DPARSF: A MATLAB Toolbox for “Pipeline” Data Analysis of Resting-State fMRI

By Chaogan Yan, Yufeng Zang. Frontiers in Systems Neuroscience, 2010

3,635 citations recorded by the source corpus.

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.

Subfields: Cognitive Neuroscience, Radiology, Nuclear Medicine and Imaging

View Paper: https://www.semanticscholar.org/paper/fc1d568021431e1fd9b4ef2623e5b706f4240239

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Citations in the corpus

DPABI: Data Processing & Analysis for (Resting-State) Brain Imaging (2016, methodology): “Based on SPM and REST functions, a MATLAB toolbox for “pipeline” data analysis of R-fMRI data, DPARSF was also developed (Yan and Zang, 2010).”
Principal components of functional connectivity: A new approach to study dynamic brain connectivity during rest (2013, methodology): “Anatomical and functional data were preprocessed using SPM8 (www.fil.ion.ucl.ac.uk/spm), and a combination of in-house MATLAB scripts and scripts from the DPARSFA toolbox (Chao-Gan and Yu-Feng, 2010).”
Dissecting the social brain: Introducing the EmpaToM to reveal distinct neural networks and brain-behavior relations for empathy and Theory of Mind (2015, neutral): “Resting state data was analyzed with SPM8 and DPARSF (Chao-Gan and Yu-Feng, 2010).”
Subregions of the human superior frontal gyrus and their connections (2013, neutral): “Resting-state functional connectivity patterns Resting-state fMRI data preprocessing The resting-state fMRI data were preprocessed using the Statistical Parametric Mapping (SPM8, http://www.fil.ion.ucl.ac.uk/spm) and Data Processing Assistant for Resting-State fMRI (DPARSF) (Yan and Zang, 2010).”
Implementation of a new parcellation of the orbitofrontal cortex in the automated anatomical labeling atlas (2015, methodology): “The AAL is available as a toolbox (http://www.gin.cnrs.fr/AAL) for SPM (Statistical Parametric Mapping http://www.fil.ion.ucl.ac.uk/spm), with MRIcron (http:// www.mccauslandcenter.sc.edu/mricro/mricron), and with the Data Processing Assistant for Resting-State fMRI (DPARSF) (Chao-Gan and Yu-Feng, 2”
A Comprehensive Assessment of Regional Variation in the Impact of Head Micromovements on Functional Connectomics (2013, positive): “Unless otherwise stated, all preprocessing was performed using the Data Processing Assistant for Resting-State fMRI (DPARSF, Yan and Zang, 2010, http://www.restfmri.net), which is based on Statistical Parametric Mapping (SPM8) (http://www.fil.ion.ucl.ac.uk/spm) and Resting-State fMRI Data Analysis…”
Automated anatomical labelling atlas 3 (2020, neutral): “The AAL is available as a toolbox (http://www.gin.cnrs.fr/tools/aal) for SPM (Statistical Parametric Mapping http://www.fil.ion.ucl.ac.uk/spm), with MRIcron (http://www.mccauslandcenter.sc.edu/mricro/mricron), and with the Data Processing Assistant for Resting-State fMRI (DPARSF) (Chao-Gan and Yu-Fe”
Deep learning in mental health outcome research: a scoping review (2020, methodology): “40 , and fMRI Preprocessing Pipeline 41 to extract useful features before feeding to the DL models.”
Toward reliable characterization of functional homogeneity in the human brain: Preprocessing, scan duration, imaging resolution and computational space (2012, methodology): “In the initial paper describing ReHo (Zang et al., 2004) and most applications published by using ReHo, preprocessing did not include removal of any nuisance signals although the functionality of removing head motion, white matter, CSF and global signal from BOLD time series is available in DPARSF (”
Hemisphere- and gender-related differences in small-world brain networks: A resting-state functional MRI study (2011, methodology): “R-fMRI data preprocessing was then conducted by SPM5 (http://www.fil.ion.ucl.ac.uk/spm/) and Data Processing Assistant for Resting-State fMRI (DPARSF) (Yan and Zang, 2010).”
Standardizing the intrinsic brain: Towards robust measurement of inter-individual variation in 1000 functional connectomes (2013, neutral): “…N C OPreprocessing Unless otherwise stated, all preprocessing was performed using the Data Processing Assistant for Resting-State fMRI (DPARSF, Yan and Zang, 2010, http://www.restfmri.net), which is based on Statistical Parametric Mapping (SPM8) (http://www.fil.ion.ucl.ac.uk/spm) and…”
Reduced default mode network functional connectivity in patients with recurrent major depressive disorder (2019, neutral): “Chao-Gan Y, Yu-Feng Z (2010) DPARSF: A MATLAB toolbox for “pipeline” data analysis of resting-state fMRI.”