Focusing on the limitation of conventional static Functional Connectivity (FC) techniques in investigating the dynamic functional brain states, an effective method based on whole-brain Dynamic Functional Connectivity (DFC) was proposed to characterize the time-varying brain states. First, the Diffusion Tensor Imaging (DTI) data were used to construct individual whole-brain networks with high accuracy and the functional Magnetic Resonance Imaging (fMRI) data of motor-related task was projected to the corresponding DTI space to extract the fMRI signals of each node for each subject. Then, one kind of sliding time window approach was applied to calculate the time-varying whole-brain functional connectivity strength matrix, and the corresponding Dynamic Functional Connectivity Vector (DFCV) samples were further extracted and collected. Finally, the DFCV samples were learned and classified by one sparse representation based method called Fisher Discriminative Dictionary Learning (FDDL). Total eight different whole-brain functional connectome patterns representing the dynamic brain states were obtained from this motor-related task experiment. The spatial distributions of functional connectivity strength showed obvious variance within different patterns. The pattern #1, pattern #2 and pattern #3 covered most of the samples (77.6%) and the similarities between each of them and the average static whole-brain functional connectivity strength matrix were obviously higher than other five patterns. Furthermore, the brain states were found to transfer from one pattern to another according to certain rules. The experimental results show that the proposed analysis method combining whole-brain DFC and FDDL learning is effective for describing and characterizing the dynamic brain states during task brain activity. It provides a foundation for exploring the dynamic information processing mechanism of the brain.

To improve the prediction ability of image memorability, a method for automatically predicting the memorability of an image was proposed by using visual saliency entropy and improved Object Bank feature. The proposed method improved the traditional Object Bank feature and extracted the visual saliency entropy feature. Then a prediction model of image memorability was constructed by using Support Vector Regression (SVR). The experimental results show that the correlation coefficiency of the proposed method is three percentage higher than the state-of-the-art method. The proposed model can be used in image memorability prediction, image retrieval ranking and advertisement assessment analysis.