Wang Yanlu, Li Tie-Qiang
Department of Clinical Science, Intervention and Technology, Karolinska Institute Stockholm, Sweden.
Department of Clinical Science, Intervention and Technology, Karolinska Institute Stockholm, Sweden ; Unit of Medical Imaging, Function, and Technology, Department of Medical Physics, Karolinska University Hospital Huddinge, Sweden.
Front Hum Neurosci. 2015 May 8;9:259. doi: 10.3389/fnhum.2015.00259. eCollection 2015.
Different machine learning algorithms have recently been used for assisting automated classification of independent component analysis (ICA) results from resting-state fMRI data. The success of this approach relies on identification of artifact components and meaningful functional networks. A limiting factor of ICA is the uncertainty of the number of independent components (NIC). We aim to develop a framework based on support vector machines (SVM) and optimized feature-selection for automated classification of independent components (ICs) and use the framework to investigate the effects of input NIC on the ICA results. Seven different resting-state fMRI datasets were studied. 18 features were devised by mimicking the empirical criteria for manual evaluation. The five most significant (p < 0.01) features were identified by general linear modeling and used to generate a classification model for the framework. This feature-optimized classification of ICs with SVM (FOCIS) framework was used to classify both group and single subject ICA results. The classification results obtained using FOCIS and previously published FSL-FIX were compared against manually evaluated results. On average the false negative rate in identifying artifact contaminated ICs for FOCIS and FSL-FIX were 98.27 and 92.34%, respectively. The number of artifact and functional network components increased almost linearly with the input NIC. Through tracking, we demonstrate that incrementing NIC affects most ICs when NIC < 33, whereas only a few limited ICs are affected by direct splitting when NIC is incremented beyond NIC > 40. For a given IC, its changes with increasing NIC are individually specific irrespective whether the component is a potential resting-state functional network or an artifact component. Using FOCIS, we investigated experimentally the ICA dimensionality of resting-state fMRI datasets and found that the input NIC can critically affect the ICA results of resting-state fMRI data.
最近，不同的机器学习算法已被用于辅助对静息态功能磁共振成像（fMRI）数据的独立成分分析（ICA）结果进行自动分类。这种方法的成功依赖于人工成分和有意义的功能网络的识别。ICA的一个限制因素是独立成分数量（NIC）的不确定性。我们旨在开发一个基于支持向量机（SVM）和优化特征选择的框架，用于独立成分（IC）的自动分类，并使用该框架研究输入NIC对ICA结果的影响。研究了七个不同的静息态fMRI数据集。通过模仿人工评估的经验标准设计了18个特征。通过一般线性模型识别出五个最显著（p < 0.01）的特征，并将其用于生成该框架的分类模型。这个使用SVM的IC特征优化分类（FOCIS）框架被用于对组和单受试者的ICA结果进行分类。将使用FOCIS和先前发表的FSL - FIX获得的分类结果与人工评估结果进行比较。平均而言，FOCIS和FSL - FIX在识别受人工成分污染的IC时的假阴性率分别为98.27%和92.34%。人工成分和功能网络成分的数量几乎随输入NIC呈线性增加。通过跟踪，我们证明当NIC < 33时，增加NIC会影响大多数IC，而当NIC增加超过NIC > 40时，只有少数有限的IC会受到直接分裂的影响。对于给定的IC，无论该成分是潜在的静息态功能网络还是人工成分，其随NIC增加的变化都是个体特异的。使用FOCIS，我们通过实验研究了静息态fMRI数据集的ICA维度，发现输入NIC会严重影响静息态fMRI数据的ICA结果。
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