Large-Scale Morphological Network Efficiency of Human Brain: Cognitive Intelligence and Emotional Intelligence

Abstract

Network efficiency characterizes how information flows within a network, and it has been used to study the neural basis of cognitive intelligence in adolescence, young adults, and elderly adults, in terms of the white matter in the human brain and functional connectivity networks. However, there were few studies investigating whether the human brain at different ages exhibited different underpins of cognitive and emotional intelligence (EI) from young adults to the middle-aged group, especially in terms of the morphological similarity networks in the human brain. In this study, we used 65 datasets (aging 18–64), including sMRI and behavioral measurements, to study the associations of network efficiency with cognitive intelligence and EI in young adults and the middle-aged group. We proposed a new method of defining the human brain morphological networks using the morphological distribution similarity (including cortical volume, surface area, and thickness). Our results showed inverted age × network efficiency interactions in the relationship of surface-area network efficiency with cognitive intelligence and EI: a negative age × global efficiency (nodal efficiency) interaction in cognitive intelligence, while a positive age × global efficiency (nodal efficiency) interaction in EI. In summary, this study not only proposed a new method of morphological similarity network but also emphasized the developmental effects on the brain mechanisms of intelligence from young adult to middle-aged groups and may promote mental health study on the middle-aged group in the future.

State and Trait Anxiety Share Common Network Topological Mechanisms of Human Brain

“…As stated in our previous study (Li et al, 2021 ), the white matter tractography could not reliably quantify long-range structural connectivity (Jeurissen et al, 2019 ) and was largely affected by head motion and might involve a large number of false-positive connections (Thomas et al, 2014 ; Maier-Hein et al, 2017 ); the structural covariance network from a large number of participants only yielded one single correlation matrix but did not reveal individual differences (Alexander-Bloch et al, 2013 ; Li et al, 2021 ). Therefore, in this study we still used macroscale morphology network that was based on distributions of cortical surface characteristics (cortical volume, thickness, and surface area) from resting-state fMRI (Li et al, 2021 ) in order to explore the neural substrates of state and trait anxiety. Corresponding to the temporal variations of human brain functional network, we changed bin number of frequency distributions of cortical surface characteristics (area, thickness, and volume) to explore the spatial variations of brain structural network.…”

“…Corresponding to functional network, we also changed bin number (spatial scales to measure morphological distribution) and gained different morphological similarity resolutions to investigate spatial variations of human brain structural network underlying state and trait anxiety. Previous studies have demonstrated that morphological similarity might be the most accurate and robust method to reflect information transfer between brain regions (Seidlitz et al, 2018 ; Li et al, 2021 ), and different spatial resolutions of measuring morphological similarity could detect spatial robustness of human brain structural network underlying state and trait anxiety. This was the first study to examine spatial robustness of human brain networks and characterized the dependence of topological mechanisms of anxiety on spatial resolution.…”

“…In contrast, existing methods to construct brain structural network are mainly white matter tractography (Li et al, 2009;Koenis et al, 2018) and structural covariance network (Mechelli et al, 2005;Alexander-Bloch et al, 2013). As stated in our previous study (Li et al, 2021), the white matter tractography could not reliably quantify long-range structural connectivity (Jeurissen et al, 2019) and was largely affected by head motion and might involve a large number of false-positive connections (Thomas et al, 2014;Maier-Hein et al, 2017); the structural covariance network from a large number of participants only yielded one single correlation matrix but did not reveal individual differences (Alexander-Bloch et al, 2013;Li et al, 2021). Therefore, in this study we still used macroscale morphology network that was based on distributions of cortical surface characteristics (cortical volume, thickness, and surface area) from resting-state fMRI (Li et al, 2021) in order to explore the neural substrates of state and trait anxiety.…”

“…We have proposed a new method to construct structural network, including cortical volume, surface area, and cortical thickness, in our previous study (Li et al, 2021 ), which assessed the distribution similarity of each morphological measurement for each pair of parcels ( Figure 1A ), consider the cortical volume as an example). First, for each pair of parcels, we uniformly divided their volumes into 30 bins.…”

“…Global efficiency, nodal efficiency, as well as betweenness centrality, eigenvector centrality, and pagerank centrality characterized functional integration across the network architecture, while local efficiency and degree centrality focus on the information flow and transfer in local brain regions and functional segregation (Rubinov and Sporns, 2010 ). These topological properties have been used to successfully detect the neural basis of various behaviors and diseases (Zuo et al, 2012 ; Guo et al, 2016 ; Weiler et al, 2018 ; Li et al, 2021 ), did state and trait anxiety share a common or distinct brain network topological mechanism in both functional integration and segregation perspectives?…”