DPABI: a toolbox for Data Processing & Analysis for Brain Imaging

Submitted by YAN Chao-Gan on

 
 
DPABI: a toolbox for Data Processing & Analysis for Brain Imaging
 
DPABI is a GNU/LGPL toolbox for Data Processing & Analysis of Brain Imaging, evolved from DPARSF (Data Processing Assistant for Resting-State fMRI) and contains DPABISurf, DPABIFiber, DPABINet and BrainImageNet. Please refer to The R-fMRI Course to know more about how to use this toolbox. Add with subfolders for DPABI in MATLAB's path setting and enter "dpabi" in the command window to enjoy this powerful toolbox.
 
 
 
You can also use the DPABI/DPABISurf/DPARSF Stand-Alone Version without purchasing a MATLAB license.
 

New features of DPABI_V8.1_240101 (download at http://rfmri.org/dpabi):    
1. DPABISurf 3.1.    
DPABISurfSlurm Updated: High Performance Computing Version of DPABISurf was updated after processing 4021 subjects on a HPC. Please read here for how to use it: http://rfmri.org/DPABISurfSlurm    
2. DPABI Harmonization module was updated, please also update ICVAE docker: docker pull cgyan/icvae.

3. To use DPABI V7, you need to use the developing version of SPM: https://github.com/spm/spm or use this alternative one.
 
Tips: 
1) For Linux or Mac OS, please start matlab from terminal in order to reach docker in DPABI (e.g., Linux: matlab; Mac: open /Applications/MATLAB_R2018a.app/).
2) Before running DPABISurf_Pipeline, you can test the docker environment by running DPABI->DPABISurf->Utilities->Volume-Surface Projector. If the file can be successfully projected to surface, then the software is all set. 
 

New features of DPABI_V8.0_231111 (download at http://rfmri.org/dpabi, please also update the docker file by: docker pull cgyan/dpabi and docker pull cgyan/dpabifiber):    
1. DPABI Harmonization module was released.    
This module could be used to harmonizing the brain images (.nii/.nii.gz/.gii/.mat) to remove site effects for big data in statistical analysis. Please read our latest Reference: Wang, Y.W., Chen, X., Yan, C.G. (2023). Comprehensive evaluation of harmonization on functional brain imaging for multisite data-fusion. Neuroimage, 274, 120089, doi:10.1016/j.neuroimage.2023.120089.   
Please see a tutorial for DPABI Harmonization Module here (in Chinese), please download the Demo Data for DPABI Harmonization Module from here    
2. DPABISurf 3.0.    
2.1. DPABISurfSlurm: High Performance Computing Version of DPABISurf was released. Please read here for how to use it: http://rfmri.org/DPABISurfSlurm    
2.2. Back engine updated to fMRIPrep V23.1.4, thus using FreeSurfer 7.3.2.    
2.3. Segment subregions (Thalamus, Hippocampus, Amygdala and Brainstem) with FreeSurfer 7.3.2 was set to the default option now.    
2.4. Added freesurfer atlases: {DPABISurf}/SurfTemplates/fsaverage5_lh_aparc_a2009s_annot.label.gii and fsaverage5_lh_aparc_annot.label.gii.    
2.5. Could make use of DPABI Harmonization module.    
3. DPABIFiber 1.1.    
3.1. Back engine updated to QSIPrep V0.19.1.    
3.2. Could make use of DPABI Harmonization module.    
4. DPABINet 1.3.    
4.1. Could make use of DPABI Harmonization module.    
5. Fixed a bug in handling surface correlation analysis in y_Correlation_Image.m.    
6. Fixed a bug of "Brace indexing is not supported for variables of this type" in DPABI_TDA.

