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 <

SUBMITTER: Wang Y 

PROVIDER: S-EPMC4424860 | biostudies-literature | 2015

REPOSITORIES: biostudies-literature