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


CONCLUSIONS
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.
College of Information Science and Technology, Beijing Normal University, Beijing 100875, China.
Yunting
Y
Liu
College of Information Science and Technology, Beijing Normal University, Beijing 100875, China; State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing 100875, China. Electronic address: wuxia@bnu.edu.cn.
Xia
X
Wu
College of Information Science and Technology, Beijing Normal University, Beijing 100875, China.
Jiacai
J
Zhang
College of Information Science and Technology, Beijing Normal University, Beijing 100875, China.
Xiaojuan
X
Guo
State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing 100875, China.
Zhiying
Z
Long
College of Information Science and Technology, Beijing Normal University, Beijing 100875, China; State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing 100875, China.
Li
L
Yao
D000328:Adult; D001714:Bipolar Disorder / Q000175:diagnosis* / Q000503:physiopathology*; D001921:Brain / Q000503:physiopathology*; D001931:Brain Mapping / Q000379:methods; D002540:Cerebral Cortex; D003863:Depression; D003866:Depressive Disorder / Q000175:diagnosis* / Q000503:physiopathology*; D003937:Diagnosis, Differential; D005260:Female; D006179:Gyrus Cinguli; D006624:Hippocampus; D006801:Humans; D008279:Magnetic Resonance Imaging / Q000379:methods*; D008297:Male; D010296:Parietal Lobe; D017397:Prefrontal Cortex; D012146:Rest
Bipolar depression; Effective connectivity; LiNGAM; Resting-state fMRI; Unipolar depression