Morphology-based hypothesis testing in discrete random fields: A non-parametric method to address the multiple-comparison problem in neuroimaging

Article history: Received 18 October 2010 Revised 25 March 2011 Accepted 29 March 2011 Available online 1 October 2011 Anewmethod for detecting activations in randomfields,whichmay beuseful for addressing the issue ofmultiple comparisons in neuroimaging, is presented. This method is based on some constructs of mathematical morphology – specifically, morphological erosions and dilations – that enable the detection of active regions in random fields possessing moderate activation levels and relatively large spatial extension, which may not be detected by the standardmethods that control the family-wise error rate. Themethod presented here permits an appropriate control of the false positive errors, without having to adjust any threshold values, other than the significance level. The method is easily adapted to permutation-based procedures (with the usual restrictions), and therefore does not require strong assumptions about the distribution and spatio-temporal correlation structure of thedata. Someexamples of applications to synthetic data, including realistic fMRI simulations, aswell as to real fMRI and electroencephalographic data arepresented, illustrating the powerof thepresented technique. Comparisonswith othermethods that combine voxel intensity and cluster size, aswell as some extensions of the method presented here based on their basic ideas are presented as well.