As a result of the ongoing development of non-invasive analysis of brain function, detailed brain images can be obtained, from which the relations between brain areas and brain functions can be understood. Researchers are trying to heuristically discover the relations between brain areas and brain functions from brain images. As the relations between brain areas and brain functions are described by rules, the discovery of relations between brain areas and brain functions from brain images is the discovery of rules from brain images. The discovery of rules from brain images is a discovery of rules from pattern data, which is a new field different from the discovery of rules from symbolic data or numerical data. This paper presents an algorithm for the discovery of rules from brain images. The algorithm consists of two steps. The first step is nonparametric regression. The second step is rule extraction from the linear formula obtained by the nonparametric regression. We have to confirm that the algorithm works well for artificial data before the algorithm is applied to real data. This paper shows that the algorithm works well for artificial data