Brain Anatomical Network and Intelligence

By Yonghui Li, Yong Liu, Jun Yu Li, Wen Qin, Kuncheng Li, Chunshui Yu, T. Jiang. PLoS Comput. Biol., 2009

626 citations recorded by the source corpus.

Intuitively, higher intelligence might be assumed to correspond to more efficient information transfer in the brain, but no direct evidence has been reported from the perspective of brain networks. In this study, we performed extensive analyses to test the hypothesis that individual differences in intelligence are associated with brain structural organization, and in particular that higher scores on intelligence tests are related to greater global efficiency of the brain anatomical network. We constructed binary and weighted brain anatomical networks in each of 79 healthy young adults utilizing diffusion tensor tractography and calculated topological properties of the networks using a graph theoretical method. Based on their IQ test scores, all subjects were divided into general and high intelligence groups and significantly higher global efficiencies were found in the networks of the latter group. Moreover, we showed significant correlations between IQ scores and network properties across all subjects while controlling for age and gender. Specifically, higher intelligence scores corresponded to a shorter characteristic path length and a higher global efficiency of the networks, indicating a more efficient parallel information transfer in the brain. The results were consistently observed not only in the binary but also in the weighted networks, which together provide convergent evidence for our hypothesis. Our findings suggest that the efficiency of brain structural organization may be an important biological basis for intelligence.

Subfields: Cognitive Neuroscience, Radiology, Nuclear Medicine and Imaging

View Paper: https://www.semanticscholar.org/paper/e330534518c445e4979f1726e91ef7fae493a2b0

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In Cognitive Neuroscience

In Radiology, Nuclear Medicine and Imaging

Citations in the corpus

Global connectivity of prefrontal cortex predicts cognitive control and intelligence (2012, background): “, 2009), or overall global connectivity (Li et al., 2009; Zalesky et al., 2011) rather than region-specific global connectivity.”
Function in the Human Connectome: Task-fMRI and Individual Differences in Behavior (2013, background): “…IQ among healthy adults 65 is associated with shorter path length and higher global efficiency in 66 measures of brain functional connectivity (Li et al., 2009) as well as 67 greater global connectivity in prefrontal cortex (Cole et al., 2012), 68 thus providing evidence that more efficient…”
Cognitive relevance of the community structure of the human brain functional coactivation network (2013, background): “It has been shown that higher intelligence quotient is correlated with shorter topological path length of structural and functional networks, which would facilitate globally efficient, integrative processing (6, 7).”
Simple models of human brain functional networks (2012, background): “…integrated processing, and several studies have shown that greater ef ﬁ ciency of network topology is associated with higher intelligence quotient (29, 30), greater accuracy of working memory task performance (31), or successful performance of more dif ﬁ cult versions of a working memory task (32).”
Graph analysis of the human connectome: Promise, progress, and pitfalls (2013, positive): “Despite this ambiguity, path length-based measures have shown replicated associations with cognitive performance (Bassett et al., 2009; Li et al., 2009; van denHeuvel et al., 2009; Zalesky et al., 2011), strong heritability (Fornito et al.”
The trees and the forest: Characterization of complex brain networks with minimum spanning trees. (2014, positive): “In particular, short average path length has been associated with higher intelligence (Li et al., 2009; van den Heuvel et al., 2009).”
Why use a connectivity-based approach to study stroke and recovery of function? (2012, positive): “While global measures have been related to behavior (Li et al., 2009), and shown to be differentially affected in different pathological states such as Alzheimer's disease (Sanz-Arigita et al., 2010); schizophrenia (Bassett et al., 2008); alcoholism (Chanraud et al., 2011); and obsessive compulsive…”
Comparing Brain Networks of Different Size and Connectivity Density Using Graph Theory (2010, background): “The significance of efficient brain network topology is emphasized by reports of correlations between corresponding graph measures and intelligence [11–13].”
Conserved and variable architecture of human white matter connectivity (2011, methodology): “The current network analysis described in this paper has focused on quantifying the reproducibility of global network properties using a combination of basic connectivity and binary graph metrics, the most commonly applied methods to date (Gong et al., 2009; Vaessen et al., 2010; Shu et al., 2009; L”
Aberrant Frontal and Temporal Complex Network Structure in Schizophrenia: A Graph Theoretical Analysis (2010, background): “As higher integration efficiency has been linked to cognitive fitness of the brain (Bassett et al., 2009; Li et al., 2009; van den Heuvel et al., 2009a), this may suggest that schizophrenia impacts the capacity of frontal brain regions to efficiently integrate information across the brain network.”
The UCLA multimodal connectivity database: a web-based platform for brain connectivity matrix sharing and analysis (2012, background): “…they contain suf-ﬁcient information to capture individual characteristics such as age (Dosenbach et al., 2010), gender (Yan et al., 2011), intelligence quotient (IQ) (Li et al., 2009; van den Heuvel et al., 2009b), and disease state (Supekar et al., 2008; Craddock et al., 2009; Lo et al., 2010).”