Abstract
During the early postnatal period, the human brain undergoes rapid and dynamic development. Over the past decades, there has been increased attention in studying the cognitive and cortical development of infants. However, accurate prediction of the infant cognitive and cortical development at an individual-level is a significant challenge, due to the huge complexities in highly irregular and incomplete longitudinal data that is commonly seen in current studies. Besides, joint prediction of cognitive scores and cortical morphology is barely investigated, despite some studies revealing the tight relationship between cognitive ability and cortical morphology and suggesting their potential mutual benefits. To tackle this challenge, we develop a flexible multi-task framework for joint prediction of cognitive scores and cortical morphological maps, namely, disentangled intensive triplet spherical adversarial autoencoder (DITSAA). First, we extract the mixed representative latent vector through a triplet spherical ResNet and further disentangles latent vector into identity-related and age-related features with an attention-based module. The identity recognition and age estimation tasks are introduced as supervision for a reliable disentanglement of the two components. Then we formulate the individualized cortical profile at a specific age by combining disentangled identity-related information and corresponding age-related information. Finally, an adversarial learning strategy is integrated to achieve a vivid and realistic prediction of cortical morphology, while a cognitive module is employed to predict cognitive scores. Extensive experiments are conducted on a public dataset, and the results affirm our method’s ability to predict cognitive scores and cortical morphology jointly and flexibly using incomplete longitudinal data.
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References
Casey, B., Tottenham, N., Liston, C., Durston, S.: Imaging the developing brain: what have we learned about cognitive development? Trends Cogn. Sci. 9(3), 104–110 (2005)
Dubois, J., Hertz-Pannier, L., Cachia, A., Mangin, J.F., Le Bihan, D., Dehaene-Lambertz, G.: Structural asymmetries in the infant language and sensorimotor networks. Cereb. Cortex 19(2), 414–423 (2009)
Smyser, C.D., Inder, T.E., Shimony, J.S., Hill, J.E., Degnan, A.J., Snyder, A.Z., et al.: Longitudinal analysis of neural network development in preterm infants. Cereb. Cortex 20(12), 2852–2862 (2010)
Gilmore, J.H., Kang, C., Evans, D.D., Wolfe, H.M., Smith, J.K., Lieberman, J.A., et al.: Prenatal and neonatal brain structure and white matter maturation in children at high risk for schizophrenia. Am. J. Psychiatry 167(9), 1083–1091 (2010)
Wang, L., Wu, Z., Chen, L., Sun, Y., Lin, W., Li, G.: iBEAT v2. 0: a multisite-applicable, deep learning-based pipeline for infant cerebral cortical surface reconstruction. Nat. Protocols 18, 1488–1509 (2023)
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)
Kanai, R., Rees, G.: The structural basis of inter-individual differences in human behaviour and cognition. Nat. Rev. Neurosci. 12(4), 231–242 (2011)
Mueller, S., Wang, D., Fox, M.D., Yeo, B.T., Sepulcre, J., Sabuncu, M.R., et al.: Individual variability in functional connectivity architecture of the human brain. Neuron 77(3), 586–595 (2013)
Fishbaugh, J., Prastawa, M., Gerig, G., Durrleman, S.: Geodesic regression of image and shape data for improved modeling of 4D trajectories. In: 2014 IEEE 11th International Symposium on Biomedical Imaging (ISBI), pp. 385–388. IEEE (2014)
Rekik, I., Li, G., Lin, W., Shen, D.: Predicting infant cortical surface development using a 4D varifold-based learning framework and local topography-based shape morphing. Med. Image Anal. 28, 1–12 (2016)
Meng, Y., Li, G., Rekik, I., Zhang, H., Gao, Y., Lin, W., et al.: Can we predict subject-specific dynamic cortical thickness maps during infancy from birth? Hum. Brain Mapp. 38(6), 2865–2874 (2017)
Lin, W., Zhu, Q., Gao, W., Chen, Y., Toh, C.H., Styner, M., et al.: Functional connectivity mr imaging reveals cortical functional connectivity in the developing brain. Am. J. Neuroradiol. 29(10), 1883–1889 (2008)
