The Development of Brain Morphometric Similarity Network in School-aged Children
Poster No:
1294
Submission Type:
Abstract Submission
Authors:
Xiao Wang1, Chu-Chung Huang1, Qing Cai1
Institutions:
1East China Normal University, Shanghai, China
First Author:
Co-Author(s):
Introduction:
The development of brain networks in early school-age children plays a pivotal role in the maturation of complex cognitive and behavioral functions (Dong et al., 2021; Shaffer & Kipp, 2013). Evidence suggests that brain structural maturation involves a process of interconnected networking, in which multiple brain regions undergo synchronized morphometric changes in a coordinated manner (Wu et al., 2023). Therefore, exploring the cortical similarity networks is likely to provide a comprehensive understanding of brain maturation trajectories. However, the developmental pattern of the brain morphometric similarity networks in school-age children has yet to be fully revealed and validated through longitudinal data.
Methods:
Utilizing the T1-weighted longitudinal imaging data (N = 639, 1-5 time points, aged 6-11) from East China Normal University, we investigated the developmental trajectories of the cortical similarity networks using linear mixed effects model (Figure 1). The Morphometric INverse Divergence (MIND) approach was employed to estimate the within-subject similarity between cortical areas based on the divergence between their multivariate distributions of multiple morphometric features (Sebenius et al., 2023). To investigate the topographical properties of MIND networks systematically, we detected the modular structure using a community detection approach. Additionally, we associated the MIND similarity with both the Raven IQ and the working memory ability.
·Flowchart of data analytical pipeline in this study.
Results:
The prominent developmental pattern observed in multiple morphometric features during the school-age period was the expansion of surface area and the thinning of cortical thickness, together with the increased gray matter volume in the higher-level transmodal areas and the decreased volume in the primary areas (Figure 2A). The modularity of MIND networks shown increased with age (Figure 2D). Five modules including Frontal-Temporal Area 1 and 2, Sensory-Motor Area, Insula-Limbic Area, and Orbital-Frontal Area were identified (Figure 2B). The morphometric similarities within and between the modular structure decline with age, except for the similarity between the Insula-Limbic Area and Frontal-Temporal Area (Figure 2C). The morphometric similarity of MIND network was decreased with age, while the regional degrees of bilateral insula and anterior cingulate cortex increased (Figure 2E-G). No significant association between cognitive scores (Raven IQ and working memory ability) and the morphometric features or the MIND network was found.
·The developmental trajectories of the morphometric features and MIND network.
Conclusions:
In summary, we longitudinally investigated the developmental trajectories of brain morphometric similarity network in school-aged children using T1w images. We observed that the MIND networks displayed a modular architecture, where most modules exhibited a decreased in similarity with age increased. Notably, higher-level transmodal areas, such as the bilateral insula and anterior cingulate cortex, demonstrated an opposite trend. These findings highlight the pattern of the brain segregation in fronto-temporal areas and increased inter-modular integration in insula-limbic areas. This study suggested that the cortical morphometric similarity can serve as a maturational marker during the school-age stage and provided insights into typical brain development.
Lifespan Development:
Normal Brain Development: Fetus to Adolescence 1
Modeling and Analysis Methods:
Connectivity (eg. functional, effective, structural) 2
Keywords:
Development
Morphometrics
STRUCTURAL MRI
Other - Brian Network
1|2Indicates the priority used for review
Provide references using author date format
Sebenius, I., Seidlitz, J., Warrier, V., Bethlehem, R. A. I., Alexander-Bloch, A., Mallard, T. T., Garcia, R. R., Bullmore, E. T., & Morgan, S. E. (2023). Robust estimation of cortical similarity networks from brain MRI. Nature Neuroscience, 26(8), 1461-1471. https://doi.org/10.1038/s41593-023-01376-7
Shaffer, D. R., & Kipp, K. (2013). Developmental psychology: Childhood and adolescence. Cengage Learning.
Wu, X., Palaniyappan, L., Yu, G., Zhang, K., Seidlitz, J., Liu, Z., Kong, X., Schumann, G., Feng, J., Sahakian, B. J., Robbins, T. W., Bullmore, E., & Zhang, J. (2023). Morphometric dis-similarity between cortical and subcortical areas underlies cognitive function and psychiatric symptomatology: a preadolescence study from ABCD. Mol Psychiatry, 28(3), 1146-1158. https://doi.org/10.1038/s41380-022-01896-x