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The Journal of Neuroscience, January 7, 2009, 29(1):98-105; doi:10.1523/JNEUROSCI.4137-08.2009
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Behavioral/Systems/Cognitive
Prelude to and Resolution of an Error: EEG Phase Synchrony Reveals Cognitive Control Dynamics during Action Monitoring
James F. Cavanagh,1 * Michael X Cohen,1,2 * andJohn J. B. Allen1 1Department of Psychology, University of Arizona, Tucson, Arizona 85721, and 2Department of Psychology, The University of Amsterdam, 1018 WB Amsterdam, The Netherlands
Abstract
Top
Abstract
Introduction
Error-related activity in the medial prefrontal cortex (mPFC) is thought to work in conjunction with lateral prefrontal cortex (lPFC) as a part of an action-monitoring network, where errors signal the need for increased cognitive control. The neural mechanism by which this mPFC–lPFC interaction occurs remains unknown. We hypothesized that transient synchronous oscillations in the theta range reflect a mechanism by which these structures interact. To test this hypothesis, we extracted oscillatory phase and power from current–source–density-transformed electroencephalographic data recorded during a Flanker task. Theta power in the mPFC was diminished on the trial preceding an error and increased immediately after an error, consistent with predictions of an action-monitoring system. These power dynamics appeared to take place over a response-related background of oscillatory theta phase coherence. Theta phase synchronization between FCz (mPFC) and F5/6 (lPFC) sites was robustly increased during error trials. The degree of mPFC–lPFC oscillatory synchronization predicted the degree of mPFC power on error trials, and both of these dynamics predicted the degree of posterror reaction time slowing. Oscillatory dynamics in the theta band may in part underlie a mechanism of communication between networks involved in action monitoring and cognitive control.
Key words: theta rhythm; oscillator; cognitive; connectivity; cingulate; human
Introduction
Recent theories of prefrontal cortex functioning postulate that the medial prefrontal cortex (mPFC) interacts with lateral prefrontal cortex (lPFC) in a dynamic loop during goal-directed performance (Botvinick et al., 2001; Ridderinkhof et al., 2004b
The integrity of the action-monitoring system appears to decline on trials preceding response errors. Error-preceding trials are characterized by progressively faster reaction times (Gehring and Fencsik, 2001
Synchronized neural oscillations may be one mechanism by which different regions within a network (e.g., mPFC and lPFC) can interact (Engel and Singer, 2001
Materials and Methods
Participants. Fourteen introductory psychology students (six female; mean age, 18.86; ±SD, 0.95) served as participants to partially fulfill a research experience requirement for their class. All participants were free from past neurological trauma, had normal or corrected-to-normal vision, and were free from current psychoactive medication use. The data from these 14 participants were taken from another study (T. O. C. Gründler, J. F. Cavanagh, C. M. Figueroa, M. J. Frank, and J. J. B. Allen, unpublished data) investigating obsessive-compulsive symptomatology. Note that the 14 participants used for the present study had normative levels of obsessive-compulsive symptomatology (obsessive-compulsive inventory-revised scores: mean, 8.6; SD, 6.8; range, 0–17). All participants gave informed consent before participating.Procedure. Participants completed a modified Eriksen Flankers task (Eriksen and Eriksen, 1974
.
Task and EEG recording. A matching algorithm was used to match trials for reaction time (RT), selecting the correct trial with RT closest to each erroneous response. Response-locked epochs were obtained for these trials (N), as well as the two trials preceding (N-2 and N-1) and after (N+1 and N+2) each error and matched correct trial. All EEG data were visually inspected for artifacts, and epoched from –1000 to +2000 ms peri-response, with large windows to accommodate edge artifacts induced by wavelet convolution. Eye blinks were corrected using a regression algorithm. All participants had EEG data for at least 29 erroneous responses and at least 20 epochs for single-trial analyses. All EEG epochs were then converted to current source density (CSD) using the methods of Kayser and Tenke (2006)
Time–frequency calculation. Time–frequency calculations were computed using custom-written Matlab (The MathWorks) routines (Cohen et al., 2008
tf e–t2/(2*
2), where t is time, f is frequency (which increased from 2.5 to 50 Hz in 30 logarithmically spaced steps), and
t = arctan(imag[z(t)]/real[z(t)]). Each epoch was then cut in length (–300 to +500 ms peri-response) and baseline corrected to the average frequency power from –300 to –100 ms before the onset of the cue (Cohen et al., 2008
Two different types of oscillation phase coherence were examined: intertrial phase coherence and interchannel phase synchrony (ICPS). ITPC measures the consistency of phase values for a given frequency band at each point in time over trials, in one particular electrode. Phase coherence values vary from 0 to 1, where 0 indicates random phases at that time–frequency point across trials, and 1 indicates identical phase values at that time–frequency point across trials. The phase coherence value is defined as follows:
where n is the number of trials for each time and each frequency band. ITPC thus reflects the extent to which oscillation phase values are consistent over trials at that point in time–frequency space (power, in contrast, represents the intensity of that signal). Note that this measure of phase coherence does not differentiate between possible biophysical mechanisms underlying phase consistency, such as phase reset or phase "smearing." Rather, this measure simply indicates the statistical probability of increased phase consistency between trial and baseline epochs. The hypotheses for the current investigation require comparing these measures of phase consistency across conditions, not comparing the difference in underlying biophysical mechanisms, per se.
