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脑-肠微生物群网络(BGMN)与精神分裂症患者的症状严重程度和神经认知相关。

The brain-gut microbiota network (BGMN) is correlated with symptom severity and neurocognition in patients with schizophrenia.

作者信息

Peng Runlin, Wang Wei, Liang Liqin, Han Rui, Li Yi, Wang Haiyuan, Wang Yuran, Li Wenhao, Feng Shixuan, Zhou Jing, Huang Yuanyuan, Wu Fengchun, Wu Kai

机构信息

School of Biomedical Sciences and Engineering, South China University of Technology, Guangzhou International Campus, Guangzhou 511442, China.

Department of Psychiatry, The Affiliated Brain Hospital, Guangzhou Medical University, Guangzhou 510370, China.

出版信息

Neuroimage. 2025 Mar;308:121052. doi: 10.1016/j.neuroimage.2025.121052. Epub 2025 Jan 26.

Abstract

The association between the human brain and gut microbiota, known as the "brain-gut-microbiota axis", is involved in the neuropathological mechanisms of schizophrenia (SZ); however, its association patterns and correlations with symptom severity and neurocognition are still largely unknown. In this study, 43 SZ patients and 55 normal controls (NCs) were included, and resting-state functional magnetic resonance imaging (rs-fMRI) and gut microbiota data were acquired for each participant. First, the brain features of brain images and functional brain networks were computed from rs-fMRI data; the gut features of gut microbiota abundance and the gut microbiota network were computed from gut microbiota data. Second, we propose a novel methodology to construct an individual brain-gut microbiota network (BGMN) for each participant by combining the brain and gut features via multiple strategies. Third, discriminative models between SZ patients and NCs were built using the connectivity matrices of the BGMN as input features. Moreover, the correlations between the most discriminative features and the scores of symptom severity and neurocognition were analyzed in SZ patients. The results showed that the best discriminative model between SZ patients and NCs was achieved using the connectivity matrices of the BGMN when all the brain and gut features were integrated, with an accuracy of 0.90 and an area under the curve value of 0.97. The most discriminative features were related primarily to the genera Faecalibacterium and Collinsella, in which the genus Faecalibacterium was linked to the visual system and subcortical cortices and the genus Collinsella was linked to the default network and subcortical cortices. Furthermore, parts of the most discriminative features were significantly correlated with the scores of neurocognition in the SZ patients. The methodology for constructing individual BGMNs proposed in this study can help us reveal the associations between the brain and gut microbiota and understand the neuropathology of SZ.

摘要

人类大脑与肠道微生物群之间的关联,即所谓的“脑-肠-微生物群轴”,参与了精神分裂症(SZ)的神经病理机制;然而,其关联模式以及与症状严重程度和神经认知的相关性在很大程度上仍不清楚。在本研究中,纳入了43名SZ患者和55名正常对照(NC),并为每位参与者获取了静息态功能磁共振成像(rs-fMRI)和肠道微生物群数据。首先,从rs-fMRI数据计算脑图像的脑特征和功能性脑网络;从肠道微生物群数据计算肠道微生物群丰度的肠道特征和肠道微生物群网络。其次,我们提出了一种新颖的方法,通过多种策略将脑特征和肠道特征相结合,为每位参与者构建个体脑-肠微生物群网络(BGMN)。第三,使用BGMN的连通性矩阵作为输入特征,建立SZ患者和NC之间的判别模型。此外,在SZ患者中分析了最具判别力的特征与症状严重程度和神经认知得分之间的相关性。结果表明,当整合所有脑特征和肠道特征时,使用BGMN的连通性矩阵在SZ患者和NC之间实现了最佳判别模型,准确率为0.90,曲线下面积值为0.97。最具判别力的特征主要与粪杆菌属和柯林斯菌属有关,其中粪杆菌属与视觉系统和皮层下皮质相关,柯林斯菌属与默认网络和皮层下皮质相关。此外,部分最具判别力的特征与SZ患者的神经认知得分显著相关。本研究中提出的构建个体BGMN的方法可以帮助我们揭示脑与肠道微生物群之间的关联,并理解SZ的神经病理学。

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