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Research ArticleNew Research, Cognition and Behavior

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

Andrew Chang, Jaime S. Ide, Hsin-Hung Li, Chien-Chung Chen and Chiang-Shan R. Li
eNeuro 16 May 2017, 4 (3) ENEURO.0061-17.2017; https://doi.org/10.1523/ENEURO.0061-17.2017
Andrew Chang
1Department of Psychology, National Taiwan University, Taipei, Taiwan 10617
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Jaime S. Ide
2Department of Psychiatry, Yale University, New Haven, CT 06520
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Hsin-Hung Li
1Department of Psychology, National Taiwan University, Taipei, Taiwan 10617
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Chien-Chung Chen
1Department of Psychology, National Taiwan University, Taipei, Taiwan 10617
3Center for Neurobiology and Cognitive Science, National Taiwan University, Taipei, Taiwan 10617
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Chiang-Shan R. Li
2Department of Psychiatry, Yale University, New Haven, CT 06520
4Department of Neuroscience, Yale University, New Haven, CT 06520
5Interdepartmental Neuroscience Program, Yale University, New Haven, CT 06520
6 Beijing Huilongguan Hospital, Beijing 100096, China
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  • Figure 1.
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    Figure 1.

    Bayesian model prediction of behavioral performance in the stop-signal task. A, Example sequence of trials. The upper panel shows the sequence of go (blue dots) or stop (red dots) trials and how Bayesian belief about encountering a stop trial [P(stop), black line] increases and decreases, respectively, after each stop and go trial. The lower panel shows the sequence of go-RT in the upper panel. Overall, go-RT tended to be prolonged with a higher P(stop). B, Correlation between P(stop) and RT across all go success trials, with each regression line representing an individual participant. C, Positive correlation between go-RT and P(stop) collapsed over all participants. The plot in the upper panel shows the mean ± SE, the histogram in the lower panel shows the distribution of P(stop), and both were binned at intervals of 0.01. D, Negative correlation between stop error (SE) rate and P(stop), with the same format as in C.

  • Figure 2.
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    Figure 2.

    Trial-by-trial oscillatory power correlates of P(stop), go-RT, and PE at channel FCz. A, Correlation between fixation-locked (onset at 0 ms) power and P(stop) at each time–frequency bin. The color represents the z-value of Spearman correlation, and the black contours represent statistically significant time–frequency clusters in the nonparametric cluster-based permutation test across participants, with clustering threshold at p < 0.02, 0.005, and 0.001 levels (see Materials and Methods for details). It showed a positive correlation in the intervals 3–5 Hz and 0–200 ms. B, No clusters showed a significant correlation between go-locked power and P(stop). C, No clusters showed a significant correlation between fixation-locked power and go-RT. D, Correlation between go-locked power and go-RT, with the permutation test showing a negative correlation in the intervals 2–8 Hz and 200–700 ms. E, The coefficient of trial-by-trial Pearson correlation between the mean power of the clusters identified in A and D of individual participants. Wilcoxon signed rank test showed that the correlation coefficients across participants were significantly below zero. F, G, Correlation between stop-locked power and PE, with the permutation test showing a positive correlation in the intervals 12–22 Hz and 300–400 ms. The topographies of each correlational cluster (p < 0.005) are shown in the bottom panel, where H, I, J, and K each show the cluster of A, D, F, and G, all with the strongest correlations at the midfrontal region. We performed the same analyses at Pz channel (Fig. 2-1).

  • Figure 3.
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    Figure 3.

    Source reconstruction and localization. EEG correlates of conflict anticipation (p < 0.001, uncorrected) were localized to the right SMG and the anterior pre-SMA. B, EEG correlates of RT slowing (p < 0.005, uncorrected) were localized to the middle and inferior frontal gyrus (MFG/IFG) and precentral and postcentral gyrus (PC).

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    Table 1.

    Source reconstruction for the EEG correlates of conflict anticipation (p < 0.001, uncorrected; cluster size >2000 mm3) and RT slowing (p < 0.005, uncorrected; cluster size >20,000 mm3)

    Cluster size
    (mm3)
    t valueMNI coordinate (mm)SideIdentified brain region
    xyz
    Conflict anticipation
    3,2774.0959–2350RSupramarginal gyrus
    2,3633.8643742R/LPresupplementary motor area
    RT slowing
    20,9273.79386125RMiddle and inferior frontal gyri
    30,9363.6261–107RCentral operculum, postcentral gyrus
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    Table 2.

    Statistical table

    LocationData structureType of testObserved power or 95% confidence interval
    aNormal distributionPaired t-test[36, 53]
    bNormal distributionPearson correlation[0.77, 0.94]
    cNormal distributionPearson correlation[–0.82, –0.97]
    d, f, g, j, k, l, m, n, oNonparametricNonparametric cluster-based permutation test100%
    eNonparametricNonparametric cluster-based permutation test98.0%
    hNonparametricWilcoxon signed rank test[–0.047, –0.002]
    iNonparametricNonparametric cluster-based permutation test95.5%

Extended Data

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  • Figure 2-1

    Trial-by-trial oscillatory power correlates of P(stop), go-RT, and PE at channel Pz. The format is the same as with Fig. 2. The color represents the z-value of Spearman correlation, and the black contours represent statistically significant time–frequency clusters in the nonparametric cluster-based permutation test across participants, with clustering threshold at p < 0.02, 0.005, and 0.001 levels (see Materials and Methods for details). A, B, Time–frequency power was not correlated with P(stop) in fixation-locked or go-locked epoch. C, Time–frequency power was not correlated with go-RT in fixation-locked epoch. D, Time–frequency power was negatively correlated with go-RT in the intervals 2–8 Hz and ∼100–500 ms in go-locked epoch. E, F, Time–frequency power was not correlated with PE in stop-locked epoch.. Download Figure 2-1, TIF file.

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Proactive Control: Neural Oscillatory Correlates of Conflict Anticipation and Response Slowing
Andrew Chang, Jaime S. Ide, Hsin-Hung Li, Chien-Chung Chen, Chiang-Shan R. Li
eNeuro 16 May 2017, 4 (3) ENEURO.0061-17.2017; DOI: 10.1523/ENEURO.0061-17.2017

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Proactive Control: Neural Oscillatory Correlates of Conflict Anticipation and Response Slowing
Andrew Chang, Jaime S. Ide, Hsin-Hung Li, Chien-Chung Chen, Chiang-Shan R. Li
eNeuro 16 May 2017, 4 (3) ENEURO.0061-17.2017; DOI: 10.1523/ENEURO.0061-17.2017
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Keywords

  • Bayesian Model
  • Electroencephalogram (EEG)
  • neural oscillation
  • proactive control
  • Stop-Signal Task

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