Causal Interactions Between the Default Mode Network and Central Executive Network in Patients with Major Depression

• Published in: Neuroscience Vol. 475; pp. 93 - 102
• Main Authors: Li, Jiaming, Liu, Jian, Zhong, Yufang, Wang, Huaning, Yan, Baoyu, Zheng, Kaizhong, Wei, Lei, Lu, Hongbing, Li, Baojuan
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 01.11.2021
• Subjects: dynamic causal modeling; effective connectivity; major depressive disorder; parametric empirical Bayes; task negative network; task positive networks; task negative network; major depressive disorder; dynamic causal modeling; task positive networks; effective connectivity; parametric empirical Bayes
• ISSN: 0306-4522; 1873-7544; 1873-7544
• DOI: 10.1016/j.neuroscience.2021.08.033

•Causal interactions between the CEN and DMN in depressed patients were investigated by spectral dynamic causal modeling.•Inhibitory influences from the CEN to the DMN were detected with node-level PEB analyses.•Patients with MDD showed increased effective connectivity within the CEN.•Decreased connectivity from regions of the CEN to DMN was found in the patients. Two different but interacting neural systems exist in the human brain: the task positive networks and task negative networks. One of the most important task positive networks is the central executive network (CEN), while the task negative network generally refers to the default mode network (DMN), which usually demonstrates task-induced deactivation. Although previous studies have clearly shown the association of both the CEN and DMN with major depressive disorder (MDD), how the causal interactions between these two networks change in depressed patients remains unclear. In the current study, 99 subjects (43 patients with MDD and 56 healthy controls) were recruited with their resting-state fMRI data collected. After data preprocessing, spectral dynamic causal modeling (spDCM) was used to investigate the causal interactions within and between the DMN and CEN. Group commonalities and differences in causal interaction patterns within and between the CEN and DMN in patients and controls were assessed by a parametric empirical Bayes (PEB) model. Both subject groups demonstrated significant effective connectivity between regions of the CEN and DMN. In particular, we detected inhibitory influences from the CEN to the DMN with node-level PEB analyses, which may help to explain the anticorrelations between these two networks consistently reported in previous studies. Compared with healthy controls, patients with MDD showed increased effective connectivity within the CEN and decreased connectivity from regions of the CEN to DMN, suggesting impaired control of the DMN by the CEN in these patients. These findings might provide new insights into the neural substrates of MDD.