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Theta oscillations shift towards optimal frequency for cognitive control

Nature Human Behaviour volume 6, pages 1000–1013 (2022)Cite this article

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Abstract

Cognitive control allows to flexibly guide behaviour in a complex and ever-changing environment. It is supported by theta band (4–7 Hz) neural oscillations that coordinate distant neural populations. However, little is known about the precise neural mechanisms permitting such flexible control. Most research has focused on theta amplitude, showing that it increases when control is needed, but a second essential aspect of theta oscillations, their peak frequency, has mostly been overlooked. Here, using computational modelling and behavioural and electrophysiological recordings, in three independent datasets, we show that theta oscillations adaptively shift towards optimal frequency depending on task demands. We provide evidence that theta frequency balances reliable set-up of task representation and gating of task-relevant sensory and motor information and that this frequency shift predicts behavioural performance. Our study presents a mechanism supporting flexible control and calls for a reevaluation of the mechanistic role of theta oscillations in adaptive behaviour.

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Fig. 1: Task and model structure.
Fig. 2: Model dynamics.
Fig. 3: Model simulations.
Fig. 4: Testing model predictions in behaviour and EEG.
Fig. 5: Testing model predictions in other datasets.
Fig. 6: Peak theta amplitude per condition in each dataset.

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Data availability

Raw behavioural, eye-tracking and EEG data can be found on the Open Science Framework repository at https://osf.io/nwh87/?view_only=b11ee1f860804da582c816fe8acdecad.

Code availability

Code of the model, the behavioural experiment and analysis scripts to reproduce all results and figures from the study can be found on Github at https://github.com/mehdisenoussi/theta_shift_cog_control.

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Acknowledgements

The authors thank C. Buc Calderon for fruitful discussions and comments on the manuscript. M.S. and T.V. were supported by grant G012816 from Research Foundation Flanders. M.S., T.V. and E.D.L. were supported by grant BOF17-GOA-004 from the Research Council of Ghent University. P.V. was supported by grant 1102519N from Research Foundation Flanders. K.D. was supported by FWO [PEGASUS]² Marie Skłodowska-Curie fellowship 12T9717N. The funders had no role in study design, data collection and analysis, decision to publish or preparation of the manuscript.

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Authors and Affiliations

  1. Department of Experimental Psychology, Ghent University, Ghent, Belgium

    Mehdi Senoussi, Pieter Verbeke, Kobe Desender, Esther De Loof, Durk Talsma & Tom Verguts

  2. Department of Neurophysiology and Pathophysiology, University Medical Center Hamburg-Eppendorf, Hamburg, Germany

    Kobe Desender

  3. Brain and Cognition, KU Leuven, Leuven, Belgium

    Kobe Desender

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  1. Mehdi Senoussi
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  2. Pieter Verbeke
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  3. Kobe Desender
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  4. Esther De Loof
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  5. Durk Talsma
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  6. Tom Verguts
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Contributions

M.S., D.T. and T.V. designed the study. M.S., P.V. and T.V. developed the model. M.S. and E.D.L. collected the data. M.S. analysed model simulations, and behavioural and EEG data. M.S. and T.V. wrote the manuscript. All of the authors discussed the results and commented on the manuscript.

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Correspondence to Mehdi Senoussi.

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Nature Human Behaviour thanks James Cavanagh, Sirawaj Itthipuripat and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Peer reviewer reports are available.

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Extended data

Extended Data Fig. 1 Rule node competition and effect of MFC theta amplitude.

a Number of time points the correct rule node won the competition in the LFC unit depending on the instructed rule. Black bars represent the 95% Confidence Interval (smaller than dot size). The colour of each dot represents the rule, as represented in the rule names on the x-axis. b Time course of proportion of correct rule node winning the competition across a sample of the ISD. Only two rule nodes are shown for clarity, one easy (green curve) and one difficult (orange curve). Shaded area represents the 95% Confidence Interval. c Effect of MFC theta amplitude and frequency on competition time window. Average competition window length, in milliseconds, for one theta cycle, that is one competition window, as a function of theta frequency for different theta amplitude. Varying the amplitude (the different lines) shows that although the competition window increases with amplitude, this effect reaches a ceiling around amplitude values of 2-3. Each line represents simulations at different levels of MFC theta frequency with a fixed theta amplitude. Line color represents MFC theta amplitude as represented in the legend. d Varying MFC theta amplitude from 0.8 to 2.0 yielded similar results concerning what was the optimal theta frequency depending on rule difficulty (n = 34 simulations per theta frequency and for each theta amplitude, two-sided Wilcoxon sign-rank test: all W > 33, all ps < 0.002). Data are presented as violin plots, left- and right-most bars represent extrema, middle bar represent the median. Distribution density is represented by violin plot width.

