EEG-fMRI Methods for the Study of Brain Networks during Sleep.
EEG-fMRI Methods for the Study of Brain Networks during Sleep.
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DOI：
10.3389/fneur.2012.00100
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发表时间：
2012
影响因子：
3.4
通讯作者：
Duyn JH
中科院分区：
文献类型：
--
作者：
Duyn JH
现代神经影像学方法可以为睡眠的机制和作用以及大脑功能的特定机制提供独特的见解。最近的许多神经影像学研究使用了并发EEG和fMRI，这带来了独特的技术挑战，从在MRI环境中诱导睡眠的困难到适当的仪器和数据处理方法以获得无伪影的数据。此外，在睡眠期间使用EEG-fMRI导致独特的数据解释问题，因为用于分析任务诱发活动的常见方法不适用于睡眠。回顾了各种统计方法，可用于表征大脑活动的fMRI数据在睡眠期间获得的，重点是方法，调查大脑区域之间的相关活动的存在。这些方法中的每一种都有优点和缺点，必须与感兴趣的理论问题一起考虑。具体而言，在设计这些研究和选择相关统计方法时，应考虑睡眠控制和功能的基本理论。例如，睡眠期间的局部大脑活动可能由清醒期间的局部、使用依赖性活动触发的概念可以通过将睡眠网络作为统计独立组件进行分析来测试。或者，可以用相关性分析来研究区域参与更全局的过程，例如唤醒。
Modern neuroimaging methods may provide unique insights into the mechanism and role of sleep, as well as into particular mechanisms of brain function in general. Many of the recent neuroimaging studies have used concurrent EEG and fMRI, which present unique technical challenges ranging from the difficulty of inducing sleep in the MRI environment to appropriate instrumentation and data processing methods to obtain artifact free data. In addition, the use of EEG-fMRI during sleep leads to unique data interpretation issues, as common approaches developed for the analysis of task-evoked activity do not apply to sleep. Reviewed are a variety of statistical approaches that can be used to characterize brain activity from fMRI data acquired during sleep, with an emphasis on approaches that investigate the presence of correlated activity between brain regions. Each of these approaches has advantages and disadvantages that must be considered in concert with the theoretical questions of interest. Specifically, fundamental theories of sleep control and function should be considered when designing these studies and when choosing the associated statistical approaches. For example, the notion that local brain activity during sleep may be triggered by local, use-dependent activity during wakefulness may be tested by analyzing sleep networks as statistically independent components. Alternatively, the involvement of regions in more global processes such as arousal may be investigated with correlation analysis.