Literature DB >> 33732136

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

Chunlin Li1,2, Kaini Qiao1,2, Yan Mu1,2, Lili Jiang1,2.   

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.
Copyright © 2021 Li, Qiao, Mu and Jiang.

Entities:  

Keywords:  MRI; aging; intelligence; morphological network; network efficiency

Year:  2021        PMID: 33732136      PMCID: PMC7959829          DOI: 10.3389/fnagi.2021.605158

Source DB:  PubMed          Journal:  Front Aging Neurosci        ISSN: 1663-4365            Impact factor:   5.750


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