Structural Covariance Network in Infants with Complex Congenital Heart Disease
Poster No:
308
Submission Type:
Abstract Submission
Authors:
Pengcheng Xue1, Bingyan Wang1, Meijiao Zhu2, Siyu Ma2, Yuting Liu2, Peng Liu2, Bing Jing3, Ming Yang2, Xuming Mo2, Daoqiang Zhang1, Xuyun Wen1
Institutions:
1Nanjing University of Aeronautics and Astronautics, Nanjing, Jiangsu, 2Children’s Hospital of Nanjing Medical University, Nanjing, Jiangsu, 3Beijing Key Laboratory of Fundamental Research on Biomechanics in Clinical Application, Beijing, Beijing
First Author:
Co-Author(s):
Bing Jing
Beijing Key Laboratory of Fundamental Research on Biomechanics in Clinical Application
Beijing, Beijing
Beijing Key Laboratory of Fundamental Research on Biomechanics in Clinical Application
Beijing, Beijing
Introduction:
Congenital heart disease (CHD) is one of the most common birth defects in newborns.(Liu, 2019) CHD not only affects cardiac function but also disrupts cortical development, increasing the risk of long-term neurodevelopmental disorders in infants (Marelli, 2016; Rollins, 2016). Consequently, understanding the mechanisms underlying abnormal brain development in CHD infants is critical for designing early intervention strategies and improving their quality of life. However, previous studies have primarily focused on structural and functional abnormalities in individual brain regions(Bonthrone, 2021; Kelly, 2017), limiting the ability to fully capture the complex interplay between different brain areas. To address these research gaps, this study analyzed structural MRI data and constructed individual structural covariance networks (SCNs) from cortical thickness (CT), cortical surface area (CSA), and gray matter volume (GMV) to evaluate co-vary changes across different brain regions. Furthermore, we introduced functional systems and aimed to detect potential system-specific structural connectivity abnormalities caused by CHD and their associations with neurodevelopmental outcomes.
Methods:
This study recruited 83 infants with complex CHD and 86 age-matched healthy controls (1–2 years old) from the Children's Hospital of Nanjing Medical University. T1-weighted imaging data were processed using an infant-specific pipeline(Zöllei, 2020), parcellating the cortex into 68 regions with the Desikan-Killiany atlas(Desikan, 2006). Morphological measurements (CSA, CT, GMV) were extracted and used to construct individual SCNs. A sparsity range of 20%-35% (2% step) was used to ensure valid network analysis, preserving connectivity, modularity, and small-world properties (Yun, 2020; Uehara, 2014; De, 2018). Based on SCNs and a predefined community network (Fig. 1A), average inter- and intra-community connection strengths were calculated, and their AUCs across the sparsity range were compared between CHD and HCs using Non-parametric permutation tests. Pearson correlation analysis was used to examine the relationship between structural connectivity strengths and neurodevelopmental outcomes. A measure was deemed significant if it correlated with cognitive scores in over five of eight densities.
Results:
Statistical analysis revealed significant differences in the CSA and CT networks between the CHD and HC cohorts (Fig. 1B-C), while no differences were observed in the gray matter volume (GMV) network. Specifically, In the CSA network, the CHD group showed significantly higher intra- and inter-community connection strengths compared to HCs. Intra-community connection strengths with significant differences were primary located in Community 1, 2, 3, and 5. Additionally, inter-community connectivity was significantly enhanced across all six communities. For the CT network, the CHD group demonstrated a reduction in inter-community connection strength, specifically between Community 4 and Community 6, compared to HCs.
Correlation analysis showed that intra- and inter-community connection strengths in the CSA network (Fig. 1D-E) were significantly associated with adaptability, with no significant correlations observed in the CT or GMV networks. Specifically, among the 15 pairs of inter-community connection strengths, all exhibited significant negative correlations with adaptability, except for the connections between Community 1 and Community 6, and between Community 3 and Community 6 (Fig. 1D). Among the six communities, intra-community connection strengths shows significantly negative correlations with adaptability in all communities except Community 2 (Fig. 1E).
Correlation analysis showed that intra- and inter-community connection strengths in the CSA network (Fig. 1D-E) were significantly associated with adaptability, with no significant correlations observed in the CT or GMV networks. Specifically, among the 15 pairs of inter-community connection strengths, all exhibited significant negative correlations with adaptability, except for the connections between Community 1 and Community 6, and between Community 3 and Community 6 (Fig. 1D). Among the six communities, intra-community connection strengths shows significantly negative correlations with adaptability in all communities except Community 2 (Fig. 1E).
Conclusions:
This study identified abnormalities in structural connectivity within the brain's structural covariance network in infants with complex congenital heart disease and demonstrated its association with neurodevelopmental outcomes. This provides a new perspective for the clinical management of CHD infants.
Disorders of the Nervous System:
Neurodevelopmental/ Early Life (eg. ADHD, autism) 1
Neuroanatomy, Physiology, Metabolism and Neurotransmission:
Anatomy and Functional Systems 2
Keywords:
Cognition
Congenital
Cortex
Development
DISORDERS
MRI
STRUCTURAL MRI
Systems
1|2Indicates the priority used for review
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