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Journal Article

Citation

Ko LW, Shih YC, Chikara RK, Chuang YT, Chang EC. Front. Hum. Neurosci. 2016; 10: e185.

Affiliation

Institute of Cognitive Neuroscience, National Central University Taoyuan, Taiwan.

Copyright

(Copyright © 2016, Frontiers Research Foundation)

DOI

10.3389/fnhum.2016.00185

PMID

27199708

PMCID

PMC4852198

Abstract

The stop-signal paradigm has been widely adopted as a way to parametrically quantify the response inhibition process. To evaluate inhibitory function in realistic environmental settings, the current study compared stop-signal responses in two different scenarios: one uses simple visual symbols as go and stop signals, and the other translates the typical design into a battlefield scenario (BFS) where a sniper-scope view was the background, a terrorist image was the go signal, a hostage image was the stop signal, and the task instructions were to shoot at terrorists only when hostages were not present but to refrain from shooting if hostages appeared. The BFS created a threatening environment and allowed the evaluation of how participants' inhibitory control manifest in this realistic stop-signal task. In order to investigate the participants' brain activities with both high spatial and temporal resolution, simultaneous functional magnetic resonance imaging (fMRI) and electroencephalography (EEG) recordings were acquired. The results demonstrated that both scenarios induced increased activity in the right inferior frontal gyrus (rIFG) and presupplementary motor area (preSMA), which have been linked to response inhibition. Notably, in right temporoparietal junction (rTPJ) we found both higher blood-oxygen-level dependent (BOLD) activation and synchronization of theta-alpha activities (4-12 Hz) in the BFS than in the traditional scenario after the stop signal. The higher activation of rTPJ in the BFS may be related to morality judgments or attentional reorienting. These results provided new insights into the complex brain networks involved in inhibitory control within naturalistic environments.


Language: en

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