Brain connectivity with functional Magnetic Resonance Imaging (fMRI) is a popular approach for detecting differences between healthy and clinical populations. Before creating a functional brain network, the fMRI time series must undergo several preprocessing steps to control for artifacts and to improve data quality. However, preprocessing may affect the results in an undesirable way. Spatial smoothing, for example, is known to alter functional network structure. Yet, its effects on group-level network differences remain unknown. Here, we investigate the effects of spatial smoothing on the difference between patients and controls for two clinical conditions: autism spectrum  and bipolar disorder, considering fMRI data smoothed with Gaussian kernels (0-32 mm). We find that smoothing affects network differences between groups. For weighted networks, incrementing the smoothing kernel makes networks more different until a peak is reached and the networks become more similar . For thresholded networks, the effects depend on the network density. Smoothing also alters the effect sizes of the individual link differences. This is independent of the ROI size, but the effects of link lengthvary . The effects of spatial smoothing are diverse, non-trivial, and difficult to predict. This has important consequences: the choice of smoothing kernel affects the findings in network differences.
Brain connectivity with functional Magnetic Resonance Imaging (fMRI) is a popular approach for detecting differences between healthy and clinical populations. Before creating a functional brain network, the fMRI time series must undergo several preprocessing steps to control for artifacts and to improve data quality. However, preprocessing may affect the results in an undesirable way. Spatial smoothing, for example, is known to alter functional network structure. Yet, its effects on group-level network differences remain unknown. Here, we investigate the effects of spatial smoothing on the difference between patients and controls for two clinical conditions: autism spectrum disorder and bipolar disorder, considering fMRI data smoothed with Gaussian kernels (0-32 mm). We find that smoothing affects network differences between groups. For weighted networks, incrementing the smoothing kernel makes networks more different . For thresholded networks, larger smoothing kernels lead to more similar networks, although this depends on the network density. Smoothing also alters the effect sizes of the individual link differences. This is independent of the ROI size, but vary with link length . The effects of spatial smoothing are diverse, non-trivial, and difficult to predict. This has important consequences: the choice of smoothing kernel affects the observed network differences.
A
disorder
D
until a peak is reached and the networks become more similar
R
the effects depend
larger smoothing kernels lead to more similar networks, although this depends
R
the effects of link lengthvary
vary with link length
R
findings in
observed