Anatomically constrained volumetric smoothing enhances fMRI reliability while avoiding smoothing artifacts.

Introduction: Smoothing fMRI data prior to analysis is a fundamental and widely used technique to increase sensitivity. Unconstrained smoothing can also reduce the spatial specificity of the analysis by introducing artifacts in the data. This study tested the effects of smoothing on the reliability and accuracy of both task fMRI and resting state data. The effects of unconstrained smoothing were compared to those of an anatomically constrained smoothing method, which prevents smoothing across the white and gray matter surfaces of the cortex. Methods: Unconstrained Gaussian smoothing and anatomically constrained smoothing were applied to simulated data, a sensory task fMRI dataset, a precision fMRI motor task mapping dataset, and a resting state fMRI dataset. Smoothing-related artifacts were tested for and compared between the smoothing methods, and the effects of the smoothing methods on the reliability and accuracy were measured. Results: In the experiments with simulated data, unconstrained Gaussian smoothing demonstrated decreased accuracy and increased white matter activation compared to constrained smoothing. In the sensory task activation analysis, both Gaussian and constrained smoothing increased the reliability of the sensory task fMRI activations, but Gaussian smoothing increased the percentage of active voxels in the white matter relative to constrained smoothing ( Conclusion: This study showed that unconstrained Gaussian smoothing spreads activation across cortical boundaries, increases white matter activation, and biases graph theory connectivity metrics. Anatomically constrained smoothing reduced some of these smoothing artifacts while still increasing reliability and may be a reasonable alternative to unconstrained Gaussian smoothing.