Increasing statistical power in medical image analysis

In this paper, we present a novel method for estimating the effective number of independent variables in imaging applications that require multiple hypothesis testing. The method increases the statistical power of the results by refuting the assumption of independence among variables, while keeping the probability of false positives low. It is based on the spectral graph theory, in which the variables are seen as the vertices of a complete undirected graph and the correlation matrix as the adjacency matrix that weights its edges. By computing the eigenvalues of the correlation matrix, it is possible to obtain valuable information about the dependence levels among the variables of the problem. The method is compared to other available models and its effectiveness illustrated in a case study on the morphology of the human corpus callosum.