DPARSF is a MATLAB toolbox designed for "pipeline" data analysis of resting-state fMRI. It integrates functions from Statistical Parametric Mapping (SPM) and the Resting-State fMRI Data Analysis Toolkit (REST) to automate the preprocessing and analysis of resting-state fMRI data. The toolbox allows users to process data by arranging DICOM files, setting parameters, and then performing tasks such as slice timing correction, head motion correction, normalization, smoothing, and filtering. DPARSF also generates reports for excluding subjects with excessive head motion and provides visual checks for normalization effects. It supports the calculation of functional connectivity (FC), regional homogeneity (ReHo), amplitude of low-frequency fluctuation (ALFF), and fractional ALFF (fALFF). Additionally, users can extract time courses from regions of interest. The toolbox is user-friendly, open-source, and compatible with SPM and REST. It is designed to streamline the analysis process, reduce manual errors, and improve efficiency in resting-state fMRI studies. DPARSF is freely available at http://www.restfmri.net and is intended to facilitate the study of resting-state fMRI, particularly in clinical settings. The tool has been validated using data from the 1000 Functional Connectomes Project, demonstrating its effectiveness in analyzing resting-state fMRI data. The results show that the default mode network (DMN) exhibits higher ReHo, ALFF, and fALFF values, consistent with previous findings. Functional connectivity analysis reveals significant correlations between the DMN and other brain regions, highlighting the competitive relationship between the DMN and anti-correlated networks. DPARSF provides a reliable and efficient solution for resting-state fMRI data analysis, making it a valuable tool for researchers and clinicians.DPARSF is a MATLAB toolbox designed for "pipeline" data analysis of resting-state fMRI. It integrates functions from Statistical Parametric Mapping (SPM) and the Resting-State fMRI Data Analysis Toolkit (REST) to automate the preprocessing and analysis of resting-state fMRI data. The toolbox allows users to process data by arranging DICOM files, setting parameters, and then performing tasks such as slice timing correction, head motion correction, normalization, smoothing, and filtering. DPARSF also generates reports for excluding subjects with excessive head motion and provides visual checks for normalization effects. It supports the calculation of functional connectivity (FC), regional homogeneity (ReHo), amplitude of low-frequency fluctuation (ALFF), and fractional ALFF (fALFF). Additionally, users can extract time courses from regions of interest. The toolbox is user-friendly, open-source, and compatible with SPM and REST. It is designed to streamline the analysis process, reduce manual errors, and improve efficiency in resting-state fMRI studies. DPARSF is freely available at http://www.restfmri.net and is intended to facilitate the study of resting-state fMRI, particularly in clinical settings. The tool has been validated using data from the 1000 Functional Connectomes Project, demonstrating its effectiveness in analyzing resting-state fMRI data. The results show that the default mode network (DMN) exhibits higher ReHo, ALFF, and fALFF values, consistent with previous findings. Functional connectivity analysis reveals significant correlations between the DMN and other brain regions, highlighting the competitive relationship between the DMN and anti-correlated networks. DPARSF provides a reliable and efficient solution for resting-state fMRI data analysis, making it a valuable tool for researchers and clinicians.