ICA-based artefact removal and accelerated fMRI acquisition for improved resting state network imaging.
ICA-based artefact removal and accelerated fMRI acquisition for improved resting state network imaging.
复制标题
基于ICA的人工制品去除和加速fMRI获取，以改善静止状态网络成像。
DOI：
10.1016/j.neuroimage.2014.03.034
复制
发表时间：
2014-07-15
期刊：
影响因子：
5.7
通讯作者：
Smith SM
中科院分区：
文献类型：
--
作者：
Griffanti L;Salimi-Khorshidi G;Beckmann CF;Auerbach EJ;Douaud G;Sexton CE;Zsoldos E;Ebmeier KP;Filippini N;Mackay CE;Moeller S;Xu J;Yacoub E;Baselli G;Ugurbil K;Miller KL;Smith SM
静息态功能磁共振成像（rfMRI）的静息态网络（RSNs）的识别和定量的功能连接严重阻碍了伪影的存在，其中许多重叠空间或频谱与RSNs。此外，最近的发展，在功能磁共振成像采集产量数据具有更高的空间和时间分辨率，但可能会增加伪影空间和/或时间。因此，正确识别和去除非神经波动是至关重要的，特别是在加速采集中。在本文中，我们研究了三个数据驱动的清洗程序的有效性，比较标准对更高的（空间和时间）分辨率加速功能磁共振成像采集，并调查不同的收购和不同的清理方法的综合效果。我们应用单主题独立成分分析（伊卡），然后使用FMRIB基于ICA的X噪声器（FIX）进行自动成分分类，以识别伪成分。然后，我们比较了两种第一级（受试者内）清洗方法，用于从数据中去除这些伪影和与运动相关的波动。使用时间序列（振幅和光谱），网络矩阵和空间图分析的清洁程序的有效性进行了评估。对于时间序列和网络分析，我们还测试了第二级清理的效果（由组级分析提供信息）。比较这些方法，噪声去除和信号损失之间的较好的平衡，通过回归出的数据的运动相关的波动的整个空间，只有独特的方差的伪伊卡组件。使用类似的分析，我们还研究了不同的清洗方法对不同采集序列的数据的影响。使用最佳清洁程序，加速数据的功能连接结果在统计学上与标准（未加速）采集相当或显著优于标准（未加速）采集，并且至关重要的是，具有更高的空间和时间分辨率。此外，我们能够使用加速数据执行更高维度的伊卡分解，这对于详细的网络分析非常有价值。
The identification of resting state networks (RSNs) and the quantification of their functional connectivity in resting-state fMRI (rfMRI) are seriously hindered by the presence of artefacts, many of which overlap spatially or spectrally with RSNs. Moreover, recent developments in fMRI acquisition yield data with higher spatial and temporal resolutions, but may increase artefacts both spatially and/or temporally. Hence the correct identification and removal of non-neural fluctuations is crucial, especially in accelerated acquisitions. In this paper we investigate the effectiveness of three data-driven cleaning procedures, compare standard against higher (spatial and temporal) resolution accelerated fMRI acquisitions, and investigate the combined effect of different acquisitions and different cleanup approaches. We applied single-subject independent component analysis (ICA), followed by automatic component classification with FMRIB’s ICA-based X-noiseifier (FIX) to identify artefactual components. We then compared two first-level (within-subject) cleaning approaches for removing those artefacts and motion-related fluctuations from the data. The effectiveness of the cleaning procedures were assessed using timeseries (amplitude and spectra), network matrix and spatial map analyses. For timeseries and network analyses we also tested the effect of a second-level cleaning (informed by group-level analysis). Comparing these approaches, the preferable balance between noise removal and signal loss was achieved by regressing out of the data the full space of motion-related fluctuations and only the unique variance of the artefactual ICA components. Using similar analyses, we also investigated the effects of different cleaning approaches on data from different acquisition sequences. With the optimal cleaning procedures, functional connectivity results from accelerated data were statistically comparable or significantly better than the standard (unaccelerated) acquisition, and, crucially, with higher spatial and temporal resolution. Moreover, we were able to perform higher dimensionality ICA decompositions with the accelerated data, which is very valuable for detailed network analyses.
登录
查看更多内容
影响因子：
5.7
作者：
Jenkinson, M;Bannister, P;Smith, S
通讯作者：
Smith, S
DOI：
10.1098/rstb.2005.1634
发表时间：
2005-05-29
影响因子：
6.3
作者：
Beckmann, CF;DeLuca, M;Smith, SM
通讯作者：
Smith, SM
影响因子：
5.7
作者：
Beall, Erik B.;Lowe, Mark J.
通讯作者：
Lowe, Mark J.
影响因子：
2.5
作者：
Cordes, D;Haughton, V;Maravilla, K
通讯作者：
Maravilla, K
影响因子：
5.7
作者：
Kundu, Prantik;Inati, Souheil J.;Evans, Jennifer W.;Luh, Wen-Ming;Bandettini, Peter A.
通讯作者：
Bandettini, Peter A.