Widespread temporal coding of cognitive control in the human prefrontal cortex

Abstract

When making decisions we often face the need to adjudicate between conflicting strategies or courses of action. Our ability to understand the neuronal processes underlying conflict processing is limited on the one hand by the spatiotemporal resolution of functional MRI and, on the other hand, by imperfect cross-species homologies in animal model systems. Here we examine the responses of single neurons and local field potentials in human neurosurgical patients in two prefrontal regions critical to controlled decision-making, the dorsal anterior cingulate cortex (dACC) and dorsolateral prefrontal cortex (dlPFC). While we observe typical modest conflict-related firing rate effects, we find a widespread effect of conflict on spike-phase coupling in the dACC and on driving spike-field coherence in the dlPFC. These results support the hypothesis that a cross-areal rhythmic neuronal coordination is intrinsic to cognitive control in response to conflict, and provide new evidence to support the hypothesis that conflict processing involves modulation of the dlPFC by the dACC.

Access options

Rent or Buy article

Get time limited or full article access on ReadCube.

from$8.99

All prices are NET prices.

Fig. 1: Recording configuration, task description, and behavioral performance.
Fig. 2: Rate coding of task-relevant variables in human dACC neurons.
Fig. 3: Robust phase coding in dACC neurons.
Fig. 4: dACC neuronal interactions within a broader control network.
Fig. 5: Temporal coding in human dlPFC neurons.
Fig. 6: Trial-to-trial encoding of conflict via population theta coherence.

Data availability

Data are available from the authors upon reasonable request and with permission of the Columbia University Medical Center Institutional Review Board.

Code availability

All analysis code is available online at http://www.github.com/elliothsmith/MSIT-analysis

References

  1. 1.

    Botvinick, M. & Braver, T. Motivation and cognitive control: from behavior to neural mechanism. Annu. Rev. Psychol. 66, 83–113 (2015).

    Article  PubMed  PubMed Central  Google Scholar 

  2. 2.

    Shenhav, A. et al. Toward a rational and mechanistic account of mental effort. Annu. Rev. Neurosci. 40, 99–124 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  3. 3.

    Miller, E. K. & Cohen, J. D. An integrative theory of prefrontal cortex function. Annu. Rev. Neurosci. 24, 167–202 (2001).

    Article  CAS  Google Scholar 

  4. 4.

    Abi-Dargham, A. & Horga, G. The search for imaging biomarkers in psychiatric disorders. Nat. Med. 22, 1248–1255 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  5. 5.

    Cole, M. W., Repovš, G. & Anticevic, A. The frontoparietal control system: a central role in mental health. Neuroscientist 20, 652–664 (2014).

    Article  PubMed  PubMed Central  Google Scholar 

  6. 6.

    Horga, G. et al. Adaptation to conflict via context-driven anticipatory signals in the dorsomedial prefrontal cortex. J. Neurosci. 31, 16208–16216 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  7. 7.

    Blanchard, T. C., Strait, C. E. & Hayden, B. Y. Ramping ensemble activity in dorsal anterior cingulate neurons during persistent commitment to a decision. J. Neurophysiol. 114, 2439–2449 (2015).

    Article  PubMed  PubMed Central  Google Scholar 

  8. 8.

    Botvinick, M., Nystrom, L. E., Fissell, K., Carter, C. S. & Cohen, J. D. Conflict monitoring versus selection-for-action in anterior cingulate cortex. Nature 402, 179–181 (1999).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  9. 9.

    Carter, C. S. et al. Anterior cingulate cortex, error detection, and the online monitoring of performance. Science 280, 747 (1998).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  10. 10.

    Kennerley, S. W., Behrens, T. E. J. & Wallis, J. D. Double dissociation of value computations in orbitofrontal and anterior cingulate neurons. Nat. Neurosci. 14, 1581 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  11. 11.

    Hayden, B. Y., Heilbronner, S. R., Pearson, J. M. & Platt, M. L. Surprise signals in anterior cingulate cortex: neuronal encoding of unsigned reward prediction errors driving adjustment in behavior. J. Neurosci. 31, 4178–4187 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  12. 12.

