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.
Highlights
Independent component analysis (ICA) is a data-driven, unsupervised analysis method for extracting resting-state functional connectivity networks (RFNs) (Calhoun et al, 2001; Beckmann et al, 2005; Kiviniemi et al, 2009; Schopf et al, 2010)
The purpose of this study is two-fold: (1) Develop a robust tracking and binary sorting framework based on feature optimized classification of Independent Component (IC) with support vector machines (SVM) (FOCIS) techniques to reduce some of the limitations overviewed above; (2) Use the developed method to investigate how the extracted RFNs are influenced by the selection of number of independent components (NIC)
We have described a new tool, feature-optimized classification of ICs with SVM (FOCIS), for the automated classification of artifact components in independent component analysis (ICA) results of restingstate fMRI data
Summary
Independent component analysis (ICA) is a data-driven, unsupervised analysis method for extracting resting-state functional connectivity networks (RFNs) (Calhoun et al, 2001; Beckmann et al, 2005; Kiviniemi et al, 2009; Schopf et al, 2010). ICA has been widely used for Feature optimized classification rs-fMRI ICA Reference Algorithm Features Applicability Performance. Mean sensitivity 0.87 Sui et al, 2009
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