 
New features of DPABI_V7.0_230110 (download at http://rfmri.org/dpabi, please also update the docker file by: docker pull cgyan/dpabi):
1. DPABIFiber 1.0.
DPABIFiber is a fiber tractography analysis toolbox based on diffusion-weighted imaging (DWI), evolved from DPABI/DPABISurf/DPABINet/DPARSF, as easy-to-use as DPABI/DPABISurf/DPABINet/DPARSF. DPABIFiber is based on QSIPrep (Cieslak et al., 2021), MRtrix3 (Tournier et al., 2019), AFQ (Yeatman et al., 2012), fMRIPprep (Esteban et al., 2019), FreeSurfer (Tustison et al., 2014), ANTs (Avants et al., 2009), FSL (Jenkinson et al., 2012), SPM12 (Ashburner, 2012), dcm2niix (Li et al., 2016), PALM (Winkler et al., 2014), GNU Parallel (Tange, 2011), MATLAB (The MathWorks Inc., Natick, MA, US), Docker (https://docker.com) and DPABI (Yan et al., 2016). DPABIFiber provides a user-friendly graphical user interface (GUI) for pipeline DWI preprocessing, fiber tractography reconstruction, tract-based spatial statistics (TBSS) (Smith et al., 2006), automating fiber-tract quantification (AFQ) (Yeatman et al., 2012), structural connectome matrix analyses, seed-based structural connectivity analyses, and tract-weighted functional connectivity (TW-FC) (Calamante et al., 2013), while requires no programming/scripting skills from the users. Please see more details at: http://rfmri.org/DPABIFiber
2. DPABISurf 2.0.
2.1. Back engine updated to fMRIPrep V22.1.1, thus using FreeSurfer 7.
2.2. Added new mode: Calculate in Native Space. The processed results in native space also fits better to the TWFC analyses with DPABIFiber Pipeline.
2.3. Could segment subregions (Thalamus, Hippocampus, Amygdala and Brainstem) with FreeSurfer 7.3. 
2.4. Could use FastSurfer instead of FreeSurfer to perform surface construction.
2.5. Could add wildcard strings in Defining ROIs.
2.6. Could select ROI Indices for specific ROI files.
2.7. Could delete first time points if start with BIDS.
2.8. Could detect TR if start with BIDS or fmriprep.
3. DPARSF 5.4.
3.1. Could add wildcard strings in Defining ROIs.
3.2. Could select ROI Indices for specific ROI files.
4. Statistical Analysis. Added option of TFCE2D for TBSS and surface-based statistical analysis.
5. y_ReadRPI. Fixed a bug in calculating new origin when flipping axes.
 
 
New features of DPABI_V6.2_220915 (download at http://rfmri.org/dpabi, you do NOT need to update the docker file as compared to V6.1):
1. This is the last release of DPABI V6, still using freesurfer 6. DPABI V7 will have more changes.
2. DPABISurf 1.8:
2.1. Fixed a bug in displaying negative values in DPABISurf_VIEW.
2.2. Added area and curv in DPABISurf templates.
3. DPABINet 1.2:
3.1. Some  weighted graph theoretical metrics calculations were changed (matrix normalization, use only non-negative functional connectivity etc.). The GTA results for weighted metrics (e.g., Eglob, betweenness, Lp) may be DIFFERENT from previous version.
4. DPABI_VIEW:
4.1. Now support rotating the image. Try More->Reorientation. Usually you only need to set Pitch, Roll and Yaw. You can also save it by More -> Save Reoriented Images.
4.2. Now support set the display color range of underlay. Set it whey you load a user-defined underlay. Also can try "User-Defined" in the drop-down menu.
5. Updated y_Meta_Image_CallR.m to handle surface file.
6. Fixed a bug in w_StatToP.m in handling r value.
 
 
New features of DPABI_V6.1_220101 (download at http://rfmri.org/dpabi, please also update the docker file by: docker pull cgyan/dpabi):
1. DPARSF V5.3.
1.1. Use 3dAutomask in AFNI to generate automasks if docker is installed.
2. DPABISurf V1.7.
2.1. fMRIPrep backend updated to version 20.2.5 (Long-Term Support).
2.2. If the TR info in NIfTI header is different from what specified by the user in DPABISurf_Pipeline GUI, then rewrite as user specified.
3. DPABINet V1.1
3.1. Fixed a bug in DPABINet_VIEW when displaying negative edges.
4. DPABI Input Preparer is here! You will love it! DPABI->Utilities->DPABI Input Preparer.
5. Fixed all GUIs displaying issues on Windows and Linux platforms.
6. Fixed a bug in ROI Signal Extractor.
 
 
New features of DPABI_V6.0_210501 (download at http://rfmri.org/dpabi):
 
1. DPABINet V1.0 released! 
DPABINet is a toolbox for brain network and graph theoretical analyses, evolved from DPABI/DPABISurf/DPARSF, as easy-to-use as DPABI/DPABISurf/DPARSF. DPABINet is based on Brain Connectivity Toolbox (Rubinov and Sporns, 2010) (RRID:SCR_004841), FSLNets (http://fsl.fmrib.ox.ac.uk/fsl/fslwiki/FSLNets; RRID: SCR_002823), BrainNet Viewer (Xia et al., 2013) (RRID:SCR_009446), circos (Krzywinski et al., 2009) (RRID:SCR_018207), SPM (Ashburner, 2012) (RRID:SCR_007037), PALM (Winkler et al., 2016), MATLAB (The MathWorks Inc., Natick, MA, US) (RRID:SCR_001622), Docker (https://docker.com) (RRID:SCR_016445) and DPABI (Yan et al., 2016) (RRID:SCR_010501). DPABINet provides user-friendly graphical user interface (GUI) for Brain network construction, graph theoretical analyses, statistical analyses and results viewing, while requires no programming/scripting skills from the users.
 
