Neuroimaging Research Center for Veterans, VA Boston Healthcare System, Boston, MA, USA.
Neuroimage. 2012 May 1;60(4):2073-85. doi: 10.1016/j.neuroimage.2012.01.139. Epub 2012 Feb 9.
Cerebellar functional circuitry has been examined in several prior studies using resting fMRI data and seed-based procedures, as well as whole-brain independent component analysis (ICA). Here, we hypothesized that ICA applied to functional data from the cerebellum exclusively would provide increased sensitivity for detecting cerebellar networks compared to previous approaches. Consistency of group-level networks was assessed in two age- and sex-matched groups of twenty-five subjects each. Cerebellum-only ICA was compared to the traditional whole-brain ICA procedure to examine the potential gain in sensitivity of the novel method. In addition to replicating a number of previously identified cerebellar networks, the current approach revealed at least one network component that was not apparent with the application of whole brain ICA. These results demonstrate the gain in sensitivity attained through specifying the cerebellum as a target structure with regard to the identification of robust and reliable networks. The use of similar procedures could be important in further expanding on previously defined patterns of cerebellar functional anatomy, as well as provide information about unique networks that have not been explored in prior work. Such information may prove crucial for understanding the cognitive and behavioral importance of the cerebellum in health and disease.
小脑功能回路已经在一些先前的研究中进行了研究,这些研究使用静息 fMRI 数据和基于种子的程序,以及全脑独立成分分析(ICA)。在这里,我们假设与以前的方法相比,仅应用于小脑功能数据的 ICA 会提高检测小脑网络的敏感性。在两个年龄和性别匹配的每组二十五名受试者中评估了组水平网络的一致性。将小脑专用 ICA 与传统的全脑 ICA 程序进行了比较,以检查新方法的潜在敏感性增益。除了复制许多先前确定的小脑网络外,当前方法还揭示了至少一个网络组件,而应用全脑 ICA 则无法明显看出该组件。这些结果表明,通过将小脑指定为目标结构,在识别稳健可靠的网络方面,灵敏度得到了提高。类似程序的使用对于进一步扩展先前定义的小脑功能解剖模式以及提供以前工作中未探索过的有关独特网络的信息可能非常重要。这些信息对于了解小脑在健康和疾病中的认知和行为重要性可能至关重要。