Data Processing Assistant for Resting-State fMRI (DPARSF)

 
 
Data Processing Assistant for Resting-State fMRI (DPARSF)
 
 
Data Processing Assistant for Resting-State fMRI (DPARSF) is a convenient plug-in software within DPABI, which is based on SPM. You just need to arrange your DICOM files, and click a few buttons to set parameters, DPARSF will then give all the preprocessed (slice timing, realign, normalize, smooth) data, functional connectivity, ReHo, ALFF/fALFF, degree centrality, voxel-mirrored homotopic connectivity (VMHC) results. 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. You can use DPARSF to extract ROI time courses efficiently if you want to perform small-world analysis. DPARSF basic edition is very easy to use while DPARSF advanced edition (alias: DPARSFA) is much more flexible and powerful. DPARSFA can parallel the computation for each subject, and can be used to reorient your images interactively or define regions of interest interactively. You can skip or combine the processing steps in DPARSF advanced edition freely. Please download a MULTIMEDIA COURSE to know more about how to use this software. As a component of DPABI, please add with subfolders for DPABI in MATLAB's path setting and enter "dpabi" in the command window to enjoy this powerful toolbox.
 
The latest release is DPARSF_V4.5_190725. Please download DPABI to enjoy it!  
 
 
New features of DPARSF_V4.5_190725  (download at http://rfmri.org/dpabi)
1. For Linux or Mac OS, if FSL is not installed, then DPARSF will call FSL's bet in dpabi docker.
Tips for Linux or Mac O: please start matlab from terminal in order to reach docker in DPABI (e.g., Linux: matlab; Mac: open /Applications/MATLAB_R2018a.app/).
 
New features of DPARSF_V4.4_180801 (download at http://rfmri.org/dpabi):
1. Added a prompt of "Congratulations, the running of DPARSFA is done!!! :)" when DPARSF finishes its processing.
2. Added a new atlas (Schaefer2018_400Parcels_7Networks_order_FSLMNI152_1mm.nii) to the V4 parameters. Please see more details at Schaefer, A., Kong, R., Gordon, E.M., Laumann, T.O., Zuo, X.N., Holmes, A.J., Eickhoff, S.B., Yeo, B.T.T., 2017. Local-Global Parcellation of the Human Cerebral Cortex from Intrinsic Functional Connectivity MRI. Cereb Cortex, 1-20.
3. The dcm2nii has been updated to the latest version in courtesy of Dr. Chris Rorden. See: Li, X., Morgan, P.S., Ashburner, J., Smith, J., Rorden, C., 2016. The first step for neuroimaging data analysis: DICOM to NIfTI conversion. J Neurosci Methods 264, 47-56.
4. As there were some parallel computing issues in calling outside command, the callings were no longer using parallel computing (i.e., downgrade from parfor to for). These includes the callings of dcm2nii and bet. 
5. Flexibility for concordance was added to the module of Temporal Dynamic Analysis (DPABI_TDA). Users can freely calculate the concordance of any combinations of ALFF, fALFF, ReHo, Degree Centrality, Global Signal Correlation and VMHC.
6. Fixed some compatibility bugs with higher versions of MATLAB. For example, Time Course error in DPABI_VIEW; uimenu parent problem when calling monkey/rat module; errors regard generating pictures for checking normalization in DPARSFA.
7. Tips for calling "bet": You should start matlab from terminal (e.g., Linux: matlab; Mac: open /Applications/MATLAB_R2018a.app/). If you installed FSL5.0, you may also need to run this: source /usr/share/fsl/5.0/etc/fslconf/fsl.sh. In addition, in some Linux versions, you may need to start matlab in this way: LD_PRELOAD="/usr/lib/x86_64-linux-gnu/libstdc++.so.6" matlab.
 
 
New features of DPARSF_V4.3_170105:
1. DPARSFA V4 Parameters (Default Parameters, also for The R-fMRI Maps Project). For ROI signals extraction, the Power 264 ROIs were added as the 1570~1833 ROIs. (Power_Neuron_264ROIs_Radius5_Mask.nii was added to {DPABI}/Templates/)
2. DPARSF Advanced Edition: Re-run with global signal regression (DPARSFA_RerunWithGSR). Fixed a bug when “Remove first X time points” was defined, the number of time points will be adjusted accordingly.
3. DPARSF. Add a “Clear All” button in the ROI List GUI for defining ROIs.
 
 
New features DPARSF_V4.2_161201
1. To let the users be more aware what kind of templates they are using, SPM Templates were included under {DPABI}/Templates/ now. If you want to USE YOUR OWN TEMPLATES, please replace the corresponding ones under this directory instead of replacing those under SPM. For example: if you are using normalize by New Segment + DARTEL, please replace {DPABI}/Templates/SPMTemplates/tpm/TPM.nii; If you are using normalize by using EPI template, please replace {DPABI}/Templates/SPMTemplates/toolbox/OldNorm/EPI.nii; If you are using normalize by using T1 image unified segmentation, please replace {DPABI}/Templates/SPMTemplates/toolbox/OldSeg/grey.nii, white.nii, and csf.nii. 
2. DPARSF Windows version. Previously need to run as administrator to get results both with and without global signal regression (GSR). Now such limitation is removed (change mklink to copyfile).
3. DPARSF Advanced Edition Preprocessing for Task fMRI data: For nuisance regression, the option of “Add mean back” is now default. The mean will be added back to the residual after nuisance regression. This is useful for circumstances of ICA or task-based analysis.
4. DPARSFA V4 Parameters (Default Parameters, also for The R-fMRI Maps Project). For ROI signals extraction, the global signal (BrainMask_05_91x109x91.img) was added as the 1569th ROI.
 
