Causal role of cross-frequency coupling in distinct components of cognitive control

Abstract

Cognitive control is the capacity to guide motor and perceptual systems towards abstract goals. High-frequency neural oscillations related to motor activity in the beta band (13-30 Hz) and to visual processing in the gamma band (>30 Hz) are known to be modulated by cognitive control signals. One proposed mechanism for cognitive control is via cross-frequency coupling whereby low frequency network oscillations in prefrontal cortex (delta from 2-3 Hz and theta from 4-8 Hz) guide the expression of motor-related activity in action planning and guide perception-related activity in memory access. However, there is no causal evidence for cross-frequency coupling in these dissociable components of cognitive control. To address this important gap in knowledge, we delivered cross-frequency transcranial alternating current stimulation (CF-tACS) during performance of a task that manipulated cognitive control demands along two dimensions: the abstraction of the rules of the task (nested levels of action selection) that increased delta-beta coupling and the number of rules (set-size held in memory) that increased theta-gamma coupling. As hypothesized, we found that CF-tACS increased the targeted phase-amplitude coupling and modulated task performance of the associated cognitive control component. These findings provide causal evidence that prefrontal cortex orchestrates different components of cognitive control via two different cross-frequency coupling modalities.

Trial registration: ClinicalTrials.gov NCT03800030.

Keywords: Cognitive control; Cross-frequency coupling; EEG; Phase-amplitude coupling; Prefrontal cortex; Transcranial alternating current stimulation.

Copyright © 2021 Elsevier Ltd. All rights reserved.

Figures

Figure 1.. Hierarchical cognitive control paradigm.

(A) The response task consisted of a mapping between button response and colored square. This low abstraction task manipulated set-size with an increase in the number of color-to-button mapping from four to eight. (B) In the dimension task, two objects were presented within a colored square. The colored square mapped onto a feature dimension: texture or shape. The objects were evaluated based on this feature and were determined to be the “same” or “different.” This high abstraction task manipulated set-size with an increase in the number of dimensions that were indicated by the colors, one or two. ITI is inter-trial interval. s is seconds. (C-D) Baseline behavioral performance was evaluated with a two-way repeated-measures ANOVA with factors: abstraction (high or low) and set-size (high or low). (C) Response time was slower as a function of abstraction (striped versus not-striped bars) and set-size (grey versus white bars). (D) For accuracy, there was no significant difference in as a function of set-size or abstraction. (E) Repeated-measured ANOVA of the amplitude of low-frequency neural oscillations with two factors, band (delta or theta) and cognitive control component (abstraction or set-size), revealed a significant interaction. Time window for analysis: 0.2 to 1.6 seconds after stimulus. Activity was extracted from prefrontal electrodes (Fz and surrounding). Error bars are SEM. Opaque lines are individual traces. *** p 0.1.

Figure 2.. Time-frequency analysis of cognitive control components.

Five-cycle Morlet wavelet convolution was run on prefrontal electrodes (Fz and surrounding). (A) As a function of abstraction, statistical analysis revealed a decrease in alpha-beta oscillations (6–30 Hz) from 0.3 to 1.6 seconds following the stimulus and an increase in delta oscillations (2–4 Hz) from 0.2 to 1.4 seconds. (B) As a function of set-size, statistical analysis revealed a decrease in alpha-beta oscillations (8–30 Hz) from 0.4 to 1.4 seconds following the stimulus and an increase in theta oscillations (3–8 Hz) from 0.6 to 1.6 seconds. Units are percent change from baseline (−0.7 to −0.3 seconds) for all panels. Graphic in the upper right corner depicts the region of interest used for time-frequency analysis. Frequencies on the y-axis are log-scale using a 1f0.05 distribution. Zero point is the time that the stimulus was presented. Solid line depicts time-frequency clusters that survived permutation-based cluster-mass correction at p < 0.05. Dashed line depicts time-frequency clusters that were significant at p < 0.05, k > 1000. (C) Topographic distribution of the amplitude of delta band as a function of abstraction shows a peak near the frontal-central midline. FCz and surrounding electrodes from 0.2 to 1.4 seconds was defined as central prefrontal cortex for delta-beta PAC analysis. (D) Topographic distribution of the amplitude of theta band as a function of set-size shows a peak near the frontal midline. Fz and surrounding electrodes from 0.4 to 1.6 seconds was defined as anterior prefrontal cortex for theta-gamma PAC analysis. Outlined polygon depicts the electrodes defined for each region of interest and black dots are the electrodes. The central electrode is written in white.

