Hsin-Long Hsieh1, 2, Pin-Yu Chen3, Jhih-Wei He1, Yao-Chia Shih1, 2, Fu-Shan Jaw1, Wen-Yih Isaac Tseng
1Institute of Biomedical Engineering, National Taiwan University, Taipei, Taiwan, Taiwan; 2Center for Optoelectronic Biomedicine, National Taiwan Univerity College of Medicine , Taipei, Taiwan, Taiwan; 3 Center for Optoelectronic Biomedicine, National Taiwan Univerity College of Medicine, Taipei, Taiwan, Taiwan
Independent component analysis (ICA) has recently been employed in the detection of the resting-state networks (RSNs) which are consistent and highly reproducible across healthy subjects. However, the identification of RSNs often involves visual inspection and/or correlating spatial maps derived from ICA with templates or seed-based results. To avoid bias caused by investigators, we employed a more objective and template-free approach to select and classify components derived from ICA as RSNs based on the component's time course correlation. Our proposed method adds value to the data-driven approach in defining RSNs, and is potentially useful in the connectome research.
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