2. DPABISurf V1.6.
2.1. Can use individual ROI definition files for each subject. The first line of the ROI definition file should be: 'Seed_ROI_List:'. An example SeedROI.txt:
Seed_ROI_List:
/Processing/fmriprep/sub-Sub001/func/sub-Sub001_task-rest_space-MNI152NLin2009cAsym_desc-aseg_dseg.nii.gz
/Processing/fmriprep/sub-Sub002/func/sub-Sub002_task-rest_space-MNI152NLin2009cAsym_desc-aseg_dseg.nii.gz
/Processing/fmriprep/sub-Sub003/func/sub-Sub003_task-rest_space-MNI152NLin2009cAsym_desc-aseg_dseg.nii.gz
...
2.2. Fixed a bug in dealing with multiple sessions of FieldMap.
2.3. Allow skipping subjects in TRInfo.tsv.
2.4. The surface for ReHo calculation were changed from pial to white.
 
3. DPARSF V5.2.
3.1. Users can threshold subjects with bad quality after reorienting.
3.2. Allow skipping subjects in TRInfo.tsv.
 
4. DICOM sorter added new layout and show demo.
 
5. A BIDS converter was added. DPABI->Utilities->DPABI BIDS Converter. Users can also deface the T1 Images before converting to BIDS. This module is good for sharing BIDS data, and is compatible with Brain Imaging Sharing Initiative: http://bisi.org.cn.
 
6. As suggested by Dr. Andrew Zalesky, the functional subcortical atlas "Tian2020_Subcortex_Atlas" was added. Ref: Tian, Y., Margulies, D.S., Breakspear, M., Zalesky, A. (2020). Topographic organization of the human subcortex unveiled with functional connectivity gradients. Nat Neurosci, 23(11), 1421-1432, doi:10.1038/s41593-020-00711-6.
 
7. A function get_components.m within the Network-based statistic (NBS) toolbox was added with Dr. Andrew Zalesky's permission. Ref: Zalesky, A., Fornito, A., Bullmore, E.T. (2010). Network-based statistic: identifying differences in brain networks. Neuroimage, 53(4), 1197-1207, doi:10.1016/j.neuroimage.2010.06.041.
 
8. Brain Connectivity Toolbox (BCT) was redistributed within DPABI with Dr. Mikail Rubinov's permission. Ref: Rubinov, M., Sporns, O. (2010). Complex network measures of brain connectivity: Uses and interpretations. Neuroimage, 52(3), 1059-1069, doi:S1053-8119(09)01074-X [pii] 10.1016/j.neuroimage.2009.10.003.
 
9. DPABI now is compatible with Dr. Mingrui Xia's latest version of BrainNet Viewer. Ref: Xia, M., Wang, J., He, Y. (2013). BrainNet Viewer: a network visualization tool for human brain connectomics. PLoS ONE, 8(7), e68910, doi:10.1371/journal.pone.0068910.
 
 
New features of DPABI_V5.1_201201 (download at http://rfmri.org/dpabi, please also update the docker file by: docker pull cgyan/dpabi):
1. DPABISurf V1.5.
1.1. fMRIPrep backend updated to version 20.2.1 (Long-Term Support).
1.2. Fixed a bug during processing data with multiple sessions.
1.3. DPABISurf_VIEW. Updated Cluster Report function. Please see here for usage.
1.4. DPABISurf_VIEW. Fixed a bug when setting p = 0.001, but still display 1. Fixed a bug to input index. Added save colorbar.
2. As discussed with Drs. Edmund Rolls and Marc Joliot, the AAL3 template was integrated into DPABI.
3. DPABI_VIEW. Added New Cluster Report function. Please see here for usage.
4. dcm2niix updated to version 2-November-2020 (v1.0.20201102)
 
