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High-frequency neuromodulation improves obsessive–compulsive behavior

Abstract

Nearly one billion people worldwide suffer from obsessive–compulsive behaviors1,2, yet our mechanistic understanding of these behaviors is incomplete, and effective therapeutics are unavailable. An emerging perspective characterizes obsessive–compulsive behaviors as maladaptive habit learning3,4, which may be associated with abnormal beta–gamma neurophysiology of the orbitofrontal–striatal circuitry during reward processing5,6. We target the orbitofrontal cortex with alternating current, personalized to the intrinsic beta–gamma frequency of the reward network, and show rapid, reversible, frequency-specific modulation of reward- but not punishment-guided choice behavior and learning, driven by increased exploration in the setting of an actor-critic architecture. Next, we demonstrate that chronic application of the procedure over 5 days robustly attenuates obsessive–compulsive behavior in a non-clinical population for 3 months, with the largest benefits for individuals with more severe symptoms. Finally, we show that convergent mechanisms underlie modulation of reward learning and reduction of obsessive–compulsive symptoms. The results contribute to neurophysiological theories of reward, learning and obsessive–compulsive behavior, suggest a unifying functional role of rhythms in the beta–gamma range, and set the groundwork for the development of personalized circuit-based therapeutics for related disorders.

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Fig. 1: Model integrating beta–gamma activity with reward learning circuitry.
Fig. 2: Monetary reinforcement learning task and orbitofrontal neuromodulation protocol.
Fig. 3: Results of experiment 1, the monetary reinforcement learning task.
Fig. 4: Results of experiment 2, change in obsessive–compulsive symptoms after HD-tACS.

Data availability

The data used for analysis in this study are freely and permanently available on Open Science Framework (https://osf.io/ctph2/). Source data are provided with this paper.

Code availability

The software code used in this study is freely and permanently available on Open Science Framework (https://osf.io/ctph2/).

References

  1. Fullana, M. A. et al. Obsessions and compulsions in the community: prevalence, interference, help-seeking, developmental stability, and co-occurring psychiatric conditions. Am. J. Psychiatry 166, 329–336 (2009).

    PubMed  Google Scholar 

  2. Ruscio, A. M., Stein, D. J., Chiu, W. T. & Kessler, R. C. The epidemiology of obsessive-compulsive disorder in the National Comorbidity Survey Replication. Mol. Psychiatry 15, 53–63 (2010).

    CAS  PubMed  Google Scholar 

  3. Burguière, E., Monteiro, P., Mallet, L., Feng, G. & Graybiel, A. M. Striatal circuits, habits, and implications for obsessive-compulsive disorder. Curr. Opin. Neurobiol. 30, 59–65 (2015).

    PubMed  Google Scholar 

  4. Robbins, T. W., Vaghi, M. M. & Banca, P. Obsessive-compulsive disorder: puzzles and prospects. Neuron 102, 27–47 (2019).

    CAS  PubMed  Google Scholar 

  5. Marco-Pallarés, J., Münte, T. F. & Rodríguez-Fornells, A. The role of high-frequency oscillatory activity in reward processing and learning. Neurosci. Biobehav. Rev. 49, 1–7 (2015).

    PubMed  Google Scholar 

  6. Cohen, M. X., Wilmes, K. & van de Vijver, I. Cortical electrophysiological network dynamics of feedback learning. Trends Cogn. Sci. 15, 558–566 (2011).

    PubMed  Google Scholar 

  7. Gillan, C. M., Fineberg, N. A. & Robbins, T. W. A trans-diagnostic perspective on obsessive-compulsive disorder. Psychol. Med. 47, 1528–1548 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  8. Bloch, M. H., McGuire, J., Landeros-Weisenberger, A., Leckman, J. F. & Pittenger, C. Meta-analysis of the dose-response relationship of SSRI in obsessive-compulsive disorder. Mol. Psychiatry 15, 850–855 (2010).

