Decoding visual network‐related dynamic functional connectivity for eyes‐open and eyes‐closed using machine learning

Eyes‐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.