Adaptive Smoothing in fMRI Data Processing Neural Networks

The use of noninvasive functional Magnetic Resonance Imaging (fMRI) techniques for determining brain activity requires a set of data processing steps that transforms raw data into validated elements suitable for statistical analysis. Gaussian filter to average local voxel intensities is an important preprocessing step. Spatial smoothing serves several purposes [1]: local averaging reduces uncorrelated random noise in the voxel, hence increasing the Signal-to-Noise Ratio (SNR) leading to improved statistical power to detect true functional brain activation; also, spatial smoothing serves to eliminate unimportant anatomical details across subjects, that are preserved despite affine and nonlinear transformations, which are common spatial normalisation steps in the pipeline; additionally, smoothing can ensure that assumptions typically made to enable multiple comparison correction using Random Field Theory (RFT) for locating brain activation are fulfilled. Notice that the resolution of each brain volume is decreased by applying the smoothing step, meaning that an appropriate tradeoff between the original volume and the degree of smoothing to be applied is sought. This compromise is governed by a single parameter stating the width of the Gaussian filter. In spite of the importance of this parameter, there is no established method to automatically select its most appropriate value for every situation, being often set according to best practices or relying on each scientist's expertise.