Evidence map›Paper›PMID 21557387›Full record

ArticleHuman brain mapping2012

Functional brain network efficiency predicts intelligence.

Nicolas Langer, Andreas Pedroni, Lorena R R Gianotti, Jürgen Hänggi, Daria Knoch, Lutz Jäncke

Abstract read
In one paragraph

Article in Human brain mapping, 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 122 papers, 4 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
122citing papers in PubMed, 4 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

122 citing papers in PubMed, 4 syntheses or guidelines pooled it.

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62 more citing papers are in PubMed but not listed here.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

6 authors.

Nicolas LangerDivision of Neuropsychology, Institute of Psychology, University of Zurich, Zurich 8050, Switzerland. n.langer@psychologie.uzh.ch
Andreas Pedroni
Lorena R R Gianotti
Jürgen Hänggi
Daria Knoch
Lutz Jäncke

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

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.

Indexed as

AdultBrainBrain MappingElectroencephalographyHumansIntelligenceIntelligence TestsMaleModels, NeurologicalNerve NetPredictive Value of Tests

Identifiers

PMID21557387
PMCPMC6870216

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.