Given a Reproducing Kernel Hilbert Space (H, h., .i) of real-valued functions and a suitable measure μ over the source space, we decompose H as sum of a subspace of centered functions for μ and its orthogonal in H. This decomposition leads to a special case of ANOVA kernels, for which the functional ANOVA representation of the minimal norm interpolator can be elegantly derived. The proposed kernels appear to be particularly convenient for analyzing the effect of each (group of) variable(s) and computing sensitivity indices without recursivity.