Disrupted Structural Brain Network Organization Behind Depressive Symptoms in Major Depressive Disorder.

Jing Liu,Binbin Xiao,Lingtao Hu,Xiaopei Xu,Bin Feng,Qi Wang,Liyuan Luo,Chunqing Zhu,Lanying Liu

doi:10.3389/fpsyt.2020.565890

Abstract

Major depressive disorder (MDD) is a severe and devastating condition. However, the anatomical basis behind the affective symptoms, cognitive symptoms, and somatic-vegetative symptoms of MDD is still unknown. 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. Twenty-six patients with MDD and 25 CN were included. 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. 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. 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). We also found that the characteristic path length of MDD patients was associated with weight loss. Moreover, significantly lower global efficiency of MDD patients was correlated with higher total HAMD score, anxiety somatization, and cognitive disturbance. The nodal degree in SFG was also found to be negatively associated with disease duration. 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.

Highlights

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”
There were no significant differences in age, gender, and education between CN and patients with MDD
Most importantly, when we studied the relationship between weighted network metrics and clinical variables, we found that the characteristic path length in MDD patients was associated (p = 0.031) with weight loss

Summary

Introduction

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”. The discrete depressive episode includes clear changes in mood, interests and pleasure, changes in cognition and vegetative symptoms. 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]. Given the heterogeneity of this disorder, identifying brain alterations is important as this may indicate potential neural networks related to diagnose and treatment response, as one aspect of the biological markers of MDD. The exploration of biomarkers and timely and effective intervention for MDD is of great importance

Methods

Results

Discussion

Conclusion