2026-07-03 中国科学院（CAS）
＜関連情報＞
- https://english.cas.cn/newsroom/research-news/202607/t20260706_1176348.shtml
- https://www.cell.com/cell-reports-medicine/fulltext/S2666-3791(26)00210-7
痛み知覚の予測的皮質脊髄路モデル A predictive corticospinal model for pain perception
Xiao-Min Lin ∙ Xiao-Shuo Zhang ∙ Hang Zhou ∙ … ∙ Ji-Xin Liu ∙ Cun-Zhi Liu ∙ Ya-Zhuo Kong
Cell Reports Medicine Published:June 17, 2026
DOI:https://doi.org/10.1016/j.xcrm.2026.102793
Highlights
- An integrated corticospinal fMRI model accurately decodes human pain intensity
- The model outperforms brain-only signatures and generalizes across pain types
- CsPIP successfully tracks TENS-induced analgesia in healthy participants
- CsPIP predicts chronic pain severity and treatment outcomes
Summary
Pain perception arises from integrated corticospinal circuits, yet most neuroimaging biomarkers are brain centric. We develop the Corticospinal Pain Intensity Pattern, a multivariate model trained and validated on 330 simultaneous corticospinal fMRI data. Across independent datasets, the model predicts pain intensity more accurately than cortical signatures and generalizes to electrical pain, while remaining insensitive to itch and observed pain. The model further tracks analgesia induced by transcutaneous electrical nerve stimulation in healthy participants. In a chronic pain cohort, corticospinal model expression derived from low-frequency spontaneous activity predicts baseline pain and longitudinal changes closely mirror treatment-induced pain relief. A corticospinal hidden Markov model reveals that dynamic transitions between pro- and anti-nociceptive states underpin static spectral abnormalities. Together, these findings establish a corticospinal biomarker that bridges experimental and clinical pain by linking task-evoked and spontaneous neural activity.
