与重度抑郁症患者自杀意念和行为相关的脑结构协变网络异常的转录模式。
Transcriptional Patterns of Brain Structural Covariance Network Abnormalities Associated With Suicidal Thoughts and Behaviors in Major Depressive Disorder.
机构信息
Department of Radiology and Huaxi MR Research Center, Functional and Molecular Imaging Key Laboratory of Sichuan Province, West China Hospital, Sichuan University, Chengdu, China; Department of Radiology, Taihe Hospital, Hubei University of Medicine, Shiyan, China; Research Unit of Psychoradiology, Chinese Academy of Medical Sciences, Chengdu, China; Department of Psychiatry and Behavioral Neuroscience, University of Cincinnati, Cincinnati, Ohio.
Department of Radiology and Huaxi MR Research Center, Functional and Molecular Imaging Key Laboratory of Sichuan Province, West China Hospital, Sichuan University, Chengdu, China; Research Unit of Psychoradiology, Chinese Academy of Medical Sciences, Chengdu, China; Department of Medical Imaging, The First Affiliated Hospital of Kunming Medical University, Kunming, China.
出版信息
Biol Psychiatry. 2024 Sep 15;96(6):435-444. doi: 10.1016/j.biopsych.2024.01.026. Epub 2024 Feb 4.
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.
背景
尽管大脑结构协变网络（SCN）异常与伴有自杀意念和行为（STBs）的重性抑郁障碍（MDD）个体有关，但基于小样本量的先前研究报告了不一致的发现，且潜在的转录模式仍知之甚少。
方法
我们使用包含 218 例伴有 STBs 的 MDD 患者、230 例无 STBs 的 MDD 患者和 263 例健康对照参与者的多中心磁共振成像数据集，基于区域形态计量学指标建立个体化 SCN，并使用图论分析评估网络拓扑度量。我们应用机器学习方法来探索和比较形态计量和拓扑特征在个体水平识别 MDD 和 STBs 的诊断价值。使用偏最小二乘回归检查与 STBs 相关的连接组学改变与基因表达之间的全脑关系。
结果
组间比较显示，与 STBs 相关的 SCN 拓扑缺陷在前额皮质、前扣带皮质和外侧颞叶皮质中被识别。结合形态计量和拓扑特征可实现 MDD 和 STBs 的个体水平特征化。拓扑特征对区分有和无 STBs 的患者做出了更大的贡献。与 STBs 相关的连接组学改变与细胞代谢和突触信号相关基因的表达呈空间相关。
结论
这些发现揭示了网络水平的稳健大脑结构缺陷，强调了 SCN 拓扑测量在个体自杀特征刻画中的重要性，并表明其与分子功能和细胞类型有关，为自杀的神经生物学基础及其预测和预防的潜在标志物提供了新的见解。
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