Analyzing Cognitive Patterns in Gifted Children Using MRI and Morphometric Similarity Networks

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

2025

Abstract

Advances in non-invasive neuroimaging, such as structural magnetic resonance imaging (sMRI), have enabled the construction of structural brain networks (SBNs), allowing in vivo mapping of anatomical connections. This study investigates brain network structural differences linked to different intelligence levels in children by individual morphometric similarity networks (MSNs) derived from sMRI data. Through group- and individual-level analyses, we aim to uncover key topological features associated with cognitive performance and to identify a suitable connection density for SBN analysis. Connection density strongly affects global and nodal topological features, with a range of p = 0.05 to 0.15 recommended for stable and optimal results. Gifted individuals exhibit stronger intra-hemispheric and intra-modular connectivity, a more balanced distribution of left-to-right intra-hemispheric connections, and lower mean versatility, supporting efficient and stable cognitive processing. Moreover, anatomical modularity analyses based on von Economo indicate that higher cognitive performance is linked to enhanced connectivity in specific areas (such as secondary sensory area, motor to association area and secondary sensory to limbic area), alongside selective reduction in certain modular connections (such as motor to insular area, association to secondary sensory area and motor to secondary sensory area). Furthermore, topological features, including participation coefficient and local efficiency, are linked to cognitive performance. These findings provide valuable insights into the SBNs underlying cognitive levels in children.

Paper Citation

in Harvard Style

Han S., Duan F., Vilaseca G., Vilaró N., Caiafa C., Sun Z. and Solé-Casals J. (2025). Analyzing Cognitive Patterns in Gifted Children Using MRI and Morphometric Similarity Networks. In Proceedings of the 18th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 1: BIOSIGNALS; ISBN 978-989-758-731-3, SciTePress, pages 729-740. DOI: 10.5220/0013169200003911

in Bibtex Style

@conference{biosignals25,
author={Shuning Han and Feng Duan and Gemma Vilaseca and Núria Vilaró and Cesar Caiafa and Zhe Sun and Jordi Solé-Casals},
title={Analyzing Cognitive Patterns in Gifted Children Using MRI and Morphometric Similarity Networks},
booktitle={Proceedings of the 18th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 1: BIOSIGNALS},
year={2025},
pages={729-740},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0013169200003911},
isbn={978-989-758-731-3},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 18th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 1: BIOSIGNALS
TI - Analyzing Cognitive Patterns in Gifted Children Using MRI and Morphometric Similarity Networks
SN - 978-989-758-731-3
AU - Han S.
AU - Duan F.
AU - Vilaseca G.
AU - Vilaró N.
AU - Caiafa C.
AU - Sun Z.
AU - Solé-Casals J.
PY - 2025
SP - 729
EP - 740
DO - 10.5220/0013169200003911
PB - SciTePress