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全面分析来自和去往小鼠顶后皮质的远程连接。

Comprehensive Analysis of Long-Range Connectivity from and to the Posterior Parietal Cortex of the Mouse.

机构信息

Grace Medical Institute, Lynnwood, WA 98037, USA.

Laboratory of Neurophysiology, Korea Brain Research Institute, Daegu 41062, Korea.

出版信息

Cereb Cortex. 2021 Jan 1;31(1):356-378. doi: 10.1093/cercor/bhaa230.

Abstract

The posterior parietal cortex (PPC) is a major multimodal association cortex implicated in a variety of higher order cognitive functions, such as visuospatial perception, spatial attention, categorization, and decision-making. The PPC is known to receive inputs from a collection of sensory cortices as well as various subcortical areas and integrate those inputs to facilitate the execution of functions that require diverse information. Although many recent works have been performed with the mouse as a model system, a comprehensive understanding of long-range connectivity of the mouse PPC is scarce, preventing integrative interpretation of the rapidly accumulating functional data. In this study, we conducted a detailed neuroanatomic and bioinformatic analysis of the Allen Mouse Brain Connectivity Atlas data to summarize afferent and efferent connections to/from the PPC. Then, we analyzed variability between subregions of the PPC, functional/anatomical modalities, and species, and summarized the organizational principle of the mouse PPC. Finally, we confirmed key results by using additional neurotracers. A comprehensive survey of the connectivity will provide an important future reference to comprehend the function of the PPC and allow effective paths forward to various studies using mice as a model system.

摘要

顶叶后皮质(PPC)是一个主要的多模态联合皮质,参与了多种高级认知功能,如视空间感知、空间注意、分类和决策。PPC 已知接收来自一系列感觉皮质以及各种皮质下区域的输入,并整合这些输入,以促进需要多样化信息的功能的执行。尽管许多最近的工作都是以小鼠作为模型系统进行的,但对小鼠 PPC 的长程连接的全面理解仍然很少,这阻碍了对快速积累的功能数据的综合解释。在这项研究中,我们对 Allen 小鼠大脑连接图谱数据进行了详细的神经解剖学和生物信息学分析,以总结来自/到 PPC 的传入和传出连接。然后,我们分析了 PPC 的子区域、功能/解剖模态和物种之间的可变性,并总结了小鼠 PPC 的组织原则。最后,我们使用额外的神经示踪剂证实了关键结果。对连接的全面调查将为理解 PPC 的功能提供一个重要的未来参考,并为使用小鼠作为模型系统的各种研究提供有效的前进途径。

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