Background & Aims

Chronic pain (CP) is a perceptual disorder with impaired sensory processing (Gunendi et al., 2019; Huang et al., 2020; McNaughton et al., 2022). Other chronic perceptual disorders such as hallucination or tinnitus are hypothesised to be a maladaptive compensation of the brain to aberrant predictive processing (Mohan & Vanneste, 2017). The local-global oddball paradigm is specifically designed to test how errors in predictions (PE) hierarchically update predictions (Wacongne et al., 2011). This study aims to provide empirical evidence that CP is a maladaptive compensation of the brain to minimise PEs.

Methods

Sixteen CP patients and HC were recruited for the study. A passive local-global oddball paradigm was applied to prime the hierarchical predictive coding system. EEG was collected and analysed to extract event-related potentials for the hierarchical PEs (mismatch negativity (MMN) and P300) and their corresponding time-frequency representation. In addition, a partial correlation analysis was performed to investigate the potential relationship between PE processing and subjective pain perception.

Results

A significantly decreased frontotemporal MNN and P300 were observed for the CP group compared to the HC. We also observed significantly increased theta-frequency phase locking during the MMN for the CP group compared to HC. Importantly, the area under the curve of the MMN wave (signifying the morphology of the wave) and its increased theta phase-locking were significantly correlated with increased pain perception.

Conclusions

This study is the first to show that CP affects hierarchical predictive processing. The significant reduction in MMN provides empirical evidence of the brain’s role in reducing PEs through CP. Furthermore, as MMN is a pre-attentive signal, CP seems to influence involuntary, unconscious states in response to stimuli from non-somatosensory domains such as the one used in this study. Finally, the correlation of the morphology of MMN and the amount of theta phase-locking with pain perception shows that neural resources become more aligned to pain processing; hence presenting us with a potential biomarker for domain-general dysfunction.

References

Includes the citations of the abstract and further relevant references.
______________________________________________________________________
Dick, B. D., Connolly, J. F., McGrath, P. J., Finley, G. A., Stroink, G., Houlihan, M. E., & Clark, A. J. (2003). The disruptive effect of chronic pain on mismatch negativity. Clinical Neurophysiology, 114(8), 1497–1506. https://doi.org/10.1016/S1388-2457(03)00133-0
Friston, K., & Kiebel, S. (2009). Predictive coding under the free-energy principle. Philosophical Transactions of the Royal Society B: Biological Sciences, 364(1521), 1211–1221. https://doi.org/10.1098/rstb.2008.0300
Gunendi, Z., Polat, M., Vuralli, D., & Cengiz, B. (2019). Somatosensory temporal discrimination is impaired in fibromyalgia. Journal of Clinical Neuroscience, 60, 44–48. https://doi.org/10.1016/j.jocn.2018.10.067
Huang, J., Zhang, Z., & Zamponi, G. W. (2020). Pain: Integration of Sensory and Affective Aspects of Pain. Current Biology, 30(9), R393–R395. https://doi.org/10.1016/j.cub.2020.02.056
Javitt, D. C., Lee, M., Kantrowitz, J. T., & Martinez, A. (2018). Mismatch negativity as a biomarker of theta band oscillatory dysfunction in schizophrenia. Schizophrenia Research, 191, 51–60. https://doi.org/10.1016/j.schres.2017.06.023
McNaughton, D., Beath, A., Hush, J., & Jones, M. (2022). Perceptual sensory attenuation in chronic pain subjects and healthy controls. Scientific Reports, 12(1), 8958. https://doi.org/10.1038/s41598-022-13175-4
Mohan, A., & Vanneste, S. (2017). Adaptive and maladaptive neural compensatory consequences of sensory deprivation—From a phantom percept perspective. Progress in Neurobiology, 153, 1–17. https://doi.org/10.1016/j.pneurobio.2017.03.010
Näätänen, R., Paavilainen, P., Rinne, T., & Alho, K. (2007). The mismatch negativity (MMN) in basic research of central auditory processing: A review. Clinical Neurophysiology, 118(12), 2544–2590. https://doi.org/10.1016/j.clinph.2007.04.026
Shipp, S., Adams, R. A., & Friston, K. J. (2013). Reflections on agranular architecture: Predictive coding in the motor cortex. Trends in Neurosciences, 36(12), 706–716. https://doi.org/10.1016/j.tins.2013.09.004
Wacongne, C., Labyt, E., Van Wassenhove, V., Bekinschtein, T., Naccache, L., & Dehaene, S. (2011). Evidence for a hierarchy of predictions and prediction errors in human cortex. Proceedings of the National Academy of Sciences, 108(51), 20754–20759. https://doi.org/10.1073/pnas.1117807108

Presenting Author

Jorge Castejon España

Poster Authors

Jorge Castejon

B.Sc; M.Sc

Trinity College Dublin

Lead Author

Feifan Chen

Trinity College Dublin

Lead Author

Anusha Mohan

Global Brain Health Institute-Trinity College Dublin

Lead Author

Colum Ó Sé

Lead Author

Sven Vanneste

Lead Author

Topics

  • Models: Transition to Chronic Pain