Methodological Evolution of Diffusion MRI in Chronic Pain: A Narrative Review
J Pain Res. 2026 Aug 22;19:624864. doi: 10.2147/JPR.S624864. eCollection 2026.
ABSTRACT
Chronic pain is a highly prevalent and clinically heterogeneous condition, and its underlying mechanisms remain incompletely understood. The lack of objective, clinically validated biomarkers has hindered diagnostic stratification, mechanistic insight, and treatment evaluation. Diffusion magnetic resonance imaging (dMRI) noninvasively quantifies water diffusion in tissue and provides indirect indices of microstructural organization and structural connectivity in the central and peripheral nervous systems, making it a widely used tool for studying pain chronification and candidate imaging markers. This narrative, methodologically oriented review traces how dMRI has been applied in chronic pain research along its technical trajectory-from diffusion tensor imaging (DTI) to multi-shell acquisition with advanced microstructural models, including diffusion kurtosis imaging (DKI), neurite orientation dispersion and density imaging (NODDI), and constrained spherical deconvolution (CSD), as well as the increasing integration with multimodal imaging. PubMed and Web of Science were searched from inception to December 2025 for studies applying dMRI in participants with chronic pain and reporting quantitative diffusion metrics. We evaluate the strengths and limitations of different dMRI approaches in the context of chronic pain and discuss their potential to improve diagnosis, biomarker discovery, and outcome prediction. Findings are appraised alongside the evidentiary limits of this literature, including small and predominantly cross-sectional samples, heterogeneous acquisition and preprocessing pipelines, and variable statistical correction. Current dMRI metrics are therefore best regarded as promising research markers rather than validated clinical tools, and standardized acquisition, multicenter replication, and prospective individual-level validation remain necessary.
PMID:42657359 | PMC:PMC13510267 | DOI:10.2147/JPR.S624864