Abstract
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.
Highlights
A functional magnetic resonance imaging experiment captures brain activity via a time series of images or a spatiotemporal series
By repeating the spatial smoothing procedure for a range of full width at half maximum (FWHM) = {1,3,6,9,12,15,20,25,30,35} mm while carrying out the other routines, we studied the effect of spatial smoothing effects
It is clear that the independent component analysis (ICA)-extracted task activation patterns are consistently reproduced at the motor cortex after different spatial smoothing (the activation blobs were displayed with orthogonal slices at the same foci, as designed with the same (x,y,z) MNI coordinates)
Summary
A functional magnetic resonance imaging (fMRI) experiment captures brain activity via a time series of images or a spatiotemporal series (represented by a 4D dataset). For a task fMRI ICA study, we can use the predefined task paradigm (a cue of timecourse) to order the ICA components in an order of task correlation, such that we may compare ICA-based FC matrices generated from different ICA outputs (where the ICA components are always disordered). Another important reason to work on task fMRI ICA is that the ICA technique can successfully extract the primary task performance component (Duff et al, 2012; Xu et al, 2013; Chen and Calhoun, 2016),which provides a useful reference point for conducting analysis
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