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A Corticospinal Signature for Interindividual Pain Sensitivity

Presented During:

Perception, Attention and Sleep

Saturday, June 28, 2025: 11:30 AM - 12:45 PM

Brisbane Convention & Exhibition Centre

Room: M1 & M2 (Mezzanine Level)

Poster No:

2044

Submission Type:

Abstract Submission

Authors:

Xiaomin Lin¹, Lingfei Guo², Bing Ni³, Zhaoxing Wei⁴, Xiaoshuo Zhang⁵, Yunyun Duan⁶, Li Hu⁵, Ming Zhang⁷, Jingyi Zhang⁷, Zaiying Jiang³, Yunjian Huang³, Jonathan Brooks⁸, Irene Tracey⁹, Tor Wager¹⁰, Yaou Liu⁶, Yazhuo Kong⁷

Institutions:

¹CAS Key Laboratory of Behavioral Science,Institute of Psychology, Chinese Academy of Sciences, Beijing, Beijing, ²Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, Jinan, ³Functional neurosurgery Department, Xuanwu Hospital, Capital Medical University, Beijing, Beijing, ⁴Department of Psychological and Brain Sciences, Dartmouth College, Hanover, NH, ⁵CAS Key Laboratory of Mental Health, Institute of Psychology, Chinese Academy of Sciences, Beijing, Beijing, ⁶Department of Radiology, Beijing Tiantan Hospital, Capital Medical University, Beijing, Beijing, ⁷CAS Key Laboratory of Behavioral Science, Institute of Psychology, Chinese Academy of Sciences, Beijing, Beijing, ⁸School of Psychology, University of East Anglia, Norwich, Norfolk, ⁹Wellcome Centre for Integrative Neuroimaging, University of Oxford, Oxford, Oxford, ¹⁰Department of Psychological and Brain Sciences, Hanover, NH

First Author:

Xiaomin Lin
CAS Key Laboratory of Behavioral Science,Institute of Psychology, Chinese Academy of Sciences
Beijing, Beijing

Co-Author(s):

Lingfei Guo
Shandong Provincial Hospital Affiliated to Shandong First Medical University
Jinan, Jinan

Bing Ni
Functional neurosurgery Department, Xuanwu Hospital, Capital Medical University
Beijing, Beijing

Zhaoxing Wei
Department of Psychological and Brain Sciences, Dartmouth College
Hanover, NH

Xiaoshuo Zhang
CAS Key Laboratory of Mental Health, Institute of Psychology, Chinese Academy of Sciences
Beijing, Beijing

Yunyun Duan
Department of Radiology, Beijing Tiantan Hospital, Capital Medical University
Beijing, Beijing

Li Hu
CAS Key Laboratory of Mental Health, Institute of Psychology, Chinese Academy of Sciences
Beijing, Beijing

Ming Zhang
CAS Key Laboratory of Behavioral Science, Institute of Psychology, Chinese Academy of Sciences
Beijing, Beijing

Jingyi Zhang
CAS Key Laboratory of Behavioral Science, Institute of Psychology, Chinese Academy of Sciences
Beijing, Beijing

Zaiying Jiang
Functional neurosurgery Department, Xuanwu Hospital, Capital Medical University
Beijing, Beijing

Yunjian Huang
Functional neurosurgery Department, Xuanwu Hospital, Capital Medical University
Beijing, Beijing

Jonathan Brooks
School of Psychology, University of East Anglia
Norwich, Norfolk

Irene Tracey
Wellcome Centre for Integrative Neuroimaging, University of Oxford
Oxford, Oxford

Tor Wager
Department of Psychological and Brain Sciences
Hanover, NH

Yaou Liu
Department of Radiology, Beijing Tiantan Hospital, Capital Medical University
Beijing, Beijing

Yazhuo Kong
CAS Key Laboratory of Behavioral Science, Institute of Psychology, Chinese Academy of Sciences
Beijing, Beijing

Introduction:

Chronic pain compromises quality of life (Kuehn, 2018), yet its subjective variability complicates both research and treatment (Kohoutová et al., 2022). Identifying neural markers of individual pain sensitivity is critical for understanding why some individuals develop chronic pain while others recover (Kehlet et la., 2006). Previous imaging studies, focusing on brain networks, reveal stable resting-state features that predict pain sensitivity (Spisak et al., 2020). However, clinical translation remains limited, and the spinal cord's crucial role is understudied. Integrated corticospinal fMRI now allows simultaneous exploration of brain and cervical spinal cord activity, potentially improving prediction models (Tinnermann et al., 2017). Our study presents a pioneering approach to understanding interindividual differences in pain sensitivity by developing a novel corticospinal pain sensitivity signature (CSps). The integration of corticospinal functional connectivity with machine learning offers a significant advancement over traditional brain-centric models. The study situates itself within the broader context of pain research by addressing gaps related to the spinal cord's contribution to pain sensitivity, promising to refine pain prediction frameworks and improve clinical interventions.