ICPS measures the extent to which oscillation phases are similar across different electrodes over time–frequency. ICPS is calculated in a similar manner as intertrial phase coherence as follows:
where n is the number of trials,
For this investigation, F5–FCz and F6–FCz sites were used to measure phase synchrony between mPFC and lPFC. To rule out an alternative explanation of volume conduction effects, ICPS was also calculated between CP3–FCz and CP4–FCz: these pairs are of similar spatial difference between F5/6 and FCz in the posterior direction (closest Euclidean distance), but no theories postulate a role for these posterior areas in the type of cognitive control under investigation here. We selected both F5 and F6 phase synchrony because the literature does not provide a clear indication about whether PFC-based cognitive control is lateralized (Garavan et al., 2002
Statistical analysis. All power, ITPC, and ICPS measures were quantified as the average value and time of peak. For clarity and simplicity, only the error/correct trials (N) and immediately surrounding trials (N-1 and N+1) in the wide (–100 to 300 ms) window were entered into statistical analyses of averaged values. Separate 2 (accuracy: error, correct) * 3 (trial: N-1, N, N+1) ANOVAs were run for each measure, with an expected accuracy by trial quadratic interaction. For ICPS measures, additional tests were conducted to examine laterality by mirroring the 2*3 ANOVA, but with an additional (hemisphere: right F6, left F5) dimension, with an expected accuracy by trial interaction in the absence of a lateralized effect. The spatial specificity of the ICPS on trial N was tested with a 2 (accuracy: error, correct) * 2 (hemisphere: right, left) * 2 (caudality: anterior F5/6, posterior CP3/4) ANOVA, with an expected accuracy by caudality interaction. Where appropriate, follow-up paired-samples t tests (error vs correct) were conducted to test specific a priori hypotheses. Single-trial analyses were performed on error and error-following trials. Individual standardized β weights were taken from bivariate regressions between a priori determined measures of conflict-control activities (ICPS and FCz power) and posterror reaction time (from posterror trials which were answered correctly), within two different analytic windows: (–100 to 300 ms) and (0 to 200 ms).
Results
Behavioral performance
Participants made an average of 60.1 (±SD, 52.2) errors on the task, with errors on incongruent trials consisting of 70% of total errors. EEG data were available for an average of 52.2 (±SD, 20.1) errors, and all subsequent behavioral analyses were performed on these errors (and RT-matched correct trials). Participants self-corrected on an average of 53% (±SD, 29%) of these error trials, although this proportion varied widely across participants. There were similar RTs for error (mean, 427.0; ±SD, 71.0) and reaction time matched correct trials (mean, 438.7; ±SD, 63.1). Participants displayed reliable posterror reaction time slowing (mean, 79.4; ±SD, 22.9; one-sample t test: t(13) = 12.47; p < 0.001; d, 3.46), shown in Figure 1 A.
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Figure 1. Time courses of CSD activities (mean ± SE). A, CSD–ERPs show decreased activity preceding an error and increased activity immediately after an error. The reaction time plot shows posterror slowing. B, Averaged FCz (mPFC) power demonstrates similar dynamics as the CSD–ERP, but F5 and F6 (lPFC) sites show no accuracy difference. C, Averaged FCz intertrial phase coherence increases after errors, but F5 and F6 sites show no difference. Note that ordinate scaling on FCz plots in B and C are 200% of the F5 and F6 ordinate. D, Differences in peak latency for power and phase coherence at the FCz site. This accuracy-related difference shows that phase coherence latency is constant, but peak power occurs much later after an error.
