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基于基线静息态功能连接预测重度抑郁症干预的治疗结果:一项荟萃分析。

Predicting the treatment outcomes of major depressive disorder interventions with baseline resting-state functional connectivity: a meta-analysis.

作者信息

Zhou Yanyao, Dong Na, Lei Letian, Chang Dorita H F, Lam Charlene L M

机构信息

Laboratory of Clinical Psychology and Affective Neuroscience, The University of Hong Kong, Hong Kong, China.

The State Key Laboratory of Brain and Cognitive Sciences, The University of Hong Kong, Hong Kong, China.

出版信息

BMC Psychiatry. 2025 Apr 7;25(1):340. doi: 10.1186/s12888-025-06728-0.

Abstract

BACKGROUND

Current interventions for major depressive disorder (MDD) demonstrate limited and heterogeneous efficacy, highlighting the need for improving the precision of treatment. Although findings have been mixed, resting-state functional connectivity (rsFC) at baseline shows promise as a predictive biomarker. This meta-analysis evaluates the evidence for baseline rsFC as a predictor of treatment outcomes of MDD interventions.

METHOD

We included MDD literature published between 2012 and 2024 that used antidepressants, non-invasive brain stimulation, and cognitive behavioral therapy. Pearson correlations or their equivalents were analyzed between baseline rsFC and treatment outcome. Nodes were categorized according to the type of brain networks they belong to, and pooled coefficients were generated for rsFC connections reported by more than three studies.

RESULT

Among the 16 included studies and 892 MDD patients, data from nine studies were used to generate pooled coefficients for the rsFC connection between the frontoparietal network (FPN) and default mode network (DMN), and within the DMN (six studies each, with three overlapping studies, involving 534 and 300 patients, respectively). The rsFC between the DMN and FPN had a pooled predictability of -0.060 (p = 0.171, fixed effect model), and the rsFC within the DMN had a pooled predictability of 0.207 (p < 0.001, fixed effect model). The rsFC between the DMN and FPN and the rsFC within the DMN had a larger effect in predicting the outcome of non-invasive brain stimulation (-0.215, p < 0.001, fixed effect model) and antidepressants (0.315, p < 0.001, fixed effect model), respectively. Heterogeneity was observed in both types of rsFC, study design, sample characteristics and data analysis pipeline.

CONCLUSION

Baseline rsFC within the DMN and between the DMN and FPN demonstrated a small but differential predictive effect on the outcome of antidepressants and non-invasive brain stimulation, respectively. The small predictability of rsFC suggested that rsFC between the FPN and DMN and the rsFC within the DMN might not be a good biomarker for predicting treatment outcome. Future research should focus on exploring treatment-specific predictions of baseline rsFC and its predictive utility for other types of MDD interventions.

TRIAL REGISTRATION

The review was pre-registered at PROSPERO CRD42022370235 (33).

摘要

背景

目前针对重度抑郁症(MDD)的干预措施显示出有限且异质性的疗效,这凸显了提高治疗精准度的必要性。尽管研究结果不一,但基线静息态功能连接(rsFC)有望成为一种预测性生物标志物。本荟萃分析评估了基线rsFC作为MDD干预治疗结果预测指标的证据。

方法

我们纳入了2012年至2024年间发表的使用抗抑郁药、非侵入性脑刺激和认知行为疗法的MDD文献。分析了基线rsFC与治疗结果之间的皮尔逊相关性或其等效指标。节点根据其所属的脑网络类型进行分类,并为三项以上研究报告的rsFC连接生成合并系数。

结果

在纳入的16项研究和892名MDD患者中,来自9项研究的数据用于生成额顶叶网络(FPN)与默认模式网络(DMN)之间以及DMN内部rsFC连接的合并系数(每项各6项研究,其中3项重叠研究,分别涉及534名和300名患者)。DMN与FPN之间的rsFC合并预测性为-0.060(p = 0.171,固定效应模型),DMN内部的rsFC合并预测性为0.207(p < 0.001,固定效应模型)。DMN与FPN之间的rsFC以及DMN内部的rsFC在预测非侵入性脑刺激(-0.215,p < 0.001,固定效应模型)和抗抑郁药(0.315,p < 0.001,固定效应模型)的结果方面分别具有更大的效应。在两种类型的rsFC、研究设计、样本特征和数据分析流程中均观察到异质性。

结论

DMN内部以及DMN与FPN之间的基线rsFC分别对抗抑郁药和非侵入性脑刺激的结果显示出较小但有差异的预测作用。rsFC的预测性较小表明,FPN与DMN之间的rsFC以及DMN内部的rsFC可能不是预测治疗结果的良好生物标志物。未来的研究应侧重于探索基线rsFC的治疗特异性预测及其对其他类型MDD干预措施的预测效用。

试验注册

该综述在PROSPERO CRD42022370235(33)进行了预注册。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4aa0/11974056/02b4f67d5fff/12888_2025_6728_Fig1_HTML.jpg

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