 
New features of DPARSF V4.1_160415:
1. Fixed a bug in DPARSF Basic Edition. The bug is that the white matter signal is always removed in nuisance regression (only exist in the Basic Edition). Thanks to the report of Liviu Badea.
2. DPARSF Advanced Edition: Add an option of “Add mean back” for nuisance regression. The mean will be added back to the residual after nuisance regression. This is useful for circumstances of ICA or task-based analysis.
3. DPARSF Advanced Edition: Re-run with global signal regression (DPARSFA_RerunWithGSR). Fixed a bug when “Remove first X time points” was defined, the number of time points will be adjusted accordingly now. Thanks to the report of Hua-Sheng Liu.
4. DPARSF Advanced Edition: Add a slice timing batch mode for MultiBand data. Users could specify a text timing file for a given participant in SliceOrderInfo.tsv. Please see http://rfmri.org/SliceTiming for more details.
 
 
New features of DPARSF_V4.0_151201 (together with DPABI_V2.0_151201) :
1. Compatible with MATLAB 2014b and later versions.
2. Process the data both with and without global signal regression (GSR). Check “Nuisance regressors setting” -> “Both with & without GSR”. Alternatively, you can call DPARSFA_RerunWithGSR.m. E.g., DPARSFA_RerunWithGSR(DPARSFACfg.mat); where DPARSFACfg.mat stores the previous parameters without GSR.
3. The processing steps are affixed to Results directories. The R-fMRI calculation parameters are also written to the header of the result files.
4. V4 template is added. No smoothing before R-fMRI measure calculation (except for VMHC). This is used for comparing across studies and accumulate processed data.
5. DPABI Statistical Analysis. Add multiple comparison test after ANOVA, e.g., 'tukey-kramer' or 'hsd', 'lsd', 'dunn-sidak', 'bonferroni’ or ‘scheffe' procedures.
6. DPABI_VIEW: compatible with BrainNet Viewer 1.5. 
7. Fixed a "File too small" bug when .hdr/.img files are used.
8. Fixed a bug in y_Standardize.m: error when multiple files are defined.
9. Fixed a bug in DPABI Image Calculator: error in standard deviation calculation along the 4th dimension.
10. Results Organizer module: with this module, the users could organize the intermediate files for future processing with DPABI. In addition, the results could be organized for future use, and to be accumulated for the future R-fMRI maps project.
 
New features of DPARSF_V3.2_150710:
1. SPM12 Compatible.
2. DPARSF for Rat data released.
The Rat module is based on a Rat T2 template generated by Dr. Adam J. Schwarz et al. Please cite this paper when appropriate: Schwarz, A.J., Danckaert, A., Reese, T., Gozzi, A., Paxinos, G., Watson, C., Merlo-Pich, E.V., Bifone, A., 2006. A stereotaxic MRI template set for the rat brain with tissue class distribution maps and co-registered anatomical atlas: application to pharmacological MRI. Neuroimage 32, 538-550. (A T1 template was included as well. It's generated by normalizing 50 rats (two scans at PND45 or PND60) to that T2 template and then averaging (by Dr. Chao-Gan Yan)).
A video for rat data processing is available at http://d.rnet.co/DPABI_RatDataProcessing_20150520.mp4.
3. Fixed a bug in generating Voxel Specific Head Motion: missing gmdmp.
 
New features in DPARSF_V3.1_141101.
1. DPARSF V3.1 Basic Edition: Fixed a bug of missing DPARSF_run.
2. DPARSF V3.1: Fixed a bug that can not find ROI templates.
 
1. New features in DPARSF 3.0 Advanced Edition.
1.1.  Quality control. Integrated GUI for QCing the functional and structural images, users can give ratings and comments during the step of interactive reorientation.
1.2.  Automask generation. For checking EPI coverage and generating group mask, the automasks (as in AFNI) will be generated based on EPI images.
1.3.  Brain extraction (Skullstrip). This step can improve the coregistration between functional and structural images. Most registration issues of previous DPARSF versions can be solved by including this step. For Linux and Mac users: Need to install FSL. For Windows users: Thanks to Chris Rorden's compiled version of bet in MRIcroN, the modified version can work on NIfTI images directly.
1.4.  Nuisance Regression. 1) Masks can be generated based on segmentation or SPM apriori masks; 2) Methods can be mean or CompCor [Note: for CompCor, detrend (demean) and variance normalization will be applied before PCA, according to (Behzadi et al., 2007)]; 3) Global Signal can be extracted based on Automasks.
 
2. New features in DPARSF 3.0 Basic Edition.
2.1.  DPARSF Basic Edition now is using the engine of DPARSF Advanced Edition.
2.2.  Nuisance Regression (in MNI space) is placed before filtering, according to (Hallquist et al., 2013).
 
3. New features in DPARSF for Monkey data.
3.1.  The monkey module is based on Rhesus Macaque Atlases for functional and structural imaging studies generated by Wisconsin ADRC Imaging Core. Please cite their papers when appropriate: (McLaren et al., 2010; McLaren et al., 2009).
3.2.  Of note, the origin of monkey atlas is different from human MNI atlas. Please make sure the correct origins are set at the steps of "reorienting Fun*" and "reorienting T1*".

Licence: GNU General Public Licence (GPL)

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Forums:

Seems there is smothing wrong with this data.

obviously there should be something wrong with the data. I checked both the raw fun images and the preprocessed filed images ad did not find any obvious wrong with them. Do you have some more suggestions? Really appreciate.

Shuxia 

 

See if the domo data works. If so, you may need to consider discard some data.

thanks a lot!

BTW, it is ok to run the functional connectivity analysis.

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