ERP analyses
The CSD–ERP 2*3 ANOVA revealed a significant accuracy by trial interaction (F(1,13) = 17.8; p < 0.001; partial2 = 0.58), and follow-up t tests indicated that the CSD–ERP was larger on error trials (t(13) = 3.4; p < 0.01; d = 0.90) and smaller on error-preceding trials (t(13) = –2.8; p < 0.01; d = 0.75) compared with each corresponding correct trial. These data replicate previous investigations of a diminished response-locked voltage negativity immediately preceding errors (Allain et al., 2004
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Figure 2. Response locked CSD grand averages for N-1 (left), N (center), and N+1 (right) trials. A, CSD–ERPs and topographic maps. Topographic maps reflect the average from 50 to 90 ms after the response. B, Power in dB. Note the latency of error trial power compared with all other power plots. The error–correct contrast reveals a large burst of power in the theta and low alpha range after an error. C, Intertrial phase coherence (R). Note the increase of phase coherence after an error, especially 4 Hz.
mPFC theta frequency dynamics
The CSD–ERN from the ERP analysis significantly correlated with theta power (r(14) = 0.57; p < 0.05, R2 = 0.32) and ITPC values (r(14) = 0.64; p < 0.01; R2 = 0.45), indicating a large degree of shared variance between ERP, power, and phase coherence measures immediately after an error. Figure 2 shows complementary perspectives of ERP, power, and phase coherence measures for error and peri-error trials. Similar to the CSD–ERN findings, the theta power value 2*3 ANOVA revealed a significant accuracy by trial interaction (F(1,13) = 22.6; p < 0.001; partialmPFC theta frequency dynamics: peak latencies
The peak power latency 2*3 ANOVA revealed a significant accuracy by trial interaction (F(1,13) = 10.3; p < 0.01; partialSummary of mPFC activities
Error trials may be predicted by a decline in response-related mPFC power on the preceding trial. Error-related mPFC activities consist of an increase in both ITPC and power values. Although ITPC occurs at the same time on error and correct trials, a prolonged peak latency of the power burst appears to be specific to error trials. There were no significant 2*3 ANOVA interactions or single-trial t tests for F5 or F6 power value, peak power time, ITPC value, or peak ITPC time, indicating that these power and ITPC effects are specific to the mPFC and are not seen in lPFC sites.Theta frequency interchannel phase coherence
The ICPS between mPFC and lPFC was robustly increased on error trials, as demonstrated by the significant accuracy by trial interaction (F(1,13) = 15.4; p < 0.01; partial
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Figure 3. Grand-averaged interchannel phase synchrony calculated as a percentage change from baseline. ICPS is increased after errors, with robust increases in the theta band at frontal sites.
Single-trial analysis
Figure 4 displays the results of the single-trial analyses. One sample t tests of the individual standardized β weights revealed that ICPS predicted FCz theta power during errors between both F5–FCz (–100 to 300 ms: t(13) = 6.71, p < 0.001, d = 1.79; 0 to 200 ms: t(13) = 7.01, p < 0.001, d = 1.87) and F6–FCz sites (–100 to 300 ms: t(13) = 2.42, p < 0.05, d = 0.64; 0 to 200 ms: t(13) = 2.48, p < 0.05, d = 0.66). Both peak theta power at FCz (–100 to 300 ms: t(13) = 2.54, p < 0.05, d = 0.68; 0 to 200 ms: t(13) = 2.04, p = 0.06, d = 0.54) and the latency of peak FCz theta power (–100 to 300 ms: t(13) = –3.43, p < 0.01, d = 0.92; 0 to 200 ms: t(13) = –5.25, p < 0.01, d = 1.4) on the error trial predicted posterror slowing, with greater peak power and faster peak latencies predicting more reaction time slowing on the following trial. F5–FCz ICPS did not significantly predict posterror slowing (–100 to 300 ms: t(13) = 0.82, p > 0.10, d = 0.22; 0 to 200 ms: t(13) = 1.13, p > 0.10, d = 0.30), but F6–FCz ICPS did predict posterror slowing in the narrow time window (–100 to 300 ms: t(13) = 1.62, p > 0.10, d = 0.43; 0 to 200 ms: t(13) = 2.86, p < 0.01, d = 0.77). Additionally, we calculated congruency effects on single-trial analyses (supplemental Fig. S2, available at www.jneurosci.org as supplemental material). Overall, in all analyses shown in Figure 4 and supplemental Figure S2, available at www.jneurosci.org as supplemental material, the relationship between ICPS and FCz power is strong and distinct, whereas ICPS and FCz power metrics each moderately predicted posterror slowing.