 
New features of DPABI_V5.0_201001 (download at http://rfmri.org/dpabi, please also update the docker file by: docker pull cgyan/dpabi):
1. New Module: BrainImageNet included! With BrainImageNet, the model can classify the sex of a participant with brain structural imaging data from anybody and any scanner with about 95% accuracy. The model fine-tuned to Alzheimer’s Disease (AD) achieved 88.4% accuracy in leave-sites-out five-fold cross-validation on the ADNI dataset and 86.1% accuracy for a direct test on an unseen independent dataset (OASIS). When directly testing this AD classifier on brain images of unseen mild cognitive impairment (MCI) patients, 63.2% who finally converted into AD were predicted as AD, versus 22.1% who did not convert into AD were predicted as AD. Predicted scores of the AD classifier showed significant correlations with severity of illness. Users can utilize our models as bases for transfer learning. Please read more details at https://www.biorxiv.org/content/10.1101/2020.08.18.256594v2.
2. DPABISurf V1.4.
2.1. fMRIPrep backend updated to version 20.2.0 (Long-Term Support).
2.2. For surface based multiple comparison correction, the method of FDR correction was added to DPABISurf. See DPABISurf_VIEW->Cluster...->Apply FDR Correction.
2.3. For surface based multiple comparison correction, the method of Monte Carlo simulation was added to DPABISurf. See DPABISurf_VIEW->Cluster...->Apply FWE (Monte Carlo Simulation) Correction.
2.4. Pre-calculated Monte Carlo Simulation tables were added for most used situations. Please see {DPABI}/StatisticalAnalysis/DPABISurf_MonteCarlo/MonteCarloTable/. Users can index the minimum cluster area for an estimated smoothness and set at DPABISurf_VIEW->Cluster...->Set Cluster Size.
3. DPARSF V5.1.
3.1. Fixed a bug in applying slice timing information from DICOM files to DPARSF settings. This bug only affected DPARSF V5.0 while not setting slice timing information (using the default) in slice timing preprocessing. Automatically applying slice timing information from DICOM files missed the unit change for SPM.
4. Scrubbing default setting changed to FD_Jenkinson, 0.2mm, no time points before or after "bad" time points.
5. PALM updated to version alpha115.
6. dcm2niix updated to version 31-March-2020 (v1.0.20200331)
7. DICOM Sorter. Now DICOM Sorter can handle files with DICOM files mixed with other kind of files when setting suffix to none.
8. Fixed a bug of "Add Image" in ROI Signals Extractor.
9. y_ANCOVA1_Multcompare_Image now is compatible for .gii.
10. Docker File: MCR files extracted for easy use in Singularity.
 
 
New features of DPABI_V4.3_200401 (download at http://rfmri.org/dpabi, please also update the docker file by: docker pull cgyan/dpabi):
This is a minor update to fix some bugs.
1. DPARSF V5.0. Fixed a bug specific in Windows OS when starting from .nii.gz files: bet fails.
2. DPABISurf V1.3. Fixed the errors of "--ignore-aroma-denoising-errors" and "--template-resampling-grid". These were caused by the input format changing in fmriprep V20.
 
