DPABISurf V1.3: An Updated Surface-Based Resting-State fMRI Data Analysis Toolbox

Presented During:

Poster No:


Submission Type:

Abstract Submission 


Chao-Gan Yan1, Xin-Di Wang2, Bin Lu3, Zhi-Kai Chang3


1Institute of Psychology, Chinese Academy of Sciences, Beijing, China, 2McGill Centre for Integrative Neuroscience, Montreal Neurological Institute, McGill University, Montréal, QC, 3Institute of Psychology, Chinese Academy of Sciences, Beijing, Beijing

First Author:

Chao-Gan Yan  
Institute of Psychology, Chinese Academy of Sciences
Beijing, China


Xin-Di Wang  
McGill Centre for Integrative Neuroscience, Montreal Neurological Institute, McGill University
Montréal, QC
Bin Lu  
Institute of Psychology, Chinese Academy of Sciences
Beijing, Beijing
Zhi-Kai Chang  
Institute of Psychology, Chinese Academy of Sciences
Beijing, Beijing


DPABISurf V1.3 is an updated surface-based resting-state fMRI data analysis toolbox evolved from DPABI/DPARSF, as easy-to-use as DPABI/DPARSF. DPABISurf is based on fMRIPprep 1.5.0 (Esteban et al., 2018) (RRID:SCR_016216), FreeSurfer 6.0.1 (Dale et al., 1999) (RRID:SCR_001847), ANTs 2.2.0 (Avants et al., 2008) (RRID:SCR_004757), FSL 5.0.9 (Jenkinson et al., 2002) (RRID:SCR_002823), AFNI 20160207 (Cox, 1996) (RRID:SCR_005927), SPM12 (Ashburner, 2012) (RRID:SCR_007037), PALM alpha112 (Winkler et al., 2016), GNU Parallel (Tange, 2011), MATLAB (The MathWorks Inc., Natick, MA, US) (RRID:SCR_001622), Docker (https://docker.com) (RRID:SCR_016445), and DPABI V4.3 (Yan et al., 2016) (RRID:SCR_010501). DPABISurf provides user-friendly graphical user interface (GUI) for pipeline surface-based preprocessing, statistical analyses and results viewing, while requires no programming/scripting skills from the users.


DPABISurf (Figure 1) is open-source and distributed under GNU/GPL, available at http://www.rfmri.org/dpabi. It supports Windows 10 Pro, MacOS and Linux operating systems. We are continuing updating the toolbox since its first release in March, 2019.
Supporting Image: Figure1.png
   ·Graphical User Interface (GUI) of DPABISurf.


The DPABISurf pipeline (Figure 2) first converts the user specified data into BIDS format (Gorgolewski et al., 2016), and then calls fMRIPprep 1.5.0 docker to preprocess the structural and functional MRI data, which integrates FreeSurfer, ANTs, FSL and AFNI. With fMRIPprep, the data is processed into FreeSurfer fsaverage5 surface space and MNI volume space. DPABISurf further performs nuisance covariates regression on the surface-based data (volume-based data is processed as well), and then calculate the commonly used R-fMRI metrics: amplitude of low frequency fluctuation (ALFF) (Zang et al., 2007), fractional ALFF (Zou et al., 2008), regional homogeneity (Zang et al., 2004), degree centrality (Zuo and Xing, 2014), and seed-based functional connectivity. DPABISurf also performs surface-based smoothing by calling FreeSurfer's mri_surf2surf command. These processed metrics then enters surfaced-based statistical analyses within DPABISurf, which could perform surfaced-based permutation test with TFCE by integrating PALM. Finally, the corrected results could be viewed by the convenient surface viewer DPABISurf_VIEW, which is derived from spm_mesh_render.m. DPABISurf V1.3 also integrated field map distortion correction and temporal dynamic analyses.
Supporting Image: Figure2.png
   ·The pipeline of DPABISurf.


DPABISurf is designed to make surface-based data analysis require minimum manual operations and almost no programming/scripting experience. We anticipate this updated open-source toolbox will assist novices and expert users alike and continue to support advancing R-fMRI methodology and its application to clinical translational studies.

Neuroinformatics and Data Sharing:

Workflows 1
Informatics Other 2



1|2Indicates the priority used for review

My abstract is being submitted as a Software Demonstration.


Please indicate below if your study was a "resting state" or "task-activation” study.

Resting state

Healthy subjects only or patients (note that patient studies may also involve healthy subjects):

Healthy subjects

Was any human subjects research approved by the relevant Institutional Review Board or ethics panel? NOTE: Any human subjects studies without IRB approval will be automatically rejected.


Was any animal research approved by the relevant IACUC or other animal research panel? NOTE: Any animal studies without IACUC approval will be automatically rejected.

Not applicable

Please indicate which methods were used in your research:

Functional MRI

For human MRI, what field strength scanner do you use?


Which processing packages did you use for your study?

Other, Please list  -   DPABI

Provide references using author date format

• Ashburner, J. (2012), 'SPM: a history', Neuroimage, vol. 62, pp. 791-800.
• Avants, B.B. (2008), 'Symmetric diffeomorphic image registration with cross-correlation: evaluating automated labeling of elderly and neurodegenerative brain', Med Image Anal, vol. 12, pp. 26-41.
• Cox, R.W. (1996), 'AFNI: software for analysis and visualization of functional magnetic resonance neuroimages', Comput Biomed Res, vol. 29, pp. 162-173.
• Dale, A.M. (1999), 'Cortical surface-based analysis. I. Segmentation and surface reconstruction', Neuroimage, vol. 9, pp. 179-194.
• Esteban, O. (2018), 'fMRIPrep: a robust preprocessing pipeline for functional MRI', Nat Methods, vol.
• Gorgolewski, K.J. (2016), 'The brain imaging data structure, a format for organizing and describing outputs of neuroimaging experiments', Sci Data, vol. 3, pp. 160044.
• Jenkinson, M. (2002), 'Improved optimization for the robust and accurate linear registration and motion correction of brain images', Neuroimage, vol. 17, pp. 825-841.
• Tange, O. (2011), 'Gnu parallel-the command-line power tool', The USENIX Magazine, vol. 36, pp. 42-47.
• Winkler, A.M. (2016), 'Faster permutation inference in brain imaging', Neuroimage, vol. 141, pp. 502-516.
• Yan, C.G. (2016), 'DPABI: Data Processing & Analysis for (Resting-State) Brain Imaging', Neuroinformatics, vol. 14, pp. 339-351.
• Zang, Y. (2004), 'Regional homogeneity approach to fMRI data analysis', Neuroimage, vol. 22, pp. 394-400.
• Zang, Y.F. (2007), 'Altered baseline brain activity in children with ADHD revealed by resting-state functional MRI', Brain Dev, vol. 29, pp. 83-91.
• Zou, Q.-H. (2008), 'An improved approach to detection of amplitude of low-frequency fluctuation (ALFF) for resting-state fMRI: Fractional ALFF', Journal of Neuroscience Methods, vol. 172, pp. 137-141.
• Zuo, X.-N. (2014), 'Test-retest reliabilities of resting-state FMRI measurements in human brain functional connectomics: A systems neuroscience perspective', Neuroscience & Biobehavioral Reviews, vol. 45, pp. 100-118.