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

• Published in: bioRxiv 2024-12
• Main Authors: Sadil, Patrick, Arfanakis, Konstantinos, Enamul Hoque Bhuiyan, Caffo, Brian, Calhoun, Vince D, Clauw, Daniel J, Delano, Mark C, d, James C, Gattu, Ramtalik, Guo, Xiaodong, Harris, Richard E, Ichesco, Eric, Johnson, Micah A, Jung, Heejung, Kahn, Ari B, Kaplan, Chelsea M, Leloudas, Nondas, Lindquist, Martin A, Luo, Qingfei, Mulderink, Todd A, Peltier, Scott J, Prasad, Pottumarthi V, Sica, Christopher, Urrutia, Joshua, Vance, Carol Gt, Wager, Tor D, Yang, Xuan, Zhou, Xiaohong J, Zhou, Yong, Shu, David C, The Acute To Chronic Pain Signatures Consortium
• Format: Article
• Language: English
• Subjects: Biomarkers; Brain mapping; Chronic pain; Functional magnetic resonance imaging; Image processing; Medical imaging; Neural networks; Neuroimaging; Quality control
• DOI: 10.1101/2024.12.19.627509

The Acute to Chronic Pain Signatures (A2CPS) project is a large-scale, multi-site initiative aimed at identifying biomarkers and biosignatures that predict the transition from acute to chronic pain. The project is collecting multimodal, longitudinal data from over 2,500 individuals at risk for developing chronic pain after surgery. Here we describe the neuroimaging component of A2CPS, including the acquisition protocols, processing pipelines, and contents of the initial data release. The imaging protocol includes structural, diffusion, resting-state and task-based functional magnetic resonance imaging (MRI) data. Data are collected across multiple clinical sites using different scanner manufacturers, with attention to protocol harmonization and quality control. The processing pipeline integrates several established neuroimaging tools to extract potential biomarkers, including measures of brain structure, connectivity, and pain-related neural signatures. The first data release includes pre-surgical imaging data for 595 participants, with high quality ratings across modalities (98.7% of sMRI, 99.8% of dMRI, and 94.6% of fMRI images were rated as acceptable or better). Initial analyses demonstrate expected relationships between brain-derived measures and clinical variables, such as associations between brain age and psychological factors. This dataset represents a valuable resource for both pain research and neuroimaging methods development, with future releases planned to include additional participants and expanded analysis pipelines and processed data derivatives.Competing Interest StatementThe authors have declared no competing interest.Footnotes* https://a2cps.org/* https://nda.nih.gov/edit_collection.html?id=5121