Methods:

Following established guidelines, we leveraged seven datasets to develop a corticospinal pain sensitivity signature (Woo et al., 2017; Spisak et al., 2020)., as described in Fig 1a. Participants included healthy individuals (n=179) and patients with pain, including diabetic and zoster-associated patients (n=46), each dataset meeting strict inclusion criteria. Corticospinal functional MRI data were acquired using a 3T scanner (Siemens Prisma, Erlangen, Germany) with simultaneous multi-slice (SMS) imaging and parallel image reconstruction, and processed using an established pipeline (Wei et al., 2024). The corticospinal connectivity features were extracted using partial correlation, allowing for more precise estimations of direct neural interactions (Marrelec et al., 2006). A machine learning pipeline optimized through grid search and cross-validation was applied, with Elastic Net regression used for feature selection and predictive modeling. The robustness of the CSps was evaluated through external validation and comparison with alternative models, ensuring its generalizability and specificity.

Results:

In multiple independent datasets, CSps accurately predicted individual differences in thermal pain thresholds, achieving robust correlations and low errors (Fig.1b, Fig. 1c and Fig. 1d). This performance extended to clinical cohorts, where CSps successfully captured clinical pain intensities (Fig. 1e). Notably, stronger functional connectivity between the motor cortex and spinal regions correlated with heightened pain sensitivity (Fig. 1f). Comparisons with a brain-only model (RPN, Spisak et al., 2020) and other restricted models (including brain-only, spinal-only, disconnected, and solely brain–spinal connectivity models) indicated that CSps more effectively reflected pain sensitivity, achieving superior predictive accuracy and explained variance (Fig. 2). Its generalizability was evident across varied populations and pain conditions, highlighting CSps as a robust biomarker of pain sensitivity capable of reliably predicting both experimental and clinical outcomes.

Conclusions:

This study successfully establishes the CSps as a reliable biomarker for pain sensitivity, integrating brain and spinal cord data to offer a holistic understanding of pain processing. By addressing longstanding gaps in pain neuroimaging, the research provides a robust framework for personalized pain assessment and management. Future directions include expanding the model's applicability to diverse populations and exploring longitudinal changes in corticospinal connectivity during the progression from acute to chronic pain.

Modeling and Analysis Methods:

Classification and Predictive Modeling ²

fMRI Connectivity and Network Modeling

Neuroanatomy, Physiology, Metabolism and Neurotransmission:

Neuroanatomy Other

Novel Imaging Acquisition Methods:

BOLD fMRI

Perception, Attention and Motor Behavior:

Perception: Pain and Visceral ¹

Keywords:

Machine Learning

Pain

Spinal Cord

^1|2Indicates the priority used for review

Abstract Information

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Please indicate below if your study was a "resting state" or "task-activation” study.

Resting state

Healthy subjects only or patients (note that patient studies may also involve healthy subjects):

Patients

Was this research conducted in the United States?

Were any human subjects research approved by the relevant Institutional Review Board or ethics panel? NOTE: Any human subjects studies without IRB approval will be automatically rejected.

Yes

Were any animal research approved by the relevant IACUC or other animal research panel? NOTE: Any animal studies without IACUC approval will be automatically rejected.

Not applicable

Please indicate which methods were used in your research:

Functional MRI

Structural MRI

Behavior

Computational modeling

Other, Please specify - Corticospinal MRI

For human MRI, what field strength scanner do you use?

3.0T

Which processing packages did you use for your study?

AFNI

FSL

Other, Please list - Deepbet, AntsPy

Provide references using APA citation style.

Kuehn, B. (2018). Chronic pain prevalence. Jama, 320(16), 1632-1632.
Kohoutová, L. et al. (2022). Individual variability in brain representations of pain. Nature neuroscience, 25(6), 749-759.
Kehlet, H. et al. (2006). Persistent postsurgical pain: risk factors and prevention. The lancet, 367(9522), 1618-1625.
Spisak, T. et al. (2020). Pain-free resting-state functional brain connectivity predicts individual pain sensitivity. Nature communications, 11(1), 187.
Tinnermann et al. (2017). Interactions between brain and spinal cord mediate value effects in nocebo hyperalgesia. Science, 358(6359), 105-108.
Woo, C. W. et al. (2017). Building better biomarkers: brain models in translational neuroimaging. Nature neuroscience, 20(3), 365-377.
Wei, Z. et al. (2024). Cortico-spinal Mechanisms of Periphery Neuromodulation induced Analgesia. bioRxiv, 2024-02.
Marrelec, G. et al. (2006). Partial correlation for functional brain interactivity investigation in functional MRI. Neuroimage, 32(1), 228-237.

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