Transcriptional Patterns of Nodal Entropy Abnormalities in Major Depressive Disorder Patients with and without Suicidal Ideation.

IF 11 1区 综合性期刊 Q1 Multidisciplinary
Research Pub Date : 2025-04-02 eCollection Date: 2025-01-01 DOI:10.34133/research.0659
Minxin Guo, Heng Zhang, Yuanyuan Huang, Yunheng Diao, Wei Wang, Zhaobo Li, Shixuan Feng, Jing Zhou, Yuping Ning, Fengchun Wu, Kai Wu
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引用次数: 0

Abstract

Previous studies have indicated that major depressive disorder (MDD) patients with suicidal ideation (SI) present abnormal functional connectivity (FC) and network organization in node-centric brain networks, ignoring the interactions among FCs. Whether the abnormalities of edge interactions affect the emergence of SI and are related to the gene expression remains largely unknown. In this study, resting-state functional magnetic resonance imaging (fMRI) data were collected from 90 first-episode, drug-naive MDD with suicidal ideation (MDDSI) patients, 60 first-episode, drug-naive MDD without suicidal ideation (MDDNSI) patients, and 98 healthy controls (HCs). We applied the methodology of edge-centric network analysis to construct the functional brain networks and calculate the nodal entropy. Furthermore, we examined the relationships between nodal entropy alterations and gene expression. The MDDSI group exhibited significantly lower subnetwork entropy in the dorsal attention network (DAN) and significantly greater subnetwork entropy in the default mode network than the MDDNSI group. The visual learning score of the measurement and treatment research to improve cognition in schizophrenia (MATRICS) consensus cognitive battery was negatively correlated with the subnetwork entropy of DAN in the MDDSI group. The support vector machine model based on nodal entropy achieved an accuracy of 81.87% when distinguishing the MDDNSI and MDDSI. Additionally, the changes in SI-related nodal entropy were associated with the expression of genes in cell signaling and interactions, as well as immune and inflammatory responses. These findings reveal the abnormalities in nodal entropy between the MDDSI and MDDNSI groups, demonstrated their association with molecular functions, and provided novel insights into the neurobiological underpinnings and potential markers for the prediction and prevention of suicide.

有或无自杀意念的重度抑郁症患者淋巴结熵异常的转录模式。
以往的研究表明,重度抑郁障碍(MDD)合并自杀意念(SI)患者在节点中心脑网络中存在异常的功能连接(FC)和网络组织,而忽略了FC之间的相互作用。边缘相互作用的异常是否影响SI的出现,是否与基因表达有关,在很大程度上仍是未知的。在这项研究中,静息状态功能磁共振成像(fMRI)数据收集了90例首发药物型MDD伴自杀意念(MDDSI)患者、60例首发药物型MDD无自杀意念(MDDNSI)患者和98例健康对照(hc)。应用边缘中心网络分析方法构建脑功能网络并计算节点熵。此外,我们还研究了节点熵改变与基因表达之间的关系。MDDSI组在背侧注意网络(DAN)的子网络熵显著低于MDDNSI组,在默认模式网络的子网络熵显著高于MDDNSI组。精神分裂症认知改善测量与治疗研究(matrix)共识认知电池的视觉学习得分与MDDSI组DAN的子网熵呈负相关。基于节点熵的支持向量机模型区分MDDNSI和MDDSI的准确率为81.87%。此外,si相关节点熵的变化与细胞信号传导和相互作用中基因的表达以及免疫和炎症反应有关。这些发现揭示了MDDSI组和MDDNSI组之间的节点熵异常,证明了它们与分子功能的关联,并为预测和预防自杀的神经生物学基础和潜在标志物提供了新的见解。
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来源期刊
Research
Research Multidisciplinary-Multidisciplinary
CiteScore
13.40
自引率
3.60%
发文量
0
审稿时长
14 weeks
期刊介绍: Research serves as a global platform for academic exchange, collaboration, and technological advancements. This journal welcomes high-quality research contributions from any domain, with open arms to authors from around the globe. Comprising fundamental research in the life and physical sciences, Research also highlights significant findings and issues in engineering and applied science. The journal proudly features original research articles, reviews, perspectives, and editorials, fostering a diverse and dynamic scholarly environment.
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