ICA-AROMA: A robust ICA-based strategy for removing motion artifacts from fMRI data
ICA-AROMA: A robust ICA-based strategy for removing motion artifacts from fMRI data
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DOI：
10.1016/j.neuroimage.2015.02.064
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发表时间：
2015-05-15
期刊：
影响因子：
5.7
通讯作者：
Beckmann, Christian F.
中科院分区：
文献类型：
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作者：
Pruim, Raimon H. R.;Mennes, Maarten;Beckmann, Christian F.
功能磁共振成像（fMRI）扫描时的头部运动可能导致虚假的发现和/或对真实效果的危害检测。已经提出了解决方案，包括从fMRI时间序列中删除（“擦洗”）或回归（“尖峰回归”）运动体积。这些策略以破坏fMRI时间序列的自相关结构和降低时间自由度为代价去除运动引起的信号变化。基于ica的fMRI去噪策略克服了这些缺点，但通常需要重新训练分类器，需要手动标记衍生成分（例如ICA-FIX； Salimi-Khorshidi等人（2014））。在这里，我们提出了一种基于ica的自动去除运动伪像的策略（ICA-AROMA），该策略使用小（n = 4）但鲁棒的理论动机时空特征集。我们的策略不需要重新训练分类器，保留了数据的自相关结构，并在很大程度上保留了时间自由度。我们描述了ICA-AROMA，它的实现和初始验证。经leave- out交叉验证，ICA-AROMA识别运动成分具有较高的准确性和鲁棒性。我们还验证了静息状态（100名参与者）和基于任务的fMRI数据（118名参与者）的ICA-AROMA。我们的方法从rfMRI和基于任务的fMRI数据中去除（与运动相关的）伪噪声，其程度大于使用24个运动参数或峰值回归的回归。此外，ICA-AROMA增加了对组水平激活的敏感性。我们的研究结果表明，ICA-AROMA有效地减少了fMRI数据中运动引起的信号变化，在不需要重新训练分类器的情况下适用于多个数据集，并保留了fMRI数据的时间特征。(C) 2015爱思唯尔公司所有
Head motion during functional MRI (fMRI) scanning can induce spurious findings and/or harm detection of true effects. Solutions have been proposed, including deleting ('scrubbing') or regressing out ('spike regression') motion volumes from fMRI time-series. These strategies remove motion-induced signal variations at the cost of destroying the autocorrelation structure of the fMRI time-series and reducing temporal degrees of freedom. ICA-based fMRI denoising strategies overcome these drawbacks but typically require re-training of a classifier, needing manual labeling of derived components (e.g. ICA-FIX; Salimi-Khorshidi et al. (2014)). Here, we propose an ICA-based strategy for Automatic Removal of Motion Artifacts (ICA-AROMA) that uses a small (n = 4), but robust set of theoretically motivated temporal and spatial features. Our strategy does not require classifier re-training, retains the data's autocorrelation structure and largely preserves temporal degrees of freedom. We describe ICA-AROMA, its implementation, and initial validation. ICA-AROMA identified motion components with high accuracy and robustness as illustrated by leave-N-out cross-validation. We additionally validated ICA-AROMA in resting-state (100 participants) and task-based fMRI data (118 participants). Our approach removed (motion-related) spurious noise from both rfMRI and task-based fMRI data to larger extent than regression using 24 motion parameters or spike regression. Furthermore, ICA-AROMA increased sensitivity to group-level activation. Our results show that ICA-AROMA effectively reduces motion-induced signal variations in fMRI data, is applicable across datasets without requiring classifier re-training, and preserves the temporal characteristics of the fMRI data. (C) 2015 Elsevier Inc. All