Gaussian kernel smoothing
This is an incremental contribution, as it reiterates the established use of Gaussian kernel smoothing in brain imaging without introducing new methods or data.
The paper addresses the problem of noise in brain imaging data by advocating for Gaussian kernel smoothing to improve signal-to-noise ratio and smoothness, which enhances statistical sensitivity and power for subsequent analysis.
Image acquisition and segmentation are likely to introduce noise. Further image processing such as image registration and parameterization can introduce additional noise. It is thus imperative to reduce noise measurements and boost signal. In order to increase the signal-to-noise ratio (SNR) and smoothness of data required for the subsequent random field theory based statistical inference, some type of smoothing is necessary. Among many image smoothing methods, Gaussian kernel smoothing has emerged as a de facto smoothing technique among brain imaging researchers due to its simplicity in numerical implementation. Gaussian kernel smoothing also increases statistical sensitivity and statistical power as well as Gausianness. Gaussian kernel smoothing can be viewed as weighted averaging of voxel values. Then from the central limit theorem, the weighted average should be more Gaussian.