What has brain diffusion magnetic resonance imaging taught us about chronic primary pain: a narrative review

Abstract

Chronic pain is a pervasive and debilitating condition with increasing implications for public health, affecting millions of individuals worldwide. Despite its high prevalence, the underlying neural mechanisms and pathophysiology remain only partly understood. Since its introduction 35 years ago, brain diffusion magnetic resonance imaging (MRI) has emerged as a powerful tool to investigate changes in white matter microstructure and connectivity associated with chronic pain. This review synthesizes findings from 58 articles that constitute the current research landscape, covering methods and key discoveries. We discuss the evidence supporting the role of altered white matter microstructure and connectivity in chronic primary pain conditions, highlighting the importance of studying multiple chronic pain syndromes to identify common neurobiological pathways. We also explore the prospective clinical utility of diffusion MRI, such as its role in identifying diagnostic, prognostic, and therapeutic biomarkers. Furthermore, we address shortcomings and challenges associated with brain diffusion MRI in chronic primary pain studies, emphasizing the need for the harmonization of data acquisition and analysis methods. We conclude by highlighting emerging approaches and prospective avenues in the field that may provide new insights into the pathophysiology of chronic pain and potential new therapeutic targets. Because of the limited current body of research and unidentified targeted therapeutic strategies, we are forced to conclude that further research is required. However, we believe that brain diffusion MRI presents a promising opportunity for enhancing our understanding of chronic pain and improving clinical outcomes.

Figure 1.

Flowchart of the article selection process, 58 final articles were included for the analyzes in this review.

Figure 2.

An interactive overview of 58 articles that met our inclusion criteria regrouped according to the latest IASP chronic primary pain definition and their main analysis method. In blue: primary headache and orofacial, red: primary visceral pain, green: primary musculoskeletal pain, purple: chronic widespread pain and orange: complex regional pain syndrome. Dynamic figure can be found at: https://osf.io/4wyqt/. IASP, International Association for the Study of Pain.

Figure 3.

An interactive visual summary of the reviewed studies in the types of chronic primary pain studied (A), the used analysis methods (B), and the tracts and regions reported as significant findings (C). In blue: primary headache and orofacial, red: primary visceral pain, green: primary musculoskeletal pain, purple: chronic widespread pain and orange: complex regional pain syndrome. Dynamic figure can be found at: https://osf.io/4wyqt/.

Figure 4.

Visual representation of the association fibers (A), the projection fibers (B), the commissural fibers (C), and the gray matter regions (D) that were mentioned by at least 3 different articles in this review (above the dashed line in the heatmap). (E) An interactive heatmap of how consistently each tract and region is reported as significant findings across the studies of this review. The tracts and regions with warm colors (towards yellow) are reported more consistently (with a maximum of 14 studies that report the corpus callosum) relatively to tracts and regions with cold colors (towards blue; with over 50% of regions mentioned only once or twice). Dynamic heatmap can be found at: https://osf.io/4wyqt/.

Figure 5.

Distribution of analyses methods (A) and distribution of alternative analyses methods to DTI (B) between chronic primary pain (CP) and healthy controls used in diffusion MRI. Dynamic figure can be found at: https://osf.io/4wyqt/. DTI, diffusion tensor imaging; MRI, magnetic resonance imaging.

Figure 6.

Overview of the different acquisition parameters used in the studies of the review. Distribution of the b-value in s/mm² (A), the number of diffusion encoding gradient directions (B), the resolution in mm³ (C), and the magnetic field strength in T (D). Dynamic figure can be found at: https://osf.io/4wyqt/.

Figure 7.

A visualization of the overlap between the FSL FA skeleton used commonly in whole-brain diffusion MRI analysis and 2 different reconstructed WM bundles taken from 2 different tractography analysis approaches. The population average right fornix (Fx) bundle used in the RecobundleX analysis pipeline is displayed in blue, with regions overlapping the FA skeleton displayed in yellow. Voxels overlapping the bundle were extracted, and the percentage of these voxels overlapping the FA skeleton are displayed as a bar graph. The same metrics are displayed for the left accumbofronal (AcF) track, which was generated in native imaging space by extracting tracks that traversed ROIs (pink) from the nucleus accumbens and the orbital frontal gyrus. Only a small proportion of these tracks overlap the whole-brain FA skeleton from FSL, suggesting that more targeted analysis approaches may be more sensitive to detecting microstructural alterations in some WM structures. FA, fractional anisotropy; MRI, magnetic resonance imaging; ROI, region of interest; WM, white matter.