    CAS  PubMed  Google Scholar 

  9. Hollander, E., Kaplan, A., Allen, A. & Cartwright, C. Pharmacotherapy for obsessive-compulsive disorder. Psychiatr. Clin. North Am. 23, 643–656 (2000).

    CAS  PubMed  Google Scholar 

  10. Reinhart, R. M. G. & Nguyen, J. A. Working memory revived in older adults by synchronizing rhythmic brain circuits. Nat. Neurosci. 22, 820–827 (2019).

    CAS  PubMed  PubMed Central  Google Scholar 

  11. Cocchi, L. et al. Transcranial magnetic stimulation in obsessive-compulsive disorder: a focus on network mechanisms and state dependence. Neuroimage Clin. 19, 661–674 (2018).

    PubMed  PubMed Central  Google Scholar 

  12. Voon, V. et al. Motivation and value influences in the relative balance of goal-directed and habitual behaviours in obsessive-compulsive disorder. Transl. Psychiatry 5, e670 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  13. Remijnse, P. L. et al. Reduced orbitofrontal-striatal activity on a reversal learning task in obsessive-compulsive disorder. Arch. Gen. Psychiatry 63, 1225–1236 (2006).

    PubMed  Google Scholar 

  14. Kubota, Y. et al. Corticostriatal-limbic correlates of sub-clinical obsessive-compulsive traits. Psychiatry Res. Neuroimaging 285, 40–46 (2019).

    PubMed  Google Scholar 

  15. Marco-Pallares, J. et al. Human oscillatory activity associated to reward processing in a gambling task. Neuropsychologia 46, 241–248 (2008).

    PubMed  Google Scholar 

  16. Cohen, M. X., Elger, C. E. & Ranganath, C. Reward expectation modulates feedback-related negativity and EEG spectra. Neuroimage 35, 968–978 (2007).

    PubMed  Google Scholar 

  17. Mas-Herrero, E., Ripollés, P., HajiHosseini, A., Rodríguez-Fornells, A. & Marco-Pallarés, J. Beta oscillations and reward processing: coupling oscillatory activity and hemodynamic responses. Neuroimage 119, 13–19 (2015).

    PubMed  Google Scholar 

  18. Graybiel, A. M. Habits, rituals, and the evaluative brain. Annu. Rev. Neurosci. 31, 359–387 (2008).

    CAS  PubMed  Google Scholar 

  19. Marco-Pallarés, J. et al. Genetic variability in the dopamine system (dopamine receptor D4, catechol-O-methyltransferase) modulates neurophysiological responses to gains and losses. Biol. Psychiatry 66, 154–161 (2009).

    PubMed  Google Scholar 

  20. Clarke, H. F. et al. Orbitofrontal dopamine depletion upregulates caudate dopamine and alters behavior via changes in reinforcement sensitivity. J. Neurosci. 34, 7663–7676 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  21. Pessiglione, M., Seymour, B., Flandin, G., Dolan, R. J. & Frith, C. D. Dopamine-dependent prediction errors underpin reward-seeking behaviour in humans. Nature 442, 1042–1045 (2006).

    CAS  PubMed  PubMed Central  Google Scholar 

  22. Sutton, R. S. & Barto, A. G. Reinforcement Learning: An Introduction (MIT Press, 1998).

  23. Gold, J. M. et al. Negative symptoms in schizophrenia result from a failure to represent the expected value of rewards: behavioral and computational modeling evidence. Arch. Gen. Psychiatry 69, 129–138 (2012).

    PubMed  PubMed Central  Google Scholar 

  24. Hernaus, D., Gold, J. M., Waltz, J. A. & Frank, M. J. Impaired expected value computations coupled with overreliance on stimulus-response learning in schizophrenia. Biol. Psychiatry Cogn. Neurosci. Neuroimaging 3, 916–926 (2018).

    PubMed  PubMed Central  Google Scholar 

  25. Foa, E. B. et al. The obsessive-compulsive inventory: development and validation of a short version. Psychol. Assess. 14, 485–496 (2002).

