title: Dimensionality of ICA in resting-state fMRI investigated by feature optimized classification of independent components with SVM.
abstract: 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.
authors.affiliation: Department of Clinical Science, Intervention and Technology, Karolinska Institute Stockholm, Sweden.
authors.forename: Yanlu
authors.initials: Y
authors.lastname: Wang
authors.affiliation: 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.
authors.forename: Tie-Qiang
authors.initials: TQ
authors.lastname: Li
keywords: functional neuroimaging; image processing; independent component analysis; machine learning; magnetic resonance imaging; pattern classification; signal processing