Center for Functional MRI, University of California San Diego, 9500 Gilman Drive MC 0677, La Jolla, CA 92093, USA.
National Institutes of Health, Bethesda, MD 20892, USA.
Neuroimage. 2018 Jul 1;174:317-327. doi: 10.1016/j.neuroimage.2018.03.012. Epub 2018 Mar 13.
Changes in vigilance or alertness during a typical resting state fMRI scan are inevitable and have been found to affect measures of functional brain connectivity. Since it is not often feasible to monitor vigilance with EEG during fMRI scans, it would be of great value to have methods for estimating vigilance levels from fMRI data alone. A recent study, conducted in macaque monkeys, proposed a template-based approach for fMRI-based estimation of vigilance fluctuations. Here, we use simultaneously acquired EEG/fMRI data to investigate whether the same template-based approach can be employed to estimate vigilance fluctuations of awake humans across different resting-state conditions. We first demonstrate that the spatial pattern of correlations between EEG-defined vigilance and fMRI in our data is consistent with the previous literature. Notably, however, we observed a significant difference between the eyes-closed (EC) and eyes-open (EO) conditions, finding stronger negative correlations with vigilance in regions forming the default mode network and higher positive correlations in thalamus and insula in the EC condition when compared to the EO condition. Taking these correlation maps as "templates" for vigilance estimation, we found that the template-based approach produced fMRI-based vigilance estimates that were significantly correlated with EEG-based vigilance measures, indicating its generalizability from macaques to humans. We also demonstrate that the performance of this method was related to the overall amount of variability in a subject's vigilance state, and that the template-based approach outperformed the use of the global signal as a vigilance estimator. In addition, we show that the template-based approach can be used to estimate the variability across scans in the amplitude of the vigilance fluctuations. We discuss the benefits and tradeoffs of using the template-based approach in future fMRI studies.
在典型的静息态 fMRI 扫描期间,警觉或觉醒状态的变化是不可避免的,并且已经发现这会影响功能脑连接的测量。由于在 fMRI 扫描期间通常不可能使用 EEG 来监测警觉度,因此仅从 fMRI 数据估计警觉度水平的方法将具有重要价值。最近一项在猕猴中进行的研究提出了一种基于模板的 fMRI 估计警觉波动的方法。在这里,我们使用同时采集的 EEG/fMRI 数据来研究相同的基于模板的方法是否可以用于估计不同静息状态条件下清醒人类的警觉波动。我们首先证明,我们数据中 EEG 定义的警觉与 fMRI 之间的相关性的空间模式与先前的文献一致。然而,值得注意的是,我们观察到闭眼 (EC) 和睁眼 (EO) 条件之间存在显著差异,与警觉负相关的区域在默认模式网络中更强,而在 EC 条件下丘脑和岛叶中的正相关更强,与 EO 条件相比。将这些相关图作为警觉估计的“模板”,我们发现基于模板的方法产生的 fMRI 基于警觉估计与 EEG 基于警觉测量显著相关,表明其从猕猴到人类的通用性。我们还表明,该方法的性能与受试者警觉状态整体变化量有关,并且基于模板的方法优于使用全局信号作为警觉估计器。此外,我们表明,基于模板的方法可用于估计警觉波动幅度在扫描之间的可变性。我们讨论了在未来 fMRI 研究中使用基于模板的方法的优缺点。