Correction Scheme for Multiple Correlated Statistical Tests in Local Shape Analysis

In neuroimaging research shape analysis has become a field of great interest due to the ability to locate morphological brain changes between different groups. Currently, most local shape analysis approaches fail to correct for their high number of correlated statistical tests. This results in an overly optimistic estimate of the local shape analysis. This paper presents a correction scheme for objects described by a parametrized 3D closed surface description. The scheme decomposes the object surface into overlapping planar images via a cylindrical equal area projection of the parameterization. The images are individually analyzed with the SnPM/SPM package using a voxel-level non-parametric multiple testing procedure based on permutation tests. The correction scheme employs conservative tests resulting in a pessimistic estimate. We present an application of the correction scheme in the analysis of the shape similarity of lateral ventricles.