    Amemori, K. & Graybiel, A. M. Localized microstimulation of primate pregenual cingulate cortex induces negative decision-making. Nat. Neurosci. 15, 776 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  13. 13.

    Nakamura, K., Roesch, M. R. & Olson, C. R. Neuronal activity in macaque SEF and ACC during performance of tasks involving conflict. J. Neurophysiol. 93, 884 (2005).

    Article  PubMed  PubMed Central  Google Scholar 

  14. 14.

    Glascher, J. et al. Lesion mapping of cognitive control and value-based decision making in the prefrontal cortex. Proc. Natl Acad. Sci. USA 109, 14681–14686 (2012).

    Article  PubMed  PubMed Central  Google Scholar 

  15. 15.

    Alexander, W. H. & Brown, J. W. Medial prefrontal cortex as an action-outcome predictor. Nat. Neurosci. 14, 1338 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  16. 16.

    Shenhav, A., Straccia, M. A., Cohen, J. D. & Botvinick, M. M. Anterior cingulate engagement in a foraging context reflects choice difficulty, not foraging value. Nat. Neurosci. 17, 1249 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  17. 17.

    Kolling, N. et al. Value, search, persistence and model updating in anterior cingulate cortex. Nat. Neurosci. 19, 1280 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  18. 18.

    Collins, A. G. E. & Frank, M. J. Cognitive control over learning: creating, clustering, and generalizing task-set structure. Psychol. Rev. 120, 190–229 (2013).

    Article  PubMed  PubMed Central  Google Scholar 

  19. 19.

    Gratton, G., Coles, M. G. & Donchin, E. Optimizing the use of information: strategic control of activation of responses. J. Exp. Psychol. Gen. 121, 480 (1992).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  20. 20.

    MacDonald, A. W. Dissociating the role of the dorsolateral prefrontal and anterior cingulate cortex in cognitive control. Science 288, 1835–1838 (2000).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  21. 21.

    Ebitz, R. B. & Hayden, B. Y. Dorsal anterior cingulate: a Rorschach test for cognitive neuroscience. Nat. Neurosci. 19, 1278 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  22. 22.

    Blanchard, T. C. & Hayden, B. Y. Neurons in dorsal anterior cingulate cortex signal postdecisional variables in a foraging task. J. Neurosci. 34, 646–655 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  23. 23.

    Wise, S. P. Forward frontal fields: phylogeny and fundamental function. Trends Neurosci. 31, 599–608 (2008).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  24. 24.

    Heilbronner, S. R., Rodriguez-Romaguera, J., Quirk, G. J., Groenewegen, H. J. & Haber, S. N. Circuit-based corticostriatal homologies between rat and primate. Biol. Psychiatry 80, 509–521 (2016).

    Article  PubMed  PubMed Central  Google Scholar 

  25. 25.

    Cole, M. W., Yeung, N., Freiwald, W. A. & Botvinick, M. Cingulate cortex: diverging data from humans and monkeys. Trends Neurosci. 32, 566–574 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  26. 26.

    Cole, M. W., Yeung, N., Freiwald, W. A. & Botvinick, M. Conflict over cingulate cortex: between-species differences in cingulate may support enhanced cognitive flexibility in humans. Brain. Behav. Evol. 75, 239–240 (2010).

    Article  PubMed  PubMed Central  Google Scholar 

  27. 27.

    Womelsdorf, T. et al. Modulation of neuronal interactions through neuronal synchronization. Science 316, 1609–1612 (2007).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  28. 28.

    Logothetis, N. K., Pauls, J., Augath, M., Trinath, T. & Oeltermann, A. Neurophysiological investigation of the basis of the fMRI signal. Nature 412, 150–157 (2001).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  29. 29.

    Logothetis, N. K. & Wandell, B. A. Interpreting the BOLD signal. Annu. Rev. Physiol. 66, 735–769 (2004).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  30. 30.

    Cavanagh, J. F. & Frank, M. J. Frontal theta as a mechanism for cognitive control. Trends Cogn. Sci. 18, 414–421 (2014).

    Article  PubMed  PubMed Central  Google Scholar 

  31. 31.

    Cavanagh, J. F., Zambrano-Vazquez, L. & Allen, J. J. B. Theta lingua franca: a common mid-frontal substrate for action monitoring processes. Psychophysiology 49, 220–238 (2012).

