Objective To investigate the temporal properties of dynamic functional connectivity of brain networks and the variability of network topology in patients with end-stage renal disease(ESRD).Methods Data of 30 ESRD patients(ESRD group)and 33 healthy subjects(control group)were retrospectively analyzed.Based on cranial resting-state functional MRI(rs-fMRI),dynamic functional connectivity(dFC)and graph theory analysis were employed,and the abnormalities in network topology and dFC in ESRD patients were assessed through comparison of groups.Pearson correlation analysis was used to observe the correlation between abnormal dFC indicators and clinical variables.Results Compared with control group,temporal scores and the mean residence time in ESRD group were significantly higher under state Ⅱ but significantly lower under state Ⅲ(both P＜0.05).The abnormal functional connectivity in ESRD patients under states Ⅱ and Ⅲ distributed mainly within and between default mode network,sensorimotor network,subcortical nuclei,execution and attention network,visual network and cerebellum networks.Network density and bilateral superior temporal gyrus nodal degrees in ESRD group were all significantly lower than those in control group(all P＜0.05).No significant correlation was found between the abnormal parameters of functional connectivity and graph theory attributes in ESRD group and clinical indicators under states Ⅱ nor Ⅲ(all P＞0.05).Conclusion ESRD patients had abnormal temporal attributes and network topology of brain dynamic networks related to cognitive impairments.

In order to overcome the difficulty in lung parenchymal segmentation due to the factors such as lung disease and bronchial interference, a segmentation algorithm for three-dimensional lung parenchymal is presented based on the integration of surfacelet transform and pulse coupled neural network (PCNN). First, the three-dimensional computed tomography of lungs is decomposed into surfacelet transform domain to obtain multi-scale and multi-directional sub-band information. The edge features are then enhanced by filtering sub-band coefficients using local modified Laplacian operator. Second, surfacelet inverse transform is implemented and the reconstructed image is fed back to the input of PCNN. Finally, iteration process of the PCNN is carried out to obtain final segmentation result. The proposed algorithm is validated on the samples of public dataset. The experimental results demonstrate that the proposed algorithm has superior performance over that of the three-dimensional surfacelet transform edge detection algorithm, the three-dimensional region growing algorithm, and the three-dimensional U-NET algorithm. It can effectively suppress the interference coming from lung lesions and bronchial, and obtain a complete structure of lung parenchyma.
Algorithms ; Neural Networks, Computer ; Tomography, X-Ray Computed