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Brain anatomical network and intelligence.

Li Y, Liu Y, Li J, Qin W, Li K, Yu C, Jiang T - PLoS Comput. Biol. (2009)

Bottom Line: 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.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.

View Article: PubMed Central - PubMed

Affiliation: LIAMA Center for Computational Medicine, National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, China.

ABSTRACT
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.

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Significant partial correlation between the total number of edges and intelligence tests scores. was found to be positively correlated to FSIQ and PIQ.
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pcbi-1000395-g005: Significant partial correlation between the total number of edges and intelligence tests scores. was found to be positively correlated to FSIQ and PIQ.

Mentions: Intelligence test scores included full scale IQ (FSIQ), performance IQ (PIQ) and verbal IQ (VIQ) (see Materials and Methods). As shown in Table 5, significant correlations between the intelligence test scores and the topological properties of the binary and the weighted anatomical brain networks were found by partial correlation analyses in all 79 subjects, when the data were controlled for age and gender (see Materials and Methods): was found to be positively correlated to FSIQ and PIQ (Fig. 5); for the binary networks, was found to be negatively correlated to FSIQ and PIQ, and for the weighted networks, was found to be negatively correlated to FSIQ, PIQ and VIQ (Fig. 6); was found to be positively correlated to FSIQ, PIQ and VIQ in the binary and the weighted networks for all subjects (Fig. 7); no significant correlation was found between and the intelligence tests scores. In most cases, the weighted networks showed a much larger absolute value of the partial correlation coefficient and a much smaller P-value than the binary networks, suggesting that the correlations were stronger and more significant in the weighted networks. Having established that changing the threshold values did not change our overall conclusions, we will use a threshold value of 3 throughout the rest of the Results section.


Brain anatomical network and intelligence.

Li Y, Liu Y, Li J, Qin W, Li K, Yu C, Jiang T - PLoS Comput. Biol. (2009)

Significant partial correlation between the total number of edges and intelligence tests scores. was found to be positively correlated to FSIQ and PIQ.
© Copyright Policy
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC2683575&req=5

pcbi-1000395-g005: Significant partial correlation between the total number of edges and intelligence tests scores. was found to be positively correlated to FSIQ and PIQ.
Mentions: Intelligence test scores included full scale IQ (FSIQ), performance IQ (PIQ) and verbal IQ (VIQ) (see Materials and Methods). As shown in Table 5, significant correlations between the intelligence test scores and the topological properties of the binary and the weighted anatomical brain networks were found by partial correlation analyses in all 79 subjects, when the data were controlled for age and gender (see Materials and Methods): was found to be positively correlated to FSIQ and PIQ (Fig. 5); for the binary networks, was found to be negatively correlated to FSIQ and PIQ, and for the weighted networks, was found to be negatively correlated to FSIQ, PIQ and VIQ (Fig. 6); was found to be positively correlated to FSIQ, PIQ and VIQ in the binary and the weighted networks for all subjects (Fig. 7); no significant correlation was found between and the intelligence tests scores. In most cases, the weighted networks showed a much larger absolute value of the partial correlation coefficient and a much smaller P-value than the binary networks, suggesting that the correlations were stronger and more significant in the weighted networks. Having established that changing the threshold values did not change our overall conclusions, we will use a threshold value of 3 throughout the rest of the Results section.

Bottom Line: 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.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.

View Article: PubMed Central - PubMed

Affiliation: LIAMA Center for Computational Medicine, National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, China.

ABSTRACT
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.

Show MeSH