    Article  PubMed  PubMed Central  Google Scholar 

  32. 32.

    Oehrn, C. R. et al. Neural communication patterns underlying conflict detection, resolution, and adaptation. J. Neurosci. 34, 10438 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  33. 33.

    Tang, H. et al. Cascade of neural processing orchestrates cognitive control in human frontal cortex. Elife 5, e12352 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  34. 34.

    Helfrich, R. F. & Knight, R. T. Oscillatory dynamics of prefrontal cognitive control. Trends Cogn. Sci. 20, 916–930 (2016).

    Article  PubMed  PubMed Central  Google Scholar 

  35. 35.

    Pesaran, B. et al. Investigating large-scale brain dynamics using field potential recordings: analysis and interpretation. Nat. Neurosci. 21, 903–919 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  36. 36.

    Kayser, C., Montemurro, M. A., Logothetis, N. K. & Panzeri, S. Spike-Phase coding boosts and stabilizes information carried by spatial and temporal spike patterns. Neuron 61, 597–608 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  37. 37.

    Shenhav, A., Botvinick, M. M. & Cohen, J. D. The expected value of control: an integrative theory of anterior cingulate cortex function. Neuron 79, 217–240 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  38. 38.

    Sheth, S. A. et al. Human dorsal anterior cingulate cortex neurons mediate ongoing behavioural adaptation. Nature 488, 218 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  39. 39.

    Jensen, O. & Lisman, J. E. Position reconstruction from an ensemble of hippocampal place cells: contribution of theta phase coding. J. Neurophysiol. 83, 2602–2609 (2000).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  40. 40.

    O’Keefe, J. & Burgess, N. Dual phase and rate coding in hippocampal place cells: theoretical significance and relationship to entorhinal grid cells. Hippocampus 15, 853–866 (2005).

    Article  PubMed  PubMed Central  Google Scholar 

  41. 41.

    Montemurro, M. A., Rasch, M. J., Murayama, Y., Logothetis, N. K. & Panzeri, S. Phase-of-Firing coding of natural visual stimuli in primary visual cortex. Curr. Biol. 18, 375–380 (2008).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  42. 42.

    Hawellek, D. J., Wong, Y. T. & Pesaran, B. Temporal coding of reward-guided choice in the posterior parietal cortex. Proc. Natl Acad. Sci. USA 113, 13492–13497 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  43. 43.

    Constantinescu, A. O., O’Reilly, J. X. & Behrens, T. E. J. Organizing conceptual knowledge in humans with a grid-like code. Science 352, 1464–1468 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  44. 44.

    O’Keefe, J. & Recce, M. L. Phase relationship between hippocampal place units and the EEG theta rhythm. Hippocampus 3, 317–330 (1993).

    Article  PubMed  PubMed Central  Google Scholar 

  45. 45.

    Cannon, J. et al. Neurosystems: brain rhythms and cognitive processing. Eur. J. Neurosci. 39, 705–719 (2014).

    Article  PubMed  PubMed Central  Google Scholar 

  46. 46.

    Eisenreich, B. R., Akaishi, R. & Hayden, B. Y. Control without controllers: toward a distributed neuroscience of executive control. J. Cogn. Neurosci. 29, 1684–1698 (2017).

    Article  PubMed  PubMed Central  Google Scholar 

  47. 47.

    Ito, T., Tiede, M. & Ostry, D. J. Somatosensory function in speech perception. Proc. Natl Acad. Sci. USA 106, 1245–1248 (2009).

    Article  PubMed  PubMed Central  Google Scholar 

  48. 48.

    Ebitz, R. B. & Platt, M. L. Neuronal activity in primate dorsal anterior cingulate cortex signals task conflict and predicts adjustments in pupil-linked arousal. Neuron 85, 628–640 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  49. 49.

    Grinvald, A., Kenet, T., Arieli, A. & Tsodyks, M. Are single cortical neurons independent or are they obedient members of a huge orchestra? in 23 Problems in Systems Neuroscience (eds J. Leo van Hemmen, J. L. & Sejnowski, T. J.) chapter 9 (Oxford Univ. Press, 2006).

