Optimization of rs-fMRI parameters in the Seed Correlation Analysis (SCA) in DPARSF toolbox: A preliminary study

Abstract

There are a number of various methods of resting-state functional magnetic resonance imaging (rs-fMRI) analysis such as independent component analysis, multivariate autoregressive models, or seed correlation analysis however their results depend on arbitrary choice of parameters. Therefore, the aim of this work was to optimize the parameters in the seed correlation analysis using the Data Processing Assistant for Resting-State fMRI (DPARSF) toolbox for rs-fMRI data received from a Siemens Magnetom Skyra 3-Tesla scanner using a whole-brain, gradient-echo echo planar sequence with a 32-channel head coil. Different ranges of the following parameters: amplitude of low-frequency fluctuation (ALFF), Gaussian kernel at FWHM and radius of spherical ROI for 109 regions were tested for 20 healthy volunteers. The highest values of functional connectivity (FC) correlations were found for ALFF 0.01-0.08, spherical ROIs with the 8-mm radius and Gaussian kernel 8 mm at FWHM in all the studied areas that is, Auditory, Sensimotor, Visual, and Default Mode Network. The dominating influence of ALFF and smoothing on values of FC correlations was noted.

Keywords: Data Processing Assistant for Resting-State fMRI (DPARSF); RRID: SCR_002372; analysis seed-based (SCA); functional connectivity (FC); resting-state functional magnetic resonance imaging (rs-fMRI).