Research On Optimizing The Connection Of Structure Brain Network Based On The Stochastic Block Model

With the rapid development of macroscopic connection group mapping of Diffusion Weighted Imaging(DWI),it is imperative to obtain the measurement of connection strength based on these reconstructed data,and verify the reliability of its connection from the network itself.In the field of macroscopic connection,the number of reconstructed fiber streamline is more used as an indicator of the interfacial strength between the ROIs(Regions of Interest),and by verifying that there was a positive correlation between the number of fiber bundles based on DWI and the tracing anatomical connection strength of animal living bodies.In recent years,diffusion weighted imaging has been widely used,but there are still some problems have not yet been resolved,for example,there is exists volumetric effect access to the image data,fiber cross problem.In the fiber tracking method,there are also some problems,such as the problem of parameter settings and so on.Due to the scanning technique and the problem of tracking technology,there are a number of false positive connections and false negative connections in the brain network constructed from the fiber method.Also,for long distance connections,it is always difficult to capture by existing techniques,and these problems will lead to a great change in the network structure,making the network structure and the conclusions is unbelievable.For non-human mammals,a real network can be obtained to compare the correctness of the constructed network.If it is not possible to obtain a real network structure(such as human brain),whether these connections in the constructed network are reliable and if the connection is reliable,how much the probability is? There are very few studies on this subject.In this paper,we compare the other DWI measures with the tracer perfusion intensity and use the stochastic block model to evaluate the DWI-derived connections,and evaluate the results.The tracing information for the connection strength of the area is obtained from two common monkey connection group data sets,(1)the Co Co Mac database,collection of data of the tracer experiments of the monkey brain,(2)High-resolution tracer data sets are provided by Markov and Kennedy and their partners.The data in the network represent the number of fiber bundles of the reconstructed fiber path,which are obtained from DWI data of 23 monkeys.The symbol network is used for the 23 individual networks to obtain the central network.Thus,the reliability of the connection was found to be positively correlated with the Co Co Mac database and the Markov dataset based on tracer-based link strength(P values were less than 0.0001),and the DWIderived connections of the other measures are also strongly correlated.The main innovations of this paper are as follows:First,the reliability of the connection in the network is calculated based on the stochastic block model.According to the idea of stochastic block model,the nodes in the network are randomly divided into the same or different groups.The reliability of the connection depend mainly on the groups which the nodes exist in.The correlation between the reliability of the connection and the real strength of the connection is analyzed.The results show that there is a strong positive correlation between the connection reliability based on the stochastic block model and the real intensity.Second,the reliability is verified in the brain network.The reliability were compared with those of the other indicators,include Fiber Number,Fractional Anisotropy,Distance and the size of ROI.The results showed that Fiber Number,Fractional Anisotropy and the size of ROI all have a strong positive correlation with the connection reliability,there is a strong negative correlation between Distance index and reliability,which is the method based on stochastic block model can be applied to brain structure network research.Thirdly,optimizing the connection of structural brain network.There are some false-positive and false-negative connections in the network using the imaging technique.In this paper,the values of the connection reliability and the symbol test are used to optimize the two types of connections in the network.The results show that the reliability value is closer to the real network and the reliability value can be used for the optimization of the brain structure network.In this paper,we propose a method to evaluate the network connectivity of DWI based on the stochastic block model,which can correctly calculate the reliability of the connection.Our results show that the evaluation value based on the stochastic block model provides a near-real estimate of the perfusion intensity of the white matter connection and other parameters of the connection.The crosscomparisons of our two models suggest that a stochastic block-based approach can be an effective methodology for connection evaluation.