Classifying mental disorder is a big issue in psychology in recent years. This article focuses on offering a relation between decision tree and encoding of fMRI that can simplify the analysis of different mental disorders and has a high ROC over 0.9. Here we encode fMRI information to the power-law distribution with integer elements by the graph theory in which the network is characterized by degrees that measure the number of effective links exceeding the threshold of Pearson correlation among voxels. When the degrees are ranked from low to high, the network equation can be fit by the power-law distribution. Here we use the mentally disordered SHR and WKY rats as samples and employ decision tree from chi2 algorithm to classify different states of mental disorder. This method not only provides the decision tree and encoding, but also enables the construction of a transformation matrix that is capable of connecting different metal disorders. Although the latter attempt is still in its fancy, it may have a contribution to unraveling the mystery of psychological processes.

In the past two decades neuroscience has offered many popular methods for the analysis of mental disorder, such as seedbased analysis, ICA, and graph methods. They are widely used in the study of brain network. We offer a new procedure that can simplify the analysis and has a high ROC index over 0.9. This method uses the graph theory to build a connectivity network, which is characterized by degrees and measures the number of effective links for each voxel. When the degree is ranked from low to high, the network equation can be fit by the power-law distribution. It has been proposed that distinct and yet robust exponents of the power law can differentiate human behavior. Using the mentally disordered SHR and WKY rats as samples, we employ chi2 algorithm and Decision Tree to classify different states of mental disorder by analyzing different traits in degree of connectivity.