Carone D, Licenik R, Suri S, Griffanti L, Filippini N, Kennedy J
Acute Vascular Imaging Centre, Radcliffe Department of Medicine, University of Oxford, Oxford, United Kingdom.
Laboratory of Experimental Stroke Research, Department of Surgery and Translational Medicine, University of Milano Bicocca, Milan Center of Neuroscience, Monza, Italy.
Neuroimage Clin. 2017 Jun 30;16:23-31. doi: 10.1016/j.nicl.2017.06.033. eCollection 2017.
Different strategies have been developed using Independent Component Analysis (ICA) to automatically de-noise fMRI data, either focusing on removing only certain components (e.g. motion-ICA-AROMA, Pruim et al., 2015a) or using more complex classifiers to remove multiple types of noise components (e.g. FIX, Salimi-Khorshidi et al., 2014 Griffanti et al., 2014). However, denoising data obtained in an acute setting might prove challenging: the presence of multiple noise sources may not allow focused strategies to clean the data enough and the heterogeneity in the data may be so great to critically undermine complex approaches. The purpose of this study was to explore what automated ICA based approach would better cope with these limitations when cleaning fMRI data obtained from acute stroke patients. The performance of a focused classifier (ICA-AROMA) and a complex classifier (FIX) approaches were compared using data obtained from twenty consecutive acute lacunar stroke patients using metrics determining RSN identification, RSN reproducibility, changes in the BOLD variance, differences in the estimation of functional connectivity and loss of temporal degrees of freedom. The use of generic-trained FIX resulted in misclassification of components and significant loss of signal (< 80%), and was not explored further. Both ICA-AROMA and patient-trained FIX based denoising approaches resulted in significantly improved RSN reproducibility ( < 0.001), localized reduction in BOLD variance consistent with noise removal, and significant changes in functional connectivity ( < 0.001). Patient-trained FIX resulted in higher RSN identifiability ( < 0.001) and wider changes both in the BOLD variance and in functional connectivity compared to ICA-AROMA. The success of ICA-AROMA suggests that by focusing on selected components the full automation can deliver meaningful data for analysis even in population with multiple sources of noise. However, the time invested to train FIX with appropriate patient data proved valuable, particularly in improving the signal-to-noise ratio.
人们已经开发出不同的策略，利用独立成分分析（ICA）自动去除功能磁共振成像（fMRI）数据中的噪声，有的策略只专注于去除特定成分（如运动ICA - AROMA，普鲁伊姆等人，2015年a），有的则使用更复杂的分类器来去除多种类型的噪声成分（如FIX，萨利米 - 霍尔希迪等人，2014年；格里凡蒂等人，2014年）。然而，对急性情况下获得的数据进行去噪可能具有挑战性：多种噪声源的存在可能使聚焦策略无法充分清理数据，而且数据中的异质性可能非常大，严重破坏复杂方法的效果。本研究的目的是探索在清理急性中风患者的fMRI数据时，哪种基于ICA的自动化方法能更好地应对这些限制。使用从20例连续的急性腔隙性中风患者获得的数据，通过确定静息态网络（RSN）识别、RSN可重复性、血氧水平依赖（BOLD）信号方差变化、功能连接估计差异以及时间自由度损失的指标，比较了聚焦分类器（ICA - AROMA）和复杂分类器（FIX）方法的性能。使用通用训练的FIX导致成分误分类和显著的信号损失（<80%），因此未作进一步研究。基于ICA - AROMA和患者训练的FIX的去噪方法都显著提高了RSN可重复性（<0.001），实现了与噪声去除一致的BOLD信号方差局部降低，以及功能连接的显著变化（<0.001）。与ICA - AROMA相比，患者训练的FIX导致更高的RSN可识别性（<0.001），以及在BOLD信号方差和功能连接方面更广泛的变化。ICA - AROMA的成功表明，通过专注于选定成分，即使在存在多种噪声源的人群中，完全自动化也能提供有意义的分析数据。然而，事实证明，投入时间用适当的患者数据训练FIX是有价值的，特别是在提高信噪比方面。
