Research On GSK3β Genes Influence Of Resting State Functional Brain Network Of Depression And Classification

Presently, a growing number of researchers are applying the complex network theory to the study of brain awareness. They regard a whole system of the brain as a complex network, and use more and more advanced technical means to collect data of human brain, and build brain functional networks, using complex network theory to analyze and study the brain networks and achieve exciting results. With the development of brain imaging techniques, the multi-modal brain imaging research methods has been widely applied to the study of brain diseases. Some studies suggest that the human brain functional network changes along with status and age, while it also has some hereditary. Therefore, more and more researchers concerned about the impact of gene on resting-state brain function network.In the society of huge competitive pressures, depression has a high incidence. It characterized mainly by a significant, lasting depressed mood and cognitive dysfunction, and has a high mortality occurs. The previous studies found that the pathogenesis of depression has some correlation with GSK3β genes, which is related with structural changes in the brain of patients with depression. However, it is still unknown now, that the affect and their clinical significance of GSK3β to brain functional network topology attributes.This article first build brain functional networks in a continuous threshold value space using functional magnetic resonance imaging data of patients with depression and the normal human. Secondly, using the knowledge of complex network basic theory analysis the network properties of all subjects’brain functional networks, such as degree, node efficiency, etc. Thirdly, statistical experiment is constructed to find the most significant network attributes in the gene of depression patients, and the most significant networks attribute between genes and diseases. Finally further research classification is made with support vector machines, artificial neural networks, decision trees, logistic regression model.The results showed that the center degree of the brain increase significantly, which related to the GSK3β gene in depression patients. These areas located in the limbic system, basal ganglia and frontal lobe, temporal lobe, occipital lobe. The previous studies also show that the associated connection in these areas is somehow disorder. Therefore, this result makes a better understanding to the role of GSK-3β gene in change brain tissue function network topology.The study of classification found that the support vector machines and artificial neural network perform better than Decision Tree and Logistic regression model. The classification results of disease status suggest that the gene has a certain impact on the classification features, turns to the depression group and the control group of brain functional network. We select the characteristics from the indicators of brain networks significantly different to classify the genes. Example shows that we can use the brain functional method to analyses the brain diseases, and also provide a diagnostic aid to clinical medicine diagnose.