A Replicable and Generalizable Neuroimaging‐Based Indicator of Pain Sensitivity Across Individuals

Humans differ in their sensitivity to pain. With six large and diverse fMRI datasets (total N = 1046), this study finds that such individual differences in pain sensitivity can be tracked by fMRI responses to painful stimuli. A highly generalizable machine learning model is further built to predict pain sensitivity across all datasets and analgesic effects of different treatments. Abstract Revealing the neural underpinnings of pain sensitivity is crucial for understanding how the brain encodes individual differences in pain and advancing personalized pain treatments. Here, six large and diverse functional magnetic resonance imaging (fMRI) datasets (total N = 1046) are leveraged to uncover the neural mechanisms of pain sensitivity. Replicable and generalizable correlations are found between nociceptive‐evoked fMRI responses and pain sensitivity for laser heat, contact heat, and mechanical pains. These fMRI responses correlate more strongly with pain sensitivity than with tactile, auditory, and visual sensitivity. Moreover, a machine learning model is developed that accurately predicts not only pain sensitivity (r = 0.20∼0.56, ps < 0.05) but also analgesic effects of different treatments in healthy individuals (r = 0.17∼0.25, ps < 0.05). Notably, these findings are influenced considerably by sample sizes, requiring >200 for univariate whole brain correlation analysis and >150 for multivariate machine learning modeling. Altogether, this study demonstrates that fMRI activations encode pain sensitivity across various types of pain, thus facilitating interpretations of subjective pain reports and promoting more mechanistically informed investigations into pain physiology. Humans differ in their sensitivity to pain. With six large and diverse fMRI datasets (total N = 1046), this study finds that such individual differences in pain sensitivity can be tracked by fMRI responses to painful stimuli. A highly generalizable machine learning model is further built to predict pain sensitivity across all datasets and analgesic effects of different treatments. Abstract Revealing the neural underpinnings of pain sensitivity is crucial for understanding how the brain encodes individual differences in pain and advancing personalized pain treatments. Here, six large and diverse functional magnetic resonance imaging (fMRI) datasets (total N = 1046) are leveraged to uncover the neural mechanisms of pain sensitivity. Replicable and generalizable correlations are found between nociceptive-evoked fMRI responses and pain sensitivity for laser heat, contact heat, and mechanical pains. These fMRI responses correlate more strongly with pain sensitivity than with tactile, auditory, and visual sensitivity. Moreover, a machine learning model is developed that accurately predicts not only pain sensitivity (r = 0.20∼0.56, ps < 0.05) but also analgesic effects of different treatments in healthy individuals (r = 0.17∼0.25, ps < 0.05). Notably, these findings are influenced considerably by sample sizes, requiring >200 for univariate whole brain correlation analysis and >150 for multivariate machine learning modeling. Altogether, this study demonstrates that fMRI activations encode pain sensitivity across various types of pain, thus facilitating interpretations of subjective pain reports and promoting more mechanistically informed investigations into pain physiology. Advanced Science, Volume 12, Issue 44, November 27, 2025.