Abstract：
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.
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.
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.
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.
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.
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.
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.
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.
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.
摘要：
目的：确定基于灰质（GM）图像约束到感觉运动网络的多元模式回归分析是否可以准确预测健康个体的三叉神经热痛敏感性。
背景：预测个体疼痛敏感性具有临床意义，因为高疼痛敏感性与术后疼痛风险增加有关，疼痛慢性化，和不良的治疗反应。然而,由于疼痛是一种主观体验，因此很难准确识别此类个体。已显示感觉运动区域的GM结构随疼痛敏感性而变化。尚不清楚这些区域内的GM结构是否可用于预测疼痛敏感性。
方法：在这项横断面研究中，我们从79名健康参与者中获得了响应左眶上区域接触性热刺激的结构性磁共振图像和疼痛阈值.使用基于体素的形态计量学来提取分割和归一化的GM图像。然后将这些约束到包含功能定义的静息状态感觉运动网络的掩模。掩蔽图像和疼痛阈值进入多变量相关向量回归分析,以定量预测个体疼痛阈值。预测疼痛阈值和实际疼痛阈值之间的对应关系由Pearson相关系数(r)和均方误差(MSE)索引。通过10倍和5倍交叉验证评估模型的可泛化性。使用非参数置换检验来估计显著性水平。
结果：从感觉运动网络内的GM结构可以预测三叉神经热痛敏感性，具有显着的准确性（10倍：r=0.53，p<0.001，MSE=10.32，p=0.001；5倍：r=0.46，p=0.001，MSE=10.54，p<0.001）。由此产生的多变量权重图表明，准确的预测依赖于感觉运动网络中的多个广泛区域。
结论：感觉运动网络中GM结构的多变量模式可用于在健康参与者的个体水平上对三叉神经热痛敏感性进行准确预测。感觉运动网络内的广泛区域有助于预测模型。
背景：预测个体疼痛敏感性具有临床意义，因为高疼痛敏感性与术后疼痛风险增加有关，疼痛慢性化，和不良的治疗反应。然而,由于疼痛是一种主观体验，因此很难准确识别此类个体。已显示感觉运动区域的GM结构随疼痛敏感性而变化。尚不清楚这些区域内的GM结构是否可用于预测疼痛敏感性。
方法：在这项横断面研究中，我们从79名健康参与者中获得了响应左眶上区域接触性热刺激的结构性磁共振图像和疼痛阈值.使用基于体素的形态计量学来提取分割和归一化的GM图像。然后将这些约束到包含功能定义的静息状态感觉运动网络的掩模。掩蔽图像和疼痛阈值进入多变量相关向量回归分析,以定量预测个体疼痛阈值。预测疼痛阈值和实际疼痛阈值之间的对应关系由Pearson相关系数(r)和均方误差(MSE)索引。通过10倍和5倍交叉验证评估模型的可泛化性。使用非参数置换检验来估计显著性水平。
结果：从感觉运动网络内的GM结构可以预测三叉神经热痛敏感性，具有显着的准确性（10倍：r=0.53，p<0.001，MSE=10.32，p=0.001；5倍：r=0.46，p=0.001，MSE=10.54，p<0.001）。由此产生的多变量权重图表明，准确的预测依赖于感觉运动网络中的多个广泛区域。
结论：感觉运动网络中GM结构的多变量模式可用于在健康参与者的个体水平上对三叉神经热痛敏感性进行准确预测。感觉运动网络内的广泛区域有助于预测模型。