  50. 50.

    Bush, G. & Shin, L. M. The multi-source interference task: an fMRI task that reliably activates the cingulo-frontal-parietal cognitive/attention network. Nat. Protoc. 1, 308–313 (2006).

    Article  PubMed  PubMed Central  Google Scholar 

  51. 51.

    Fu, Z. et al. Single-Neuron correlates of error monitoring and post-error adjustments in human medial frontal cortex. Neuron 101, 165–177.e5 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  52. 52.

    Mian, M. K. et al. Encoding of rules by neurons in the human dorsolateral prefrontal cortex. Cereb. Cortex 24, 807–816 (2014).

    Article  PubMed  PubMed Central  Google Scholar 

  53. 53.

    Jacobs, J. et al. Direct recordings of grid-like neuronal activity in human spatial navigation. Nat. Neurosci. 16, 1188–1190 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  54. 54.

    Schevon, C. A. et al. Evidence of an inhibitory restraint of seizure activity in humans. Nat. Commun. 3, 1060 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  55. 55.

    Smith, E. H. et al. The ictal wavefront is the spatiotemporal source of discharges during spontaneous human seizures. Nat. Commun. 7, 11098 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  56. 56.

    House, P. A., MacDonald, J. D., Tresco, P. A. & Normann, R. A. Acute microelectrode array implantation into human neocortex: preliminary technique and histological considerations. Neurosurg. Focus 20, 1–4 (2006).

    Article  Google Scholar 

  57. 57.

    Shoham, S., Fellows, M. R. & Normann, R. A. Robust, automatic spike sorting using mixtures of multivariate t-distributions. J. Neurosci. Methods 127, 111–122 (2003).

    Article  PubMed  PubMed Central  Google Scholar 

  58. 58.

    Mitra, P. P. & Pesaran, B. Analysis of dynamic brain imaging data. Biophys. J. 76, 691–708 (1999).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  59. 59.

    Maris, E. & Oostenveld, R. Nonparametric statistical testing of EEG- and MEG-data. J. Neurosci. Methods 164, 177–190 (2007).

    Article  PubMed  PubMed Central  Google Scholar 

  60. 60.

    Izenman, A. J. Linear discriminant analysis. Modern Multivariate Statistical Techniques: Regression, Classification, and Manifold Learning (ed. Izenman, A. J.) 237–280 (Springer, 2008).

  61. 61.

    Okun, M., Naim, A. & Lampl, I. The subthreshold relation between cortical local field potential and neuronal firing unveiled by intracellular recordings in awake rats. J. Neurosci. 30, 4440–4448 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  62. 62.

    Okun, M. et al. Diverse coupling of neurons to populations in sensory cortex. Nature 521, 511 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

Download references

Acknowledgements

This work was supported by NIH R01 MH106700 (S.A.S.), NIH K12 NS080223 (S.A.S.), NIH S10 OD018211 (C.A.S.), NIH R01 NS084142 (C.A.S.), NIH R01 DA038615 (B.Y.H.), the Dana Foundation (S.A.S.), the McNair Foundation (S.A.S.), and a Young Investigator grant from the Brain & Behavior Research Foundation (E.H.S). Special thanks to C. Casadei, D. K. Peprah, and T. G. Dyster, all at Columbia University Medical Center, for coordination and data collection efforts.

Author information

Affiliations

Authors

Contributions

S.A.S., C.B.M., M.J.Y., and E.H.S. designed the experiments. E.H.S., C.B.M., M.J.Y., Y.J.P., C.A.S., G.M.M., and S.A.S were involved with collecting the data. E.H.S., G.P.B., and G.H. analyzed the data. E.H.S., S.A.S., G.H., B.Y.H., M.M.B., and M.J.Y., wrote the manuscript. All authors provided edits to the manuscript.

Corresponding author

Correspondence to Sameer A. Sheth.

Ethics declarations

Competing interests

The authors declare no competing interests.

Additional information

Peer review information Nature Neuroscience thanks Tobias Egner and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Integrated supplementary information

Supplementary Figure 1 Example dACC neurons.

