Gene expression profiles have been widely used in functional genomic studies. However, not much work in traditional gene expression profiling takes into account the location information of a gene's expressions in the brain. Gene expression maps, which contain spatial information regarding the expression of genes in mice's brain, are obtained by combining voxelation and microarrays. Based on the idea that genes with similar gene expression maps may have similar gene functions, we propose an approach to identify gene functions. A gene function can potentially be associated with a specific gene expression profile. We name this specific gene expression profile, Functional Expression Profile (FEP). A functional expression profile can be obtained either by directly finding genes with a certain function, or by analyzing clusters of genes that have similar expression maps and similar functions. By taking advantage of the identified FEPs, we can annotate gene functions with high accuracy. Compared to the traditional K-nearest neighbor method, our approach shows higher accuracy in predicting functions. The images of FEPs are in good agreement with anatomical components of mice's brain, and provide valuable insight in terms of function prediction to biological scientists.