Abstract
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 region of interest (ROI) size, but varies with link length. The effects of spatial smoothing are diverse, nontrivial, and difficult to predict. This has important consequences: The choice of smoothing kernel affects the observed network differences.
Highlights
Neuroscientists commonly model the brain as a network
Using functional magnetic resonance imaging (fMRI) data from two clinical populations and healthy controls, we show that the between-group differences in network structure depend on the amount of spatial smoothing applied during preprocessing in a nontrivial way
When examining which links were significantly different between the two groups, we found that 6 links were consistently different at all smoothing levels and 37 links were consistent at 12 smoothing levels, which represents 75% of the levels; these 6 most stable links are between the subcortical regions, superior frontal gyrus, precentral gyrus, paracentral lobule, and postcentral gyrus; and fusiform gyrus and postcentral gyrus
Summary
Neuroscientists commonly model the brain as a network. The brain can be seen as a system of segregated, specialized neuronal groups that interact to produce intricate behaviors (Sporns, 2013). These groups and their interactions form a network that can be studied with the tools of network science. One of the applications of brain network studies is to investigate connectivity similarities between subjects and groups Bullmore & Sporns, 2009) This may lead to the identification of abnormalities in the connectivity of clinical populations (Bassett & Bullmore, 2009)
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
