Dimensionality of ICA in resting-state fMRI investigated by feature optimized classification of independent components with SVM

Yanlu Wang; Tie-Qiang Li

doi:10.3389/fnhum.2015.00259

Dimensionality of ICA in resting-state fMRI investigated by feature optimized classification of independent components with SVM

Front Hum Neurosci. 2015 May 8:9:259. doi: 10.3389/fnhum.2015.00259. eCollection 2015.

Authors

Yanlu Wang¹, Tie-Qiang Li²

Affiliations

¹ Department of Clinical Science, Intervention and Technology, Karolinska Institute Stockholm, Sweden.
² 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.

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.

Keywords: functional neuroimaging; image processing; independent component analysis; machine learning; magnetic resonance imaging; pattern classification; signal processing.