Abnormal functional integration across core brain networks in migraine without aura. Issue 1 (October 2017)

• Record Type: Journal Article
• Authors: Yu, Dahua; Yuan, Kai; Luo, Lin; Zhai, Jinquan; Bi, Yanzhi; Xue, Ting; Ren, Xiaoying; Zhang, Ming; Ren, Guoyin; Lu, Xiaoqi
• Is Part Of: Molecular pain. Volume 13:Issue 1(2017)
• Publication Date: 2017-10
• Subjects: Intrinsic connectivity networks -- independent component analysis -- functional connectivity -- Granger causality analyses; Pain -- Molecular aspects -- Periodicals; Pain -- Pathophysiology -- Periodicals; Pain -- Physiological aspects -- Periodicals; 616.0472
• DOI: 10.1177/1744806917737461
• Languages: English
• ISSNs: 1744-8069

Abstract

Background: As a complex subjective experience, pain processing may be related to functional integration among intrinsic connectivity networks of migraine patients without aura. However, few study focused on the pattern alterations in the intrinsic connectivity networks of migraine patients without aura. Results: Thirty-one migraine patients without aura and 31 age- and education-matched healthy controls participated in this study. After identifying the default mode network, central executive network and salience network as core intrinsic connectivity networks by using independent component analysis, functional connectivity, and effective connectivity during the resting state were used to investigate the abnormalities in intrinsic connectivity network interactions. Migraine patients without aura showed decreased functional connectivity among intrinsic connectivity networks compared with healthy controls. The strength of causal influences from the right frontoinsular cortex to the right anterior cingulate cortex became weaker, and the right frontoinsular cortex to the right medial prefrontal cortex became stronger in migraine patients without aura. Conclusions: These changes suggested that the salience network may play a major role in the pathophysiological features of migraine patients without aura and helped us to synthesize previous findings into an aberrant network dynamical framework.