Spatial Smoothing Effect on Group-Level Functional Connectivity during Resting and Task-Based fMRI

Spatial smoothing is a preprocessing step applied to neuroimaging data to enhance data quality by reducing noise and artifacts. However, selecting an appropriate smoothing kernel size can be challenging as it can lead to undesired alterations in final images and functional connectivity networks. However, there is no sufficient information about the effects of the Gaussian kernel size on group-level results for different cases yet. This study investigates the influence of kernel size on functional connectivity networks and network parameters in whole-brain rs-fMRI and tb-fMRI analyses of healthy adults. The analysis includes {0, 2, 4, 6, 8, 10} mm kernels, commonly used in practical analyses, covering all major brain networks. Graph theoretical measures such as betweenness centrality, global/local efficiency, clustering coefficient, and average path length are examined for each kernel. Additionally, principal component analysis (PCA) and independent component analysis (ICA) parameters, namely kurtosis and skewness, are evaluated for the functional images. The findings demonstrate that kernel size directly affects node connections, resulting in modifications to functional network structures and PCA/ICA parameters. However, network metrics exhibit greater resilience to these changes.

Automated summary

Spatial smoothing is a preprocessing step that improves the quality of neuroimaging data by reducing noise and artifacts. However, selecting the right size for smoothing kernel can be challenging as it can lead to undesired changes in final images and functional connectivity networks. This study investigates the impact of kernel size on functional connectivity networks and network parameters in whole-brain resting-state and task-based fMRI analyses of healthy adults.