Ecker, C., Shahidiani, A., Feng, Y., Daly, E., Murphy, C., D’Almeida, V., et al.: The effect of age, diagnosis, and their interaction on vertex-based measures of cortical thickness and surface area in autism spectrum disorder. J. Neural Transm. 121, 1157–1170 (2014)
Querbes, O., et al.: Early diagnosis of alzheimer’s disease using cortical thickness: impact of cognitive reserve. Brain 132(8), 2036–2047 (2009)
Girault, J.B., Cornea, E., Goldman, B.D., Jha, S.C., Murphy, V.A., Li, G., et al.: Cortical structure and cognition in infants and toddlers. Cereb. Cortex 30(2), 786–800 (2020)
Kagan, J., Herschkowitz, N.: A Young Mind in a Growing Brain. Psychology Press (2006)
Cheng, J., Zhang, X., Ni, H., Li, C., Xu, X., Wu, Z., et al.: Path signature neural network of cortical features for prediction of infant cognitive scores. IEEE Trans. Med. Imaging 41(7), 1665–1676 (2022)
Mullen, E.M., et al.: Mullen Scales of Early Learning. AGS Circle Pines, MN (1995)
Howell, B.R., Styner, M.A., Gao, W., Yap, P.T., Wang, L., Baluyot, K., et al.: The UNC/UMN baby connectome project (BCP): an overview of the study design and protocol development. Neuroimage 185, 891–905 (2019)
Hu, D., et al.: Disentangled intensive triplet autoencoder for infant functional connectome fingerprinting. In: Martel, A.L., et al. (eds.) MICCAI 2020. LNCS, vol. 12267, pp. 72–82. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-59728-3_8
He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. pp. 770–778 (2016)
Zhao, F., Wu, Z., Wang, L., Lin, W., Gilmore, J.H., Xia, S., et al.: Spherical deformable U-net: application to cortical surface parcellation and development prediction. IEEE Trans. Med. Imaging 40(4), 1217–1228 (2021)
Zhao, F., Xia, S., Wu, Z., Duan, D., Wang, L., Lin, W., et al.: Spherical U-net on cortical surfaces: methods and applications. In: Chung, A., Gee, J., Yushkevich, P., Bao, S. (eds.) IPMI 2019. LNCS, vol. 11492, pp. 855–866. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-20351-1_67
Hu, J., Shen, L., Sun, G.: Squeeze-and-excitation networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition,pp. 7132–7141 (2018)
Woo, S., Park, J., Lee, J.Y., Kweon, I.S.: CBAM: convolutional block attention module. In: Proceedings of the European Conference on Computer Vision (ECCV). pp. 3–19 (2018)
Huang, Z., Zhang, J., Shan, H.: When age-invariant face recognition meets face age synthesis: a multi-task learning framework and a new benchmark. IEEE Trans. Pattern Anal. Mach. Intell. 45(6), 7917–7932 (2023)
Mao, X., Li, Q., Xie, H., Lau, R.Y., Wang, Z., Paul Smolley, S.: Least squares generative adversarial networks. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2794–2802 (2017)
Rothe, R., Timofte, R., Van Gool, L.: DEX: deep expectation of apparent age from a single image. In: Proceedings of the IEEE International Conference on Computer Vision Workshops, pp. 10–15 (2015)
Fischl, B.: Freesurfer. NeuroImage 62(2), 774–781 (2012)
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)
Jiang, C.M., Huang, J., Kashinath, K., Prabhat, Marcus, P., Nießner, M.: Spherical CNNs on unstructured grids. In: ICLR (Poster) (2019)
Cheng, J., et al.: Spherical transformer on cortical surfaces. In: Lian, C., Cao, X., Rekik, I., Xu, X., Cui, Z. (eds.) MLMI 2022. LNCS, vol. 13583, pp. 406–415. Springer, Cham (2022). https://doi.org/10.1007/978-3-031-21014-3_42
Acknowledgements
This work was supported in part by NIH grants (MH116225, MH117943, MH127544, and MH123202). This work also utilizes approaches developed by an NIH grant (1U01MH110274) and the efforts of the UNC/UMN Baby Connectome Project Consortium.
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Yuan, X. et al. (2023). Multi-task Joint Prediction of Infant Cortical Morphological and Cognitive Development. In: Greenspan, H., et al. Medical Image Computing and Computer Assisted Intervention – MICCAI 2023. MICCAI 2023. Lecture Notes in Computer Science, vol 14228. Springer, Cham. https://doi.org/10.1007/978-3-031-43996-4_52
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