Abstract
Delineating the normative developmental profile of functional con- nectome is important for both standardized assessment of individual growth and early detection of diseases. However, functional connectome has been mostly studied using functional connectivity (FC), where undirected connectivity strengths are estimated from statistical correlation of resting-state functional MRI (rs-fMRI) signals. To address this limitation, we applied regression dynamic causal modeling (rDCM) to delineate the developmental trajectories of effective connectivity (EC), the directed causal influence among neuronal populations, in whole-brain networks from infancy to adolescence (0–22 years old) based on high-quality rs-fMRI data from Baby Connectome Project (BCP) and Human Connectome Project Development (HCP-D). Analysis with linear mixed model demonstrates significant age effect on the mean nodal EC which is best fit by a “U” shaped quadratic curve with minimal EC at around 2 years old. Further analysis indicates that five brain regions including the left and right cuneus, left precuneus, left supramarginal gyrus and right inferior temporal gyrus have the most significant age effect on nodal EC (p < 0.05, FDR corrected). Moreover, the frontoparietal control (FPC) network shows the fastest increase from early childhood to adolescence followed by the visual and salience networks. Our findings suggest complex nonlinear developmental profile of EC from infancy to adolescence, which may reflect dynamic structural and functional maturation during this critical growth period.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Disclosure of Interests
The authors have no competing interests to disclose.
References
Sydnor VJ, Larsen B, Bassett DS, Alexander-Bloch A, Fair DA, Liston C, Mackey AP, Milham MP, Pines A, Roalf DR, Seidlitz J, Xu T, Raznahan A, Satterthwaite TD. Neurodevelopment of the association cortices: Patterns, mechanisms, and implications for psychopathology. Neuron 9(18), 2820-2846 (2021).
Nielsen, A.N., et al. Maturation of large-scale brain systems over the first month of life. Cereb. Cortex, bhac242 (2022).
Gilmore JH, Knickmeyer RC, Gao W. Imaging structural and functional brain development in early childhood. Nat Rev Neurosci 19(3), 123-137 (2018).
Monk C, Lugo-Candelas C, Trumpff C. Prenatal developmental origins of future psycho- pathology: mechanisms and pathways. Annu Rev Clin Psychol 15, 317-344 (2019).
Fair DA, Cohen AL, Power JD, Dosenbach NU, Church JA, Miezin FM, Schlaggar BL, Petersen SE. Functional brain networks develop from a “local to distributed” organization. PLoS Comput Biol 5(5), e1000381 (2009).
Hwang K, Hallquist MN, Luna B. The development of hub architecture in the human func- tional brain network. Cereb Cortex. 23(10), 2380-93 (2013).
Bhana A. Middle chilldhood and pre-adolescence. HSRC Press (2010).
Fan F, Liao X, Lei T, Zhao T, Xia M, Men W, Wang Y, Hu M, Liu J, Qin S, Tan S, Gao JH, Dong Q, Tao S, He Y. Development of the default-mode network during childhood and adolescence: A longitudinal resting-state fMRI study. Neuroimage 226, 117581 (2021).
Xiao Y, Zhai H, Friederici AD, Jia F. The development of the intrinsic functional connec- tivity of default network subsystems from age 3 to 5. Brain Imaging Behav 10, 50–59 (2016).
Li G and Yap PT. From descriptive connectome to mechanistic connectome: Generative modeling in functional magnetic resonance imaging analysis. Front Hum Neurosci 16, 940842 (2022).
Friston KJ, Harrison L, Penny W. Dynamic causal modeling. Neuroimage 19, 1273–1302 (2003).
Frässle S, Lomakina EI, Razi A, Friston KJ, Buhmann JM, Stephan KE. Regression DCM for fMRI. Neuroimage 155, 406–421 (2017).
Frässle S, Lomakina EI, Kasper L, Manjaly ZM, Leff A, Pruessmann KP, Buhmann JM, Stephan KE. A generative model of whole-brain effective connectivity. Neuroimage 179, 505–529 (2018).
Frässle S, Harrison SJ, Heinzle J, Clementz BA, Tamminga CA, Sweeney JA, Gershon ES, Keshavan MS, Pearlson GD, Powers A, Stephan KE. Regression dynamic causal modeling for resting-state fMRI. Hum Brain Mapp 42, 2159-2180 (2021).
Howell BR et al. The UNC/UMN Baby Connectome Project (BCP): An overview of the study design and protocol development. Neuroimage 185, 891–905 (2019).
Somerville LH et al. The Lifespan Human Connectome Project in Development: A large- scale study of brain connectivity development in 5–21 year olds. Neuroimage 183, 456– 468 (2018).
Zhang, H., et al.: Infant resting-state FMRI analysis pipeline for UNC/UMN baby connectome project. In: OHBM, Rome, Italy, 9–13 June 2019 (2019).
Glasser MF et al. The minimal preprocessing pipelines for the Human Connectome Pro- ject. Neuroimage 80, 105-124 (2013).
Desikan RS, Ségonne F, Fischl B, Quinn BT, Dickerson BC, Blacker D, Buckner RL, Dale AM, Maguire RP, Hyman BT, Albert MS, Killiany RJ. An automated labeling system for subdividing the human cerebral cortex on MRI scans into gyral based regions of interest. Neuroimage 31, 968–980 (2006).
Yeo BT, Krienen FM, Sepulcre J, Sabuncu MR, Lashkari D, Hollinshead M, et al. The or- ganization of the human cerebral cortex estimated by intrinsic functional connectivity. J Neurophysiol 106, 1125–1165 (2011).
Bethlehem RAI, Seidlitz J, White SR, Vogel JW, Anderson KM, Adamson C, et al. Brain charts for the human lifespan. Nature 604, 525-533 (2022).
Li G, Liu Y, Zheng Y, Li D, Liang X, Chen Y, Cui Y, Yap P, Qiu S, Zhang H, Shen D. Large-scale dynamic causal modeling of major depressive disorder based on resting-state fMRI. Hum Brain Mapp 41, 865-881 (2022).
LaMantia AS, Rakic P. Axon overproduction and elimination in the corpus callosum of the developing rhesus monkey. J Neurosci 10(7), 2156-2175 (1990).
Supekar K, Uddin LQ, Prater K, Amin H, Greicius MD, Menon V. Development of func- tional and structural connectivity within the default mode network in young children. Neu- roimage 52(1), 290-301 (2010).
Acknowledgments
This work was supported in part by the United States National Institutes of Health (NIH) through grants R01 MH125479, R01 EB008374, R01 MH133836 and R21 AG083589.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2024 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Li, G. et al. (2024). Development of Effective Connectome from Infancy to Adolescence. In: Linguraru, M.G., et al. Medical Image Computing and Computer Assisted Intervention – MICCAI 2024. MICCAI 2024. Lecture Notes in Computer Science, vol 15003. Springer, Cham. https://doi.org/10.1007/978-3-031-72384-1_13
Download citation
DOI: https://doi.org/10.1007/978-3-031-72384-1_13
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-72383-4
Online ISBN: 978-3-031-72384-1
eBook Packages: Computer ScienceComputer Science (R0)