Article of the Year awards 2023: Dissociated brain functional connectivity of fast versus slow frequencies underlying individual differences in fluid intelligence: a DTI and MEG study

Nordic Mensa Fund awarded two article of the year- awards in 2023. Leonardo Bonetti from the University of Aarhus, Denmark was awarded for "Dissociated brain functional connectivity of fast versus slow frequencies underlying individual differences in fluid intelligence: a DTI and MEG study” published in Scientific Reports.

The article presents an array of key brain features crucially differentiating highly (IQ > 115) versus average (IQ = 100) intelligent individuals. Specifically, our work focused on the relationship between fluid intelligence and the brain structural and functional networks measured employing state-of-the-art neuroscientific techniques such as magnetoencephalography (MEG) and diffusion tensor imaging (DTI).

After acquiring and pre-processing the data, the researchers’ procedure comprised the following main steps. First, the brain was divided into 90 regions. Second, the structural connectivity network of the brain of each participant was identified measuring the strength of the physical connections between those 90 brain regions. The functional connectivity networks were instead measured for each participant by computing the correlations between their brain activity over time, for each pair of the 90 brain regions. This was done in five discrete frequency bands, to detect the functional connectivity and communication between brain regions at the following different neural rhythms: delta, theta, alpha, beta, gamma. Third, three key measures of the brain networks were computed and assessed in relation to intelligence. These measures were: degree, modularity, and segregation coefficient. The degree describes how connected a brain region is to the other regions of the whole brain network and provides information about the functional integration properties of the brain. Modularity measures how the brain network can be subdivided into clearly defined, non-overlapping sub-networks, providing the ideal set of brain sub-networks for both structural and functional connectivity. The segregation coefficient indicates whether a brain region is mainly connected to the other brain regions of the same sub-network or is more connected to the brain regions of the other sub-networks. In other words, a high segregation coefficient tells that the brain region is mainly relevant for its sub-networks, while a low segregation coefficient suggests that the brain region links different sub-networks, allowing communication between distant partitions of the brain.

The results showed that highly intelligent individuals had stronger degree and lower segregation coefficient compared to average intelligent people in a higher number of brain areas. This happened with regards to structural connectivity and to the slower frequency bands of functional connectivity (delta, theta, alpha, beta). The opposite result was observed for higher-frequency (gamma) functional networks, with higher intelligent individuals showing lower degree and higher segregation across the brain. These results suggest that fast neural communication (gamma) in more intelligent individuals supports local processing of the information in segregated sub-networks of the brain, while slower neural communication (delta, theta, alpha, beta) is responsible for a more effective transfer of information between brain sub-networks, and thus allows stronger integration of the information across the whole brain.

In conclusion, this study greatly advanced our understanding of the neural basis of human intelligence, revealing that efficient brain communication in different neural rhythms is a crucial property of the brain of highly intelligent people.