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Functional brain network efficiency predicts intelligence

Langer, Nicolas and Pedroni, Andreas and Gianotti, Lorena R. R. and Hänggi, Jürgen and Knoch, Daria and Jäncke, Lutz. (2012) Functional brain network efficiency predicts intelligence. Human brain mapping : a journal devoted to functional neuroanatomy and neuroimaging, Vol. 33, No. 6 , S. 1393–1406.

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Official URL: http://edoc.unibas.ch/dok/A5844599

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Abstract

The neuronal causes of individual differences in mental abilities such as intelligence are complex and profoundly important. Understanding these abilities has the potential to facilitate their enhancement. The purpose of this study was to identify the functional brain network characteristics and their relation to psychometric intelligence. In particular, we examined whether the functional network exhibits efficient small-world network attributes (high clustering and short path length) and whether these small-world network parameters are associated with intellectual performance. High-density resting state electroencephalography (EEG) was recorded in 74 healthy subjects to analyze graph-theoretical functional network characteristics at an intracortical level. Ravens advanced progressive matrices were used to assess intelligence. We found that the clustering coefficient and path length of the functional network are strongly related to intelligence. Thus, the more intelligent the subjects are the more the functional brain network resembles a small-world network. We further identified the parietal cortex as a main hub of this resting state network as indicated by increased degree centrality that is associated with higher intelligence. Taken together, this is the first study that substantiates the neural efficiency hypothesis as well as the Parieto-Frontal Integration Theory (P-FIT) of intelligence in the context of functional brain network characteristics. These theories are currently the most established intelligence theories in neuroscience. Our findings revealed robust evidence of an efficiently organized resting state functional brain network for highly productive cognitions. Hum Brain Mapp, 2011. © 2011 Wiley-Liss, Inc.
Faculties and Departments:07 Faculty of Psychology > Departement Psychologie > Ehemalige Einheiten Psychologie > Social and Affective Neuroscience (Knoch)
UniBasel Contributors:Pedroni, Andreas and Gianotti, Lorena R. R. and Knoch, Daria
Item Type:Article, refereed
Article Subtype:Research Article
Bibsysno:Link to catalogue
Publisher:Wiley-Liss
ISSN:1065-9471
Note:Publication type according to Uni Basel Research Database: Journal article
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Last Modified:14 Sep 2012 07:20
Deposited On:14 Sep 2012 07:04

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