The measurements of the similarity of dynamic brain functional network / 生物医学工程学杂志

Brain functional network changes over time along with the process of brain development, disease, and aging. However, most of the available measurements for evaluation of the difference (or similarity) between the individual brain functional networks are for charactering static networks, which do not work with the dynamic characteristics of the brain networks that typically involve a long-span and large-scale evolution over the time. The current study proposes an index for measuring the similarity of dynamic brain networks, named as dynamic network similarity (DNS). It measures the similarity by combining the "evolutional" and "structural" properties of the dynamic network. Four sets of simulated dynamic networks with different evolutional and structural properties (varying amplitude of changes, trend of changes, distribution of connectivity strength, range of connectivity strength) were generated to validate the performance of DNS. In addition, real world imaging datasets, acquired from 13 stroke patients who were treated by transcranial direct current stimulation (tDCS), were used to further validate the proposed method and compared with the traditional similarity measurements that were developed for static network similarity. The results showed that DNS was significantly correlated with the varying amplitude of changes, trend of changes, distribution of connectivity strength and range of connectivity strength of the dynamic networks. DNS was able to appropriately measure the significant similarity of the dynamics of network changes over the time for the patients before and after the tDCS treatments. However, the traditional methods failed, which showed significantly differences between the data before and after the tDCS treatments. The experiment results demonstrate that DNS may robustly measure the similarity of evolutional and structural properties of dynamic networks. The new method appears to be superior to the traditional methods in that the new one is capable of assessing the temporal similarity of dynamic functional imaging data.

Structural modeling and dynamic evolution analysis of hypoxia response networks / 军事医学

Objective To construct an executable model of a hypoxia response network (HRN) and to analyze the dynamic evolution mechanism of an HRN including randomness as well as concurrency based on computer simulation. Methods Specific evolution rules and Gillespie algorithm were adoped to study the dynamic evolution of the structural model based on the construction of a structural model of an HRN using stochastic Petri net ( SPN ) .Results Dynamic evolution laws of an HRN were obtained and the simulation results were consistent with laboratory results in response to descript switch-like behavior of an HRN .Conclusion A visualization model of the HRN can be achieved using SPN method.Simulation results achieved by executing the model based on stochastic simulation using specific kinetic parameters can serve as a nice complement to traditional laboratory results , which can help shed light on the structure and function characteristics of an HRN.