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小鼠大脑的三重网络组织。

A triple-network organization for the mouse brain.

机构信息

Singapore Bioimaging Consortium, Agency for Science, Technology and Research, 11 Biopolis Way, Singapore, 138667, Singapore.

Faculty of Biology, Medicine and Health, The University of Manchester, Manchester, UK.

出版信息

Mol Psychiatry. 2022 Feb;27(2):865-872. doi: 10.1038/s41380-021-01298-5. Epub 2021 Oct 14.

Abstract

The triple-network model of psychopathology is a framework to explain the functional and structural neuroimaging phenotypes of psychiatric and neurological disorders. It describes the interactions within and between three distributed networks: the salience, default-mode, and central executive networks. These have been associated with brain disorder traits in patients. Homologous networks have been proposed in animal models, but their integration into a triple-network organization has not yet been determined. Using resting-state datasets, we demonstrate conserved spatio-temporal properties between triple-network elements in human, macaque, and mouse. The model predictions were also shown to apply in a mouse model for depression. To validate spatial homologies, we developed a data-driven approach to convert mouse brain maps into human standard coordinates. Finally, using high-resolution viral tracers in the mouse, we refined an anatomical model for these networks and validated this using optogenetics in mice and tractography in humans. Unexpectedly, we find serotonin involvement within the salience rather than the default-mode network. Our results support the existence of a triple-network system in the mouse that shares properties with that of humans along several dimensions, including a disease condition. Finally, we demonstrate a method to humanize mouse brain networks that opens doors to fully data-driven trans-species comparisons.

摘要

精神病理学的三重网络模型是一个解释精神和神经障碍的功能和结构神经影像学表型的框架。它描述了三个分布式网络内部和之间的相互作用:突显网络、默认模式网络和中央执行网络。这些网络与患者的大脑障碍特征有关。在动物模型中已经提出了同源网络,但它们尚未整合到三重网络组织中。我们使用静息态数据集,证明了人类、猕猴和小鼠三重网络元素之间具有保守的时空特性。该模型的预测也适用于抑郁小鼠模型。为了验证空间同源性,我们开发了一种数据驱动的方法,将小鼠大脑图谱转换为人类标准坐标。最后,我们使用小鼠中的高分辨率病毒示踪剂,对这些网络的解剖模型进行了细化,并在小鼠中使用光遗传学和人类中的示踪技术对其进行了验证。出乎意料的是,我们发现 5-羟色胺存在于突显网络中,而不是默认模式网络中。我们的研究结果支持在小鼠中存在三重网络系统,该系统在多个维度上与人类具有相似的特性,包括疾病状态。最后,我们展示了一种将小鼠大脑网络人性化的方法,为全面的数据驱动跨物种比较开辟了道路。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5d06/9054663/d111574e0ffe/41380_2021_1298_Fig1_HTML.jpg

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