225: Image Processing in the Acute to Chronic Pain Signatures (A2CPS) Project

Topic: Clinical Science
Subtopic: Pain category (ICD-11), Chronic postsurgical pain, Imaging (e.g. fMRI, PET, MRS)

Background and Aims: The Acute to Chronic Pain Signatures (A2CPS) project is a large-scale, multi-site study designed to identify biomarkers that predict the transition from acute to chronic pain following surgery. Chronic post-surgical pain remains a major clinical challenge, yet reliable predictive markers are lacking. A2CPS integrates multimodal neuroimaging with deep clinical, psychosocial, and biological phenotyping in over 2,400 surgical patients. Here, we describe the neuroimaging component of the project and summarize the contents and quality of the initial public data release.

Methods: We outline the A2CPS neuroimaging acquisition protocols, harmonization procedures, processing pipelines, and data release structure. Imaging includes structural MRI, diffusion MRI, resting-state fMRI, and task-based fMRI acquired pre- and post-surgically across multiple scanner manufacturers and sites. Standardized protocols and centralized quality control procedures were implemented to ensure cross-site harmonization. Processing integrates established tools for structural morphometry, white matter microstructure, functional connectivity, and multivariate pain-related neural signatures. Derived measures include brain age, network connectivity metrics, and validated pain neuromarkers. The initial release provides raw and processed derivatives along with comprehensive metadata to facilitate reproducible analyses.

Results: The first data release includes pre-surgical imaging from 595 participants. Data quality was high across modalities, with 98.7% of structural MRI, 99.8% of diffusion MRI, and 94.6% of functional MRI scans rated acceptable or better following centralized review. Preliminary analyses demonstrate expected associations between brain-derived measures and clinical variables, including relationships between brain age and psychological factors. Harmonization procedures yielded consistent imaging metrics across sites and scanner platforms. These findings support the reliability and translational potential of the dataset for investigating neural predictors of chronic pain risk.

Conclusions: A2CPS provides one of the largest longitudinal neuroimaging datasets in surgical pain populations. The combination of high-quality multimodal imaging, harmonized acquisition, and deep phenotyping creates a powerful platform for biomarker discovery and methodological development. Future releases will expand to the full cohort of over 2,400 participants and include post-surgical imaging, additional derived measures, and enhanced processing pipelines. This open resource is positioned to accelerate discovery of brain-based predictors of chronic pain and to support collaborative advances in precision pain medicine.

References: Berardi, G., Frey-Law, L., Sluka, K. A., Bayman, E. O., Coffey, C. S., Ecklund, D., Vance, C. G. T., Dailey, D. L., Burns, J., Buvanendran, A., McCarthy, R. J., Jacobs, J., Zhou, X. J., Wixson, R., Balach, T., Brummett, C. M., Clauw, D., Colquhoun, D., Harte, S. E., … Wandner, L. D. (2022). Multi-site observational study to assess biomarkers for susceptibility or resilience to chronic pain: The Acute to Chronic Pain Signatures (A2CPS) study protocol. Frontiers in Medicine, 9, 849214. https://doi.org/10.3389/fmed.2022.849214
Sluka, K. A., Wager, T. D., Sutherland, S. P., Labosky, P. A., Balach, T., Bayman, E. O., Berardi, G., Brummett, C. M., Burns, J., Buvanendran, A., Caffo, B., Calhoun, V. D., Clauw, D., Chang, A., Coffey, C. S., Dailey, D. L., Ecklund, D., Fiehn, O., Fisch, K. M., … Zhou, X. J. (2023). Predicting chronic postsurgical pain: Current evidence and a novel program to develop predictive biomarker signatures. Pain, 164(9), 1912–1926.

Presenting Author: Michael Sun
Submitting Author: Michael Sun

Senior Author: Tor D. Wager

IASP 2026 Global Year theme of Neuropathic Pain: No

• Patrick Sadil - John Hopkins University
• Konstantinos Arfanakis - Rush University Medical School
• Enamul Hoque Bhuiyan - University of Illinois at Chicago
• Brian Caffo - The Mind Research Network
• Vince D Calhoun - The Mind Research Network
• Daniel J Clauw - University of Michigan
• Mark C DeLano - Corewell Health
• James C Ford - Dartmouth College
• Ramtalik Gattu - Wayne State University
• Xiaodong Guo - University of Chicago
• Richard E Harris - University of Michigan
• Eric Ichesco - University of Michigan
• Micah A Johnson - Johns Hopkins University
• Heejung Jung - Stanford University
• Ari B Kahn - TACC, University of Texas at Austin
• Chelsea M Kaplan - University of Michigan
• Nondas Leloudas - Endeavor
• Martin A Lindquist - Johns Hopkins University
• Qingfei Luo - University of Illinois at Chicago
• Todd A Mulderink - Corewell Health
• Scott J Peltier - University of Michigan
• Pottumarthi V Prasad - Endeavor
• Christopher Sica - Rush University Medical School
• Joshua Urrutia - TACC, University of Texas at Austin
• Carol GT Vance - University of Iowa
• Yang Xuan - Wayne State University
• Xiaohong J Zhou - University of Illinois at Chicago
• Yong Zhou - Corewell Health
• David C Shu - Albert Einstein College of Medicine
• Tor D. Wager - Dartmouth College