Midline frontal cortex low-frequency activity drives subthalamic nucleus oscillations during conflict

Abstract

Making the right decision from conflicting information takes time. Recent computational, electrophysiological, and clinical studies have implicated two brain areas as being crucial in assuring sufficient time is taken for decision-making under conditions of conflict: the medial prefrontal cortex and the subthalamic nucleus (STN). Both structures exhibit an elevation of activity at low frequencies (<10 Hz) during conflict that correlates with the amount of time taken to respond. This suggests that the two sites could become functionally coupled during conflict. To establish the nature of this interaction we recorded from deep-brain stimulation electrodes implanted bilaterally in the STN of 13 Parkinson's disease patients while they performed a sensory integration task involving randomly moving dots. By gradually increasing the number of dots moving coherently in one direction, we were able to determine changes in the STN associated with response execution. Furthermore, by occasionally having 10% of the dots move in the opposite direction as the majority, we were able to identify an independent increase in STN theta-delta activity triggered by conflict. Crucially, simultaneous midline frontal electroencephalographic recordings revealed an increase in the theta-delta band coherence between the two structures that was specific to high-conflict trials. Activity over the midline frontal cortex was Granger causal to that in STN. These results establish the cortico-subcortical circuit enabling successful choices to be made under conditions of conflict and provide support for the hypothesis that the brain uses frequency-specific channels of communication to convey behaviorally relevant information.

Keywords: conflict; midline frontal cortex; subthalamic nucleus; theta oscillations.

Copyright © 2014 Zavala et al.

Figures

Figure 1.

Task and behavior. Randomly moving dots were displayed continuously on screen. During low-conflict trials (A, top), dots began moving coherently in one horizontal direction until either 50% of the dots moved coherently, the subject indicated the direction he or she perceived the dots were moving in, or the trial timed out after 14 s. During high-conflict trials (A, bottom), up to 10% of the dots gradually moved in the opposite direction to the above population. Conflict was highest during the first 833 ms of the trial and then gradually decreased over time as the number of dots moving in the correct direction exceeded the number of dots moving in the conflicting direction (bottom, black trace). Low- and high-conflict trials were randomly interleaved throughout the experiment without the subject knowing what the current trial type was. B, Cumulative distribution function of the response times for all of the low- and high-conflict trials (collapsed across all subjects) used in the analysis.

Figure 2.

Granger connectivity analysis. The GCCA toolbox (Seth, 2010) was used to analyze the LFP and EEG recordings made while patients performed a decision making task. Along with the analysis of the real data, the toolbox was also used to analyze two simulated signals of known connectivity where one signal (signal A) Granger caused 30 and 70 Hz oscillations in signal B. The left column, which shows the coherence and Granger causality analysis of signals A and B, shows high coherence and A to B Granger causality for these two signals in the 30 and 70 Hz bands. The right column shows the results for the same analysis when signal A was manipulated in a way that resulted in signal B Granger causing 1–5 Hz oscillations in signal A (to this end signal B was filtered, time shifted by 40 ms and then added to signal A, whereas signal B was left unchanged). In line with these manipulations to signal A, the right column shows elevated coherence in the lower frequencies (

Figure 3.

Group average percentage power changes in STN LFP. A, Low-conflict trials showed a decrease in beta power and an increase in delta power beginning just before response onset. B, High-conflict trials showed a response locked theta-delta power increase that began even earlier. The beta power decrease was unchanged. C, Differences were significant (p < 0.05, unmasked area). D, Delta and theta power change over time. Mean ± SEM are shown. T = 0 corresponds to response onset.

Figure 4.

Group average normalized changes in EEG-STN LFP coherence estimated from the Granger causality MVAR matrix. A, High-conflict trials showed a relative increase in response locked STN-frontal (FCz-Cz) cortex coherence compared with low-conflict trials. Differences were significant (p < 0.05, unmasked area, third row). B, There were no conflict-related changes in STN-parietal (Pz-Cz) cortex coupling. Bottom, Mean ± SEM theta-delta coherence changes over time. T = 0 corresponds to response onset.

Figure 5.

Group averaged normalized changes in continuous EEG:STN LFP theta-delta band coherence estimated using the Hilbert transform. A, top, High-conflict trials showed a relative increase in response locked STN-frontal (FCz-Cz) cortex coherence compared with low-conflict trials. Differences were significant (p < 0.05, horizontal bar). A, Bottom, Similar results were obtained when only the consistency of the intersite phase difference was considered. B, There were no conflict related changes in STN-parietal (Pz-Cz) cortex coupling. All mean ± SEM theta-delta coherence changes over time. T = 0 corresponds to response onset.

Figure 6.

Group average directed coherence between FCz-Cz and STN LFP. A, DTF analysis shows a conflict related increase in response aligned directed coherence in the frontal cortex-to-STN direction. Differences between low- and high-conflict trials were significant (p < 0.05, unmasked area, third row). B, There were no conflict-related changes in response aligned directed coherence in the STN-to-frontal cortex direction. Bottom, Mean ± SEM theta-delta directed coherence changes over time. T = 0 is response onset.