Extended Data Fig. 2 Reaction times and control analyses with DDM.

Reaction times and DDM parameters (bound, drift rate and non-decision time) estimated in model and participant data with the EZ-Diffusion model (see Methods). a Reaction time and DDM parameters of model performance by rule difficulty (same-side, different-side) and theta frequency (4 to 7Hz, steps of 0.5Hz), n = 34 simulations per theta frequency. We ran a repeated-measure 2x7 ANOVA with factors rule difficulty (2 levels) and theta frequency (7 levels). There was a main effect of rule difficulty in all measures, that is reaction times, bound, drift rate and non-decision time, (all Fs(1, 33) > 98.96, all ps < 10-10, all η2 > 0.20). There was a main effect of theta frequency in reaction times, drift rate and nondecision time (all Fs(6, 33) > 3.20, all ps < 0.006, all η 2 > 0.01). There was a significant rule-difficulty – theta-frequency interaction in drift rate (F(6, 33) = 6.69, p < 0.001, η2 = 0.021). Error bars represent standard deviation across simulations. b Reaction time and DDM parameters (bound, drift rate and nondecision time) estimated on participants’ data grouped by rule (n = 34 participants). Data were collapsed across ISD to avoid data sparsity. We ran a repeated-measure 2x2 ANOVA with factors target-location (2 levels: Left, Right) and hand (2 levels: Left, Right). Only drift rate showed a significant interaction between hand and target-location (F(1, 33) = 31.86, p < 0.001, η2 = 0.21), as well as a main effect of hand (F(1, 33) = 6.65, p = 0.014, η2 = 0.02). Reaction time and bound showed a significant effect of hand (Reaction time: F(1, 33) = 4.62, p = 0.039, η2 = 0.04; bound: F(1, 33) = 4.84, p = 0.034, η2 = 0.03). All other effects were not significant (all Fs(1, 33) < 3.29, all ps > 0.079). Data are presented as mean values, error bars represent s.e.m. Smaller grey dots represent individual participants’ data.

Extended Data Fig. 3 Raw spectra of individual participants per rule in Dataset 1.

The grey area represents the theta frequency band.

Extended Data Fig. 4 Control analyses on the effect of peak frequency and amplitude of nearby frequency bands.

Panels a to f: delta frequency band (1-3Hz). Panels g to l: alpha frequency band (8-12Hz). Two-way repeated-measure ANOVAs were used for all Dataset 1 data. Two-sided Wilcoxon sign-rank tests were used for all Dataset 2 and 3 data. a Peak frequency of delta band oscillations in Dataset 1 (all Fs < 0.78, all ps > 0.384). b Peak amplitude of delta band oscillations in Dataset 1 (all Fs < 0.76, all ps > 0.390). c Peak frequency of delta band oscillations in Dataset 2 (W = 26, p = 0.104, r = 0.50, 95% CI = (−0.00, 0.04)). d Peak amplitude of delta band oscillations in Dataset 2 (W = 49, p = 0.855, r = 0.07, 95% CI = (−0.02, 0.03)). e Peak frequency of delta band oscillations in Dataset 3 (W = 173, p = 0.091, r = − 0.34, 95% CI = (−0.07, 0.01)). f Peak amplitude of delta band oscillations in Dataset 3 (W = 195, p = 0.200, r = −0.26, 95% CI = (−0.04, 0.01)). g Peak frequency of alpha band oscillations in Dataset 1 (all Fs < 1.71, all ps > 0.199). h Peak amplitude of alpha band oscillations in Dataset 1. There was a main effect of target-location (F(1, 33) = 5.32, p = 0.027, η 2 = 0.051; uncorrected for multiple comparisons). i Peak frequency of alpha band oscillations in Dataset 2 (W = 29, p = 0.153, r = 0.44, 95% CI = (−0.05, 0.13)). j Peak amplitude of alpha band oscillations in Dataset 2 (W = 51, p = 0.951, r = 0.03, 95% CI = (−0.02, 0.02)). k Peak frequency of alpha band oscillations in Dataset 3 (W = 244, p = 0.715, r = − 0.07, 95% CI = (−0.05, 0.03)). l Peak amplitude of alpha band oscillations in Dataset 3 (W = 218, p = 0.394, r = −0.17, 95% CI = (−0.03, 0.01)). Data are presented as mean values, error bars represent s.e.m. computed over n = 34, 14 and 33 participants for Dataset 1, 2 and 3, respectively. Smaller grey dots represent individual participants’ data.

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Senoussi, M., Verbeke, P., Desender, K. et al. Theta oscillations shift towards optimal frequency for cognitive control. Nat Hum Behav 6, 1000–1013 (2022). https://doi.org/10.1038/s41562-022-01335-5

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