The exploration of biomarkers and timely and effective intervention for MDD is of great importance. Given the heterogeneity of this disorder, identifying brain alterations is particularly important as this may indicate potential neural networks related to diagnose and treatment response, as one aspect of the biological markers of MDD. However, the results are inconsistent ( 9– 11) regarding the network change of MDD, and the pathogenesis of MDD is still not clear. Now, MDD is regarded as a disorder of dysregulated neural networks, including the default mode network (DMN), the salience network (SN), the cognitive control network (CCN), the affective network (AN), and parts of the limbic system, rather than as a destruction of single brain regions ( 8). Among all the studies trying to find the root of depressive symptoms of MDD, a significant amount of attention has been focused on the functional network changes in frontal-subcortical circuit ( 4, 5) and the role of network disruption in MDD has also been continuously explored using functional magnetic resonance imaging (fMRI) ( 6, 7). Large-scale longitudinal studies indicated that MDD increases the risk of diabetes mellitus, heart disease, stroke, hypertension, obesity, cancer, cognitive impairment, and Alzheimer disease ( 3). The discrete depressive episode includes clear changes in mood, interests and pleasure, changes in cognition and vegetative symptoms. MDD is characterized by at least one discrete depressive episode lasting for at least 2 weeks that is present across most situations ( 2). Major depressive disorder (MDD), which affects more than 322 million people worldwide, has become the second leading contributor to chronic disease burden among all medical conditions ( 1) as measured “by years lived with disability”. In conclusion, our results demonstrated that MDD patients had impaired structural network features compared to CN, and disruption of optimal network architecture might be the mechanism behind the depressive symptoms and emotion disturbance in MDD patients. The nodal degree in SFG was also found to be negatively associated with disease duration. Moreover, significantly lower global efficiency of MDD patients was correlated with higher total HAMD score, anxiety somatization, and cognitive disturbance. We also found that the characteristic path length of MDD patients was associated with weight loss. Furthermore, MDD patients demonstrated significantly lower nodal degree and nodal efficiency in multiple brain regions including superior frontal gyrus (SFG), supplementary motor area (SMA), calcarine fissure, middle temporal gyrus (MTG), and inferior temporal gyrus (ITG). When compared with healthy subjects, significantly higher characteristic path length and clustering coefficient as well as significantly lower network efficiencies were observed in patients with MDD. A baseline 24-item Hamilton rating scale for depression (HAMD-24) score ≥ 21 and seven factors (anxiety/somatization, weight loss, cognitive disturbance, diurnal variation, retardation, sleep disturbance, hopelessness) scores were assessed. Twenty-six patients with MDD and 25 CN were included. To explore the mechanism behind the depressive symptoms in MDD, we used diffusion tensor imaging (DTI)–based structural brain connectivity analysis to investigate the network difference between MDD patients and healthy controls (CN), and to explore the association between network metrics and patients’ clinical symptoms.

However, the anatomical basis behind the affective symptoms, cognitive symptoms, and somatic-vegetative symptoms of MDD is still unknown. Major depressive disorder (MDD) is a severe and devastating condition. 2Department of Radiology, The Second Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, China.1Department of Psychiatry, Tongde Hospital of Zhejiang Province, Hangzhou, China.Jing Liu 1† Xiaopei Xu 2† Chunqing Zhu 1 Liyuan Luo 1 Qi Wang 1 Binbin Xiao 1 Bin Feng 1 Lingtao Hu 1 Lanying Liu 1*

Major depressive episode length