Reducing Inter-subject Anatomical Variation : Analysis of the Functional Activity in Auditory Cortex and Superior Temporal Region using HAMMER

Conventional group analysis of functional MRI (fMRI) data often involves spatial matching of individual data by registering every subject to an anatomical reference. Due to the high degree of intersubject anatomical variability, a low-resolution average anatomical model is typically used as the target template, and/or smoothing kernels are applied to the fMRI data to spatially blur images. However, such smoothing can make it difficult to detect small regions such as auditory cortex when anatomical morphology varies among subjects. Here, we investigate the impact of using a high-dimensional (high-d) registration technique (HAMMER) on fMRI data analysis. It is shown that HAMMERbased analysis results in an enhanced functional signal-to-noise ratio (fSNR) with more localized activation patterns. The technique is validated against a commonly used low-dimensional (low-d) normalization (SPM2). The comparison also includes the effect of template spatial resolution, and the effect of smoothing on fSNR and on activation localization accuracy. The results demonstrate significant improvement in fSNR using HAMMER compared to conventional analysis using SPM, with more precisely localized activation foci.