DPARSF: a MATLAB toolbox for “pipeline” data analysis of resting-state fMRI

TLDR

Resting‑state fMRI is a non‑invasive, effective, and simple method for probing the brain’s intrinsic functional architecture, yet a user‑friendly pipeline toolbox is still lacking. DPARSF was developed to provide a MATLAB‑based, user‑friendly pipeline for resting‑state fMRI data analysis. Built on SPM and REST functions, DPARSF automates preprocessing (slice timing, realignment, normalization, smoothing) and computes functional connectivity, regional homogeneity, ALFF/fALFF, generates motion‑exclusion reports, normalization quality images, and allows ROI time‑course extraction.

Abstract

Resting-state functional magnetic resonance imaging (fMRI) has attracted more and more attention because of its effectiveness, simplicity and non-invasiveness in exploration of the intrinsic functional architecture of the human brain. However, user-friendly toolbox for "pipeline" data analysis of resting-state fMRI is still lacking. Based on some functions in Statistical Parametric Mapping (SPM) and Resting-State fMRI Data Analysis Toolkit (REST), we have developed a MATLAB toolbox called Data Processing Assistant for Resting-State fMRI (DPARSF) for "pipeline" data analysis of resting-state fMRI. After the user arranges the Digital Imaging and Communications in Medicine (DICOM) files and click a few buttons to set parameters, DPARSF will then give all the preprocessed (slice timing, realign, normalize, smooth) data and results for functional connectivity, regional homogeneity, amplitude of low-frequency fluctuation (ALFF), and fractional ALFF. DPARSF can also create a report for excluding subjects with excessive head motion and generate a set of pictures for easily checking the effect of normalization. In addition, users can also use DPARSF to extract time courses from regions of interest.