Wu, Z., Wang, L., Lin, W., Gilmore, J.H., Li, G., Shen, D.: Construction of 4D infant cortical surface atlases with sharp folding patterns via spherical patch-based group-wise sparse representation. Hum. Brain Mapp. 40(13), 3860–3880 (2019)
Acknowledgments
This work was partially supported by NIH grants (MH116225, MH117943, MH104324, MH109773). This work also utilizes approaches developed by an NIH grant (1U01MH110274) and the efforts of the UNC/UMN Baby Connectome Project Consortium.
Author information
Authors and Affiliations
Consortia
Corresponding authors
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Switzerland AG
About this paper
Cite this paper
Hu, D. et al. (2020). Disentangled Intensive Triplet Autoencoder for Infant Functional Connectome Fingerprinting. In: Martel, A.L., et al. Medical Image Computing and Computer Assisted Intervention – MICCAI 2020. MICCAI 2020. Lecture Notes in Computer Science(), vol 12267. Springer, Cham. https://doi.org/10.1007/978-3-030-59728-3_8
Download citation
DOI: https://doi.org/10.1007/978-3-030-59728-3_8
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-59727-6
Online ISBN: 978-3-030-59728-3
eBook Packages: Computer ScienceComputer Science (R0)
