Network growth of cortical morphology across the human lifespan
Poster No:
945
Submission Type:
Abstract Submission
Authors:
Xinyuan Liang1, Lianglong Sun1, Mingrui Xia1, Qiongling Li1, Tengda Zhao1, Xuhong Liao2, Gaolang Gong1, Zaixu Cui3, Yong He1
Institutions:
1Beijing Normal University, Beijing, Beijing, 2School of Systems Science, Beijing Normal University, Beijing, Beijing, 3Chinese Institute for Brain Research, Beijing, Beijing, Beijing
First Author:
Co-Author(s):
Introduction:
The morphology of the human cortex shows remarkable structural covariance between regions (Sebenius et al., 2024; Wang & He, 2024). However, the form and extent of the network remodeling of the cortical morphology throughout the lifespan remain unknown.
Methods:
Structural MRI images of 33,937 healthy participants (aged 0-80 years) were employed to delineate lifespan growth patterns of morphometric networks. A subsample of 32,887 participants with both structural and functional images for investigating structure-function coupling across the lifespan(Sun et al., 2023). Structural images of 1,202 patients, including 180 patients with Alzheimer's disease (AD), 622 with major depressive disorder (MDD), and 400 with autism spectrum disorder, were used to validate the clinical relevance of network-based normative models. The individual cortex was parcellated into 318 regions using the modified Desikan-Kiliany atlas(Romero-Garcia, Atienza, Clemmensen, & Cantero, 2012). For each participant, a morphometric inverse divergence (MIND) network was generated by estimating the pairwise divergence between the multivariate distributions of five morphological features: cortical thickness, surface area, gray matter volume, mean curvature, and sulcal depth(Sebenius et al., 2023). To investigate the lifespan normative growth of the MIND network, we applied a GAMLSS model, with network phenotype as the dependent variable, age as a smoothing term, sex and Euler number as covariates, and scanner site as a random effect. Growth rate of each network phenotype was calculated from the first derivatives of the normative growth curve. Structure–function coupling was estimate by correlating morphometric similarity profiles with functional connectivity profiles. Individual deviation z-scores of morphometric metrics were characterized for patients and matched HCs (Rutherford et al., 2022). Using SVR models, we investigated the potential of MIND network phenotype deviations to predict clinical scores. To evaluate the robustness of the main results, we employed bootstrap resampling, split-half, leave-one-site-out, balanced resampling, different cortical parcellation strategies, and leave-one-feature-out analyses.
Results:
From birth to early adulthood, the global network architecture gradually matures through increasing modularity and small-worldness(Fig. 1a). We established the normative model of regional morphometric similarity strength (MSS), calculated as the average similarity with all other regions. Cytoarchitecturally distinct zones show heterogeneous, substantial remodeling during early development, recapitulated by increased structural differentiation in the sensory cortices, increased morphological similarity in the paralimbic cortices, and the persistence of hubs in the association cortices(Fig. 1b and 1d). Hierarchical clustering of the spatial correlation matrix of age-specific MSS maps identified four clusters(Fig. 1c): birth to infancy, early childhood, late childhood to adolescence, and adulthood. The principal axis of network growth of cortical morphology is anchored by sensorimotor areas at one end and transmodal areas at the other(Fig. 1e). Structure‒function coupling increases until early adolescence and then decreases. Stronger couplings were observed in the occipital, medial prefrontal, and insula cortex(Fig. 2a-c). Significant negative correlations were found between MSS maps and structure-function coupling maps and growth rate maps of MSS and structure-function coupling across the lifespan(Fig. 2d-e). Compared to HCs, all three disease types exhibited greater extreme deviations. The connectome-based deviations in AD and MDD patients significantly predicted clinical scores. Our results show high robustness across the different sensitivity analyses.
Conclusions:
These data provide a blueprint for using cortical morphology to elucidate the network reconfiguration that occurs across the lifespan and serve as a benchmark for quantifying interindividual network variation.
Lifespan Development:
Early life, Adolescence, Aging 1
Modeling and Analysis Methods:
Connectivity (eg. functional, effective, structural) 2
Novel Imaging Acquisition Methods:
Anatomical MRI
BOLD fMRI
Keywords:
Development
Morphometrics
MRI
Other - brain chart, connectome, structural covariance, normative model
1|2Indicates the priority used for review
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Were any human subjects research approved by the relevant Institutional Review Board or ethics panel? NOTE: Any human subjects studies without IRB approval will be automatically rejected.
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Provide references using APA citation style.
Rutherford, S., Kia, S. M., Wolfers, T., Fraza, C., Zabihi, M., Dinga, R., . . . Ruhe, H. G. (2022). The normative modeling framework for computational psychiatry. Nature protocols, 17(7), 1711-1734.
Sebenius, I., Dorfschmidt, L., Seidlitz, J., Alexander-Bloch, A., Morgan, S. E., & Bullmore, E. (2024). Structural MRI of brain similarity networks. Nature reviews neuroscience. doi:10.1038/s41583-024-00882-2
Sebenius, I., Seidlitz, J., Warrier, V., Bethlehem, R. A., Alexander-Bloch, A., Mallard, T. T., . . . Morgan, S. E. (2023). Robust estimation of cortical similarity networks from brain MRI. Nature neuroscience, 26(8), 1461-1471.
Sun, L., Zhao, T., Liang, X., Xia, M., Li, Q., Liao, X., . . . Yu, Q. (2023). Functional connectome through the human life span. Biorxiv.
Wang, J., & He, Y. (2024). Toward individualized connectomes of brain morphology. Trends in neurosciences.