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Structural brain network of gifted children has a more integrated and versatile topology
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Gifted children learn more rapidly and effectively than others, presumably due to neurophysiological differences that affect efficiency in neuronal communication. Identifying the topological features that support its capabilities is relevant to understanding how the brain structure is related to intelligence. We proposed the analysis of the structural covariance network to assess which organizational patterns are characteristic of gifted children. The graph theory was used to analyse topological properties of structural covariance across a group of gifted children. The analysis was focused on measures of brain network integration, such as, participation coefficient and versatility, which quantifies the strength of specific modular affiliation of each regional node. We found that the gifted group network was more integrated (and less segregated) than the control group network. Brain regional nodes in the gifted group network had higher versatility and participation coefficient, indicating greater inter-modular communication mediated by connector hubs with links to many modules. Connector hubs of the networks of both groups were located mainly in association with neocortical areas (which had thicker cortex), with fewer hubs in primary or secondary neocortical areas (which had thinner cortex), as well as a few connector hubs in limbic cortex and insula. In the group of gifted children, a larger proportion of connector hubs were located in association cortex. In conclusion, gifted children have a more integrated and versatile brain network topology. This is compatible with the global workspace theory and other data linking integrative network topology to cognitive performance.
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ADAN PUIG, Ana, SOLÉ CASALS, Jordi, SERRA Y GRABULOSA, Josep ma., ROMERO GARCÍA, Rafael, VILASECA, Gemma, VILARÓ, Núria, BARGALLÓ ALABART, Núria, BULLMORE, Edward t.. Structural brain network of gifted children has a more integrated and versatile topology. _Brain Structure and Function_. 2019. Vol. 224, núm. 7, pàgs. 2373-2383. [consulta: 24 de gener de 2026]. ISSN: 1863-2653. [Disponible a: https://hdl.handle.net/2445/218225]