DPABISurf V1.3: An Updated Surface-Based Resting-State fMRI Data Analysis Toolbox
Poster No:
1913
Submission Type:
Abstract Submission
Authors:
Chao-Gan Yan1, Xin-Di Wang2, Bin Lu3, Zhi-Kai Chang3
Institutions:
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:
Co-Author(s):
Xin-Di Wang
McGill Centre for Integrative Neuroscience, Montreal Neurological Institute, McGill University
Montréal, QC
McGill Centre for Integrative Neuroscience, Montreal Neurological Institute, McGill University
Montréal, QC
Introduction:
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.
Methods:
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.
·Graphical User Interface (GUI) of DPABISurf.
Results:
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.
·The pipeline of DPABISurf.
Conclusions:
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
Keywords:
FUNCTIONAL MRI
Informatics
Workflows
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.
Healthy subjects only or patients (note that patient studies may also involve 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.
Please indicate which methods were used in your research:
For human MRI, what field strength scanner do you use?
Which processing packages did you use for your study?
Provide references using author date format
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