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2 Z.S. Saad, R.C. Reynolds / NeuroImage xxx (2011) xxx–xxx1996) voxelwise programs can handle surface-based data, while spatialoperations such as smoothing, clustering, and regions-of-interest drawinghave their own implementation in SUMA. The visualization part ofSUMA allows for very fast and simultaneous display of multiple surfacemodels and corresponding multitudes of datasets, all while maintaininga direct two-way link to volumetric data from which surface modelsand data originated.In this retrospective article, we touch briefly on the motivation forthe use of surface models and the circumstances prompting the creationof SUMA a little less than 10 years ago, and expand on a few aspectsof data analysis and presentation that are particular to SUMA.To keep the tedium of software descriptions to a minimum, we includelinks to downloadable self-executing interactive demonstrationsof some features described herein.Why surfaces?The cortical surface is a highly convoluted sheet comprised of gyriand deeply buried sulci that make it difficult to appreciate its topographicorganization. Mapping such data onto cortical surface modelsis the natural way to view organizations such as retinotopy, somatotopy,and tonotopy. More recently, cortical resting state FMRI correlationpatterns were used to define functional areas (Cohen et al.,2008). Anatomists who sought to understand the cortical layout bystudying nonhuman primates first appreciated the value of cortical surfacemodels. In the early days, brain contours of each slice were outlinedmanually with flexible wires which were then painstaking strengthened,or with pencil and tracing paper (Van Essen and Maunsell,1980)—tasks reserved for the most patient and meticulously organizedamong us. Fortunately, computational approaches arrived to make thisprocess simpler. The group of Van Essen, long a pioneer of the use ofsurface models, produced software known as SureFit/CARET to createcortical surface models from MRI data (Van Essen et al., 2001). Aboutthe same time, Anders Dale, Martin Sereno, Bruce Fischl (Dale et al.,1999) and colleagues produced the FreeSurfer software (http://surfer.nmr.mgh.harvard.edu), which also created high-quality surfacesfrom MRI data. A multitude of surface creation software followed; famousamong them are BrainVoyager (http://www.brainvoyager.com),BrainVisa (http://brainvisa.info), and mrVista (http://white.stanford.edu/software).Although surface-based analysis is restricted to the cortex, it offersadvantages with imaging data: 1—The preservation, enhancement,and visualization of topological detail in FMRI volumes. Neighboringvoxels in the volume are not necessarily sampling the signal of neighboringregions of the cortex. This often leads to a spatial aliasing ofthe FMRI signal, that gets more pronounced when one isotropicallysmoothes the data in the volume. By mapping data from the volumedomain onto the surface domain, one can then smooth along the surface,thereby preserving and enhancing topological detail present inthe brain. An example of this would be the analysis of retinotopicdata, where isotropic smoothing in the volume can destroy the finetopology of the activation pattern. 2—Increase in statistical power.Multiple studies have shown increases in statistical power whengroup statistics (level-2) are carried out on the cortical surface comparedto the volume domain (Anticevic et al., 2008; Fischl et al.,1999b). This appears to be the case whether or not nonlinear warpingis utilized (Argall et al., 2006). The increase in power is likely due toimproved smoothing and domain matching across subjects. In otherwords, the data at each node in the surface comes from gray mattervoxels (assuming proper alignment between surfaces and volumesand neglecting partial volume effects), even if nonlinear warping isnot used. The same is not true of affine registered volumes. Proper domainmatching is important, especially when comparing data fromgroups with consistent differences in the anatomy, e.g. young vs. oldsubjects. Without proper alignment and relevant tissue matching, thesame voxel in volume template space from a young subject group willlikely reflect different brain tissue than in the elderly group, potentiallyleading to artificial differences in FMRI measures between the twogroups. 3—Cortical thickness. Using two surfaces modeling the innerand outer layers of gray matter, cortical thickness estimates can readilybe obtained and compared across subjects. 4—Beautiful renditions ofcortex and data.To be sure, all these advantages can be achieved in volume-basedapproaches, but at a greater computational complexity. For exampledomain matching in the volume can be improved under highly nonlinearwarps, but that is still rarely done despite marked improvementsin registration software (Ashburner, 2007; Klein et al., 2009).It remains easier to deform the surface for the purposes of alignmentbecause there is simply less to align than in the volume.And then came SUMAIn the early days of FMRI surface-based analysis, much of the effortwent into creating the surfaces out of MRI volumes—a complex taskto be sure—but manipulating surfaces or data, or relating the surfacesto the volumetric data from which they originated was cumbersomeat best. In the mid to late nineties, by the time one got done analyzingretinotopic data on a flattened version of the cortical surface, multiplesteps were needed to find out where a particular node on the