Predictive coding models for pain perception

Abstract

Pain is a complex, multidimensional experience that involves dynamic interactions between sensory-discriminative and affective-emotional processes. Pain experiences have a high degree of variability depending on their context and prior anticipation. Viewing pain perception as a perceptual inference problem, we propose a predictive coding paradigm to characterize evoked and non-evoked pain. We record the local field potentials (LFPs) from the primary somatosensory cortex (S1) and the anterior cingulate cortex (ACC) of freely behaving rats—two regions known to encode the sensory-discriminative and affective-emotional aspects of pain, respectively. We further use predictive coding to investigate the temporal coordination of oscillatory activity between the S1 and ACC. Specifically, we develop a phenomenological predictive coding model to describe the macroscopic dynamics of bottom-up and top-down activity. Supported by recent experimental data, we also develop a biophysical neural mass model to describe the mesoscopic neural dynamics in the S1 and ACC populations, in both naive and chronic pain-treated animals. Our proposed predictive coding models not only replicate important experimental findings, but also provide new prediction about the impact of the model parameters on the physiological or behavioral read-out—thereby yielding mechanistic insight into the uncertainty of expectation, placebo or nocebo effect, and chronic pain.

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Code availability

The custom MATLAB code for implementing two described computational models is distributed online (https://github.com/yuru-eats-celery/pain-coding-model and https://github.com/ymch815/predictive-coding-mean-field-model.git).

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Acknowledgements

This work was partially supported by the National Science Foundation (NSF)-CBET grant 1835000 (ZSC, JW), National Institutes of Health (NIH) R01-NS100065 (ZSC, JW), R01-MH118928 (ZSC), and a fellowship of the NIH Training Program in Computational Neuroscience (HK) supported by NIH T90/R90 DA043219 and DA043849. Preliminary version of this work was presented in Proceedings of IEEE EMBC’19, Berlin, July 23-28, 2019 Song et al. (2019).

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Conceived and designed the experiments: ZSC, JW. Supervised the project: ZSC. Performed the experiments and collected the data: QZ, ZX, AS. Analyzed the data: YS, MY, HK, ZX. Contributed the software: YS, MY, AB. Wrote the paper: ZSC.

Corresponding author

Correspondence to Zhe S. Chen.

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No conflict of interest, financial or otherwise, are declared by the authors.

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Action Editor: Nicolas Brunel.

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Song, Y., Yao, M., Kemprecos, H. et al. Predictive coding models for pain perception. J Comput Neurosci (2021). https://doi.org/10.1007/s10827-021-00780-x

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Keywords

  • Predictive coding
  • Pain perception
  • Somatosensory cortex
  • Anterior cingulate cortex
  • Mean field model
  • Placebo
  • Chronic pain