    PubMed  Google Scholar 

  26. Andersson, E. et al. Internet-based cognitive behaviour therapy for obsessive-compulsive disorder: a randomized controlled trial. Psychol. Med. 42, 2193–2203 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  27. Simpson, H. B. et al. A randomized, controlled trial of cognitive-behavioral therapy for augmenting pharmacotherapy in obsessive-compulsive disorder. Am. J. Psychiatry 165, 621–630 (2008).

    PubMed  PubMed Central  Google Scholar 

  28. Hajcak, G., Huppert, J. D., Simons, R. F. & Foa, E. B. Psychometric properties of the OCI-R in a college sample. Behav. Res. Ther. 42, 115–123 (2004).

    PubMed  Google Scholar 

  29. Grossman, N. et al. Noninvasive deep brain stimulation via temporally interfering electric fields. Cell 169, 1029–1041.e16 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  30. McDannald, M. A., Lucantonio, F., Burke, K. A., Niv, Y. & Schoenbaum, G. Ventral striatum and orbitofrontal cortex are both required for model-based, but not model-free, reinforcement learning. J. Neurosci. 31, 2700–2705 (2011).

    CAS  PubMed  PubMed Central  Google Scholar 

  31. McClure, S. M., Gilzenrat, M. S. & Cohen, J. D. in Advances in Neural Information Processing Systems 18 (eds Weiss, Y., Schölkopf, B. & Platt, J. C.) 867–874 (MIT Press, 2006).

  32. Cohen, J. D., McClure, S. M. & Yu, A. J. Should I stay or should I go? How the human brain manages the trade-off between exploitation and exploration. Philos. Trans. R. Soc. Lond. B Biol. Sci. 362, 933–942 (2007).

    PubMed  PubMed Central  Google Scholar 

  33. Schreiner, D. C. & Gremel, C. M. Orbital frontal cortex projections to secondary motor cortex mediate exploitation of learned rules. Sci. Rep. 8, 10979 (2018).

    PubMed  PubMed Central  Google Scholar 

  34. Morris, L. S. et al. Biases in the explore-exploit tradeoff in addictions: the role of avoidance of uncertainty. Neuropsychopharmacology 41, 940–948 (2016).

    PubMed  Google Scholar 

  35. Thompson, S. L. et al. Btbd3 expression regulates compulsive-like and exploratory behaviors in mice. Transl. Psychiatry 9, 222 (2019).

    PubMed  PubMed Central  Google Scholar 

  36. Howe, M. W., Atallah, H. E., McCool, A., Gibson, D. J. & Graybiel, A. M. Habit learning is associated with major shifts in frequencies of oscillatory activity and synchronized spike firing in striatum. Proc. Natl Acad. Sci. U. S. A. 108, 16801–16806 (2011).

    CAS  PubMed  PubMed Central  Google Scholar 

  37. Vossen, A., Gross, J. & Thut, G. Alpha power increase after transcranial alternating current stimulation at alpha frequency (α-tACS) reflects plastic changes rather than entrainment. Brain Stimul. 8, 499–508 (2015).

    PubMed  PubMed Central  Google Scholar 

  38. Axmacher, N. et al. Intracranial EEG correlates of expectancy and memory formation in the human hippocampus and nucleus accumbens. Neuron 65, 541–549 (2010).

    CAS  PubMed  Google Scholar 

  39. Camara, E., Rodriguez-Fornells, A., Ye, Z. & Münte, T. F. Reward networks in the brain as captured by connectivity measures. Front. Neurosci. 3, 350–362 (2009).

    PubMed  PubMed Central  Google Scholar 

  40. Insel, T. et al. Research domain criteria (RDoC): toward a new classification framework for research on mental disorders. Am. J. Psychiatry 167, 748–751 (2010).

    PubMed  Google Scholar 

  41. Toffolo, M. B. J., van den Hout, M. A., Engelhard, I. M., Hooge, I. T. C. & Cath, D. C. Uncertainty, checking, and intolerance of uncertainty in subclinical obsessive compulsive disorder: an extended replication. J. Obsessive Compuls. Relat. Disord. 3, 338–344 (2014).

