Interindividual reaction time variability is related to resting-state network topology: An electroencephalogram study

G. Zhou, P. Liu, J. He, M. Dong, X. Yang, B. Hou, K. M. Von Deneen, W. Qin*, J. Tian

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

23 Scopus citations

Abstract

Both anatomical and functional brain network studies have drawn great attention recently. Previous studies have suggested the significant impacts of brain network topology on cognitive function. However, the relationship between non-task-related resting-state functional brain network topology and overall efficiency of sensorimotor processing has not been well identified. In the present study, we investigated the relationship between non-task-related resting-state functional brain network topology and reaction time (RT) in a Go/Nogo task using an electroencephalogram (EEG). After estimating the functional connectivity between each pair of electrodes, graph analysis was applied to characterize the network topology. Two fundamental measures, clustering coefficient (functional segregation) and characteristic path length (functional integration), as well as "small-world-ness" (the ratio between the clustering coefficient and characteristic path length) were calculated in five frequency bands. Then, the correlations between the network measures and RT were evaluated in each band separately. The present results showed that increased overall functional connectivity in alpha and gamma frequency bands was correlated with a longer RT. Furthermore, shorter RT was correlated with a shorter characteristic path length in the gamma band. This result suggested that human RTs were likely to be related to the efficiency of the brain integrating information across distributed brain regions. The results also showed that a longer RT was related to an increased gamma clustering coefficient and decreased small-world-ness. These results provided further evidence of the association between the resting-state functional brain network and cognitive function.

Original languageEnglish (US)
Pages (from-to)276-282
Number of pages7
JournalNeuroscience
Volume202
DOIs
StatePublished - Jan 27 2012
Externally publishedYes

Keywords

  • Cognitive function
  • EEG
  • Functional connectivity
  • Graph analysis
  • Phase lag index

ASJC Scopus subject areas

  • Neuroscience(all)

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