Neurons that are selective for a, response identity, and b, feedback valence. Black dotted lines indicate the timing of the stimulus and colored dotted lines indicate the mean time of the response in a and time of feedback in b. Shaded regions indicate s.e.m over balanced numbers of trials (100 in each condition in a and 150 in each condition in b). c, example unit waveform recorded from the dACC (mean and standard error of 4278 spikes), and d, the corresponding inter-spike interval histogram. e, action potential waveform amplitude plotted against the threshold on the channel upon which the unit was recorded. This threshold corresponded to four times the RMS of the high-pass filtered data.

Supplementary Figure 2 Temporal coding of response identity and feedback valence in dACC neurons and dACC LFP.

Example spike field coherograms for feedback valence (a: valenced, b: neutral) and response identity (c: 1st button, d: 2nd button, e: 3rd button). Proportions of dACC neurons that exhibited phase coding (f) and SFC coding (g). h, Time course of linear discriminability of decision conflict among subgroups of phase-coding neurons (green: beta and blue: theta) and rate coding neurons (salmon). Permutation-based confidence interval is shown as a dotted line. Chance performance is 0.25.

Supplementary Figure 3 Decision conflict related LFP effects.

Colors in all figure panels represent conflict conditions and are color-coded in the same manner as in Fig. 1. a, example evoked potential from a dACC electrode averaged across trials within each conflict condition. b, example evoked potential from a dACC electrode averaged across trials within each conflict condition. c, spectrum of the electrode averaged across trials within each conflict condition. Dotted lines indicate the upper 95% confidence bound and the dashed lines indicate the lower confidence bounds (approximately 75 trials for each condition). d, same as c, for an electrode in dlPFC. Eight of the 29 examined ECoG contacts exhibited significant changes in LFP related to decision conflict (2-way ANOVA, F(3,5) > 2.68, all p < 0.05). All eight of these contacts exhibited conflict-related changes in the theta band, four of these contacts exhibited significant conflict related effects in the beta band, and two of these contacts exhibited changes in the gamma band (Tukey’s Range Tests, all p< 0.05). In dlPFC, three of the 29 examined ECoG contacts exhibited significant changes in LFP related to decision conflict (2-way ANOVA, F(3,5) > 2.76, all p < 0.05). Three of these contacts exhibited conflict-related changes in the theta band, and two of these contacts exhibited significant conflict related effects in the beta and gamma bands (Tukey’s Range Tests, all p< 0.05). Spectra from each contact averaged across trials are shown in e, for dACC and f, for dlPFC. (π±σ trials = 647.5±582.5) g, mean and s.e.m. coherence between dACC and dlPFC LFPs between stimulus and response for each patient and conflict condition, color coded as in Fig. 1.

Supplementary Figure 4 Electrode locations in patients from whom single units in dACC were recorded.

The top row of brains shows the distal tip of depth electrodes projected onto the medial surface of the MNI brain. The bottom row shows lateral most contact projected onto the lateral surface of the MNI brain. The left column shows all electrodes in the left hemisphere (L) and the right column shows all electrodes in the right hemisphere (R). Each color represents electrodes from an individual patient (legend above, center).

Supplementary Figure 5 stLFP for all classes of neuronal selectivity, as determined by the GLM.

a, Local stLFP between dACC neurons and dACC LFPs for the different classes of neuronal selectivity, as classified by the GLM. b, Distant stLFP between dACC neurons and dlPFC LFPs for the different classes of neuronal selectivity, as classified by the GLM. Same sample sizes as in Fig. 4. c, normalized stLFP amplitude distributions for dACC and dlPFC LFPs in black and gray, respectively. The mean trial by trial Pearson correlation between dACC and dlPFC LFPs are shown in green (π±σ trials = 647.5±582.5), indicating that changes in LFP correlations in both medial and lateral PFC do not underlie similarities in stLFP results. Box plot boxes indicate interquartile range, lines indicate medians, and ticks indicate data extrema.

