Objective: To determine whether multivariate pattern regression analysis based on gray matter (GM) images constrained to the sensorimotor network could accurately predict trigeminal heat pain sensitivity in healthy individuals. Background: Prediction of individual pain sensitivity is of clinical relevance as high pain sensitivity is associated with increased risks of postoperative pain, pain chronification, and a poor treatment response. However, as pain is a subjective experience accurate identification of such individuals can be difficult. GM structure of sensorimotor regions have been shown to vary with pain sensitivity. It is unclear whether GM structure within these regions can be used to predict pain sensitivity. Methods: In this cross-sectional study, structural magnetic resonance images and pain thresholds in response to contact heat stimulation of the left supraorbital area were obtained from 79 healthy participants. Voxel-based morphometry was used to extract segmented and normalized GM images. These were then constrained to a mask encompassing the functionally defined resting-state sensorimotor network. The masked images and pain thresholds entered a multivariate relevance vector regression analysis for quantitative prediction of the individual pain thresholds. The correspondence between predicted and actual pain thresholds was indexed by the Pearson correlation coefficient (r) and the mean squared error (MSE). The generalizability of the model was assessed by 10-fold and 5-fold cross-validation. Non-parametric permutation tests were used to estimate significance levels. Results: Trigeminal heat pain sensitivity could be predicted from GM structure within the sensorimotor network with significant accuracy (10-fold: r = 0.53, p < 0.001, MSE = 10.32, p = 0.001; 5-fold: r = 0.46, p = 0.001, MSE = 10.54, p < 0.001). The resulting multivariate weight maps revealed that accurate prediction relied on multiple widespread regions within the sensorimotor network. Conclusion: A multivariate pattern of GM structure within the sensorimotor network could be used to make accurate predictions about trigeminal heat pain sensitivity at the individual level in healthy participants. Widespread regions within the sensorimotor network contributed to the predictive model.
- heat pain threshold
- multivariate pattern analysis
- primary sensorimotor cortex
- relevance vector regression
- voxel-based morphometry