Evidence map›Paper›PMID 42309066›Full record

ArticleCell reports. Medicine2026

A predictive corticospinal model for pain perception.

Xiao-Min Lin, Xiao-Shuo Zhang, Hang Zhou, Xiu-Yi Han, Zhao-Xing Wei, Tor D Wager, Irene Tracey, Ji-Xin Liu, Cun-Zhi Liu, Ya-Zhuo Kong

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Cell reports. Medicine, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

10 authors.

Xiao-Min LinState Key Laboratory of Cognitive Science and Mental Health, Institute of Psychology, Chinese Academy of Sciences, Beijing 100101, China; Department of Psychology, University of Chinese Academy of Sciences, Beijing 100049, China.
Xiao-Shuo ZhangState Key Laboratory of Cognitive Science and Mental Health, Institute of Psychology, Chinese Academy of Sciences, Beijing 100101, China; Department of Psychology, University of Chinese Academy of Sciences, Beijing 100049, China.
Hang ZhouInternational Acupuncture and Moxibustion Innovation Institute, School of Acupuncture-Moxibustion and Tuina, Beijing University of Chinese Medicine, Beijing 100029, China.
Xiu-Yi HanState Key Laboratory of Cognitive Science and Mental Health, Institute of Psychology, Chinese Academy of Sciences, Beijing 100101, China; Department of Psychology, University of Chinese Academy of Sciences, Beijing 100049, China.
Zhao-Xing WeiState Key Laboratory of Cognitive Science and Mental Health, Institute of Psychology, Chinese Academy of Sciences, Beijing 100101, China; Department of Psychology, University of Chinese Academy of Sciences, Beijing 100049, China; Department of Psychological and Brain Sciences, Dartmouth College, Hanover, NH 03755, USA.
Tor D WagerDepartment of Psychological and Brain Sciences, Dartmouth College, Hanover, NH 03755, USA.
Irene TraceyWellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford OX3 9DU, UK.
Ji-Xin LiuCenter for Brain Imaging, School of Life Science and Technology, Xidian University, Xi'an 710126, China. Electronic address: liujixin@xidian.edu.cn.
Cun-Zhi LiuInternational Acupuncture and Moxibustion Innovation Institute, School of Acupuncture-Moxibustion and Tuina, Beijing University of Chinese Medicine, Beijing 100029, China. Electronic address: lcz623780@126.com.
Ya-Zhuo KongState Key Laboratory of Cognitive Science and Mental Health, Institute of Psychology, Chinese Academy of Sciences, Beijing 100101, China; Department of Psychology, University of Chinese Academy of Sciences, Beijing 100049, China; Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford OX3 9DU, UK. Electronic address: kongyz@psych.ac.cn.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

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.

Indexed as

Chronic PainPain PerceptionPyramidal TractsAdultFemaleHumansMagnetic Resonance ImagingMaleTranscutaneous Electric Nerve Stimulationacupuncturechronic paincorticospinal fMRIneuromodulationpain biomarker

Identifiers

PMID42309066
PMCPMC13400141

What Socratic holds

Textmetadata
LicenceCC BY-NC-ND
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.