Effects of spatial smoothing on functional brain networks

Abstract

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 functional magnetic resonance imaging (fMRI) data, such networks are typically built by aggregating the blood-oxygen-level dependent signal time series of voxels into larger entities (such as Regions of Interest in some brain atlas) and determining their connection strengths from some measure of time-series correlations. Although it is evident that the outcome 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, a standard preprocessing method for fMRI. We apply various levels of spatial smoothing to resting-state fMRI data and measure the changes induced in functional networks. We show that the level of spatial smoothing clearly affects the degrees and other centrality measures of functional network nodes; 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.

A Comparison of Functional Networks Derived From Representational Similarity, Functional Connectivity, and Univariate Analyses

“…Preprocessing was conducted as described in our previous study (Lee , but again optimized to fit the requirements of FCA: no smoothing was carried out to avoid a spillover effect (Alakörkkö et al, 2017). The outlier scans were detected based on the global signal spike and motion in the functional data by the Artifact Detection Toolbox (ART) software package 1 .…”

Internodular functional connectivity in heterotopia‐related epilepsy

“…ART‐based functional outlier detection and scrubbing was used in the preprocessing pipeline in CONN to account for movement of individual patients. Spatial smoothing was not performed to avoid contamination of blood oxygenation level‐dependent signal in the PNH by adjacent cerebrospinal fluid in the ventricles …”

Unhealthy Yet Avoidable – How Cognitive Bias Modification Alters Behavioral And Brain Responses To Food Cues In Obesity

“…We investigated seed-based connectivity using predefined ROIs (see supplementary materials ‘regions of interest definition’) as seeds. Unsmoothed 24 functional time series was entered into the analysis. Analysis was performed using Nipype and Nilearn algorithms.…”