The chronic fatigue syndrome (CFS)/myalgic encephalomyelitis is a debilitating disease with unknown pathophysiology and no diagnostic test. This study investigated the default mode network (DMN) to understand the pathophysiology of CFS and to identify potential biomarkers. Using functional MRI (fMRI) collected from 72 subjects (45 CFS and 27 controls) with a temporal resolution of 0.798 sec, we evaluated the DMN using static functional connectivity (FC), dynamic functional connectivity (DFC) and DFC complexity, blood oxygenation level dependent (BOLD) activation maps, and complexity of activity. General linear model univariate analysis was used for intergroup comparison to account for age and gender differences. Hierarchical regression analysis was used to test whether fMRI measures could be used to explain variances of health scores. BOLD signals in the posterior cingulate cortex (PCC), the driving hub in the DMN, were more complex in CFS in both resting state and task (p < 0.05). The FCs between medial prefrontal cortex (mPFC) and both inferior parietal lobules (IPLs) were weaker (p < 0.05) during resting state, whereas during task mPFC-left IPL and mPFC-PCC were weaker (p < 0.05). The DFCs between the DMN hubs were more complex in CFS (p < 0.05) during task. Each of these differences accounted for 7-11% variability of health scores. This study showed that DMN activity is more complex and less coordinated in CFS, suggesting brain network analysis could be potentially used as a diagnostic biomarker for CFS.
Keywords: chronic fatigue syndrome; complexity; connectivity; default mode network; dysfunction.