A validation framework for probabilistic maps using Heschl's gyrus as a model.

Probabilistic maps are useful in functional neuroimaging research for anatomical labeling and for data analysis. The degree to which a probability map can accurately estimate the location of a structure of interest in a new individual depends on many factors, including variability in the morphology of the structure of interest over subjects, the registration (normalization procedure and template) applied to align the brains among individuals for constructing a probability map, and the registration used to map a new subject's data set to the frame of the probabilistic map. Here, we take Heschl's gyrus (HG) as our structure of interest, and explore the impact of different registration methods on the accuracy with which a probabilistic map of HG can approximate HG in a new individual. We assess and compare the goodness of fit of probability maps generated using five different registration techniques, as well as evaluating the goodness of fit of a previously published probabilistic map of HG generated using affine registration (Penhune et al., 1996). The five registration techniques are: three groupwise registration techniques (implicit reference-based or IRG, DARTEL, and BSpline-based); a high-dimensional pairwise registration (HAMMER) as well as a segmentation-based registration (unified segmentation of SPM5). The accuracy of the resulting maps in labeling HG was assessed using evidence-based diagnostic measures within a leave-one-out cross-validation framework. Our results demonstrated the out performance of IRG and DARTEL compared to other registration techniques in terms of sensitivity, specificity and positive predictive value (PPV). All the techniques displayed relatively low sensitivity rates, despite high PPV, indicating that the generated probability maps provide accurate but conservative estimates of the location and extent of HG in new individuals.