Altered effective connectivity model in the default mode network between bipolar and unipolar depression based on resting-state fMRI

Abstract

Background: Bipolar depression (BD) is characterized by alternating episodes of depression and mania. Patients who spend the majority of their time in episodes of depression rather than mania are often misdiagnosed with unipolar depression (UD) that only exhibits depressive episodes. It would be important to explore the construction of more objective biomarkers which can be used to more accurately differentiate BD and UD.

Methods: The effective connectivity model of BD and UD in the default mode network (DMN) was constructed based on resting-state fMRI data of 17 BD (32.12±8.57 years old) and 17 UD (32.59±9.77 years old) patients using a linear non-Gaussian acyclic model (LiNGAM). The effective connectivity differences were obtained by conducting a permutation test.

Results: The following connections were stronger in the BD group than in the UD group: medial prefrontal cortex (MPFC) →posterior cingulate cortex (PCC), right inferior parietal cortex (rIPC)→left hippocampus (lHC) and rIPC→right insula (rInsula). In contrast, the following connections were weak or unapparent in the BD group: MPFC→lHC, rHC→MPFC, rHC→rInsula and rInsula→lHC.

Limitations: First, the medication effect is a confounding factor. Second, as with most fMRI studies, the subjects׳ thoughts during imaging are difficult to control.

Conclusions: The brain regions in these altered connections, such as the HC, insula, MPFC and IPC, all play important roles in emotional processing, suggesting that these altered connections may be conducive to better distinguish between BD and UD.

Keywords: Bipolar depression; Effective connectivity; LiNGAM; Resting-state fMRI; Unipolar depression.