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<p>The study of structural brain networks (SBNs) offers critical insights into brain-cognition relationships. However, a comprehensive comparison of these methods in terms of their topological properties, cognitive relevance, and sensitivity to connection density remains lacking. This study compares two types of individual-level SBNs–morphometric similarity networks (MSNs) and morphometric inverse divergence (MIND) networks–by analyzing their associations with cognitive performance using sMRI data from 29 male children. Group- and individual-level analyses were conducted to evaluate differences in hemispheric connectivity, topological features, and their correlations with cognitive performance across different connection densities. In our analyses, a connection density of <InlineEquation ID="IEq1"> <EquationSource Format="TEX">\(p=0.05\sim 0.15\)</EquationSource> </InlineEquation> appeared optimal for stabilizing network properties and maximizing cognitive correlations in both MSN and MIND. Moreover, advanced network segregation and integration metrics (such as local efficiency and node versatility, along with their global summaries) demonstrated greater sensitivity to cognitive performance. However, MSNs appeared to provide a more reliable framework, demonstrating more stable associations across connection densities in topological and hemispheric dimensions. Specifically, higher cognitive performance may be linked to stronger left intra-hemispheric connectivity, weaker inter-hemispheric connectivity, and more modular network organization–consistent with established theories of hemispheric specialization and efficient modularity. In contrast, MIND networks exhibit reduced effectiveness and stability across metrics and densities in our data. These preliminary insights enhance our understanding of brain-cognition relationships and provide practical guidelines for parameter selection and metric identification in network-based cognitive analyses.</p>

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Revealing Structural Brain-Cognition Relationships in Children: A Comparison of Morphometric Similarity and INverse Divergence Networks

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年1月8日

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  • Shuning Han,
  • Hao Jia,
  • Gemma Vilaseca,
  • Núria Vilaró,
  • Feng Duan,
  • Zhe Sun,
  • Cesar F. Caiafa,
  • Jordi Solé-Casals

摘要

The study of structural brain networks (SBNs) offers critical insights into brain-cognition relationships. However, a comprehensive comparison of these methods in terms of their topological properties, cognitive relevance, and sensitivity to connection density remains lacking. This study compares two types of individual-level SBNs–morphometric similarity networks (MSNs) and morphometric inverse divergence (MIND) networks–by analyzing their associations with cognitive performance using sMRI data from 29 male children. Group- and individual-level analyses were conducted to evaluate differences in hemispheric connectivity, topological features, and their correlations with cognitive performance across different connection densities. In our analyses, a connection density of \(p=0.05\sim 0.15\) appeared optimal for stabilizing network properties and maximizing cognitive correlations in both MSN and MIND. Moreover, advanced network segregation and integration metrics (such as local efficiency and node versatility, along with their global summaries) demonstrated greater sensitivity to cognitive performance. However, MSNs appeared to provide a more reliable framework, demonstrating more stable associations across connection densities in topological and hemispheric dimensions. Specifically, higher cognitive performance may be linked to stronger left intra-hemispheric connectivity, weaker inter-hemispheric connectivity, and more modular network organization–consistent with established theories of hemispheric specialization and efficient modularity. In contrast, MIND networks exhibit reduced effectiveness and stability across metrics and densities in our data. These preliminary insights enhance our understanding of brain-cognition relationships and provide practical guidelines for parameter selection and metric identification in network-based cognitive analyses.