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Common medial frontal mechanisms of adaptive control in humans and rodents


In this report we describe how common brain networks within the medial frontal cortex (MFC) facilitate adaptive behavioral control in rodents and humans. We demonstrate that after errors, low-frequency oscillations below 12 Hz are modulated over the midfrontal cortex in humans and within the prelimbic and anterior cingulate regions of the MFC in rats. These oscillations were phase locked between the MFC and motor areas in both rats and humans. In rats, single neurons that encoded prior behavioral outcomes were phase coherent with low-frequency field oscillations, particularly after errors. Inactivating the medial frontal regions in rats led to impaired behavioral adjustments after errors, eliminated the differential expression of low-frequency oscillations after errors and increased low-frequency spike-field coupling within the motor cortex. Our results describe a new mechanism for behavioral adaptation through low-frequency oscillations and elucidate how medial frontal networks synchronize brain activity to guide performance.

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Figure 1: Common mechanisms of medial frontal cortical oscillations during adaptive control in rats and humans.
Figure 2: Time-frequency analysis revealing enhanced low-frequency power after errors.
Figure 3: Medial frontal single neuron spiking is coupled with low-frequency oscillations.
Figure 4: Encoding of previous outcomes in the medial frontal and motor cortices.
Figure 5: Loss of adaptive control after inactivation of the MFC.
Figure 6: Inactivation of the MFC eliminated post-error increases in low-frequency oscillations in the motor cortex.
Figure 7: The rat MFC directly influences post-error low-frequency oscillations in the motor cortex in the service of adaptive control.


  1. 1

    Bellman, R. & Kalaba, R. A mathematical theory of adaptive control processes. Proc. Natl. Acad. Sci. USA 45, 1288–1290 (1959).

    CAS  Article  Google Scholar 

  2. 2

    Ridderinkhof, K.R., Ullsperger, M., Crone, E.A. & Nieuwenhuis, S. The role of the medial frontal cortex in cognitive control. Science 306, 443–447 (2004).

    CAS  Article  Google Scholar 

  3. 3

    Holroyd, C.B. & Coles, M.G.H. The neural basis of human error processing: reinforcement learning, dopamine, and the error-related negativity. Psychol. Rev. 109, 679–709 (2002).

    Article  Google Scholar 

  4. 4

    Rushworth, M.F.S. & Behrens, T.E.J. Choice, uncertainty and value in prefrontal and cingulate cortex. Nat. Neurosci. 11, 389–397 (2008).

    CAS  Article  Google Scholar 

  5. 5

    van Meel, C.S., Heslenfeld, D.J., Oosterlaan, J. & Sergeant, J.A. Adaptive control deficits in attention-deficit/hyperactivity disorder (ADHD): the role of error processing. Psychiatry Res. 151, 211–220 (2007).

    Article  Google Scholar 

  6. 6

    Velligan, D.I., Ritch, J.L., Sui, D., DiCocco, M. & Huntzinger, C.D. Frontal Systems Behavior Scale in schizophrenia: relationships with psychiatric symptomatology, cognition and adaptive function. Psychiatry Res. 113, 227–236 (2002).

    Article  Google Scholar 

  7. 7

    Fitzgerald, K.D. et al. Error-related hyperactivity of the anterior cingulate cortex in obsessive-compulsive disorder. Biol. Psychiatry 57, 287–294 (2005).

    Article  Google Scholar 

  8. 8

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

    CAS  Article  Google Scholar 

  9. 9

    Robbins, T.W. Shifting and stopping: fronto-striatal substrates, neurochemical modulation and clinical implications. Phil. Trans. R. Soc. Lond. B 362, 917–932 (2007).

    CAS  Article  Google Scholar 

  10. 10

    Narayanan, N.S. & Laubach, M. Top-down control of motor cortex ensembles by dorsomedial prefrontal cortex. Neuron 52, 921–931 (2006).

    CAS  Article  Google Scholar 

  11. 11

    Narayanan, N.S. & Laubach, M. Neuronal correlates of post-error slowing in the rat dorsomedial prefrontal cortex. J. Neurophysiol. 100, 520–525 (2008).

    Article  Google Scholar 

  12. 12

    Carter, C.S. et al. Anterior cingulate cortex, error detection, and the online monitoring of performance. Science 280, 747–749 (1998).

    CAS  Article  Google Scholar 

  13. 13

    Sheth, S.A. et al. Human dorsal anterior cingulate cortex neurons mediate ongoing behavioural adaptation. Nature 488, 218–221 (2012).

    CAS  Article  Google Scholar 

  14. 14

    Rabbitt, P.M. Errors and error correction in choice-response tasks. J. Exp. Psychol. 71, 264–272 (1966).

    CAS  Article  Google Scholar 

  15. 15

    Danielmeier, C. & Ullsperger, M. Post-error adjustments. Front. Psychol. 2, 233 (2011).

    Article  Google Scholar 

  16. 16

    Dutilh, G. et al. Testing theories of post-error slowing. Atten. Percept. Psychophys. 74, 454–465 (2012).

