As one of the critical steps in brain imaging analysis and processing, brain image registration plays a significant role. In this paper, we proposed a technique of human brain image registration based on tissue morphology in vivo to address the problems of previous image registration. First, different feature points were extracted and combined, including those at the boundary of different brain tissues and those of the maximum or minimum from the original image. Second, feature points were screened through eliminating their wrong matching pairs between moving image and reference image. Finally, the remaining matching pairs of feature points were used to generate the model parameters of spatial transformation, with which the brain image registration can be finished by combining interpolation techniques. Results showed that compared with the Surf, Demons, and Sift algorithms, the proposed method can perform better not only for four quantitative indicators (mean square differences, normalized cross correlation, normalized mutual information and mutual information) but also in spatial location, size, appearance contour, and registration details. The findings may suggest that the proposed method will be of great value for brain image reconstruction, fusion, and statistical comparison analysis.

Most of the existing methods about the causal relationship based on functional magnetic resonance imaging (fMRI) data are either the hypothesis-driven methods or based on a linear model, which can result in the deviation for detecting the original brain activity. Therefore, it is necessary to develop a new method for detecting the effective connectivity (EC) of the brain activity by the nonlinear calculation. In this study, we firstly proposed a new technology evaluating effective connectivity of the human brain based on back-propagation neural network with nonlinear model, named EC-BP. Next, we simulated four time series for assessing the feasibility and accuracy of EC-BP compared to Granger causality analysis (GCA). Finally, the proposed EC-BP was applied to the brain fMRI from 60 healthy subjects. The results from the four simulated time series showed that the proposed EC-BP can detect the originally causal relationship, consistent with the actual causality. However, the GCA can not find nonlinear causality. Based on the analysis of the fMRI data from the healthy participants, EC-BP and GCA showed the huge differences in the top 50 connections in descending order of EC. EC-BP showed all ECs related to hippocampus and parahippocampus, whereas GCA showed most ECs related to the paracentral lobule, caudate, putamen, and pallidum, which represents the brain regions with most frequent information passing measured by different methods. The proposed EC-BP method can provide supplementary information to GCA, which will promote more comprehensive detection and evaluation of brain EC.