Supplementary Figure 6 dACC phase coding relative to dlPFC LFPs.

a, Mean phase of SFC across 43 beta-coherent neurons. Shading indicates standard error. b, Schematic showing where the mean spikes fall for each conflict condition in beta-coherent neurons. c, Mean phase of SFC across 49 theta-coherent neurons. Shading indicates standard error. d, Schematic showing where the mean spikes fall for each conflict condition in theta-coherent neurons. e-f, For each neuron, the maximum F statistic from the firing rate GLM plotted against the maximum F statistic from the beta (i) or theta (j) phase code (LMM t-tests; t510 > 2.6 for beta and t510 > 2.1 for theta, both p < 0.05; colors as in Fig. 3; Spearman’s correlation coefficient for theta: ρ = −0.08; p = 0.33; correlation coefficient for beta: ρ = −0.01; p = 0.92; both-sided t-tests). g, Venn diagram for dACC-dlPFC rate and phase coding neurons selective for decision conflict (colors as in Fig. 3; significantly more temporal coding than rate coding: 70 vs 14 neurons; McNemar’s Test, χ2=13.6, p < 10−3).

Supplementary Figure 7 All reaction times across sessions and subjects.

a, Violin plots of RT distributions for the seventeen task sessions over six subjects in which dACC units were recorded. Each violin represents approximately 75 trials. Distributions are color coded as in Fig. 1, and the circles and bars show estimates of the mean and s.e.m. for each distribution. The effect of increasing RT with increasing level of conflict is evident at the individual subject level. b, Violin plots of RT distributions for the eleven task sessions over nine subjects in which dlPFC units were recorded. The conflict effect was also detected in these RTs (LMM t-test, RT ~ 1 + condition * session + (condition * session | subject), t3073 = 5.00, p = 6.17*10−7). Again, the conflict level-RT effect is evident at the individual subject level. There was no significant overall RT difference between groups of subjects (that is the groups of patients from which dACC units were recorded and the group of patients from which dlPFC units were recorded; Mann Whitney U test, w = 40, p = 0.25). Error rates were slightly higher in this sample (that is from which dlPFC units were recorded; 4.7%), yet were not significantly greater than in patients in which dACC units were recorded (two-sided Mann-Whitney U test, w = 90.5, p = 0.58). White circles and intersecting black lines indicate the means and standard errors for all violin plots.

Supplementary Figure 8 Example dlPFC neurons.

that are selective for a, response identity, and b, block valence. Black dotted lines indicate the timing of the stimulus and colored dotted lines indicate the mean time of the response in a and mean time of feedback in b. Shaded regions indicate s.e.m over balanced numbers of trials (100 in each condition in a and 150 in each condition in b). c, example unit waveform recorded from the dlPFC (mean and standard error of 6424 spikes), and d, the corresponding inter-spike interval histogram. e, action potential waveform amplitude plotted against the threshold on the channel upon which the unit was recorded. This threshold corresponded to four times the RMS of the high-pass filtered data.

Supplementary Figure 9 dlPFC local spike-field coding controls.

Examining the same measures for local spike field interactions in dlPFC yielded different results from dACC. a, mean stLFP waveforms averaged across dlPFC units that were selective for particular elements of the task (colors) or not (gray). The conflict selective stLFP comprises 11,596 spikes, the response selective stLFP comprises 10,348 spikes, the feedback selective stLFP comprises 6,130 spikes, and the non-rate-coding stLFP comprises 78,786 spikes. While an interesting voltage shift and temporal patterns of deflections in the theta range were observed in b, scatter plots of the stLFP amplitudes induced by different classes of neurons, as defined by the GLM, which were not significantly different among neuron classes (LMM, t78,182 < 1.27, p > 0.21). c, phase coding was not apparent in dlPFC. Rather dlPFC neural firing precessed slightly, from the trough of the LFP to slightly ahead of the trough (mean angle±s.d. = 0.38±0.12), in response to the stimulus, without a significant difference in phase among conflict conditions (LMM t-test, t886 = −1.71, p = 0.09). d, mean and s.e.m. stimulus-aligned evoked potentials from LFP averaged across 888 trials of dlPFC UMA (red) and overlying macrocontact (blue) data.

Supplementary information

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Smith, E.H., Horga, G., Yates, M.J. et al. Widespread temporal coding of cognitive control in the human prefrontal cortex. Nat Neurosci 22, 1883–1891 (2019). https://doi.org/10.1038/s41593-019-0494-0

Download citation

Further reading

Search

Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing