Spatial accuracy of fMRI activation influenced by volume- and surface-based spatial smoothing techniques

Abstract

As improvements in cortical surface modeling allowed accurate cortical topology in brain imaging studies, surface-based methods for the analysis of functional magnetic resonance imaging (fMRI) were introduced to overcome the topological deficiency of commonly used volume-based methods. The difference between the two methods is mainly due to the smoothing techniques applied. For practical applications, the surface-based methods need to quantitatively validate the accuracy of localizing activation. In this study, we evaluated the spatial accuracy of activation detected by the volume- and surface-based methods using simulated blood oxygenation level-dependent (BOLD) signals and MRI phantoms focusing on the influence of their smoothing techniques. T1- and T2-weighted phantoms were acquired from BrainWeb ( http://www.bic.mni.mcgill.ca/brainweb/) and used to extract cortical surfaces and to generate echo planar imaging (EPI) data. Simulated BOLD signals as the gold standard of activation in our experiment were applied to the surfaces and projected to the volume space with random noise. Three-dimensional isotropic Gaussian kernel smoothing and two-dimensional heat kernel smoothing were applied to the volume- and surface-based methods. Sensitivity and 1-specificity, which are truly and falsely detected activations, and similarity measures, which are spatially and statistically similar for the gold standard and detected activations, were calculated. In the results, the surface-based method showed the sensitivity and similarity scores of about 12% higher than the volume-based method. In conclusion, the surface-based method guarantees better spatial accuracy for the localization of BOLD signal sources within the cortex than the volume-based method.