Surface-based brain imaging analysis offers the advantages of preserving the topology of cortical activation, increasing statistical power of group-level statistics, estimating cortical thickness, and visualizing with ease the pattern of activation across the whole cortex. SUMA is an open-source suite of programs for performing surface-based analysis and visualization. It was designed since its inception to allow for a fine control over the mapping between volume and surface domains, and for very fast and simultaneous display of multiple surface models and corresponding multitudes of datasets, all while maintaining a direct two-way link to volumetric data from which surface models and data originated. SUMA provides tools for performing spatial operations such as controlled smoothing, clustering, and interactive ROI drawing on folded surfaces in 3D, in addition to the various level-1 and level-2 FMRI statistics including FDR and FWE correction for multiple comparisons. In our contribution to this commemorative issue of Neuroimage we touch on the importance of surface-based analysis and provide a historic backdrop that motivated the creation of SUMA. We also highlight features that are particular to SUMA, notably the standardization procedure of meshes to greatly facilitate group-level analyses, and the ability to control SUMA's graphical interface from external programs making it possible to handle large collections of data with relative ease.
Published by Elsevier Inc.