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eNeuro. 2017 May 26;4(3). pii: ENEURO.0061-17.2017. doi: 10.1523/ENEURO.0061-17.2017. eCollection 2017 May-Jun.

Proactive Control: Neural Oscillatory Correlates of Conflict Anticipation and Response Slowing.

Chang A1, Ide JS2, Li HH1, Chen CC1,3, Li CR2,4,5,6.

Author information

1
Department of Psychology, National Taiwan University, Taipei, Taiwan 10617.
2
Department of Psychiatry, Yale University, New Haven, CT 06520.
3
Center for Neurobiology and Cognitive Science, National Taiwan University, Taipei, Taiwan 10617.
4
Department of Neuroscience, Yale University, New Haven, CT 06520.
5
Interdepartmental Neuroscience Program, Yale University, New Haven, CT 06520.
6
Beijing Huilongguan Hospital, Beijing 100096, China.

Abstract

Proactive control allows us to anticipate environmental changes and adjust behavioral strategy. In the laboratory, investigators have used a number of different behavioral paradigms, including the stop-signal task (SST), to examine the neural processes of proactive control. Previous functional MRI studies of the SST have demonstrated regional responses to conflict anticipation-the likelihood of a stop signal or P(stop) as estimated by a Bayesian model-and reaction time (RT) slowing and how these responses are interrelated. Here, in an electrophysiological study, we investigated the time-frequency domain substrates of proactive control. The results showed that conflict anticipation as indexed by P(stop) was positively correlated with the power in low-theta band (3-5 Hz) in the fixation (trial onset)-locked interval, and go-RT was negatively correlated with the power in delta-theta band (2-8 Hz) in the go-locked interval. Stimulus prediction error was positively correlated with the power in the low-beta band (12-22 Hz) in the stop-locked interval. Further, the power of the P(stop) and go-RT clusters was negatively correlated, providing a mechanism relating conflict anticipation to RT slowing in the SST. Source reconstruction with beamformer localized these time-frequency activities close to brain regions as revealed by functional MRI in earlier work. These are the novel results to show oscillatory electrophysiological substrates in support of trial-by-trial behavioral adjustment for proactive control.

KEYWORDS:

Bayesian model; Electroencephalogram (EEG); neural oscillation; proactive control; stop-signal task

PMID:
28560315
PMCID:
PMC5446487
DOI:
10.1523/ENEURO.0061-17.2017
[Indexed for MEDLINE]
Free PMC Article

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