    Article  Google Scholar 

  17. 17

    Modirrousta, M. & Fellows, L.K. Dorsal medial prefrontal cortex plays a necessary role in rapid error prediction in humans. J. Neurosci. 28, 14000–14005 (2008).

    CAS  Article  Google Scholar 

  18. 18

    Hampson, R.E. et al. Facilitation and restoration of cognitive function in primate prefrontal cortex by a neuroprosthesis that utilizes minicolumn-specific neural firing. J. Neural Eng. 9, 056012 (2012).

    Article  Google Scholar 

  19. 19

    McClintock, S.M., Freitas, C., Oberman, L., Lisanby, S.H. & Pascual-Leone, A. Transcranial magnetic stimulation: a neuroscientific probe of cortical function in schizophrenia. Biol. Psychiatry 70, 19–27 (2011).

    Article  Google Scholar 

  20. 20

    Kornblum, S. Simple reaction time as a race between signal detection and time estimation: a paradigm and a model. Percept. Psychophys. 13, 108–112 (1973).

    Article  Google Scholar 

  21. 21

    Cavanagh, J.F., Cohen, M.X. & Allen, J.J.B. Prelude to and resolution of an error: EEG phase synchrony reveals cognitive control dynamics during action monitoring. J. Neurosci. 29, 98–105 (2009).

    CAS  Article  Google Scholar 

  22. 22

    Buzsaki, G. Rhythms of the Brain (Oxford University Press, 2011).

  23. 23

    Fujisawa, S., Amarasingham, A., Harrison, M.T. & Buzsáki, G. Behavior-dependent short-term assembly dynamics in the medial prefrontal cortex. Nat. Neurosci. 11, 823–833 (2008).

    CAS  Article  Google Scholar 

  24. 24

    Narayanan, N.S., Kimchi, E.Y. & Laubach, M. Redundancy and synergy of neuronal ensembles in motor cortex. J. Neurosci. 25, 4207–4216 (2005).

    CAS  Article  Google Scholar 

  25. 25

    Laubach, M., Wessberg, J. & Nicolelis, M.A. Cortical ensemble activity increasingly predicts behaviour outcomes during learning of a motor task. Nature 405, 567–571 (2000).

    CAS  Article  Google Scholar 

  26. 26

    Stefanics, G. et al. Phase entrainment of human delta oscillations can mediate the effects of expectation on reaction speed. J. Neurosci. 30, 13578–13585 (2010).

    CAS  Article  Google Scholar 

  27. 27

    Womelsdorf, T., Johnston, K., Vinck, M. & Everling, S. Theta-activity in anterior cingulate cortex predicts task rules and their adjustments following errors. Proc. Natl. Acad. Sci. USA 107, 5248–5253 (2010).

    CAS  Article  Google Scholar 

  28. 28

    Rosenberg, J.R., Amjad, A.M., Breeze, P., Brillinger, D.R. & Halliday, D.M. The Fourier approach to the identification of functional coupling between neuronal spike trains. Prog. Biophys. Mol. Biol. 53, 1–31 (1989).

    CAS  Article  Google Scholar 

  29. 29

    Narayanan, N.S., Horst, N.K. & Laubach, M. Reversible inactivations of rat medial prefrontal cortex impair the ability to wait for a stimulus. Neuroscience 139, 865–876 (2006).

    CAS  Article  Google Scholar 

  30. 30

    Allen, T.A. et al. Imaging the spread of reversible brain inactivations using fluorescent muscimol. J. Neurosci. Methods 171, 30–38 (2008).

    CAS  Article  Google Scholar 

  31. 31

    von Stein, A., Chiang, C. & König, P. Top-down processing mediated by interareal synchronization. Proc. Natl. Acad. Sci. USA 97, 14748–14753 (2000).

    CAS  Article  Google Scholar 

  32. 32

    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  Google Scholar 

  33. 33

    Cavanagh, J.F. et al. Subthalamic nucleus stimulation reverses mediofrontal influence over decision threshold. Nat. Neurosci. 14, 1462–1467 (2011).

    CAS  Article  Google Scholar 

  34. 34

    Allman, J.M., Hakeem, A., Erwin, J.M., Nimchinsky, E. & Hof, P. The anterior cingulate cortex. The evolution of an interface between emotion and cognition. Ann. NY Acad. Sci. 935, 107–117 (2001).

    CAS  Article  Google Scholar 

  35. 35

    Davidson, R.J., Pizzagalli, D., Nitschke, J.B. & Putnam, K. Depression: perspectives from affective neuroscience. Annu. Rev. Psychol. 53, 545–574 (2002).

    Article  Google Scholar 

  36. 36

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

    CAS  Article  Google Scholar 

  37. 37

    Vertes, R.P., Hoover, W.B., Do Valle, A.C., Sherman, A. & Rodriguez, J.J. Efferent projections of reuniens and rhomboid nuclei of the thalamus in the rat. J. Comp. Neurol. 499, 768–796 (2006).

    Article  Google Scholar 

  38. 38

    Dzirasa, K. et al. Noradrenergic control of cortico-striato-thalamic and mesolimbic cross-structural synchrony. J. Neurosci. 30, 6387–6397 (2010).

