http://www.nitrc.org/forum/forum.php?forum_id=5202 <table border="0" width="100%"><tr><td align="left"/></tr></table> <p><b>Dimensionality of ICA in resting-state fMRI investigated by feature optimized classification of independent components with SVM.</b></p> <p>Front Hum Neurosci. 2015;9:259</p> <p>Authors: Wang Y, Li TQ</p> <p>Abstract<br/> 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 &lt; 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 &lt; 33, whereas only a few limited ICs are affected by direct splitting when NIC is incremented beyond NIC &gt; 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. <br/> </p><p>PMID: 26005413 [PubMed]</p> en-us Copyright 2000-2026 NITRC OSI Sat, 02 May 2026 12:20:47 GMT http://blogs.law.harvard.edu/tech/rss NITRC RSS generator