Toward a complete taxonomy of resting state networks across wakefulness and sleep: an assessment of spatially distinct resting state networks using independent component analysis
Toward a complete taxonomy of resting state networks across wakefulness and sleep: an assessment of spatially distinct resting state networks using independent component analysis
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DOI：
10.1093/sleep/zsy235
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发表时间：
2019-03-01
期刊：
影响因子：
5.6
通讯作者：
Fogel, Stuart M.
中科院分区：
文献类型：
--
作者：
Houldin, Evan;Fang, Zhuo;Fogel, Stuart M.
静息状态网络(RSN)的功能连通性(FC)已在各种不同的健康和受损条件下进行了研究。这类研究通常依赖于所谓的规范RSN的定义的空间边界和节点，它们本身是对功能磁共振成像(FMRI)噪声和神经信号之间的区别进行广泛讨论的产物，特别是在健康清醒状态的背景下。然而，在其他状态下，仍需要对噪音和网络进行类似的公正分类，特别是睡眠，这是一种健康的大脑替代模式，支持与清醒截然不同的操作，如做梦和巩固记忆。这项研究的目的是明确地检验这一假设，即存在睡眠特有的RSN。使用同步脑电(EEG)和功能磁共振成像(FMRI)记录非睡眠剥夺参与者的脑活动。对快速眼动(REM；N=7)和非REM睡眠期fMRI数据(非REM2；N=28，非REM3；N=11)进行独立成分分析，得到的成分与标准RSN空间相关，目的是识别空间上不同的RSN。令人惊讶的是，所有低相关性成分都被肯定地识别为噪声，而所有高相关性成分都包括通常在清醒时观察到的典型RSN集，这表明尽管在睡眠期间执行了独特的操作，但睡眠由与唤醒大致相同的RSN架构支持。这进一步表明先前研究的隐含假设，即典范RSN适用于睡眠FC分析，是有效的，并且没有忽略睡眠特定RSN。
Resting state network (RSN) functional connectivity (FC) has been investigated under a wealth of different healthy and compromised conditions. Such investigations are often dependent on the defined spatial boundaries and nodes of so-called canonical RSNs, themselves the product of extensive deliberations over distinctions between functional magnetic resonance imaging (fMRI) noise and neural signal, specifically in the context of the healthy waking state. However, a similar unbiased cataloging of noise and networks remains to be done in other states, particularly sleep, a healthy alternate mode of the brain that supports distinct operations from wakefulness, such as dreaming and memory consolidation. The purpose of this study was to explicitly test the hypothesis that there are RSNs unique to sleep. Simultaneous electroencephalography (EEG) and fMRI was used to record brain activity of non-sleep-deprived participants. Independent component analysis was performed on both rapid eye movement (REM; N = 7) and non-REM sleep stage fMRI data (non-REM2; N = 28, non-REM3; N = 11), with the resulting components spatially correlated with the canonical RSNs, for the purpose of identifying spatially distinct RSNs. Surprisingly, all low-correlation components were positively identified as noise, and all high-correlation components comprised the canonical set of RSNs typically observed in wake, indicating that sleep is supported by much the same RSN architecture as wakefulness, despite the unique operations performed during sleep. This further indicates that the implicit assumptions of prior studies, i.e. that the canonical RSNs apply to sleep FC analysis, are valid and have not overlooked sleep-specific RSNs.