重度抑郁症中的区域结构-功能连接耦合与神经递质和基因谱相关。

Regional Structural-Functional Connectivity Coupling in Major Depressive Disorder Is Associated With Neurotransmitter and Genetic Profiles.

作者信息

Chu Tongpeng, Si Xiaopeng, Xie Haizhu, Ma Heng, Shi Yinghong, Yao Wei, Xing Dong, Zhao Feng, Dong Fanghui, Gai Qun, Che Kaili, Guo Yuting, Chen Danni, Ming Dong, Mao Ning

机构信息

Academy of Medical Engineering and Translational Medicine, Tianjin University, Tianjin, China; Department of Radiology, Yantai Yuhuangding Hospital, Qingdao University, Yantai, Shandong, China; State Key Laboratory of Advanced Medical Materials and Devices, Tianjin, China; Haihe Laboratory of Brain-computer Interaction and Human-machine Integration, Tianjin, China; Tianjin Key Laboratory of Brain Science and Neuroengineering, Tianjin University, Tianjin, China; Shandong Provincial Key Medical and Health Laboratory of Intelligent Diagnosis and Treatment for Women's Diseases, Yantai Yuhuangding Hospital, Yantai, Shandong, China; Big Data and Artificial Intelligence Laboratory, Yantai Yuhuangding Hospital, Qingdao University, Yantai, Shandong, China.

Academy of Medical Engineering and Translational Medicine, Tianjin University, Tianjin, China; State Key Laboratory of Advanced Medical Materials and Devices, Tianjin, China; Haihe Laboratory of Brain-computer Interaction and Human-machine Integration, Tianjin, China; Tianjin Key Laboratory of Brain Science and Neuroengineering, Tianjin University, Tianjin, China.

出版信息

Biol Psychiatry. 2025 Feb 1;97(3):290-301. doi: 10.1016/j.biopsych.2024.08.022. Epub 2024 Aug 30.

DOI:10.1016/j.biopsych.2024.08.022

PMID:39218135

Abstract

BACKGROUND

Abnormalities in structural-functional connectivity (SC-FC) coupling have been identified globally in patients with major depressive disorder (MDD). However, investigations have neglected the variability and hierarchical distribution of these abnormalities across different brain regions. Furthermore, the biological mechanisms that underlie regional SC-FC coupling patterns are not well understood.

METHODS

We enrolled 182 patients with MDD and 157 healthy control participants and quantified the intergroup differences in regional SC-FC coupling. Extreme gradient boosting (XGBoost), support vector machine, and random forest models were constructed to assess the potential of SC-FC coupling as biomarkers for MDD diagnosis and symptom prediction. Then, we examined the link between changes in regional SC-FC coupling in patients with MDD, neurotransmitter distributions, and gene expression.

RESULTS

We observed increased regional SC-FC coupling in the default mode network (t = 3.233) and decreased coupling in the frontoparietal network (t = -3.471) in patients with MDD compared with healthy control participants. XGBoost (area under the receiver operating characteristic curve = 0.853), support vector machine (area under the receiver operating characteristic curve = 0.832), and random forest (p < .05) models exhibited good prediction performance. The alterations in regional SC-FC coupling in patients with MDD were correlated with the distributions of 4 neurotransmitters (p < .05) and expression maps of specific genes. These enriched genes were implicated in excitatory neurons, inhibitory neurons, cellular metabolism, synapse function, and immune signaling. These findings were replicated on 2 brain atlases.

CONCLUSIONS

This work enhances our understanding of MDD and paves the way for the development of additional targeted therapeutic interventions.

摘要

背景

在重度抑郁症（MDD）患者中，已在全球范围内发现结构-功能连接（SC-FC）耦合异常。然而，研究忽略了这些异常在不同脑区的变异性和层级分布。此外，区域SC-FC耦合模式背后的生物学机制尚不清楚。

方法

我们招募了182名MDD患者和157名健康对照参与者，并量化了组间区域SC-FC耦合的差异。构建了极端梯度提升（XGBoost）、支持向量机和随机森林模型，以评估SC-FC耦合作为MDD诊断和症状预测生物标志物的潜力。然后，我们研究了MDD患者区域SC-FC耦合变化、神经递质分布和基因表达之间的联系。

结果

与健康对照参与者相比，我们观察到MDD患者默认模式网络中的区域SC-FC耦合增加（t = 3.233），额顶网络中的耦合减少（t = -3.471）。XGBoost（受试者操作特征曲线下面积 = 0.853）、支持向量机（受试者操作特征曲线下面积 = 0.832）和随机森林（p <.05）模型表现出良好的预测性能。MDD患者区域SC-FC耦合的改变与4种神经递质的分布（p <.05）和特定基因的表达图谱相关。这些富集基因与兴奋性神经元、抑制性神经元、细胞代谢、突触功能和免疫信号有关。这些发现在2个脑图谱上得到了重复。