Resting‐state functional magnetic resonance imaging (rs‐fMRI) has been successfully used to probe the intrinsic functional organization of the brain and to study brain development. Here, we implemented a combination of individual and group independent component analysis (ICA) of FSL on a 6‐min resting‐state data set acquired from 21 naturally sleeping term‐born (age 26 ± 6.7 d), healthy neonates to investigate the emerging functional resting‐state networks (RSNs). In line with the previous literature, we found evidence of sensorimotor, auditory/language, visual, cerebellar, thalmic, parietal, prefrontal, anterior cingulate as well as dorsal and ventral aspects of the default‐mode‐network. Additionally, we identified RSNs in frontal, parietal, and temporal regions that have not been previously described in this age group and correspond to the canonical RSNs established in adults. Importantly, we found that careful ICA‐based denoising of fMRI data increased the number of networks identified with group‐ICA, whereas the degree of spatial smoothing did not change the number of identified networks. Our results show that the infant brain has an established set of RSNs soon after birth.

中文翻译：

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新生儿大脑的静止状态网络使用独立成分分析来识别。

静止状态功能磁共振成像（rs-fMRI）已成功用于探测大脑的内在功能组织和研究大脑发育。在这里，我们对从21名自然睡眠足月龄（26±6.7 d）健康新生儿获得的6分钟静息状态数据集进行了FSL的个体和组独立成分分析（ICA）的组合，以调查新兴功能静止状态网络（RSN）。与以前的文献一致，我们发现了默认模式网络的感觉运动，听觉/语言，视觉，小脑，丘脑，顶叶，前额叶，前扣带回以及背侧和腹侧方面的证据。此外，我们在额叶，顶叶，和这个年龄组中以前没有描述过的时空区域，与成年人中建立的规范RSN相对应。重要的是，我们发现对fMRI数据进行基于ICA的仔细去噪会增加使用group-ICA识别的网络的数量，而空间平滑度不会改变所识别的网络的数量。我们的结果表明，婴儿脑在出生后不久就具有一套成熟的RSN。