Transcriptional Patterns of Brain Structural Covariance Network Abnormalities Associated With Suicidal Thoughts and Behaviors in Major Depressive Disorder

IF 9.6 1区 医学 Q1 NEUROSCIENCES
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Abstract

Background

Although brain structural covariance network (SCN) abnormalities have been associated with suicidal thoughts and behaviors (STBs) in individuals with major depressive disorder (MDD), previous studies have reported inconsistent findings based on small sample sizes, and underlying transcriptional patterns remain poorly understood.

Methods

Using a multicenter magnetic resonance imaging dataset including 218 MDD patients with STBs, 230 MDD patients without STBs, and 263 healthy control participants, we established individualized SCNs based on regional morphometric measures and assessed network topological metrics using graph theoretical analysis. Machine learning methods were applied to explore and compare the diagnostic value of morphometric and topological features in identifying MDD and STBs at the individual level. Brainwide relationships between STBs-related connectomic alterations and gene expression were examined using partial least squares regression.

Results

Group comparisons revealed that SCN topological deficits associated with STBs were identified in the prefrontal, anterior cingulate, and lateral temporal cortices. Combining morphometric and topological features allowed for individual-level characterization of MDD and STBs. Topological features made a greater contribution to distinguishing between patients with and without STBs. STBs-related connectomic alterations were spatially correlated with the expression of genes enriched for cellular metabolism and synaptic signaling.

Conclusions

These findings revealed robust brain structural deficits at the network level, highlighting the importance of SCN topological measures in characterizing individual suicidality and demonstrating its linkage to molecular function and cell types, providing novel insights into the neurobiological underpinnings and potential markers for prediction and prevention of suicide.

与重度抑郁障碍患者自杀想法和行为相关的大脑结构协方差网络异常的转录模式
背景虽然大脑结构协方差网络(SCN)异常与重度抑郁障碍(MDD)患者的自杀想法和行为(STB)有关,但之前的研究因样本量小而报告的结果不一致,而且对其潜在的转录模式仍然知之甚少。方法利用一个多中心 MRI 数据集(包括 218 名有 STB 的 MDD 患者(MDD-STB)、230 名无 STB 的 MDD 患者(MDD-nSTB)和 263 名健康对照(HC)),我们根据区域形态计量建立了个体化的 SCN,并利用图论分析评估了网络拓扑指标。我们应用机器学习方法探索和比较了形态计量和拓扑特征在个体水平上识别 MDD 和 STB 的诊断价值。结果分组比较显示,与 STB 相关的 SCN 拓扑缺陷在前额叶、前扣带回和外侧颞叶皮层被识别出来。结合形态计量学和拓扑学特征,可以对 MDD 和 STB 进行个体水平的特征描述。拓扑特征对区分STB患者和非STB患者有更大的帮助。结论 这些研究结果揭示了网络水平上强大的大脑结构缺陷,强调了SCN拓扑特征在描述个体自杀倾向方面的重要性,并证明了其与分子功能和细胞类型的联系,为预测和预防自杀的神经生物学基础和潜在标记提供了新的见解。
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来源期刊
Biological Psychiatry
Biological Psychiatry 医学-精神病学
CiteScore
18.80
自引率
2.80%
发文量
1398
审稿时长
33 days
期刊介绍: Biological Psychiatry is an official journal of the Society of Biological Psychiatry and was established in 1969. It is the first journal in the Biological Psychiatry family, which also includes Biological Psychiatry: Cognitive Neuroscience and Neuroimaging and Biological Psychiatry: Global Open Science. The Society's main goal is to promote excellence in scientific research and education in the fields related to the nature, causes, mechanisms, and treatments of disorders pertaining to thought, emotion, and behavior. To fulfill this mission, Biological Psychiatry publishes peer-reviewed, rapid-publication articles that present new findings from original basic, translational, and clinical mechanistic research, ultimately advancing our understanding of psychiatric disorders and their treatment. The journal also encourages the submission of reviews and commentaries on current research and topics of interest.
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