Introduction: It is well known that even small head movements introduce artifacts in resting-state functional magnetic resonance imaging data, and over the years, numerous methods were introduced to correct for this issue. The field of robust statistics, however, has not yet received much attention in this regard. In this article, we tested a recently developed statistical method called wrapping and compared it with two already established methods: data scrubbing and an independent component analysis-based approach for the automatic removal of motion artifacts (ICA-AROMA).

中文翻译：

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静息状态 fMRI 脑网络中链接定义的稳健相关性可以减少与运动相关的伪影

简介：众所周知，即使是很小的头部运动也会在静息状态功能磁共振成像数据中引入伪影，多年来，引入了许多方法来纠正这个问题。然而，稳健统计领域在这方面尚未受到太多关注。在本文中，我们测试了一种最近开发的称为包裹的统计方法，并将其与两种已经建立的方法进行了比较：数据清理和基于独立分量分析的自动去除运动伪影的方法 (ICA-AROMA)。