Tracking ongoing spontaneous pain in individuals with chronic pain is challenging. Using intensive longitudinal functional magnetic resonance imaging (fMRI) combined with continuous spontaneous pain ratings in two individuals with fibromyalgia, we trained personalized brain decoding models that could track moment-to-moment spontaneous pain fluctuations. Underscoring the need for precision approaches, neither decoding model generalized from one individual to the other.
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References
Mun, C. J. et al. Investigating intraindividual pain variability: methods, applications, issues, and directions. Pain 160, 2415–2429 (2019). A review that presents conceptual and methodological frameworks for analyzing within-individual pain variability using intensive longitudinal designs.
Davis, K. D. et al. Discovery and validation of biomarkers to aid the development of safe and effective pain therapeutics: challenges and opportunities. Nat. Rev. Neurol. 16, 381–400 (2020). A consensus statement that provides guidance for the discovery and validation of pain biomarkers.
Tracey, I. Neuroimaging enters the pain biomarker arena. Sci. Transl. Med. 13, eabj7358 (2021). A perspective that presents a critical overview of the promise and challenges of brain imaging-based pain biomarkers.
Wager, T. D. et al. An fMRI-based neurologic signature of physical pain. N. Engl. J. Med. 368, 1388–1397 (2013). A paper that reports an fMRI-based biomarker that predicts experimentally evoked physical pain across multiple independent test datasets.
Cheng, J. C. et al. Multivariate machine learning distinguishes cross-network dynamic functional connectivity patterns in state and trait neuropathic pain. Pain 159, 1764–1776 (2018). A paper that reports an fMRI-based biomarker that predicts state (current) and trait (average) pain in patients with chronic pain.
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This is a summary of: Lee, J.-J. et al. Personalized brain decoding of spontaneous pain in individuals with chronic pain. Nat. Neurosci. https://doi.org/10.1038/s41593-026-02221-3 (2026).
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Intensive longitudinal brain imaging reveals personalized signatures of chronic pain. Nat Neurosci (2026). https://doi.org/10.1038/s41593-026-02238-8
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DOI: https://doi.org/10.1038/s41593-026-02238-8
