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Front Hum Neurosci. 2019 Jul 12;13:241. doi: 10.3389/fnhum.2019.00241. eCollection 2019.

Topological Modification of Brain Networks Organization in Children With High Intelligence Quotient: A Resting-State fMRI Study.

Author information

1
Univ. Lyon, INSA-Lyon, Université Claude Bernard Lyon 1, UJM-Saint Étienne, CNRS, INSERM, CREATIS UMR 5220, Lyon, France.
2
Univ. Grenoble Alpes, INSERM, U1216, Grenoble Institut Neurosciences, Grenoble, France.
3
Nehme and Therese Tohme Multiple Sclerosis Center, Faculty of Medicine, American University of Beirut, Beirut, Lebanon.
4
GIPSA-Lab, UMR CNRS 5216, Université Grenoble Alpes, Grenoble, France.
5
Centre de Recherche de l'Institut Universitaire de Gériatrie de Montréal, Université de Montréal, Montreal, QC, Canada.
6
Service de Psychopathologie du Développement de l'Enfant et de l'Adolescent, Hospices Civils de Lyon, Lyon, France.
7
Laboratoire Parcours Santé Systémique (Equipe d'Accueil 4129), Université de Lyon, Université Claude Bernard-Lyon 1, Lyon, France.
8
Centre PSYRENE, Lyon, France.
9
CERMEP - Imagerie du Vivant, Université de Lyon, Lyon, France.

Abstract

The idea that intelligence is embedded not only in a single brain network, but instead in a complex, well-optimized system of complementary networks, has led to the development of whole brain network analysis. Using graph theory to analyze resting-state functional MRI data, we investigated the brain graph networks (or brain networks) of high intelligence quotient (HIQ) children. To this end, we computed the "hub disruption index κ," an index sensitive to graph network modifications. We found significant topological differences in the integration and segregation properties of brain networks in HIQ compared to standard IQ children, not only for the whole brain graph, but also for each hemispheric graph, and for the homotopic connectivity. Moreover, two profiles of HIQ children, homogenous and heterogeneous, based on the differences between the two main IQ subscales [verbal comprehension index (VCI) and perceptual reasoning index (PRI)], were compared. Brain network changes were more pronounced in the heterogeneous than in the homogeneous HIQ subgroups. Finally, we found significant correlations between the graph networks' changes and the full-scale IQ (FSIQ), as well as the subscales VCI and PRI. Specifically, the higher the FSIQ the greater was the brain organization modification in the whole brain, the left hemisphere, and the homotopic connectivity. These results shed new light on the relation between functional connectivity topology and high intelligence, as well as on different intelligence profiles.

KEYWORDS:

brain networks; children; functional MRI; functional connectivity; hub disruption index; intelligence; resting state

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