The Influence of Spatial Smoothing Kernel Size on the Temporal Features of Intrinsic Connectivity Networks

Spatial smoothing is a common preprocessing step in the analysis of functional magnetic resonance imaging (fMRI) data. However, little is known about the effect of spatial smoothing kernel size on the temporal properties of functional brain networks. This study presents a pilot investigation on the influence of spatial smoothing using independent component analysis (ICA) as a data-driven technique to extract functional networks of brain in the form of intrinsic connectivity networks (ICNs). BOLD resting state fMRI data were collected from 22 healthy subjects on a 3.0 T MRI scanner. 3D spatial smoothing was applied using a Gaussian filter with full width at half maximum (FWHM) kernel sizes of 4 mm, 8 mm, and 12 mm in the preprocessing step. Group ICA with the Infomax algorithm was performed at 75-IC decomposition. Network temporal features including functional network connectivity (FNC) and BOLD power spectra were calculated and compared pairwise using a paired t-test with a false discovery rate (FDR) correction for multiple comparisons. Results revealed robust effects of smoothing kernel size on FNC measures of most ICNs, largely indicating a decrease in inter-network connectivity as the smoothing kernel size decreased. Power spectra analysis showed increased high-frequency power (0.15 – 0.25 Hz) but decreased low-frequency power (0.01 – 0.10 Hz) with a decrease in the smoothing kernel size (corrected p< 0.01). These findings provide a preliminary observation on the effect of spatial smoothing kernel size on the FNC and power spectra.