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High-frequency neuromodulation improves obsessive–compulsive behavior

Nature Medicine volume 27, pages 232–238 (2021)Cite this article

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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/).

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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.

Author information

Authors and Affiliations

  1. Department of Psychological and Brain Sciences, Boston University, Boston, MA, USA

    Shrey Grover, John A. Nguyen, Vighnesh Viswanathan & Robert M. G. Reinhart

  2. Center for Systems Neuroscience, Boston University, Boston, MA, USA

    Robert M. G. Reinhart

  3. Cognitive Neuroimaging Center, Boston University, Boston, MA, USA

    Robert M. G. Reinhart

  4. Center for Research in Sensory Communication and Emerging Neural Technology, Boston University, Boston, MA, USA

    Robert M. G. Reinhart

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  1. Shrey Grover
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  2. John A. Nguyen
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  4. Robert M. G. Reinhart
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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.

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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.

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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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