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Functional networks for cognitive control in a stop signal task: Independent component analysis

Authors

  • Sheng Zhang,

    1. Department of Psychiatry, Yale University, New Haven, Connecticut
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  • Chiang-shan R. Li

    Corresponding author
    1. Department of Psychiatry, Yale University, New Haven, Connecticut
    2. Department of Neurobiology, Yale University, New Haven, Connecticut
    3. Interdepartmental Neuroscience Program, Yale University, New Haven, Connecticut
    • Connecticut Mental Health Center S112, 34 Park Street, New Haven, Connecticut
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  • The NIH had no role in study design; in the collection, analysis, and interpretation of data; in the writing of the report; or in the decision to submit the paper for publication.

Abstract

Cognitive control is a critical executive function of the human brain. Many studies have combined general linear modeling and the stop signal task (SST) to delineate the component processes of cognitive control. For instance, by contrasting stop success (SS) and stop error (SE) trials in the SST, investigators examined the neural processes underlying stop signal inhibition (SS > SE) and error processing (SE > SS). To complement this parameterized approach, here, we employed a data-driven method—independent component analysis (ICA)—to elucidate neural networks and the relationship between neural networks subserving cognitive control. In 59 adults performing the SST during fMRI, we characterized six independent components with ICA. These functional networks, temporally sorted for go success, SS, and SE trials as the events of interest, included a motor cortical network for motor preparation and execution; a right fronto-parietal network for attentional monitoring; a left fronto-parietal network for response inhibition; a midline cortico-subcortical network for error processing; a cuneus–precuneus network for behavioral engagement; and a “default” network for self-referential processing. Across subjects the event-associated weights of these functional networks showed a distinct pattern of correlation. These results provide new insight into the component processes of cognitive control. Hum Brain Mapp, 2012. © 2011 Wiley Periodicals, Inc.

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