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首发和复发性重度抑郁症中大规模网络的不同静息态有效连接性:来自REST-meta-MDD联盟的证据。

Distinct resting-state effective connectivity of large-scale networks in first-episode and recurrent major depression disorder: evidence from the REST-meta-MDD consortium.

作者信息

Zhu Yao, Huang Tianming, Li Ruolin, Yang Qianrong, Zhao Chaoyue, Yang Ming, Lin Bin, Li Xuzhou

机构信息

School of Psychology and Cognitive Science, East China Normal University, Shanghai, China.

Department of General Psychiatry, Shanghai Changning Mental Health Center, Shanghai, China.

出版信息

Front Neurosci. 2023 Dec 11;17:1308551. doi: 10.3389/fnins.2023.1308551. eCollection 2023.

DOI:10.3389/fnins.2023.1308551
PMID:38148946
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC10750394/
Abstract

INTRODUCTION

Previous studies have shown disrupted effective connectivity in the large-scale brain networks of individuals with major depressive disorder (MDD). However, it is unclear whether these changes differ between first-episode drug-naive MDD (FEDN-MDD) and recurrent MDD (R-MDD).

METHODS

This study utilized resting-state fMRI data from 17 sites in the Chinese REST-meta-MDD project, consisting of 839 patients with MDD and 788 normal controls (NCs). All data was preprocessed using a standardized protocol. Then, we performed a granger causality analysis to calculate the effectivity connectivity (EC) within and between brain networks for each participant, and compared the differences between the groups.

RESULTS

Our findings revealed that R-MDD exhibited increased EC in the fronto-parietal network (FPN) and decreased EC in the cerebellum network, while FEDN-MDD demonstrated increased EC from the sensorimotor network (SMN) to the FPN compared with the NCs. Importantly, the two MDD subgroups displayed significant differences in EC within the FPN and between the SMN and visual network. Moreover, the EC from the cingulo-opercular network to the SMN showed a significant negative correlation with the Hamilton Rating Scale for Depression (HAMD) score in the FEDN-MDD group.

CONCLUSION

These findings suggest that first-episode and recurrent MDD have distinct effects on the effective connectivity in large-scale brain networks, which could be potential neural mechanisms underlying their different clinical manifestations.

摘要

引言

先前的研究表明,重度抑郁症(MDD)患者的大规模脑网络中有效连接性遭到破坏。然而,尚不清楚这些变化在首发未用药的MDD(FEDN-MDD)和复发性MDD(R-MDD)之间是否存在差异。

方法

本研究利用了中国REST-meta-MDD项目中17个站点的静息态功能磁共振成像数据,该数据包括839例MDD患者和788例正常对照(NC)。所有数据均使用标准化方案进行预处理。然后,我们进行了格兰杰因果分析,以计算每个参与者脑网络内部和之间的有效连接性(EC),并比较组间差异。

结果

我们的研究结果显示,R-MDD患者额顶叶网络(FPN)的EC增加,小脑网络的EC降低,而FEDN-MDD患者与NC相比,感觉运动网络(SMN)到FPN的EC增加。重要的是,两个MDD亚组在FPN内部以及SMN和视觉网络之间的EC存在显著差异。此外,在FEDN-MDD组中,扣带回-脑岛网络到SMN的EC与汉密尔顿抑郁量表(HAMD)评分呈显著负相关。

结论

这些发现表明,首发和复发性MDD对大规模脑网络中的有效连接性有不同影响,这可能是它们不同临床表现背后的潜在神经机制。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8256/10750394/34e50417d8e2/fnins-17-1308551-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8256/10750394/9640acff9101/fnins-17-1308551-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8256/10750394/8d9094c20798/fnins-17-1308551-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8256/10750394/2029141812b3/fnins-17-1308551-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8256/10750394/34e50417d8e2/fnins-17-1308551-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8256/10750394/9640acff9101/fnins-17-1308551-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8256/10750394/8d9094c20798/fnins-17-1308551-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8256/10750394/2029141812b3/fnins-17-1308551-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8256/10750394/34e50417d8e2/fnins-17-1308551-g004.jpg

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