Common functional networks in the mouse brain revealed by multi-centre resting-state fMRI analysis
Common functional networks in the mouse brain revealed by multi-centre resting-state fMRI analysis
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DOI：
10.1016/j.neuroimage.2019.116278
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发表时间：
2020-01-15
期刊：
影响因子：
5.7
通讯作者：
Gozzi, Alessandro
中科院分区：
文献类型：
--
作者：
Grandjean, Joanes;Canella, Carola;Gozzi, Alessandro
静息态功能磁共振成像（rsfMRI）的临床前应用提供了非侵入性探测全脑网络动力学和研究在人类研究中观察到的改变网络签名的决定因素的可能性。小鼠rsfMRI已越来越多地被世界各地的许多实验室采用。在这里，我们描述了一个多中心比较17小鼠rsfMRI数据集通过一个共同的图像处理和分析管道。尽管在设备和成像程序方面存在显著的跨实验室差异，但我们报告了在大多数数据集中对几个大规模静息状态网络（RSN）（包括小鼠默认模式网络）的可重复识别。多种因素的组合与功能连接性参数估计的重现性增强相关，包括动物处理程序和设备性能。RSN空间特异性增强的数据集采集在较高的场强，冷冻探针，在通气的动物，并在美托咪定-异氟烷联合镇静。我们的工作描述了一组具有代表性的RSN在小鼠大脑中，并强调关键的实验参数，可以严格指导设计和分析未来的啮齿动物rsfMRI调查。
Preclinical applications of resting-state functional magnetic resonance imaging (rsfMRI) offer the possibility to non-invasively probe whole-brain network dynamics and to investigate the determinants of altered network signatures observed in human studies. Mouse rsfMRI has been increasingly adopted by numerous laboratories worldwide. Here we describe a multi-centre comparison of 17 mouse rsfMRI datasets via a common image processing and analysis pipeline. Despite prominent cross-laboratory differences in equipment and imaging procedures, we report the reproducible identification of several large-scale resting-state networks (RSN), including a mouse default-mode network, in the majority of datasets. A combination of factors was associated with enhanced reproducibility in functional connectivity parameter estimation, including animal handling procedures and equipment performance. RSN spatial specificity was enhanced in datasets acquired at higher field strength, with cryoprobes, in ventilated animals, and under medetomidine-isoflurane combination sedation. Our work describes a set of representative RSNs in the mouse brain and highlights key experimental parameters that can critically guide the design and analysis of future rodent rsfMRI investigations.