Background & Aims
Pain sensitivity varies widely across individuals[1]. Can we find a reliable index for this variability in pain sensitivity? Identifying it would be important for objectively measuring pain sensitivity, and thereby screening high-risk individuals for chronic pain[2]. Previous studies have attempted to find neural indicators of pain sensitivity with fMRI, but it is still controversial whether pain-evoked brain activations can reflect interindividual pain sensitivity[3,4], and whether pain-evoked brain responses selectively track pain sensitivity rather than modality-general stimulus factors. To address these issues, we used five large fMRI datasets and aimed to answer four key questions: (1) Do pain-evoked fMRI responses index variability in pain sensitivity? (2) If so, is this index selective to pain? (3) Can we develop a machine learning model accurately predicting pain sensitivity? (4) Which sample size is needed to index pain sensitivity using pain-evoked fMRI responses?
Methods
We used five large fMRI datasets (total N=1010) from previous studies where healthy subjects received transient nociceptive stimuli: laser heat in Datasets 1&2[5], mechanical pain in Dataset 3[6], contact heat in Datasets 4[3] and 5[7]. In Datasets 1&2, subjects also received transient tactile, auditory, and visual stimuli; in Dataset 3&5, subjects also received pain treatments (placebo in Dataset 3, and transcutaneous electric nerve stimulation in Dataset 4). In Datasets 1~3, we first examined whether pain-evoked fMRI activity indexes pain sensitivity. In Datasets 1&2, we examined whether this index is selective to pain by relating tactile, auditory, and visual stimuli-evoked fMRI responses with the corresponding sensory sensitivity. We next developed a machine learning model to predict pain sensitivity using Datasets 1&2, and tested its generalizability to different types of pain stimuli in Datasets 3~5 and transferability to predicting analgesic effects in Datasets 3&5.
Results
We found that, given a large sample size, pain-evoked fMRI responses reliably correlated with pain sensitivity across individuals. Importantly, this correlation was replicated in multiple independent datasets, and generalized to different types of pain stimuli, namely laser heat, contact heat, and mechanical pains. Furthermore, fMRI responses indexed pain sensitivity better than tactile, auditory, and visual sensitivity, although significant correlations with across-subject perceptual variability could also be observed in non-pain modalities. The machine learning model that we developed (neuroimaging-based indicator of pain sensitivity [NIPS]) could significantly predict not only pain sensitivity to laser heat, contact heat, and mechanical stimuli, but also pain relief from different treatments, including placebo and transcutaneous electric nerve stimulation. Notably, a sample size >150 healthy volunteers was required for machine learning models to robustly predict across-subject pain variability, and a sample size of ~200 healthy volunteers was needed to detect a quarter of voxels whose responses significantly correlated with pain sensitivity.
Conclusions
We demonstrated that when large sample sizes are considered, pain-evoked fMRI responses can reflect pain sensitivity across individuals, and pain-evoked fMRI responses contain more information about pain sensitivity than non-pain fMRI responses about sensory sensitivity in non-pain modalities. Our model, NIPS, is versatile enough to predict pain sensitivity and pain relief. Importantly, sample sizes are crucial for detecting correlations between fMRI and pain sensitivity.
References
1. Mogil, J. S. (2021). Sources of individual differences in pain. Annual Review of Neuroscience, 44(1), 1–25. https://doi.org/10.1146/annurev-neuro-092820-105941
2. Davis, K. D., Aghaeepour, N., Ahn, A. H., Angst, M. S., Borsook, D., Brenton, A., Burczynski, M. E., Crean, C., Edwards, R., Gaudilliere, B., Hergenroeder, G. W., Iadarola, M. J., Iyengar, S., Jiang, Y., Kong, J.-T., Mackey, S., Saab, C. Y., Sang, C. N., Scholz, J., … Pelleymounter, M. A. (2020). Discovery and validation of biomarkers to aid the development of safe and effective pain therapeutics: Challenges and opportunities. Nature Reviews Neurology, 16(7), Article 7. https://doi.org/10.1038/s41582-020-0362-2
3. Gim, S., Lee, D. H., Lee, S., & Woo, C.-W. (2023). Interindividual differences in pain can be explained by fMRI, sociodemographic, and psychological factors (p. 2023.07.06.547919). bioRxiv. https://doi.org/10.1101/2023.07.06.547919
4. Hoeppli, M. E., Nahman-Averbuch, H., Hinkle, W. A., Leon, E., Peugh, J., Lopez-Sola, M., King, C. D., Goldschneider, K. R., & Coghill, R. C. (2022). Dissociation between individual differences in self-reported pain intensity and underlying fMRI brain activation. Nature Communications, 13(1), Article 1. https://doi.org/10.1038/s41467-022-31039-3
5. Zhang, L.-B., Lu, X.-J., Huang, G., Zhang, H.-J., Tu, Y.-H., Kong, Y.-Z., & Hu, L. (2022). Selective and replicable neuroimaging-based indicators of pain discriminability. Cell Reports Medicine, 3(12), 100846. https://doi.org/10.1016/j.xcrm.2022.100846
6. Botvinik-Nezer, R., Petre, B., Ceko, M., Lindquist, M. A., Friedman, N. P., & Wager, T. D. (2023). Placebo treatment affects brain systems related to affective and cognitive processes, but not nociceptive pain (p. 2023.09.21.558825). bioRxiv. https://doi.org/10.1101/2023.09.21.558825
7. Wei, Z., Duan, Y., Zhu, Y., Lin, X., Zhang, M., Brooks, J. C. W., Liu, Y., Hu, L., & Kong, Y. (2024). Cortico-spinal Mechanisms of Periphery Neuromodulation induced Analgesia (p. 2024.02.06.579059). bioRxiv. https://doi.org/10.1101/2024.02.06.579059
Presenting Author
Li-Bo Zhang
Poster Authors
Li-Bo Zhang
PhD
Italian Institute of Technology
Lead Author
Xuejing Lu
PhD
Institute of Psychology, Chinese Academy of Sciences
Lead Author
Huijuan Zhang
PhD
Institute of Psychology, Chinese Academy of Sciences
Lead Author
Zhaoxing Wei
PhD
Institute of Psychology, Chinese Academy of Sciences
Lead Author
Yazhuo Kong
Institute of Psychology, Chinese Academy of Sciences
Lead Author
Yiheng Tu
Institute of Psychology, Chinese Academy of Sciences
Lead Author
Giandomenico Iannetti
Italian Institute of Technology
Lead Author
Li Hu
Institute of Psychology, Chinese Academy of Sciences
Lead Author
Topics
- Pain Imaging