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.
This is a preview of subscription content, access via your institution
Subscribe to Nature+
Get immediate online access to the entire Nature family of 50+ journals
Subscribe to Journal
Get full journal access for 1 year
only $8.25 per issue
All prices are NET prices.
VAT will be added later in the checkout.
Tax calculation will be finalised during checkout.
Get time limited or full article access on ReadCube.
All prices are NET prices.
The software code used in this study is freely and permanently available on Open Science Framework (https://osf.io/ctph2/).
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).
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).
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).
Robbins, T. W., Vaghi, M. M. & Banca, P. Obsessive-compulsive disorder: puzzles and prospects. Neuron 102, 27–47 (2019).
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).
Cohen, M. X., Wilmes, K. & van de Vijver, I. Cortical electrophysiological network dynamics of feedback learning. Trends Cogn. Sci. 15, 558–566 (2011).
Gillan, C. M., Fineberg, N. A. & Robbins, T. W. A trans-diagnostic perspective on obsessive-compulsive disorder. Psychol. Med. 47, 1528–1548 (2017).
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).
Hollander, E., Kaplan, A., Allen, A. & Cartwright, C. Pharmacotherapy for obsessive-compulsive disorder. Psychiatr. Clin. North Am. 23, 643–656 (2000).
Reinhart, R. M. G. & Nguyen, J. A. Working memory revived in older adults by synchronizing rhythmic brain circuits. Nat. Neurosci. 22, 820–827 (2019).
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).
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).
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).
Kubota, Y. et al. Corticostriatal-limbic correlates of sub-clinical obsessive-compulsive traits. Psychiatry Res. Neuroimaging 285, 40–46 (2019).
Marco-Pallares, J. et al. Human oscillatory activity associated to reward processing in a gambling task. Neuropsychologia 46, 241–248 (2008).
Cohen, M. X., Elger, C. E. & Ranganath, C. Reward expectation modulates feedback-related negativity and EEG spectra. Neuroimage 35, 968–978 (2007).
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).
Graybiel, A. M. Habits, rituals, and the evaluative brain. Annu. Rev. Neurosci. 31, 359–387 (2008).
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).
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).
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).
Sutton, R. S. & Barto, A. G. Reinforcement Learning: An Introduction (MIT Press, 1998).
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).
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).
Foa, E. B. et al. The obsessive-compulsive inventory: development and validation of a short version. Psychol. Assess. 14, 485–496 (2002).
Andersson, E. et al. Internet-based cognitive behaviour therapy for obsessive-compulsive disorder: a randomized controlled trial. Psychol. Med. 42, 2193–2203 (2012).
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).
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).
Grossman, N. et al. Noninvasive deep brain stimulation via temporally interfering electric fields. Cell 169, 1029–1041.e16 (2017).
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).
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).
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).
Schreiner, D. C. & Gremel, C. M. Orbital frontal cortex projections to secondary motor cortex mediate exploitation of learned rules. Sci. Rep. 8, 10979 (2018).
Morris, L. S. et al. Biases in the explore-exploit tradeoff in addictions: the role of avoidance of uncertainty. Neuropsychopharmacology 41, 940–948 (2016).
Thompson, S. L. et al. Btbd3 expression regulates compulsive-like and exploratory behaviors in mice. Transl. Psychiatry 9, 222 (2019).
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).
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).
Axmacher, N. et al. Intracranial EEG correlates of expectancy and memory formation in the human hippocampus and nucleus accumbens. Neuron 65, 541–549 (2010).
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).
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).
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).
Frane, J. W. A method of biased coin randomization, its implementation, and its validation. Ther. Innov. Regul. Sci. 32, 423–432 (1998).
Karabanov, A. N., Saturnino, G. B., Thielscher, A. & Siebner, H. R. Can transcranial electrical stimulation localize brain function? Front. Psychol. 10, 213 (2019).
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).
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).
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).
Brainard, D. H. The psychophysics toolbox. Spat. Vis. 10, 433–436 (1997).
Huppert, J. D. et al. The OCI-R: validation of the subscales in a clinical sample. J. Anxiety Disord. 21, 394–406 (2007).
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).
Reinhart, R. M. G. Disruption and rescue of interareal theta phase coupling and adaptive behavior. Proc. Natl Acad. Sci. USA. 114, 11542–11547 (2017).
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).
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).
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).
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).
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).
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).
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).
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).
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).
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).
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.
The authors declare no competing interests.
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.
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.
About this article
Cite this article
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
This article is cited by
Nature Reviews Neuroscience (2022)
Nature Reviews Neuroscience (2022)
Optogenetic inhibition of indirect pathway neurons in the dorsomedial striatum reduces excessive grooming in Sapap3-knockout mice
Adolescent social isolation induces distinct changes in the medial and lateral OFC-BLA synapse and social and emotional alterations in adult mice
Long-lasting, dissociable improvements in working memory and long-term memory in older adults with repetitive neuromodulation
Nature Neuroscience (2022)