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Figure 4. Single-trial analyses of the proposed cognitive control and action-monitoring network. Data from two different analytic windows are included: (–100 to 300 ms) and (0 to 200 ms). The degree of theta-band synchrony between lPFC and mPFC sites (ICPS) predicts the amount of FCz theta power increase. The degree of posterror slowing is predicted by both FCz theta power dynamics and theta ICPS.
Discussion
This investigation revealed that oscillatory synchrony in the theta band between mid-frontal and lateral frontal sites is increased immediately after response errors of commission. The degree of this oscillatory synchrony correlated with mid-frontal theta power increases, and both of these measures predicted the degree of posterror behavioral adjustment. The waning and waxing of mid-frontal activity during the prelude to and resolution of an error suggests that theta-band phase coherence and power dynamics reflect variability in the integrity of the action-monitoring system. Theta-band oscillatory synchrony between mPFC and lPFC may reflect a mechanism of communication between action-monitoring and cognitive control networks (Botvinick et al., 2001The roles of the action-monitoring and cognitive control networks
The conflict-control model of Botvinick et al. (2001)Multiple accounts of the action-monitoring system predict that the prelude to an error occurs when the integrity of the action-monitoring system diminishes over time, leading to high-conflict situations or errors (Botvinick et al., 2001
Oscillatory dynamics of the medial prefrontal action-monitoring system
The ACC has been shown to generate neural oscillations in the theta band (Wang et al., 2005The pattern of error-preceding and error-following mPFC oscillatory dynamics in this investigation suggests that demanding motor responses might always be characterized by phasic mid-frontal power and phase coherence increases, and that errors represent a special case of altered oscillatory dynamics. An ongoing background of oscillatory perturbation would fit with neurological models of the ERN, which have characterized the oscillatory perturbation and power increase specific to error trials (Luu et al., 2004
The dissociation between power and phase coherence peak times suggests that phase coherence is a temporally constant response-related phenomenon, and that inherent increases in power and phase coherence are enhanced after errors. We were able to reveal this dissociation by using precue (instead of preresponse) baselines, because previous work has shown that oscillatory processes related to conflict monitoring may arise preresponse (Cohen et al., 2008
Oscillatory phase coherence between action-monitoring and cognitive control networks
Synchronous oscillations are thought to reflect a mechanism for entrained interregional activity. Rhythmic excitability may allow temporal windows of coordinated spike timing across spatially separate neural networks (Fries, 2005Adjusting future behavior
Given that mPFC–lPFC oscillatory coherence correlated with the degree of mPFC power increase, and both of these predicted the degree of posterror slowing, it is highly likely that these EEG dynamics reflect interrelated processes relevant to the detection and correction of errors. These effects were also larger on posterror trials that were incongruent (supplemental Fig. S2, available at www.jneurosci.org as supplemental material), similar to a recent study which revealed ACC–lPFC coupling in the theta band after incongruent trials in a Stroop task (Hanslmayr et al., 2008Conclusions
We propose that inter-region oscillatory synchrony in the theta band may be one mechanism by which action-monitoring and cognitive control networks interact in the prefrontal cortex. This long-range oscillatory synchrony may capitalize on an inherent background of medial frontal theta-band oscillatory perturbation and power increase during manual responses. Errors may induce altered oscillatory and power dynamics in this system, which in turn support enhanced computation and interregional communication. The findings of this investigation suggest that the dynamic oscillatory interplay between medial and lateral frontal regions underlies our ability to detect errors and adjust behavior accordingly.
Footnotes
Received Aug. 29, 2008; revised Oct. 30, 2008; accepted Nov. 26, 2008.*J.F.C. and M.X.C. contributed equally to this work.
This work was supported by infrastructure provided by National Institute of Mental Health (NIMH) Grant R01-MH066902. J.F.C. is supported by NIMH Grant F31MH082560. We thank Jürgen Kayser and Craig Tenke for their help with the application of their CSD scripts, and Christina Figueroa for help with data collection.
Correspondence should be addressed to James F. Cavanagh, Department of Psychology, University of Arizona, 1503 University Avenue, Tucson, AZ 85721. Email: jimcav{at}email.arizona.edu
Copyright © 2009 Society for Neuroscience 0270-6474/09/290098-08$15.00/0
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