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Dparsfa

Manufactured by MathWorks
Sourced in United States

DPARSFA is a software tool for the analysis of functional magnetic resonance imaging (fMRI) data. It provides a comprehensive set of tools for preprocessing, statistical modeling, and functional connectivity analysis of fMRI data. The core function of DPARSFA is to facilitate the analysis of fMRI data in a standardized and efficient manner.

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2 protocols using dparsfa

1

Preprocessing pipeline for rs-fMRI data

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Data preprocessing was performed using the DPARSFA toolbox version 2.21 on MATLAB (MathWorks Inc., Natick, MA, USA) platform, which involved: (1) Removal of the first 10 time points for signal stabilization. (2) Slice timing correction. (3) Realignment. This step realigns individual images such that each part of the brain in every volume is in the same position. (4) T1 segmentation and spatial normalization. The 3D-T1 images were co-registered to the mean Rs-fMRI data for each subject. Specifically, 3D T1-weighted images were divided into white matter (WM), gray matter (GM), and cerebrospinal fluid (CSF) probability maps using SPM DARTEL segmentation, and CSF images were resampled to 1.5-mm isotropic voxels, spatially normalized to the MNI space using affine transformation and non-linear deformation, and then, resampled to 3-mm isotropic voxels resolution with Rs-fMRI and the deformation field was applied to the Rs-fMRI data. (5) Spatial smoothing with a 4-mm full-width at half-maximum Gaussian kernel. (6) Regressing out nuisance covariates: global, WM, CSF signals, and Friston 24 head motion parameters. (7) Detrending and filtering. Linear detrending and temporal band-pass filtering at 0.01–0.08 Hz was used to remove low-frequency drift and high-frequency physiological noise.
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2

rs-fMRI Data Preprocessing Protocol

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Preprocessing of the rs-fMRI data was performed using the advanced version of rs-fMRI Data Processing Assistant (DPARSFA, version 5.3, http://www.restfmri.net) [29 (link)] based on the MATLAB platform (The Math Works, Inc., Natick, MA, USA). Slice-timing, head motion correction, nuisance regression with adding mean back (white matter and cerebrospinal fluid (CSF) signals and Friston 24 head motion parameters), and spatial normalization to the standard Montreal Neurological Institute (MNI) EPI template were performed sequentially with a resampled voxel size of 3 × 3 × 3 mm conducted for the 230 time-points. Then, all images were smoothed with a 6 mm full-width at half-maximum (FWMH) Gaussian kernel. Realignment parameters were checked, and two participants showed displacement greater than 3.0 mm or angular rotation more than 3.0°. Thus, we excluded these two subjects from the study. Two-sample t-tests indicated no significant differences in the mean framewise displacement (Jenkinson) between the CSVD and NC groups (0.12 ± 0.05 vs 0.12 ± 0.06 mm, p = 0.856).
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