Figure 3.. Phase-amplitude coupling during hierarchical cognitive control.

(A) The phase of delta oscillations (2–3 Hz) in central prefrontal electrodes (cPFC) to the amplitude of beta oscillations (15–25 Hz) across the scalp as a function of abstraction revealed a significant increase in coupling over frontal-midline and right motor electrodes (rM1), and (B) theta phase (4–7 Hz) in anterior prefrontal electrodes (aPFC) to gamma amplitude (35–58 Hz) as a function of set-size revealed a significant increase in coupling in parietal-occipital electrodes (ParOcc). cPFC and aPFC regions of interest are highlighted with a black hexagon and marked with a Greek letter referring to the canonical band. Black dots depict electrodes with a significant increase in coupling for high versus low abstraction/set-size (p

Figure 4.. Causal Investigation using Cross-Frequency Transcranial Alternating Current Stimulation.

Cross-frequency transcranial alternating current stimulation (CF-tACS) was delivered using individualized waveforms designed to mimic and enhance endogenous delta-beta and theta-gamma phase-amplitude coupling. (A) Peak phase-amplitude coupling for delta-beta was extracted from the high abstraction, high set-size condition at baseline, and was used to generate a custom electric waveform. (B) Peak theta-gamma coupling was extracted from the low abstraction, high set-size condition. (C) In a crossover design, participants received CF-tACS during performance of the cognitive control tasks. Resting-state periods without stimulation were included after each task block to quantify the effect of CF-tACS on neural activity. Delta-beta CF-tACS in green, theta-gamma CF-tACS in purple, and sham in dark grey. (D) A three-electrode montage was used with two stimulation devices, in order to deliver identical current to the prefrontal (anterior to F4) and the motor cortex (posterior to C4). The return electrode centered on the central midline, FCz. (E) CF-tACS was targeted to increase coupling between right lateral prefrontal cortex and right posterior cortex via in-phase stimulation. Peak electric field was calculated to be along the right middle frontal gyrus and the hand region of the right motor cortex. Units are volts per meter.

Figure 5.. Impact of CF-tACS on behavior and phase-amplitude coupling.

(A-B) The impact of CF-tACS on behavioral performance was investigated as a function of different components of cognitive control (abstraction in green and set-size in purple). (A) Delta-beta CF-tACS increased response time only as a function of abstraction. (B) Theta-gamma CF-tACS increased accuracy only as a function of set-size. Opaque colored background depicts the condition for which an effect of stimulation was expected. * p

Figure 6.. Topography of CF-tACS on phase-amplitude couplings.

(A) With a delta-phase seed in central prefrontal cortex (cPFC; green hexagon), there was an increase in coupling to beta-amplitude over left motor electrodes. The homologue to right motor electrodes (rM1) is indicated with a dashed green hexagon. (B) There was no increase in delta-beta coupling with theta-gamma tACS. (C) Delta-beta tACS did not have a significant impact on theta-gamma coupling. Theta-frequency phase in anterior prefrontal cortex (aPFC seed; purple heptagon) was investigated for coupling with gamma-frequency amplitude across the scalp. The theta-gamma coupled regions identified in baseline are outlined in purple. (D) With a theta-phase seed in aPFC, there was an increase in coupling to gamma-amplitude in right parietal-occipital electrodes. Parietal-occipital electrodes (ParOcc) identified in baseline analysis are outlined with purple pentagons. Black dots are p

Figure 7.. Low-frequency functional connectivity analysis.

(A) Investigation of delta frequency (2–3 Hz) functional connectivity between a seed in a left motor electrode (C3) revealed an increase in connectivity strength for delta-beta tACS relative to sham. (B) For theta-gamma tACS versus sham, there was no change in delta-frequency functional connectivity with the left motor region. Black rectangle shows the region excluded from analysis due to artifacts arising from local connectivity estimates. Black dots represent electrodes with a significant effect at p