effect of spatial smoothing on task fmri ica and functional connectivity

Spatial smoothing is a widely used preprocessing step in functional magnetic resonance imaging (fMRI) data analysis. In this work, we report on the spatial smoothing effect on task-evoked fMRI brain functional mapping and functional connectivity. Initially, we decomposed the task fMRI data into a collection of components or networks by independent component analysis (ICA). The designed task paradigm helps identify task-modulated ICA components (highly correlated with the task stimuli). For the ICA-extracted primary task component, we then measured the task activation volume at the task response foci. We used the task timecourse (designed) as a reference to order the ICA components according to the task correlations of the ICA timecourses. With the re-ordered ICA components, we calculated the inter-component function connectivity (FC) matrix (correlations among the ICA timecourses). By repeating the spatial smoothing of fMRI data with a Gaussian smoothing kernel with a full width at half maximum (FWHM) of {1, 3, 6, 9, 12, 15, 20, 25, 30, 35} mm, we measured the spatial smoothing effects. Our results show spatial smoothing reveals the following effects: (1) It decreases the task extraction performance of single-subject ICA more than that of multi-subject ICA; (2) It increases the task volume of multi-subject ICA more than that of single-subject ICA; (3) It strengthens the functional connectivity of single-subject ICA more than that of multi-subject ICA; and (4) It impacts the positive-negative imbalance of single-subject ICA more than that of multi-subject ICA. Our experimental results suggest a 2~3 voxel FWHM spatial smoothing for single-subject ICA in achieving an optimal balance of functional connectivity, and a wide range (2~5 voxels) of FWHM for multi-subject ICA.