Functional brain network efficiency predicts intelligence

Langer, Nicolas; Pedroni, Andreas; Gianotti, Lorena; Hänggi, Jürgen; Knoch, Daria; Jäncke, Lutz (2012). Functional brain network efficiency predicts intelligence. Human brain mapping, 33(6), pp. 1393-1406. Wiley-Blackwell 10.1002/hbm.21297

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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.

Item Type:

Journal Article (Original Article)

Division/Institute:

07 Faculty of Human Sciences > Institute of Psychology > Social Psychology and Social Neuroscience

UniBE Contributor:

Gianotti, Lorena and Knoch, Daria

Subjects:

100 Philosophy > 150 Psychology

ISSN:

1065-9471

Publisher:

Wiley-Blackwell

Language:

English

Submitter:

Lorena Gianotti

Date Deposited:

24 Dec 2014 10:06

Last Modified:

24 Dec 2014 10:06

Publisher DOI:

10.1002/hbm.21297

PubMed ID:

21557387

Uncontrolled Keywords:

small-world network, intelligence, EEG, neuroscience

BORIS DOI:

10.7892/boris.61177

URI:

https://boris.unibe.ch/id/eprint/61177

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