Abstract
The human brain demonstrates higher spatial and functional heterogeneity during the first two postnatal years than any other period of life. Infant cortical developmental regionalization is fundamental for illustrating brain microstructures and reflecting functional heterogeneity during early postnatal brain development. It aims to establish smooth cortical parcellations based on the local homogeneity of brain development. Therefore, charting infant cortical developmental regionalization can reveal neurodevelopmentally meaningful cortical units and advance our understanding of early brain structural and functional development. However, existing parcellations are solely built based on either local structural properties or single-view functional connectivity (FC) patterns due to limitations in neuroimage analysis tools. These approaches fail to capture the diverse consistency of local and global functional development. Hence, we aim to construct a multi-view functional brain parcellation atlas, enabling a better understanding of infant brain functional organization during early development. Specifically, a novel fMRI fingerprint is proposed to fuse complementary regional functional connectivities. To ensure the smoothness and interpretability of the discovered map, we employ non-negative matrix factorization (NNMF) with dual graph regularization in our method. Our method was validated on the Baby Connectome Project (BCP) dataset, demonstrating superior performance compared to previous functional and structural parcellation approaches. Furthermore, we track functional development trajectory based on our brain cortical parcellation to highlight early development with high neuroanatomical and functional precision.
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References
Biswal, B., Zerrin Yetkin, F., Haughton, V.M., Hyde, J.S.: Functional connectivity in the motor cortex of resting human brain using echo-planar MRI. Magn. Reson. Med. 34(4), 537–541 (1995)
Boutsidis, C., Gallopoulos, E.: Svd based initialization: a head start for nonnegative matrix factorization. Pattern Recogn. 41(4), 1350–1362 (2008)
Chen, L., et al.: A 4d infant brain volumetric atlas based on the unc/umn baby connectome project (bcp) cohort. Neuroimage 253, 119097 (2022)
Fischl, B.: Freesurfer. Neuroimage 62(2), 774–781 (2012)
Herculano-Houzel, S., Collins, C.E., Wong, P., Kaas, J.H., Lent, R.: The basic nonuniformity of the cerebral cortex. Proc. Natl. Acad. Sci. 105(34), 12593–12598 (2008)
Hill, J., Inder, T., Neil, J., Dierker, D., Harwell, J., Van Essen, D.: Similar patterns of cortical expansion during human development and evolution. Proc. Natl. Acad. Sci. 107(29), 13135–13140 (2010)
Howell, B.R., et al.: The unc/umn baby connectome project (bcp): an overview of the study design and protocol development. Neuroimage 185, 891–905 (2019)
Huang, Z., Wang, Q., Zhou, S., Tang, C., Yi, F., Nie, J.: Exploring functional brain activity in neonates: a resting-state fmri study. Dev. Cogn. Neurosci. 45, 100850 (2020)
Joel, S.E., Caffo, B.S., Van Zijl, P.C., Pekar, J.J.: On the relationship between seed-based and ica-based measures of functional connectivity. Magn. Reson. Med. 66(3), 644–657 (2011)
Johnson, M.H.: Functional brain development in infants: elements of an interactive specialization framework. Child Dev. 71(1), 75–81 (2000)
Johnson, M.H.: Functional brain development in humans. Nat. Rev. Neurosci. 2(7), 475–483 (2001)
Kendall, M.G.: Rank correlation methods (1948)
Lee, D., Seung, H.S.: Algorithms for non-negative matrix factorization. In: Advances in Neural Information Processing Systems 13 (2000)
Li, G., et al.: Mapping region-specific longitudinal cortical surface expansion from birth to 2 years of age. Cereb. Cortex 23(11), 2724–2733 (2013)
Long, X., Benischek, A., Dewey, D., Lebel, C.: Age-related functional brain changes in young children. Neuroimage 155, 322–330 (2017)
Ouyang, M., Dubois, J., Yu, Q., Mukherjee, P., Huang, H.: Delineation of early brain development from fetuses to infants with diffusion mri and beyond. Neuroimage 185, 836–850 (2019)
Shi, F., Salzwedel, A.P., Lin, W., Gilmore, J.H., Gao, W.: Functional brain parcellations of the infant brain and the associated developmental trends. Cereb. Cortex 28(4), 1358–1368 (2018)
Smith, S.M., et al.: Correspondence of the brain’s functional architecture during activation and rest. Proc. Natl. Acad. Sci. 106(31), 13040–13045 (2009)
Wang, F., et al.: Revealing developmental regionalization of infant cerebral cortex based on multiple cortical properties. In: Shen, D., et al. (eds.) MICCAI 2019. LNCS, vol. 11765, pp. 841–849. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-32245-8_93
Wang, F., et al.: Developmental topography of cortical thickness during infancy. Proc. Natl. Acad. Sci. 116(32), 15855–15860 (2019)
Wu, Y., Ahmad, S., Yap, P.-T.: Highly reproducible whole brain parcellation in individuals via voxel annotation with fiber clusters. In: de Bruijne, M., et al. (eds.) MICCAI 2021. LNCS, vol. 12907, pp. 477–486. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-87234-2_45
Acknowledgements
This work is partially supported by the STI 2030-Major Projects (No. 2022ZD0209000), National Natural Science Foundation of China (No. 62203355), Shanghai Pilot Program for Basic Research - Chinese Academy of Science, Shanghai Branch (No. JCYJ-SHFY-2022-014), Open Research Fund Program of National Innovation Center for Advanced Medical Devices (No. NMED2021ZD-01-001), Shenzhen Science and Technology Program (No. KCXFZ 20211020163408012), and Shanghai Pujiang Program (No. 21PJ1421400). This work utilizes data acquired with support from an NIH grant (1U01MH110274) and the efforts of the UNC/UMN Baby Connectome Project (BCP) Consortium.
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Tao, T. et al. (2024). Regionalized Infant Brain Cortical Development Based on Multi-view, High-Level fMRI Fingerprint. In: Cao, X., Xu, X., Rekik, I., Cui, Z., Ouyang, X. (eds) Machine Learning in Medical Imaging. MLMI 2023. Lecture Notes in Computer Science, vol 14349. Springer, Cham. https://doi.org/10.1007/978-3-031-45676-3_47
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