Effects of spatial smoothing on group-level differences in functional brain networks

TL;DR

This study examines how spatial smoothing in fMRI preprocessing influences the detection of group differences in brain networks, revealing that smoothing can significantly alter results depending on the network analysis method.

Contribution

It provides a detailed analysis of how different levels of spatial smoothing affect group-level brain network differences in clinical populations.

Findings

Smoothing increases differences in weighted networks with larger kernels.

Larger smoothing kernels make thresholded networks more similar, depending on network density.

Smoothing effects vary with link length and are unpredictable.

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 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.