  Graph-theoretical methods have rapidly become a standard tool in studies of
the structure and function of the human brain. Whereas the structural
connectome can be fairly straightforwardly mapped onto a complex network, there
are more degrees of freedom in constructing networks that represent functional
connections between brain areas. For fMRI data, such networks are typically
built by aggregating the BOLD signal time series of voxels into larger entities
(such as Regions of Interest in some brain atlas), and determining the
connection strengths between these from some measure of time-series
correlations. Although it is evident that the outcome of this procedure must be
affected by how the voxel-level time series are treated at the preprocessing
stage, there is a lack of systematic studies of the effects of preprocessing on
network structure. Here, we focus on the effects of spatial smoothing, which is
a standard preprocessing method for fMRI. We apply various levels of spatial
smoothing to resting-state fMRI data, and measure the changes induced in the
corresponding functional networks. We show that the level of spatial smoothing
clearly affects the degrees and other centrality measures of the nodes of the
functional networks; these changes are non-uniform, systematic, and depend on
the geometry of the brain. The composition of the largest connected network
component is also affected in a way that artificially increases the similarity
of the networks of different subjects. Our conclusion is that wherever
possible, spatial smoothing should be avoided when preprocessing fMRI data for
network analysis.
