• Dorine Lenoir, Barbara Cagnie, Helena Verhelst, and Robby De Pauw.
• Pain in Motion International Research Group; Department of Rehabilitation sciences, Ghent University, Campus Heymans, Ghent, Belgium; Department of Physiotherapy, Human Physiology and Anatomy (KIMA), Brussels, Belgium; Bijzonder onderzoeksfonds Gent (BOF), Belgium.
• Pain Physician. 2021 Nov 1; 24 (7): E1037-E1058.

BackgroundChronic pain affects 20 to 30% of the adult population worldwide and is consequently the leading cause of disability. Current developments in brain imaging technology are increasing the understanding of the pathophysiology of (chronic) pain and enabling the possibility to objectify pain. As a result, our view of the brain has evolved from a static organ to a dynamic organ that constitutes an adaptable network of linked regions. Graph theory has emerged as a framework to analyze such networks and can be applied to investigate a range of topological properties of both the functional and structural brain network or connectome, thus providing meaningful information about the organization of human brain networks.ObjectivesThe aim of this systematic review is to determine whether connectivity differs between chronic pain patients and healthy controls by integrating previous studies that performed graph analyses on structural or functional connectivity. A secondary aim was to determine whether graph measures correlate to clinical outcomes.Study DesignSystematic review.MethodsRelevant articles were searched for in PubMed and Web of Science. These were screened against certain criteria and assessed for quality.ResultsOn a global level the transitivity, betweenness centrality, intramodular degree, and rich club organization differed between chronic pain patients and healthy controls, but the path length, modularity, degree, and (Hub Disruption Index [HDI] of) participation coefficient did not differ between both groups, along with the small-worldness. Conflicting evidence still remains about a number of global graph measures, namely the global efficiency, local efficiency, clustering coefficient, and HDI of degree. Significant correlations were found between several nodal and global graph measures on one hand, and clinical outcomes related to pain, disability, and motor control on the other hand.LimitationsNo clear conclusions could be made about the majority of the nodal measures, as they were often based on single studies.ConclusionDifferences between chronic pain patients and healthy controls were mostly observed for the global graph measures. Future research is still needed to validate the obtained findings and to expand this knowledge to the chronic pain populations that were not discussed in the included papers.

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