    Google Scholar 

  42. Frane, J. W. A method of biased coin randomization, its implementation, and its validation. Ther. Innov. Regul. Sci. 32, 423–432 (1998).

    Google Scholar 

  43. Karabanov, A. N., Saturnino, G. B., Thielscher, A. & Siebner, H. R. Can transcranial electrical stimulation localize brain function? Front. Psychol. 10, 213 (2019).

    PubMed  PubMed Central  Google Scholar 

  44. Dmochowski, J. P., Datta, A., Bikson, M., Su, Y. & Parra, L. C. Optimized multi-electrode stimulation increases focality and intensity at target. J. Neural Eng. 8, 046011 (2011).

    PubMed  Google Scholar 

  45. Edwards, D. et al. Physiological and modeling evidence for focal transcranial electrical brain stimulation in humans: a basis for high-definition tDCS. Neuroimage 74, 266–275 (2013).

    PubMed  Google Scholar 

  46. Villamar, M. F. et al. Focal modulation of the primary motor cortex in fibromyalgia using 4×1-ring high-definition transcranial direct current stimulation (HD-tDCS): immediate and delayed analgesic effects of cathodal and anodal stimulation. J. Pain 14, 371–383 (2013).

    PubMed  Google Scholar 

  47. Brainard, D. H. The psychophysics toolbox. Spat. Vis. 10, 433–436 (1997).

    CAS  PubMed  Google Scholar 

  48. Huppert, J. D. et al. The OCI-R: validation of the subscales in a clinical sample. J. Anxiety Disord. 21, 394–406 (2007).

    PubMed  Google Scholar 

  49. Chasson, G. S., Tang, S., Gray, B., Sun, H. & Wang, J. Further validation of a Chinese version of the obsessive-compulsive inventory-revised. Behav. Cogn. Psychother. 41, 249–254 (2013).

    PubMed  Google Scholar 

  50. Reinhart, R. M. G. Disruption and rescue of interareal theta phase coupling and adaptive behavior. Proc. Natl Acad. Sci. USA. 114, 11542–11547 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  51. Reinhart, R. M. G., Cosman, J. D., Fukuda, K. & Woodman, G. F. Using transcranial direct-current stimulation (tDCS) to understand cognitive processing. Atten. Percept. Psychophys. 79, 3–23 (2017).

    PubMed  PubMed Central  Google Scholar 

  52. Reinhart, R. M. G., Xiao, W., McClenahan, L. & Woodman, G. F. Electrical stimulation of visual cortex can immediately improve spatial vision. Curr. Biol. 25, 1867–1872 (2016).

    Google Scholar 

  53. Dayan, E., Censor, N., Buch, E. R., Sandrini, M. & Cohen, L. G. Noninvasive brain stimulation: from physiology to network dynamics and back. Nat. Neurosci. 16, 838–844 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  54. Poreisz, C., Boros, K., Antal, A. & Paulus, W. Safety aspects of transcranial direct current stimulation concerning healthy subjects and patients. Brain Res. Bull. 72, 208–214 (2007).

    PubMed  Google Scholar 

  55. Gandiga, P. C., Hummel, F. C. & Cohen, L. G. Transcranial DC stimulation (tDCS): a tool for double-blind sham-controlled clinical studies in brain stimulation. Clin. Neurophysiol. 117, 845–850 (2006).

    PubMed  Google Scholar 

  56. Ali, M. M., Sellers, K. K. & Fröhlich, F. Transcranial alternating current stimulation modulates large-scale cortical network activity by network resonance. J. Neurosci. 33, 11262–11275 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  57. 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).

    PubMed  Google Scholar 

  58. Kayser, J. & Tenke, C. E. Principal components analysis of Laplacian waveforms as a generic method for identifying ERP generator patterns: I. Evaluation with auditory oddball tasks. Clin. Neurophysiol. 117, 348–368 (2006).