 
New features of DPABI_V4.3_200301 (download at http://rfmri.org/dpabi, please also update the docker file by: docker pull cgyan/dpabi):
1. Stability Analysis module was added. You can calculate volume-based and surface-based stability from DPABI->Dynamic & Stability Analyses. You can read our recent work for the details of stability measure: Li, L., Lu, B., Yan, C.G. (2019). Stability of dynamic functional architecture differs between brain networks and states. Neuroimage, 116230, doi:10.1016/j.neuroimage.2019.116230.
2. Field map correction was added, both for DPARSF and DPABISurf. If you want to perform field map Correction, you need to arrange each subject's field map DICOM files in one directory, and then put them in "FieldMap" directory under the working directory. i.e.:';...
    '{Working Directory}\FieldMap\PhaseDiffRaw\Subject001\xxxxx001.dcm';...
    '{Working Directory}\FieldMap\PhaseDiffRaw\Subject001\xxxxx002.dcm';...
    '...';...
    '{Working Directory}\FieldMap\PhaseDiffRaw\Subject002\xxxxx001.dcm';...
    '{Working Directory}\FieldMap\PhaseDiffRaw\Subject002\xxxxx002.dcm';...
    '...';...
    '{Working Directory}\FieldMap\Magnitude1Raw\Subject001\xxxxx001.dcm';...
    '{Working Directory}\FieldMap\Magnitude1Raw\Subject001\xxxxx002.dcm';...
    '...';...
    '{Working Directory}\FieldMap\Magnitude1Raw\Subject002\xxxxx001.dcm';...
    '{Working Directory}\FieldMap\Magnitude1Raw\Subject002\xxxxx002.dcm';...
    '...';...
    '...';...
Then you can click the button of “FieldMap” button to set field map correction parameters. In most cases, you can use the default  “0” value to let the program read the parameters (e.g., echo times) from the DICOM files.
3. Check data organization function added. For the new users of DPARSF and DPABISurf, most of the errors were caused by data organization! Please use DPABI->Utilities->Check Data Organization to check your data organization before running DPARSF or DPABISurf. This program will lead you to organize your data correctly with prompting messages!
4. Slice timing information read from DICOM files. If you are starting with DICOM files, you no longer need to set the slice timing correction parameters. Just leave it as default (slice number: 0), then DPARSF or DPABISurf will read the parameters from DICOM files. This new feature thanks to Dr. Chris Rorden's new dcm2niiX program (version v1.0.20190902).
5. DPARSF V5.0 now is compatible with BIDS format. You can start with BIDS format data by checking checkbox “BIDS to DPARSF” and setting  “Starting Directory Name” to “BIDS”.
6. DPABISurf V1.3. Check and re-run fmriprep failed subjects. If for any reason, the program failed fmriprep running in DPABISurf, you just need to re-run starting with the step “Preprocessing with fmriprep” and set the “Starting Directory Name” to “BIDS” in DPABISurf_Pipeline. Alternatively, you can run a single checking step from DPABI->DPABISurf->Utilities->Re-Run fmriprep Failed Subjects.
7. DPABI->Utilities->DICOM Sorter. Now DICOM Sorter will remove illegal characters for file names in DICOM sorter. E.g., a subject id of “Wang’#$#’s” will no longer cause a problem in sorting DICOM files.
8. y_Call_DPABISurf_VIEW_FromVolume.m and y_Call_DPABISurf_VIEW.m were added. Now you can use script to call DPABISurf_VIEW to generate surface maps (with higher quality now) in batch. Remember to add “close all” in the for loop to prevent too many windows.
 
 
New features of DPABI_V4.2_190919 (download at http://rfmri.org/dpabi, please also update the docker file by: docker pull cgyan/dpabi):
1. DPABISurf_V1.2_190919 updated.
1.1. A quality control module was added to DPABISurf. Now users can quality control surface reconstruction, EPI to T1 registration and T1 to MNI registration for all the subjects in one HTML file, respectively (based on fmriprep 1.5.0). For volume-based analysis, users can also generate group mask for DPABISurf, and exclude subjects by thresholding coverage and head motion.
1.2. DPABISurf now also output sulcus depth and volume in fsaverage and fsaverage5 spaces for statistical analysis.
1.3. In results organizer of DPABISurf, the redundant files would not be organized now. In addition, the fmriprep and freesurfer files were backed up, while excluding T1 image that may have privacy information such as face.
2. DPABI_VIEW has a new function "Surface View with DPABISurf_VIEW" now. This function will convert the files to fsaverage surface using freesurfer's mri_vol2surf command. Then the results were displayed by calling DPABISurf_VIEW to generate surface-based picture. 
 
