The Key Locus of Common Response Inhibition Network for No-go and Stop Signals

Response inhibition is one of the highest evolved executive functions of human beings. Previous studies revealed a wide variety of brain regions related to response inhibition, although some of them may not be directly related to inhibition but to task-specific effects or noninhibitory cognitive fun...

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Bibliographic Details
Published inJournal of cognitive neuroscience Vol. 20; no. 8; pp. 1434 - 1442
Main Authors Zheng, Dongming, Oka, Tatsuro, Bokura, Hirokazu, Yamaguchi, Shuhei
Format Journal Article
LanguageEnglish
Published One Rogers Street, Cambridge, MA 02142-1209, USA MIT Press 01.08.2008
MIT Press Journals, The
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ISSN0898-929X
1530-8898
DOI10.1162/jocn.2008.20100

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Summary:Response inhibition is one of the highest evolved executive functions of human beings. Previous studies revealed a wide variety of brain regions related to response inhibition, although some of them may not be directly related to inhibition but to task-specific effects or noninhibitory cognitive functions such as attention, response competition, or error detection. Here, we conducted event-related functional magnetic resonance imaging studies in which all subjects performed both stop-signal and go/no-go tasks in order to explore key neural correlates within the response inhibition network irrelevant to task designs and other cognitive processes. The successful inhibition in the stop-signal and go/no-go tasks, respectively, activated a set of predominantly right-lateralized hemispheric cortices. The common inhibitory regions across the two tasks included the right middle prefrontal cortex in addition to the right middle occipital cortex. Correlation analysis was carried out within these areas between intensity of activation and behavioral performance in the two tasks. Only the region located in the middle prefrontal cortex showed significant correlations in both tasks. We believe this region is the key locus for execution of response inhibition in the distributed inhibitory neural network.
Bibliography:August, 2008
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ISSN:0898-929X
1530-8898
DOI:10.1162/jocn.2008.20100