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转录组相似性揭示抑郁症生物型中的神经形态偏差。

Transcriptomic Similarity Informs Neuromorphic Deviations in Depression Biotypes.

作者信息

Li Jiao, Long Zhiliang, Sheng Wei, Du Lian, Qiu Jiang, Chen Huafu, Liao Wei

机构信息

Clinical Hospital of Chengdu Brain Science Institute, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, P.R. China; MOE Key Laboratory for Neuroinformation, High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, University of Electronic Science and Technology of China, Chengdu, P.R. China.

Key Laboratory of Cognition and Personality, Faculty of Psychology, Southwest University, Chongqing, P.R. China.

出版信息

Biol Psychiatry. 2024 Mar 1;95(5):414-425. doi: 10.1016/j.biopsych.2023.08.003. Epub 2023 Aug 10.

Abstract

BACKGROUND

Major depressive disorder (MDD) is complicated by population heterogeneity, motivating the investigation of biotypes through imaging-derived phenotypes. However, neuromorphic heterogeneity in MDD remains unclear, and how the correlated gene expression (CGE) connectome constrains these neuromorphic anomalies in MDD biotypes has not yet been studied.

METHODS

Here, we related cortical thickness deviations in MDD biotypes to a pattern of CGE connectome. Cortical thickness was estimated from 3-dimensional T1-weighted magnetic resonance images in 2 independent cohorts (discovery cohort: N = 425; replication cohort: N = 217). The transcriptional activity was measured according to Allen Human Brain Atlas. A density peak-based clustering algorithm was used to identify MDD biotypes.

RESULTS

We found that patients with MDD were clustered into 2 replicated biotypes based on single-patient regional deviations from healthy control participants across 2 datasets. Biotype 1 mainly exhibited cortical thinning across the brain, whereas biotype 2 mainly showed cortical thickening in the brain. Using brainwide gene expression data, we found that deviations of transcriptionally connected neighbors predicted regional deviation for both biotypes. Furthermore, putative CGE-informed epicenters of biotype 1 were concentrated on the cognitive control circuit, whereas biotype 2 epicenters were located in the social perception circuit. The patterns of epicenter likelihood were separately associated with depression- and anxiety-response maps, suggesting that epicenters of MDD biotypes may be associated with clinical efficacies.

CONCLUSIONS

Our findings linked the CGE connectome and neuromorphic deviations to identify distinct epicenters in MDD biotypes, providing insight into how microscale gene expressions informed MDD biotypes.

摘要

背景

重度抑郁症(MDD)存在人群异质性,这促使人们通过影像衍生表型来研究生物型。然而,MDD中的神经形态异质性仍不明确,且相关基因表达(CGE)连接组如何在MDD生物型中约束这些神经形态异常尚未得到研究。

方法

在此,我们将MDD生物型中的皮质厚度偏差与CGE连接组模式相关联。从2个独立队列(发现队列:N = 425;重复队列:N = 217)的三维T1加权磁共振图像中估计皮质厚度。根据艾伦人类脑图谱测量转录活性。使用基于密度峰值的聚类算法来识别MDD生物型。

结果

我们发现,基于2个数据集中单个患者与健康对照参与者的区域偏差,MDD患者被聚类为2种重复的生物型。生物型1主要表现为全脑皮质变薄,而生物型2主要表现为脑内皮质增厚。利用全脑基因表达数据,我们发现转录连接邻居的偏差预测了两种生物型的区域偏差。此外,生物型1的假定CGE信息中心集中在认知控制回路,而生物型2的信息中心位于社会感知回路。中心可能性模式分别与抑郁和焦虑反应图谱相关,这表明MDD生物型的中心可能与临床疗效相关。

结论

我们的研究结果将CGE连接组与神经形态偏差联系起来,以识别MDD生物型中不同的中心,为微观尺度基因表达如何影响MDD生物型提供了见解。

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