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First-in-human prediction of chronic pain state using intracranial neural biomarkers

Nature Neuroscience volume 26, pages 1090–1099 (2023)Cite this article

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Abstract

Chronic pain syndromes are often refractory to treatment and cause substantial suffering and disability. Pain severity is often measured through subjective report, while objective biomarkers that may guide diagnosis and treatment are lacking. Also, which brain activity underlies chronic pain on clinically relevant timescales, or how this relates to acute pain, remains unclear. Here four individuals with refractory neuropathic pain were implanted with chronic intracranial electrodes in the anterior cingulate cortex and orbitofrontal cortex (OFC). Participants reported pain metrics coincident with ambulatory, direct neural recordings obtained multiple times daily over months. We successfully predicted intraindividual chronic pain severity scores from neural activity with high sensitivity using machine learning methods. Chronic pain decoding relied on sustained power changes from the OFC, which tended to differ from transient patterns of activity associated with acute, evoked pain states during a task. Thus, intracranial OFC signals can be used to predict spontaneous, chronic pain state in patients.

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Fig. 1: Long-term ambulatory tracking of chronic pain metrics.
Fig. 2: Ambulatory neural recordings from ACC and OFC predict chronic pain state.
Fig. 3: Acute pain-state prediction with ACC and OFC neural recordings is unreliable.
Fig. 4: Decoding of chronic and acute pain states are differentially supported by ACC and OFC features across participants.
Fig. 5: Temporal dynamics of power features distinguish chronic from acute pain states.

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Data availability

The datasets generated during and/or analyzed during the current study will be available in the NIH Brain Initiative data sharing platform within 1 month of publication at https://dabi.loni.usc.edu/. Datasets will include raw neurophysiology data including metadata.

Code availability

MATLAB and Python analytical software code used to generate the main results and figures is available on the NIH Brain Initiative platform above and at GitHub: https://github.com/shirvalkarlab/ChronicPain2023_NatNeuro.git/.

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Acknowledgements

We thank H. Fields, K. Sellers and J. Motzkin for advice during study design and data analysis and for critical editing of the paper. This study was funded by the National Institutes of Health (NIH) Brain Initiative Grant UH3NS109556 (to E.F.C., P. Shirvalkar, P. Starr), NIH HEAL Initiative Grant UH3NS115631 (to P. Shirvalkar) and DARPA grant W911NF-14-2-0043 (to E.F.C.). The funders had no role in study design, data collection and analysis, decision to publish or preparation of the manuscript.

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Authors and Affiliations

  1. UCSF Department of Anesthesiology and Perioperative Care, Division of Pain Medicine, University of California San Francisco, San Francisco, CA, USA

    Prasad Shirvalkar

  2. UCSF Department of Neurology, University of California San Francisco, San Francisco, CA, USA

    Prasad Shirvalkar

  3. UCSF Department of Neurological Surgery, University of California San Francisco, San Francisco, CA, USA

    Prasad Shirvalkar, Jordan Prosky, Gregory Chin, Ashlyn Schmitgen, Heather Dawes, Philip A. Starr & Edward F. Chang

  4. UCSF Weill Institute for Neurosciences, University of California San Francisco, San Francisco, CA, USA

    Prasad Shirvalkar, Jordan Prosky, Ashlyn Schmitgen, Heather Dawes, Philip A. Starr & Edward F. Chang

  5. Departments of Electrical and Computer Engineering, University of Southern California, Los Angeles, CA, USA

    Parima Ahmadipour, Omid G. Sani & Maryam M. Shanechi

  6. Department of Speech, Language, and Hearing Sciences, The University of Texas at Austin, Austin, TX, USA

    Maansi Desai

  7. UCSF Department of Physiology, University of California San Francisco, San Francisco, CA, USA

    Philip A. Starr & Edward F. Chang

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Contributions

P. Shirvalkar, P. Starr and E.F.C. conceptualized and designed the study and acquired funding. P. Shirvalkar, G.C., M.D. and A.S. collected all data and maintained data integrity. P. Shirvalkar, J.P., G.C., P.A. and O.G.S. had full access to all the data and performed formal analysis. M.M.S. supervised the state space modeling of data. P. Shirvalkar drafted the paper. All authors participated substantially in the critical revision of the paper for intellectual content. H.D. performed project administration. Study supervision was conducted by P. Starr and E.F.C.

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Correspondence to Prasad Shirvalkar.

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Competing interests

Medtronic provided research devices for use in this study and technical support through a research agreement with UCSF (with E.F.C. and P. Shirvalkar) but no financial support. Medtronic had no role in study design, data collection and analysis, decision to publish or preparation of the manuscript. All authors declare no other competing interests.

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Nature Neuroscience thanks Ueli Rutishauser, Tor Wager, and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

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Supplementary Methods, Figs. 1–14, Tables 1–4 and References.

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Shirvalkar, P., Prosky, J., Chin, G. et al. First-in-human prediction of chronic pain state using intracranial neural biomarkers. Nat Neurosci 26, 1090–1099 (2023). https://doi.org/10.1038/s41593-023-01338-z

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