Department of Psychology, University of Michigan, Ann Arbor MI, USA.
Front Neuroanat. 2012 Aug 10;6:31. doi: 10.3389/fnana.2012.00031. eCollection 2012.
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, 2009; O'Reilly et al., 2010; Buckner et al., 2011). 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., 2011), 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.
小脑在各种复杂行为中都发挥着作用。为了更好地理解小脑在人类行为中的作用,了解该结构如何与大脑皮层和其他皮质下区域相互作用非常重要。迄今为止,已有几项研究使用静息态功能磁共振成像 (fcMRI; Krienen 和 Buckner, 2009; O'Reilly 等人, 2010; Buckner 等人, 2011) 来研究小脑。然而,这些研究都没有采用解剖驱动的叶区方法。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的大脑皮层和小脑网络的详细图谱 (Buckner 等人, 2011),但尚不清楚解剖学叶区划分是否能最好地涵盖小脑的网络。在这里,我们使用 fcMRI 来创建小脑叶的解剖驱动连接图谱。时间序列是从右半球和蚓部的叶区中提取的。我们发现每个叶区都有独特的网络,并有明确的“运动”和“非运动”区域划分。我们还使用自组织映射 (SOM) 算法对小脑进行分割。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,虽然在前小脑的解剖边界提供了使用我们的 SOM 算法定义的更大运动分组的功能细分,但在后小脑,叶区由与不同功能网络相关联的子区域组成。总的来说,我们的结果表明,人类小脑的叶区边界不一定表示功能边界,尽管解剖学划分可能是有用的。此外,从小脑驱动分析是确定结构内功能连接全貌的关键。