1337-Predictive Corticospinal Model for Pain Perception

The research paper introduces the Corticospinal Pain Intensity Pattern (CsPIP), a novel neuroimaging biomarker that integrates activity from both the brain and spinal cord to decode human pain. Developed using advanced machine learning on simultaneous fMRI data, this model significantly outperforms traditional brain-centric signatures by capturing the bidirectional communication across the entire neuroaxis. The studies demonstrate that CsPIP effectively predicts pain intensity across various sensory modalities, such as thermal and electrical stimuli, while remaining specific enough to distinguish pain from itching or empathy. Furthermore, the researchers show that the model can track pain relief induced by clinical treatments like TENS and predict the severity of chronic pain in patients with irritable bowel syndrome. By linking task-evoked responses to spontaneous neural fluctuations, the authors establish a robust framework for objective pain assessment and personalized medical interventions. This comprehensive approach highlights the spinal cord's critical role not just as a relay station, but as an active participant in shaping the subjective experience of pain.

References:

• Lin X M, Zhang X S, Zhou H, et al. A predictive corticospinal model for pain perception[J]. Cell Reports Medicine, 2026.