Frequency-Specific Resting Connectome in Bipolar Disorder: An MEG Study - Sunaga, Masakazu; Takei, Yuichi; Kato, Yutaka; Tagawa, Minami; Suto, Tomohiro; Hironaga, Naruhito; Ohki, Takefumi; Takahashi, Yumiko; Fujihara, Kazuyuki; Sakurai, Noriko; Ujita, Koichi; Tsushima, Yoshito; et al. (Frontiers in Psychiatry, 2020)
Background: Bipolar disorder (BD) is a serious psychiatric disorder, especially due to the high rate of associated suicides; however, a clinical biomarker has not yet been identified for this disease. Here, we propose a new prospective tool to address this issue, magnetoencephalography (MEG). MEG is used to evaluate frequency-specific connectivity between brain regions; however, no previous study has investigated the frequency-specific resting-state connectome in BD. This resting-state MEG study aimed to explore the oscillatory representations of clinical symptoms of BD using graph analysis. Methods: In this prospective case-control study, 17 patients with BD and 22 healthy controls (HCs) underwent resting-state MEG and evaluations for depressive and manic symptoms. After estimating the source current distribution, orthogonalized envelope correlations among multiple brain regions were evaluated for each frequency band. We separated regions-of-interest into 7 left and right network modules, including the frontoparietal network (FPN), limbic network (LM), salience network (SAL), and default mode network (DMN), to compare intra- and inter-community edges between the two groups. Results: In the BD group, significantly increased inter-community edges of right LM-right DMN at the gamma band and decreased inter-community edges of right SAL-right FPN at the delta band and left SAL-right SAL at the theta band were observed. Intra-community edges in the left LM at the high beta band were significantly higher in the BD group than in the HC group and the increased number of connections in the left LM at the high beta band showed positive correlations with subjective and objective depressive symptoms in the BD group. Conclusion: We introduced graph theory into resting-state MEG studies to investigate functional connectivity in the BD population. To the best of our knowledge, this is a brand-new approach that may be beneficial in the diagnosis of BD. This study describes the spontaneous oscillatory brain networks that compensate for the time-domain issues associated with functional magnetic resonance imaging. These findings suggest that the connectivity of the LM at the beta band may be a good objective biological biomarker of depressive symptoms associated with BD.