    CAS  Article  Google Scholar 

  39. 39

    Newman, E.L., Gupta, K., Climer, J.R., Monaghan, C.K. & Hasselmo, M.E. Cholinergic modulation of cognitive processing: insights drawn from computational models. Front. Behav. Neurosci. 6, 24 (2012).

    Article  Google Scholar 

  40. 40

    Lee, T.G. & D'Esposito, M. The dynamic nature of top-down signals originating from prefrontal cortex: a combined fMRI-TMS study. J. Neurosci. 32, 15458–15466 (2012).

    CAS  Article  Google Scholar 

  41. 41

    Cohen, M.X. & Cavanagh, J.F. Single-trial regression elucidates the role of prefrontal theta oscillations in response conflict. Front. Psychol. 2, 30 (2011).

    Article  Google Scholar 

  42. 42

    Delorme, A. & Makeig, S. EEGLAB: an open source toolbox for analysis of single-trial EEG dynamics including independent component analysis. J. Neurosci. Methods 134, 9–21 (2004).

    Article  Google Scholar 

  43. 43

    Narayanan, N.S. & Laubach, M. Delay activity in rodent frontal cortex during a simple reaction time task. J. Neurophysiol. 101, 2859–2871 (2009).

    Article  Google Scholar 

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We thank S. Masters for help with human data acquisition, N. Horst for technical editing and G.R. Yang for help with spike-field coherence code and simulations. This work was funded by National Institute of Neurological Disorders and Stroke grant K08 NS078100 to N.S.N., US National Institutes of Health (NIH) grant MH080066-01 and National Science Foundation (NSF) grant 1125788 to M.J.F., and NIH grant P01-AG030004-01A1 and NSF grant 1121147 to M.L.

Author information




N.S.N., J.F.C., M.J.F. and M.L. designed experiments and wrote the paper. N.S.N. and J.F.C. conducted experiments. N.S.N., J.F.C. and M.L. analyzed data.

Corresponding author

Correspondence to Mark Laubach.

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Competing interests

The authors declare no competing financial interests.

Integrated supplementary information

Supplementary Figure 1 Recording sites in the rat medial frontal cortex.

Supplementary Figure 2 Human phase coherence between mid-frontal (electrode Cz) and target sites.

a) As demonstrated in the main text, there was a significant increase in theta-band phase coherence between mid-frontal (Cz) and motor (C4) sites during the motor action to initiate the trial after errors vs. correct responses (mean phase locking value=.08). b) This enhancement in phase consistency with mid-frontal areas was not observed with an intermediate electrode (C2), and in fact the directionality was reversed (magenta area: mean phase locking value=-.02). This pattern demonstrates that the mid-frontal – motor coherence observed in (a) is not due to volume conduction, as the intermediary site would be enhanced in that case.

Supplementary Figure 3 Amplitudes of medial frontal low-frequency oscillations (LFO) were larger prior to post-error trials compared to post-correct trials.

The left plot shows the average amplitude of local field potentials (below 12 Hz) for the 3-sec period before the trials for data from 6 rats. The right plot shows the ratios of amplitude on post-error and post-correct trials. Amplitudes were significantly larger on post-error trials (paired t-test: t=-4.23, df=5, p<0.01). Ratios for local fields from all 6 rats were larger than 1. The mean ratio is depicted by the + symbol.

Supplementary Figure 4 Spectral analysis of motor cortical field potentials.

Whereas Figure 7 showed spike-field coupling, here we detail power. There was elevated power between 1 and 12 Hz on post-error trials in a) control sessions and b) with medial frontal cortex inactivated. Power was more similar between post-error and post-correct trials when medial frontal cortex was inactivated. c) Direct comparison of differential post-error vs. post-correct trials on control sessions vs sessions with medial frontal cortex inactivated. Differential power between post-correct and post-error trials diminished in medial frontal inactivation sessions. Black contours indicate significant differences via a t-test between control and medial frontal inactivation sessions (p<0.05).

Supplementary Figure 5 Direct comparison of differentials in spike-field coherence on post-error vs. post-correct trials in control sessions vs. sessions with medial frontal cortex inactivated.

Black contours indicate significant differences via a t-test between control and medial frontal inactivation sessions (p<0.05).

Supplementary Figure 6 Summary of findings.

a) After errors, low frequency oscillations in medial frontal cortex are characterized by: 1) increased power, 2) enhanced influence over slowing of responses, 3) robust spike-field coherence of neurons which contain information about the need for control, and 4) phase consistency with low frequency oscillations in motor cortex that influences adjustments in response latency. b) When medial frontal cortex is inactivated, adaptive post-error slowing of responses is specifically diminished, low frequency oscillations are no longer specific to post-error trials and spike-field coupling is changed. However, spikes in motor cortex continue to predict response latency. These findings reveal mechanisms how the medial frontal cortex influences and regulates motor cortex in service of adaptive control.

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Narayanan, N., Cavanagh, J., Frank, M. et al. Common medial frontal mechanisms of adaptive control in humans and rodents. Nat Neurosci 16, 1888–1895 (2013).

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