Altered network architecture of functional brain communities in chronic nociplastic pain

NeuroImage

Andrew Schrepf¹  Eric Ichesco²  Chelsea M. Kaplan³  Richard E. Harris³  Tony E. Larkin⁴  George A. Mashour⁴  Ishtiaq Mawla⁴  Steven E. Harte⁴  Daniel J. Clauw⁴ 

[1] Center for Consciousness Science, Department of Anesthesiology, University of Michigan, Ann Arbor, MI, United States;Corresponding author at: UM/CPFRC, 24 Frank Lloyd Wright Dr., PO Box 385, Lobby M, Ann Arbor, MI, 48106, United States.;Neuroscience Graduate Program, University of Michigan, Ann Arbor, MI, United States;Chronic Pain and Fatigue Research Center, Department of Anesthesiology, University of Michigan, Ann Arbor, MI, United States;

关键词: Chronic pain;    Resting state fMRI;    Community structure;    Normalized mutual information;    Fibromyalgia;    Graph theory;   

DOI  :  

来源: DOAJ

【 摘 要 】

Neuroimaging has enhanced our understanding of the neural correlates of pain. Yet, how neural circuits interact and contribute to persistent pain remain largely unknown. Here, we investigate the mesoscale organization of the brain through intrinsic functional communities generated from resting state functional MRI data from two independent datasets, a discovery cohort of 43 Fibromyalgia (FM) patients and 20 healthy controls (HC) as well as a replication sample of 34 FM patients and 21 HC. Using normalized mutual information, we found that the global network architecture in chronic pain patients is less stable (more variable). Subsequent analyses of node community assignment revealed the composition of the communities differed between FM and HC. Furthermore, differences in network organization were associated with the changes in the composition of communities between patients with varying levels of clinical pain. Together, this work demonstrates that intrinsic network communities differ substantially between patients with FM and controls. These differences may represent a novel aspect of the pathophysiology of chronic nociplastic pain.

【 授权许可】

Unknown