Abstract
AbstractEyes‐open (EO) and eyes‐closed (EC) are the two experimental conditions during resting state functional magnetic resonance imaging (fMRI) scan sessions. However, the dynamic neural mechanisms of EO/EC based on intrinsic connectivity networks (ICNs) remains largely unexplored. This paper aimed to decode the dynamic internetwork neural mechanisms for EO/EC using data mining and to identify EO/EC resting state fMRI scans based on machine learning. To achieve these goals, the two states were analyzed using the discriminative models, resulting in total accuracy of 85.87%, a sensitivity of 91.3%, and a specificity of 80.43%. In addition, the discriminative features discovered using data mining were related to previous findings. In summary, we applied visual network‐related inter‐ICN features to decode the neural mechanisms of EO/EC. The reproducible results suggested that visual network‐related inter‐ICN dynamic features could be beneficial for decoding visual attentions, and had potential as neuroimaging‐markers to identify EO/EC resting state fMRI scans.
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