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Resting state cortico-cerebellar functional connectivity networks: a comparison of anatomical and self-organizing map approaches

机译:静止状态的皮质-小脑功能连接网络:解剖图和自组织图方法的比较

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摘要

The cerebellum plays a role in a wide variety of complex behaviors. In order to better understand the role of the cerebellum in human behavior, it is important to know how this structure interacts with cortical and other subcortical regions of the brain. To date, several studies have investigated the cerebellum using resting-state functional connectivity magnetic resonance imaging (fcMRI; Krienen and Buckner, ; O'Reilly et al., ; Buckner et al., ). However, none of this work has taken an anatomically-driven lobular approach. Furthermore, though detailed maps of cerebral cortex and cerebellum networks have been proposed using different network solutions based on the cerebral cortex (Buckner et al., ), it remains unknown whether or not an anatomical lobular breakdown best encompasses the networks of the cerebellum. Here, we used fcMRI to create an anatomically-driven connectivity atlas of the cerebellar lobules. Timecourses were extracted from the lobules of the right hemisphere and vermis. We found distinct networks for the individual lobules with a clear division into “motor” and “non-motor” regions. We also used a self-organizing map (SOM) algorithm to parcellate the cerebellum. This allowed us to investigate redundancy and independence of the anatomically identified cerebellar networks. We found that while anatomical boundaries in the anterior cerebellum provide functional subdivisions of a larger motor grouping defined using our SOM algorithm, in the posterior cerebellum, the lobules were made up of sub-regions associated with distinct functional networks. Together, our results indicate that the lobular boundaries of the human cerebellum are not necessarily indicative of functional boundaries, though anatomical divisions can be useful. Additionally, driving the analyses from the cerebellum is key to determining the complete picture of functional connectivity within the structure.
机译:小脑在各种各样的复杂行为中起作用。为了更好地了解小脑在人类行为中的作用,重要的是要知道这种结构如何与大脑的皮质和其他皮质下区域相互作用。迄今为止,一些研究已经使用静止状态功能连接磁共振成像(fcMRI; Krienen和Buckner,; O'Reilly等,; Buckner等,)对小脑进行了研究。但是,这些工作都没有采用解剖学驱动的小叶方法。此外,尽管已经提出了使用基于大脑皮层的不同网络解决方案来绘制大脑皮层和小脑网络的详细图谱(Buckner等人),但解剖小叶破裂是否最好地涵盖了小脑网络仍然是未知的。在这里,我们使用fcMRI创建了小脑小叶的解剖学驱动的连接图集。时程从右半球和ver的小叶中提取。我们为单个小叶找到了不同的网络,并将其清楚地分为“运动”和“非运动”区域。我们还使用了自组织映射(SOM)算法来分割小脑。这使我们能够研究解剖学识别的小脑网络的冗余性和独立性。我们发现,虽然小脑前部的解剖边界提供了使用我们的SOM算法定义的较大运动组的功能细分,但在小脑后部,小叶由与不同功能网络相关的子区域组成。在一起,我们的结果表明,尽管解剖上的划分可能有用,但人类小脑的小叶边界并不一定表示功能边界。此外,从小脑进行分析是确定结构内功能连接性完整图景的关键。

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