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

## TL;DR

This study compares two brain network methods in children to understand how brain structure relates to cognitive performance.

## Contribution

The paper provides a novel comparison of morphometric similarity and inverse divergence networks in capturing brain-cognition relationships in children.

## Key findings

- A connection density of p=0.05–0.15 optimizes network stability and cognitive correlations in both MSN and MIND.
- MSN networks show more stable associations with cognitive performance across connection densities and hemispheric dimensions.
- Higher cognitive performance correlates with stronger left intra-hemispheric connectivity and more modular organization.

## Abstract

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 \documentclass[12pt]{minimal}
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\begin{document}$$p=0.05\sim 0.15$$\end{document} 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.