  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.