flat (orinflated) map came from in the volume. Even surface display was difficultand sluggish: one moved a slider bar to set viewing angles andthen clicked a button to refresh the display. Now the user simplyclicks on the brain and moves it around until the desired view isachieved, or with a mouse flick sends the brain twirling gently inspace—a task particularly mesmerizing for toddlers and grizzled scientistsalike. This interactivity requires high-performance graphicshardware that is now readily available on handheld devices. However,in the early days displaying surface models was only available on expensivehigh-end Silicon Graphics Incorporated machines, and interactivitywas very limited. For instance, as a graduate student in the midnineties, where home-grown tools were the norm for creating andmapping data onto cortical surfaces, I (author ZSS) was asked to compareFMRI signal properties in visual areas that had been delineatedby FMRI retinotopy on flattened models of the occipital cortex. To mychagrin, the only way to read the data values off of the flattened occipitalmaps was by reverse mapping node colors back to data values perthe color scale! That was because a colorized rendering of the datawas the end point of the mapping process; data values were not preservedin the process. Needless to say, that project did not go far. Suchdifficulties in relating surfaces to the volume made it harder to detectanomalies in the segmentation, or in the functional data. It was also difficultto view in detail the meshes modeling the cortical surfaces, aproblem particularly vexing when trying to understand the source oferrors in the geometry or the triangulation of dense surface meshes.Such frustrations, coupled with an interest in the spatial propertiesof the BOLD response and the irresistible lure of beautiful 3D corticalrenderings begat SUMA and shaped its structure and features.SUMA was first released on the 4th of March 2002, about 1 yearafter I joined Bob Cox at the Scientific and Statistical ComputingCore at NIMH. Unencumbered by the task of creating surface modelsand their myriad derivates, SUMA's design focus was on controllingthe mapping between volume and surface domains, concurrent displayof all geometric variants of the same surface and maintainingan interactive and direct link between the surface and volume domains.Initially SUMA used surface models created by Caret (VanEssen et al., 2001), FreeSurfer, and BrainVoyager. With the adventand adoption of GIFTI http://www.nitrc.org/projects/gifti, surfacesand data from other platforms such as BrainVisa are now readily utilized.The rapid development and the high level of interactivity ofSUMA at the first release were due to multiple factors: 1—A firsthandexperience with what was missing in surface-based analysis:the ability to see and access the data on the surface and relate surfacePlease cite this article as: Saad, Z.S., Reynolds, R.C., SUMA, NeuroImage (2011), doi:10.1016/j.neuroimage.2011.09.016
Z.S. Saad, R.C. Reynolds / NeuroImage xxx (2011) xxx–xxx3and data to the volume. 2—A MATLAB-based prototype of the mappingprocess that I had developed for my own use during a post-doc in PeterBandettini's group. 3—An efficient organization of surfaces and datastructures and the use of C language and basic OpenGL libraries. 4—Leveraging AFNI's existing functions for handling and rendering volumetricdata.Standardizing surface meshes for group comparisonsBefore discussing the handling of data on surface models, we describethe process by which we take a set of surfaces and re-createthem with a new ‘standard’ mesh that is shared by all subjects in agroup study. A standard-mesh version of a surface is virtually identicalin 3D shape to the original one; however, each node of the newmesh encodes the same cortical location across subjects, within theaccuracy of the warping-to-template step. We detail this procedure,which is particular to SUMA, because this simple process greatly facilitatesthe handling of group statistics when multiple subject surfacesare not created to be topologically isomorphic, as is the case withFreeSurfer and Caret (for example).Fig. 1 shows the common approach for performing group analysisin the surface domain. The first step (Fig. 1A) involves inflating a subject'ssurface to a sphere, then deforming the spherical mesh (Fig. 1B)so that sulcal depth patterns match those of a template (Fischl et al.,1999a). In volume-based analysis, the analogous step is transformingthe brain to a standard-template space. Other approaches match selectedsulcal patterns or patterns derived from functional data. Towhat extent warping improves the final results remains the subjectof debate. However, it has been repeatedly shown that group analysison the cortical surface, even without nonlinear registration, results inincreased statistical power. Regardless of the warping approach, the procedurefor collecting group data then involves mapping each subject'sFig. 1. The process of transforming original- to standard-mesh surfaces. See text for details. a 1 , a 2 ,anda 3 are the barycentric coordinates of node n in the triangle formed by nodesp 1 ,p 2 ,p 3 . Node colors of original-mesh surfaces (top two rows) show FreeSurfer's cortical parcellations. Colors on standard-mesh surfaces (bottom row) reflect each node's index.Please cite this article as: Saad, Z.S., Reynolds, R.C., SUMA, NeuroImage (2011), doi:10.1016/j.neuroimage.2011.09.016
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