Critical factors in achieving fine‐scale functional MRI: Removing sources of inadvertent spatial smoothing

Ultra‐high Field (≥7T) functional magnetic resonance imaging (UHF‐fMRI) provides opportunities to resolve fine‐scale features of functional architecture such as cerebral cortical columns and layers, in vivo. While the nominal resolution of modern fMRI acquisitions may appear to be sufficient to resolve these features, several common data preprocessing steps can introduce unwanted spatial blurring, especially those that require interpolation of the data. These resolution losses can impede the detection of the fine‐scale features of interest. To examine quantitatively and systematically the sources of spatial resolution losses occurring during preprocessing, we used synthetic fMRI data and real fMRI data from the human visual cortex—the spatially interdigitated human V2 “thin” and “thick” stripes. The pattern of these cortical columns lies along the cortical surface and thus can be best appreciated using surface‐based fMRI analysis. We used this as a testbed for evaluating strategies that can reduce spatial blurring of fMRI data. Our results show that resolution losses can be mitigated at multiple points in preprocessing pathway. We show that unwanted blur is introduced at each step of volume transformation and surface projection, and can be ameliorated by replacing multi‐step transformations with equivalent single‐step transformations. Surprisingly, the simple approaches of volume upsampling and of cortical mesh refinement also helped to reduce resolution losses caused by interpolation. Volume upsampling also serves to improve motion estimation accuracy, which helps to reduce blur. Moreover, we demonstrate that the level of spatial blurring is nonuniform over the brain—knowledge which is critical for interpreting data in high‐resolution fMRI studies. Importantly, our study provides recommendations for reducing unwanted blurring during preprocessing as well as methods that enable quantitative comparisons between preprocessing strategies. These findings highlight several underappreciated sources of a spatial blur. Individually, the factors that contribute to spatial blur may appear to be minor, but in combination, the cumulative effects can hinder the interpretation of fine‐scale fMRI and the detectability of these fine‐scale features of functional architecture. In order to evaluate the strategies to reduce spatial resolution losses during fMRI data preprocessing for high resolution surface‐based columnar mapping, we quantified inadvertent blur using both syn