| Summary: | Purpose: Neuroimaging studies employing analyses dependent on regional assumptions and specific neuronal circuits could miss characteristics of whole-brain structural connectivity critical to the pathophysiology of fibromyalgia (FM). This study applied the whole-brain graph-theoretical approach to identify whole-brain structural connectivity disturbances in FM. Methods: This cross-sectional study used probabilistic diffusion tractography and graph theory analysis to evaluate the topological organization of brain white matter networks in 20 patients with FM and 20 healthy controls (HCs). The relationship between brain network metrics and clinical variables was evaluated. Results: Compared with HCs, FM patients had lower clustering coefficient, local efficiency, hierarchy, synchronization, and higher normalized characteristic path length. Regionally, patients demonstrated a significant reduction in nodal efficiency and centrality; these regions were mainly located in the prefrontal, temporal cortex, and basal ganglia. The network-based statistical analysis (NBS) identified decreased structural connectivity in a subnetwork of prefrontal cortex, basal ganglia, and thalamus in FM. There was no correlation between network metrics and clinical variables (false discovery rate corrected). Conclusions : The current research demonstrated disrupted topological architecture of white matter networks in FM. Our results suggested compromised neural integration and segregation and reduced structural connectivity in FM.
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