Quantitative evaluation of fMRI retinotopic maps, from V1 to V4, for cognitive experiments.

FMRI retinotopic mapping is a non-invasive technique for the delineation of low-level visual areas in individual subjects. It generally relies upon the analysis of functional responses to periodic visual stimuli that encode eccentricity or polar angle in the visual field. This technique is used in vision research when the precise assignation of brain activation to retinotopic areas is an issue. It involves processing steps computed with different algorithms and embedded in various software suites. Manual intervention may be needed for some steps. Although the diversity of the available processing suites and manual interventions may potentially introduce some differences in the final delineation of visual areas, no documented comparison between maps obtained with different procedures has been reported in the literature. To explore the effect of the processing steps on the quality of the maps obtained, we used two tools, BALC, which relies on a fully automated procedure, and BrainVoyager, where areas are delineated "by hand" on the brain surface. To focus on the mapping procedures specifically, we used the same SPM pipeline for pretreatment and the same tissue segmentation tool. We document the consistency and differences of the fMRI retinotopic maps obtained from "routine retinotopy" experiments on 10 subjects. The maps obtained by skilled users are never fully identical. However, the agreement between the maps, around 80% for low-level areas, is probably sufficient for most applications. Our results also indicate that assigning cognitive activations, following a specific experiment (here, color perception), to individual retinotopic maps is not free of errors. We provide measurements of this error, that may help for the cautious interpretation of cognitive activation projection onto fMRI retinotopic maps. On average, the magnitude of the error is about 20%, with much larger differences in a few subjects. More variability may even be expected with less trained users or using different acquisition parameters and preprocessing chains.