PDF(Pubmed)
Abstract：
BACKGROUND: Cerebral resting-state networks were suggested to be strongly associated with depressive disorders. However, the causal relationship between cerebral networks and depressive disorders remains controversial. In this study, we aimed to investigate the effect of resting-state networks on depressive disorders using a bidirectional Mendelian randomization (MR) design.
METHODS: Updated summary-level genome-wide association study (GWAS) data correlated with resting-state networks were obtained from a meta-analysis of European-descent GWAS from the Complex Trait Genetics Lab. Depression-related GWAS data were obtained from the FinnGen study involving participants with European ancestry. Resting-state functional magnetic resonance imaging and multiband diffusion imaging of the brain were performed to measure functional and structural connectivity in seven well-known networks. Inverse-variance weighting was used as the primary estimate, whereas the MR-Pleiotropy RESidual Sum and Outliers (PRESSO), MR-Egger, and weighted median were used to detect heterogeneity, sensitivity, and pleiotropy.
RESULTS: In total, 20,928 functional and 20,573 structural connectivity data as well as depression-related GWAS data from 48,847 patients and 225,483 controls were analyzed. Evidence for a causal effect of the structural limbic network on depressive disorders was found in the inverse variance-weighted limbic network (odds ratio, [Formula: see text]; 95% confidence interval, [Formula: see text]; [Formula: see text]), whereas the causal effect of depressive disorders on SC LN was not found(OR=1.0025; CI,1.0005-1.0046; P=0.012). No significant associations between functional connectivity of the resting-state networks and depressive disorders were found in this MR study.
CONCLUSIONS: These results suggest that genetically determined structural connectivity of the limbic network has a causal effect on depressive disorders and may play a critical role in its neuropathology.
METHODS: Updated summary-level genome-wide association study (GWAS) data correlated with resting-state networks were obtained from a meta-analysis of European-descent GWAS from the Complex Trait Genetics Lab. Depression-related GWAS data were obtained from the FinnGen study involving participants with European ancestry. Resting-state functional magnetic resonance imaging and multiband diffusion imaging of the brain were performed to measure functional and structural connectivity in seven well-known networks. Inverse-variance weighting was used as the primary estimate, whereas the MR-Pleiotropy RESidual Sum and Outliers (PRESSO), MR-Egger, and weighted median were used to detect heterogeneity, sensitivity, and pleiotropy.
RESULTS: In total, 20,928 functional and 20,573 structural connectivity data as well as depression-related GWAS data from 48,847 patients and 225,483 controls were analyzed. Evidence for a causal effect of the structural limbic network on depressive disorders was found in the inverse variance-weighted limbic network (odds ratio, [Formula: see text]; 95% confidence interval, [Formula: see text]; [Formula: see text]), whereas the causal effect of depressive disorders on SC LN was not found(OR=1.0025; CI,1.0005-1.0046; P=0.012). No significant associations between functional connectivity of the resting-state networks and depressive disorders were found in this MR study.
CONCLUSIONS: These results suggest that genetically determined structural connectivity of the limbic network has a causal effect on depressive disorders and may play a critical role in its neuropathology.
摘要：
背景：大脑静息状态网络被认为与抑郁症密切相关。然而,脑网络与抑郁障碍之间的因果关系仍存在争议.在这项研究中,我们旨在通过双向孟德尔随机化(MR)设计,研究静息态网络对抑郁障碍的影响.
方法：更新的与静息态网络相关的汇总水平全基因组关联研究（GWAS）数据来自复杂性状遗传学实验室对欧洲裔GWAS的荟萃分析。抑郁症相关的GWAS数据来自FinnGen研究，涉及具有欧洲血统的参与者。进行了大脑的静息状态功能磁共振成像和多波段扩散成像，以测量七个众所周知的网络中的功能和结构连通性。方差反加权被用作主要估计，而MR-Pleiothypeotropival和异常值(PRESSO)，MR-Egger,加权中位数用于检测异质性，灵敏度,和多功能性。
结果：总计，分析了来自48,847名患者和225,483名对照的20,928个功能和20,573个结构连接数据以及与抑郁相关的GWAS数据。在逆方差加权边缘网络（优势比，[公式：见正文]；95%置信区间，[公式:见文本]；[公式:见文本])，而未发现抑郁障碍对SCLN的因果影响(OR=1.0025;CI,1.0005-1.0046;P=0.012)。在这项MR研究中，没有发现静息状态网络的功能连通性与抑郁症之间的显着关联。
结论：这些结果表明，遗传决定的边缘网络的结构连通性对抑郁症具有因果效应，并且可能在其神经病理学中起关键作用。
方法：更新的与静息态网络相关的汇总水平全基因组关联研究（GWAS）数据来自复杂性状遗传学实验室对欧洲裔GWAS的荟萃分析。抑郁症相关的GWAS数据来自FinnGen研究，涉及具有欧洲血统的参与者。进行了大脑的静息状态功能磁共振成像和多波段扩散成像，以测量七个众所周知的网络中的功能和结构连通性。方差反加权被用作主要估计，而MR-Pleiothypeotropival和异常值(PRESSO)，MR-Egger,加权中位数用于检测异质性，灵敏度,和多功能性。
结果：总计，分析了来自48,847名患者和225,483名对照的20,928个功能和20,573个结构连接数据以及与抑郁相关的GWAS数据。在逆方差加权边缘网络（优势比，[公式：见正文]；95%置信区间，[公式:见文本]；[公式:见文本])，而未发现抑郁障碍对SCLN的因果影响(OR=1.0025;CI,1.0005-1.0046;P=0.012)。在这项MR研究中，没有发现静息状态网络的功能连通性与抑郁症之间的显着关联。
结论：这些结果表明，遗传决定的边缘网络的结构连通性对抑郁症具有因果效应，并且可能在其神经病理学中起关键作用。
