Zhou Zhe Charles, Salzwedel Andrew P, Radtke-Schuller Susanne, Li Yuhui, Sellers Kristin K, Gilmore John H, Shih Yen-Yu Ian, Fröhlich Flavio, Gao Wei
Department of Psychiatry, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, United States; Neurobiology Curriculum, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, United States.
Biomedical Imaging Research Institute, Cedars-Sinai Medical Center, Los Angeles, CA 90048, United States; Department of Biomedical Sciences, Cedars-Sinai Medical Center, Los Angeles, CA 90048, United States.
Neuroimage. 2016 Dec;143:70-81. doi: 10.1016/j.neuroimage.2016.09.003. Epub 2016 Sep 2.
Resting state functional magnetic resonance imaging (rsfMRI) has emerged as a versatile tool for non-invasive measurement of functional connectivity patterns in the brain. RsfMRI brain dynamics in rodents, non-human primates, and humans share similar properties; however, little is known about the resting state functional connectivity patterns in the ferret, an animal model with high potential for developmental and cognitive translational study. To address this knowledge-gap, we performed rsfMRI on anesthetized ferrets using a 9.4T MRI scanner, and subsequently performed group-level independent component analysis (gICA) to identify functionally connected brain networks. Group-level ICA analysis revealed distributed sensory, motor, and higher-order networks in the ferret brain. Subsequent connectivity analysis showed interconnected higher-order networks that constituted a putative default mode network (DMN), a network that exhibits altered connectivity in neuropsychiatric disorders. Finally, we assessed ferret brain topological efficiency using graph theory analysis and found that the ferret brain exhibits small-world properties. Overall, these results provide additional evidence for pan-species resting-state networks, further supporting ferret-based studies of sensory and cognitive function.
静息态功能磁共振成像（rsfMRI）已成为一种用于无创测量大脑功能连接模式的多功能工具。啮齿动物、非人灵长类动物和人类的rsfMRI脑动力学具有相似的特性；然而，对于雪貂这种在发育和认知转化研究方面具有巨大潜力的动物模型，其静息态功能连接模式却知之甚少。为了填补这一知识空白，我们使用9.4T MRI扫描仪对麻醉后的雪貂进行了rsfMRI检查，随后进行了组水平独立成分分析（gICA）以识别功能连接的脑网络。组水平ICA分析揭示了雪貂大脑中分布的感觉、运动和高阶网络。随后的连接性分析显示，相互连接的高阶网络构成了一个假定的默认模式网络（DMN），该网络在神经精神疾病中表现出连接性改变。最后，我们使用图论分析评估了雪貂大脑的拓扑效率，发现雪貂大脑具有小世界特性。总体而言，这些结果为泛物种静息态网络提供了更多证据，进一步支持了基于雪貂的感觉和认知功能研究。
