Delineating conditions and subtypes in chronic pain using neuroimaging

Abstract

Differentiating subtypes of chronic pain still remains a challenge-both from a subjective and objective point of view. Personalized medicine is the current goal of modern medical care and is limited by the subjective nature of patient self-reporting of symptoms and behavioral evaluation. Physiology-focused techniques such as genome and epigenetic analyses inform the delineation of pain groups; however, except under rare circumstances, they have diluted effects that again, share a common reliance on behavioral evaluation. The application of structural neuroimaging towards distinguishing pain subtypes is a growing field and may inform pain-group classification through the analysis of brain regions showing hypertrophic and atrophic changes in the presence of pain. Analytical techniques such as machine-learning classifiers have the capacity to process large volumes of data and delineate diagnostically relevant information from neuroimaging analysis. The issue of defining a "brain type" is an emerging field aimed at interpreting observed brain changes and delineating their clinical identity/significance. In this review, 2 chronic pain conditions (migraine and irritable bowel syndrome) with similar clinical phenotypes are compared in terms of their structural neuroimaging findings. Independent investigations are compared with findings from application of machine-learning algorithms. Findings are discussed in terms of differentiating patient subgroups using neuroimaging data in patients with chronic pain and how they may be applied towards defining a personalized pain signature that helps segregate patient subgroups (eg, migraine with and without aura, with or without nausea; irritable bowel syndrome vs other functional gastrointestinal disorders).

Keywords: Chronic pain; IBS; Machine learning; Migraine.

Figure 1.

Regional structural changes in IBS and migraine cohorts presented using chord diagrams. Chords represent reported changes with the center of each chord being the frontal (F) cortex presented for convenience. The machine-learning diagram presents both increases and decreases together. Line weights are proportional to the number of studies reported. IBS, irritable bowel syndrome.

Figure 2.

Coordinate evaluated in meta-analysis. Three sections are presented going from the anterior (blue) through the middle (red) and posterior (green) aspects of the brain. Black dots represent stereotaxic coordinates reported in the included studies within the window presented on the left. Image is presented in MNI space.