Automatic resting-state fMRI Independent Component Classification using Support Vector Machines

To facilitate the identification of meaningful components from ICA analysis for resting-state fMRI data, we have developed a supervised classification framework based on support vector machines for automatic identification of noise/artifact components. By using classifiers that reflect typical instructions for visual inspection and are invariant of training dataset, our framework achieved zero false negative rates and consistently low false positive rates for identifying noise/artifact components. Our framework facilitates ICA-based analysis of resting-state fMRI data with high model orders, and can be used for automatic removal of noise/artifact components without risking discarding any potentially interesting and meaningful components.

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