Suprano Ilaria, Delon-Martin Chantal, Kocevar Gabriel, Stamile Claudio, Hannoun Salem, Achard Sophie, Badhwar Amanpreet, Fourneret Pierre, Revol Olivier, Nusbaum Fanny, Sappey-Marinier Dominique
Univ. Lyon, INSA-Lyon, Université Claude Bernard Lyon 1, UJM-Saint Étienne, CNRS, INSERM, CREATIS UMR 5220, Lyon, France.
Univ. Grenoble Alpes, INSERM, U1216, Grenoble Institut Neurosciences, Grenoble, France.
Front Hum Neurosci. 2019 Jul 12;13:241. doi: 10.3389/fnhum.2019.00241. eCollection 2019.
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.
智力不仅嵌入在单个脑网络中，而是嵌入在一个复杂且优化良好的互补网络系统中，这一观点推动了全脑网络分析的发展。我们使用图论来分析静息态功能磁共振成像数据，研究了高智商（HIQ）儿童的脑图谱网络（或脑网络）。为此，我们计算了“枢纽破坏指数κ”，这是一个对图谱网络变化敏感的指数。我们发现，与标准智商儿童相比，HIQ儿童脑网络在整合和分离特性方面存在显著的拓扑差异，不仅在全脑图谱方面，而且在每个半球图谱以及同位连接性方面。此外，基于两个主要智商子量表[言语理解指数（VCI）和知觉推理指数（PRI）]之间的差异，对HIQ儿童的两种类型，即同质型和异质型进行了比较。脑网络变化在异质型HIQ亚组中比在同质型中更为明显。最后，我们发现图谱网络变化与全量表智商（FSIQ）以及子量表VCI和PRI之间存在显著相关性。具体而言，FSIQ越高，全脑、左半球和同位连接性中的脑组织改变就越大。这些结果为功能连接拓扑与高智力之间的关系以及不同智力类型提供了新的见解。
