PreSurgMapp: a MATLAB Toolbox for Presurgical Mapping of Eloquent Functional Areas Based on Task-Related and Resting-State Functional MRI
The article in this issue of the American Journal of Neuroradiology (AJNR) entitled “Simple fMRI Postprocessing Suffices for Normal Clinical Practice” is an important contribution to the current blood oxygen level– dependent (BOLD) fMRI literature because it highlights an emerging trend in functional neuroimaging software development that will likely promote increasing clinical use of fMRI in the near future. Although many attempts have been made during the past decade to standardize fMRI paradigms across institutions, streamline BOLD fMRI preprocessing and postprocessing, validate fMRI by using intraoperative electrophysiologic criterion standards, and establish standards for fMRI image acquisition, processing, and interpretation, only some of these have been reasonably successful. The greatest strength of the BOLD fMRI technique—its immense versatility with respect to assessment of cognitive networks, paradigm design, and signalprocessing approaches— has also proved to be its greatest weakness from a clinical standardization standpoint. Presurgical planning for resectable brain lesions was the original clinical application of BOLD fMRI and remains the sole widely accepted clinical application (if we consider the broader category of pretherapeutic planning, including planning for radiation therapy and hemispheric language lateralization for epilepsy) for which Current Procedural Terminology codes were established in 2007. Currently, expansion of clinical indications to include posttherapeutic monitoring of eloquent cortex has also been accepted. In a nutshell, clinical functional MR imaging enables neuroradiologists to go beyond mere characterization of anatomic findings and instead provide critical functional-anatomic correlation that is essential for accurate assessment of the risks of neurosurgical or radiation treatment. The relatively cumbersome nature of research-level BOLD fMRI postprocessing software (which typically requires graduatelevel experience in image processing, including computer programming within environments such as Matlab [MathWorks, Natick, Massachusetts] for generation of custom-made scripts for semiautomated execution of multiple processing steps for clinical applications) has been a limitation recently overcome by more streamlined commercially available FDA-approved packages. These newer packages allow more user-friendly interaction; better image overlays, including interactive 3D viewing; and greater compatibility with PACS servers and neuronavigation software for facilitation of image export and viewing by referring physicians and patients alike. These developments have led to the capability of importing functional images into the operating room in a fashion that neurosurgeons can easily use in planning their surgery. With brain shift occurring following violation of the dura, the original exact landmarks provided by the preoperative functional imaging may not be accurate. Overlays with postgadolinium anatomic images allow neurosurgeons to rely on venous and gyral anatomic landmarks that are useful even as resection progresses. This is particularly true when higher field (1.5T and 3T) intraoperative MR imaging systems that allow nearly real-time monitoring of the extent of lesion resection are used. Of course, these advances have certainly not made the use of research software obsolete because the relatively “turnkey” semiautomated processing available with the commercial software packages has many limitations that, in many cases, need to be addressed by additional processing by using more sophisticated research packages. The reason is that such powerful research software allows alteration of many preset default parameters (that cannot be adjusted with the more streamlined commercial packages) that may not always be optimal for every clinical case. Sometimes clinical interpretation may be dependent on additional information that the streamlined packages may not be able to provide. An example provided in this article is the need for quantitative assessment of suprathreshold-activated voxels in clusters of interest for laterality index computation. In other words, while the commercial packages may allow qualitative assessment of hemispheric lateralization, they frequently do not allow voxeland cluster-level quantitative assessments that would be necessary for accurate computation of laterality indices or even determination of centroids (or centers of mass) of activation on the basis of both individual voxel t values and spatial extent of suprathreshold voxels within a cluster or group of adjacent clusters. Another example would be the need to vary spatial-extent thresholds (ie, clustering thresholds) at a given P value or t statistic threshold after analysis by using a general linear model (GLM) approach.