Spatial smoothing systematically biases the localization of reward-related brain activity

Abstract

Neuroimaging methods with enhanced spatial resolution such as functional magnetic resonance imaging (FMRI) suggest that the subcortical striatum plays a critical role in human reward processing. Analysis of FMRI data requires several preprocessing steps, some of which entail tradeoffs. For instance, while spatial smoothing can enhance statistical power, it may also bias localization towards regions that contain more gray than white matter. In a meta-analysis and reanalysis of an existing dataset, we sought to determine whether spatial smoothing could systematically bias the spatial localization of foci related to reward anticipation in the nucleus accumbens (NAcc). An activation likelihood estimate (ALE) meta-analysis revealed that peak ventral striatal ALE foci for studies that used smaller spatial smoothing kernels (i.e. <6mm FWHM) were more anterior than those identified for studies that used larger kernels (i.e. >7mm FWHM). Additionally, subtraction analysis of findings for studies that used smaller versus larger smoothing kernels revealed a significant cluster of differential activity in the left relatively anterior NAcc (Talairach coordinates: -10, 9, -1). A second meta-analysis revealed that larger smoothing kernels were correlated with more posterior localizations of NAcc activation foci (p<0.015), but revealed no significant associations with other potentially relevant parameters (including voxel volume, magnet strength, and publication date). Finally, repeated analysis of a representative dataset processed at different smoothing kernels (i.e., 0-12mm) also indicated that smoothing systematically yielded more posterior activation foci in the NAcc (p<0.005). Taken together, these findings indicate that spatial smoothing can systematically bias the spatial localization of striatal activity. These findings have implications both for historical interpretation of past findings related to reward processing and for the analysis of future studies.