DataSheet_1_Frequency-Specific Resting Connectome in Bipolar Disorder: An MEG Study.docx (1.35 MB)

DataSheet_1_Frequency-Specific Resting Connectome in Bipolar Disorder: An MEG Study.docx

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posted on 25.06.2020, 08:42 by Masakazu Sunaga, Yuichi Takei, Yutaka Kato, Minami Tagawa, Tomohiro Suto, Naruhito Hironaga, Takefumi Ohki, Yumiko Takahashi, Kazuyuki Fujihara, Noriko Sakurai, Koichi Ujita, Yoshito Tsushima, Masato Fukuda
Background

Bipolar disorder (BD) is a serious psychiatric disorder that is associated with a high suicide rate, and for which no clinical biomarker has yet been identified. To address this issue, we investigated the use of magnetoencephalography (MEG) as a new prospective tool. MEG has been used to evaluate frequency-specific connectivity between brain regions; however, no previous study has investigated the frequency-specific resting-state connectome in patients with BD. This resting-state MEG study explored the oscillatory representations of clinical symptoms of BD via 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 between multiple brain regions were evaluated for each frequency band. We separated regions-of-interest into seven left and right network modules, including the frontoparietal network (FPN), limbic network (LM), salience network (SAL), and default mode network (DMN), to compare the intra- and inter-community edges between the two groups.

Results

In the BD group, we found significantly increased inter-community edges of the right LM–right DMN at the gamma band, and decreased inter-community edges of the right SAL–right FPN at the delta band and the left SAL–right SAL at the theta band. Intra-community edges in the left LM at the high beta band were significantly higher in the BD group than in the HC group. The number of connections in the left LM at the high beta band showed positive correlations with the subjective and objective depressive symptoms in the BD group.

Conclusion

We introduced graph theory into resting-state MEG studies to investigate the functional connectivity in patients with BD. To the best of our knowledge, this is a novel 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 the depressive symptoms associated with BD.

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