Authors:
Shuning Han
1
;
2
;
Feng Duan
3
;
Gemma Vilaseca
4
;
5
;
Núria Vilaró
5
;
Cesar F. Caiafa
6
;
Zhe Sun
2
;
7
and
Jordi Solé-Casals
1
;
8
Affiliations:
1
Data and Signal Processing Research Group, University of Vic-Central University of Catalonia, Vic, Catalonia, Spain
;
2
Image Processing Research Group, RIKEN Center for Advanced Photonics, RIKEN, Wako-Shi, Saitama, Japan
;
3
Tianjin Key Laboratory of Brain Science and Intelligent Rehabilitation, Nankai University, Tianjin, China
;
4
Psychological Department, Oms and Prat school, Fundació Catalunya - La Pedrera, Manresa, Catalonia, Spain
;
5
Oms Foundation, Manresa, Catalonia, Spain
;
6
Instituto Argentino de Radioastronomía-CCT La Plata, CONICET / CIC-PBA / UNLP, Argentina
;
7
Faculty of Health Data Science, Juntendo University, Urayasu, Chiba, Japan
;
8
Department of Psychiatry, University of Cambridge, Cambridge, U.K.
Keyword(s):
Gifted Children, Structural Magnetic Resonance Imaging, Morphometric Similarity Network, Connection Density, Anatomical Modularity, Topological Features.
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. Moreove
r, 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.
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