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Abnormal dynamic functional network connectivity in unmedicated bipolar and major depressive disorders based on the triple-network model

  • Junjing Wang (a1) (a2), Ying Wang (a3), Huiyuan Huang (a2), Yanbin Jia (a4), Senning Zheng (a2), Shuming Zhong (a4), Guanmao Chen (a3), Li Huang (a3) and Ruiwang Huang (a2)...



Previous studies have analyzed brain functional connectivity to reveal the neural physiopathology of bipolar disorder (BD) and major depressive disorder (MDD) based on the triple-network model [involving the salience network, default mode network (DMN), and central executive network (CEN)]. However, most studies assumed that the brain intrinsic fluctuations throughout the entire scan are static. Thus, we aimed to reveal the dynamic functional network connectivity (dFNC) in the triple networks of BD and MDD.


We collected resting state fMRI data from 51 unmedicated depressed BD II patients, 51 unmedicated depressed MDD patients, and 52 healthy controls. We analyzed the dFNC by using an independent component analysis, sliding window correlation and k-means clustering, and used the parameters of dFNC state properties and dFNC variability for group comparisons.


The dFNC within the triple networks could be clustered into four configuration states, three of them showing dense connections (States 1, 2, and 4) and the other one showing sparse connections (State 3). Both BD and MDD patients spent more time in State 3 and showed decreased dFNC variability between posterior DMN and right CEN (rCEN) compared with controls. The MDD patients showed specific decreased dFNC variability between anterior DMN and rCEN compared with controls.


This study revealed more common but less specific dFNC alterations within the triple networks in unmedicated depressed BD II and MDD patients, which indicated their decreased information processing and communication ability and may help us to understand their abnormal affective and cognitive functions clinically.


Corresponding author

Author for correspondence: Ying Wang, E-mail: and Ruiwang Huang, E-mail:


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These authors contributed equally to this work.



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