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A prototype closed-loop brain–machine interface for the study and treatment of pain

Nature Biomedical Engineering volume 7, pages 533–545 (2023)Cite this article

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Abstract

Chronic pain is characterized by discrete pain episodes of unpredictable frequency and duration. This hinders the study of pain mechanisms and contributes to the use of pharmacological treatments associated with side effects, addiction and drug tolerance. Here, we show that a closed-loop brain–machine interface (BMI) can modulate sensory-affective experiences in real time in freely behaving rats by coupling neural codes for nociception directly with therapeutic cortical stimulation. The BMI decodes the onset of nociception via a state-space model on the basis of the analysis of online-sorted spikes recorded from the anterior cingulate cortex (which is critical for pain processing) and couples real-time pain detection with optogenetic activation of the prelimbic prefrontal cortex (which exerts top–down nociceptive regulation). In rats, the BMI effectively inhibited sensory and affective behaviours caused by acute mechanical or thermal pain, and by chronic inflammatory or neuropathic pain. The approach provides a blueprint for demand-based neuromodulation to treat sensory-affective disorders, and could be further leveraged for nociceptive control and to study pain mechanisms.

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Fig. 1: Design of a closed-loop BMI to detect and treat pain.
Fig. 2: Closed-loop BMI control of acute thermal pain.
Fig. 3: Closed-loop BMI control of acute mechanical pain.
Fig. 4: Closed-loop BMI control of evoked pain in a model of chronic inflammatory pain.
Fig. 5: Closed-loop BMI control of evoked pain in a model of chronic neuropathic pain.
Fig. 6: Closed-loop BMI control of spontaneous pain in the chronic inflammatory pain model.
Fig. 7: Closed-loop BMI control of spontaneous pain in the chronic neuropathic pain model.

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Data availability

The main data supporting the results in this study are available within the paper and its Supplementary Information. The raw datasets generated during the study are too large to be publicly shared, yet they are available for research purposes from the corresponding authors on reasonable request.

Code availability

The custom BMI client software used in this study is available at https://github.com/wangresearch1/onlinePainDecodingGUI.

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Acknowledgements

This work was supported by NIH grants R01-GM115384 (J.W.), R01-NS100065 (Z.S.C. and J.W.) and R01-MH118928 (Z.S.C.) and NSF grant CBET-1835000 (Z.S.C. and J.W.).

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Authors and Affiliations

  1. Department of Anesthesiology, Perioperative Care and Pain, New York University School of Medicine, New York, NY, USA

    Qiaosheng Zhang, Robert Talay, Yaling Liu, Anna Li, Amrita Singh & Jing Wang

  2. Department of Psychiatry, New York University School of Medicine, New York, NY, USA

    Sile Hu, Zhengdong Xiao, David Rosenberg, Guanghao Sun, Bassir Caravan & Zhe S. Chen

  3. College of Arts and Sciences, New York University, New York, NY, USA

    Jonathan D. Gould

  4. Department of Neuroscience & Physiology, New York University School of Medicine, New York, NY, USA

    Zhe S. Chen & Jing Wang

  5. Neuroscience Institute, New York University School of Medicine, New York, NY, USA

    Zhe S. Chen & Jing Wang

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Contributions

J.W. and Z.S.C. conceived and designed the study. Q.Z. designed the 3D drive, performed the surgeries and implemented the BMI hardware system. Q.Z., S.H., R.T., A.S., B.C., Z.X., D.R., Y.L., G.S., A.L. and J.D.G. collected the data. Q.Z., A.S., Z.X., J.D.G. and R.T. analysed the data. S.H., Z.X., Q.Z. and Z.S.C. contributed to BMI software development. J.W. and Z.S.C. supervised the project. J.W. and Z.S.C. wrote the manuscript, with input from the other authors.

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Correspondence to Zhe S. Chen or Jing Wang.

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Zhang, Q., Hu, S., Talay, R. et al. A prototype closed-loop brain–machine interface for the study and treatment of pain. Nat. Biomed. Eng 7, 533–545 (2023). https://doi.org/10.1038/s41551-021-00736-7

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