| Literature DB >> 31107712 |
Yiheng Tu1,2, Minyoung Jung1, Randy L Gollub1, Vitaly Napadow2, Jessica Gerber2, Ana Ortiz1, Courtney Lang1, Ishtiaq Mawla1, Wei Shen1, Suk-Tak Chan2, Ajay D Wasan3, Robert R Edwards4, Ted J Kaptchuk5, Bruce Rosen2, Jian Kong1,2.
Abstract
Accumulating evidence has shown that complicated brain systems are involved in the development and maintenance of chronic low back pain (cLBP), but the association between brain functional changes and clinical outcomes remains unclear. Here, we used resting-state functional magnetic resonance imaging (fMRI) and multivariate pattern analysis to identify abnormal functional connectivity (FC) between the default mode, sensorimotor, salience, and central executive brain networks in cLBP and tested whether abnormal FCs are related to pain and comorbid symptoms. Fifty cLBP patients and 44 matched healthy controls (HCs) underwent an fMRI scan, from which brain networks were identified by independent component analysis. Multivariate pattern analysis, graph theory approaches, and correlation analyses were applied to find abnormal FCs that were associated with clinical symptoms. Findings were validated on a second cohort of 30 cLBP patients and 30 matched HCs. Results showed that the medial prefrontal cortex/rostral anterior cingulate cortex had abnormal FCs with brain regions within the default mode network and with other brain networks in cLBP patients. These altered FCs were also correlated with pain duration, pain severity, and pain interference. Finally, we found that resting-state FC could discriminate cLBP patients from HCs with 91% accuracy in the first cohort and 78% accuracy in the validation cohort. Our findings suggest that the medial prefrontal cortex/rostral anterior cingulate cortex may be an important hub for linking the default mode network with the other 3 networks in cLBP patients. Elucidating the altered FCs and their association with clinical outcomes will enhance our understanding of the pathophysiology of cLBP and may facilitate the development of pain management approaches.Entities:
Mesh:
Year: 2019 PMID: 31107712 PMCID: PMC6530583 DOI: 10.1097/j.pain.0000000000001507
Source DB: PubMed Journal: Pain ISSN: 0304-3959 Impact factor: 7.926