    PubMed  Google Scholar 

  59. Srinivasan, R., Winter, W. R., Ding, J. & Nunez, P. L. EEG and MEG coherence: measures of functional connectivity at distinct spatial scales of neocortical dynamics. J. Neurosci. Methods 166, 41–52 (2007).

    PubMed  PubMed Central  Google Scholar 

  60. Oostenveld, R., Fries, P., Maris, E. & Schoffelen, J. M. FieldTrip: open source software for advanced analysis of MEG, EEG, and invasive electrophysiological data. Comput. Intell. Neurosci. 2011, 1–9 (2011).

    Google Scholar 

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Acknowledgements

This work was supported by a grant from the National Institutes of Health (R01-MH114877) and a generous gift from an individual philanthropist awarded to R.M.G.R.

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Contributions

R.M.G.R. and S.G. conceived the experiments and analyses. R.M.G.R., J.A.N. and V.V. performed the experiments. R.M.G.R., S.G. and J.A.N. analyzed and interpreted the data, and R.M.G.R. and S.G. wrote the paper.

Corresponding author

Correspondence to Robert M. G. Reinhart.

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The authors declare no competing interests.

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Peer review information Nature Medicine thanks the anonymous reviewers for their contribution to the peer review of this work. Kate Gao and Jerome Staal were the primary editors on this article and managed its editorial process and peer review in collaboration with the rest of the editorial team.

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

Extended data

Extended Data Fig. 1 Neuromodulation effects on temperature.

Mean temperature shown across pre-modulation, modulation, and post-modulation periods for each group (passive sham, green; active control/alpha, blue; personalized beta–gamma, red). A significant time × group interaction was observed (F4,114 = 3.066, P = 0.027, \(\eta _p^2\) = 0.097, n = 60). There were significant time × group interactions for the beta–gamma and alpha groups (F2,76 = 4.942, P = 0.012, \(\eta _p^2\) = 0.115, n = 40) and the beta–gamma and sham groups (F2,76 = 4.119, P = 0.027, \(\eta _p^2\) = 0.098, n = 40), but not for the alpha and sham groups (F2,76 = 0.282, P = 0.686, \(\eta _p^2\) = 0.007, n = 40). A significant enhancement in temperature was evident only during beta–gamma HD-tACS relative to sham (F1,38 = 6.409, P = 0.016, \(\eta _p^2\) = 0.144, n = 40) or to alpha (F1,38 = 6.311, P = 0.016, \(\eta _p^2\) = 0.142, n = 40). The effect was rapidly extinguished upon switching off HD-tACS, given that no differences were observed between the alpha and beta–gamma (F1,38 = 0.056, P = 0.814, \(\eta _p^2\) = 0.001, n = 40) or the sham and beta–gamma groups (F1,38 = 0.762, P = 0.388, \(\eta _p^2\) = 0.02, n = 40) in the post-modulation period. Of note, baseline temperature values were relatively stable and did not significantly differ between groups during the pre-modulation period (alpha versus beta–gamma, F1,38 = 1.528, P = 0.224, \(\eta _p^2\) = 0.039, n = 40; alpha versus sham, F1,38 = 0.072, P = 0.790, \(\eta _p^2\) = 0.002, n = 40; sham versus beta–gamma, F1,38 = 1.421, P = 0.241, \(\eta _p^2\) = 0.036, n = 40). No other parameters showed significant effects. Mixed ANOVAs used the within-participants factor of time (pre-modulation, modulation, post-modulation) and the between-participants factor of group (sham, alpha, beta–gamma). Follow-up univariate ANOVAs within individual modulation periods used the between-participants factor of group (alpha, beta–gamma; alpha, sham; or beta–gamma, sham). Error bars show ±1 s.e.m. *P < 0.05. NS, not significant.

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Grover, S., Nguyen, J.A., Viswanathan, V. et al. High-frequency neuromodulation improves obsessive–compulsive behavior. Nat Med 27, 232–238 (2021). https://doi.org/10.1038/s41591-020-01173-w

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