 
New features of DPABI_V4.1_190725 (download at http://rfmri.org/dpabi):
1. DPABISurf_V1.1_190725 updated.
1.1. New module for Surface-Based Temporal Dynamic Analysis (DPABI_TDA_Surf) was added. Dynamic regional indices (ALFF, fALFF, ReHo, Degree Centrality and Global Signal Correlation) and dynamic functional connectivity could be automatically calculated by one click through DPABI_TDA_Surf (with DPABISurf preprocessed data). The statistics maps (CV, Mean and SD) of the dynamic regional indices would also be generated by DPABI_TDA_Surf. A neuroimaging index which measures the concordance of the dynamic regional indices is incorporated into DPABI_TDA_Surf. Please see more details at: Yan, C.-G., Yang, Z., Colcombe, S.J., Zuo, X.-N., Milham, M.P., 2017. Concordance among indices of intrinsic brain function: insights from inter-individual variation and temporal dynamics. Sci Bull 62, 1572-1584.
1.2. The calculation of degree centrality now considers a vertex's correlation to both left and right hemispheres.
1.3. Standardization considers bilateral hemispheres. 
1.4. Smooth function after Standardization was added.
1.5. If ICA-AROMA was chosen, no head motion realign parameters would be regressed out.
1.6. Fixed compatibility issues with old matlab versions.
1.7. The default surface-based smoothing kernel changed to 6mm instead of 10mm.
1.8. The DPABISurf results organizing function was added to the R-fMRI Maps Project.
1.9. Output an excel table for the volume of subcortical structures (calculated by freesurfer): {WorkingDir}/Results/AnatVolu/Anat_Segment_Volume.tsv.
1.10. DPABISurf_VIEW, the surface-based viewer now has a function to yoke between different viewers.
1.11. Docker updated basing on fMRIPrep 1.4.1. Besides pull from docker hub, the docker file can be also downloaded form baidu (extract code: enmn).
1.12. Besides the stand alone version of DPABI (with GUI), the compiled version of DPABISurf_run was also added to docker. Users can run DPABISurf_run with scripting. E.g.,
docker run -it --rm -v /My/FreeSurferLicense/Path/license.txt:/opt/freesurfer/license.txt -v /My/Data/Path:/data cgyan/dpabi /bin/bash
/opt/DPABI/DPABI_StandAlone/run_DPABISurf_run_StandAlone.sh ${MCRPath} /data/DPABISurf_Cfg.mat
2. DPARSF_V4.5_190725 updated.
2.1. 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 OS: 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 DPABI_V4.0_190305 (download at http://rfmri.org/dpabi):
1. DPABISurf was released! DPABISurf is a surface-based resting-state fMRI data analysis toolbox evolved from DPABI/DPARSF, as easy-to-use as DPABI/DPARSF. DPABISurf is based on fMRIPprep 1.3.0.post3 (Esteban et al., 2018)(RRID:SCR_016216), and based on 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 V4.0 (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. The DPABISurf pipeline first converts the user specified data into BIDS format (Gorgolewski et al., 2016), and then calls fMRIPprep 1.3.0.post3 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 (including ICA-AROMA) 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. Please see details at http://rfmri.org/DPABISurf.
2. DICOM Sorter: In case PatientID is not defined, use PatientName.FamilyName instead.
 
 
New features of DPABI_V3.1_180801 and 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 DPABI_V3.0_171210 (download at http://rfmri.org/dpabi):
1. New module for Temporal Dynamic Analysis (DPABI_TDA) was added. Dynamic regional indices (ALFF, fALFF, ReHo, Degree Centrality, Global Signal Correlation and VMHC) and dynamic functional connectivity could be automatically calculated by one click through DPABI_TDA (with DPARSF preprocessed data). The statistics maps (CV, Mean and SD) of the dynamic regional indices would also be generated by DPABI_TDA. A new neuroimaging index which measures the concordance of the dynamic regional indices is incorporated into DPABI_TDA. Please see more details at: Yan CG, Yang Z, Colcombe S, Zuo XN, Milham MP (2017) Concordance among indices of intrinsic brain function: insights from inter-individual variation and temporal dynamics. Science Bulletin. In press.
2. The default setting of permutation test with PALM was changed to two-tailed test. According to our recent study, permutation test with Threshold-Free Cluster Enhancement (TFCE) reaches the best balance between family-wise error rate (under 5%) and test-retest reliability / replicability, thus outperforms the other multiple comparison correction strategies. Please consider use it. Chen, X., Lu, B., Yan, C.G.*, 2018. Reproducibility of R-fMRI metrics on the impact of different strategies for multiple comparison correction and sample sizes. Hum Brain Mapp 39, 300-318.
3. Statistical Analysis Module. Also output effect size maps: Cohen’s f2 maps.
4. Statistical Analysis Module. Added a function for image-based meta analysis: y_Meta_Image_CallR.m. This functional is calling R package ‘metansue’, please install ‘metansue’ and ‘R.matlab’ first.
5. Statistical Analysis Module. Mixed Effect Analysis: Fixed a bug of OtherCovariates and CovVolume.
6. y_ExtractROISignal.m. Remove the voxels with “NaN” values.
7. DPARSF_V4.3_171210: If the file name before realignment is initialed with 'r', then move the 'rr*' files for the next step.
8. DPARSF has a docker version now: https://github.com/BIDS-Apps/DPARSF. Users can run it without installing MATLAB. It also has been deployed at OpenNeuro.org, users can try that online processing system.
 
 
New features of DPABI_V2.3_170105 and DPARSF_V4.3_170105 (download at http://rfmri.org/dpabi):
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.
4. Statistical Analysis: Fixed a bug when other covariates were defined in y_MixedEffectsAnalysis_Image.m.
5. Added Yeo2011_7Networks_Colormap_ForDPABI.mat and Yeo2011_17Networks_Colormap_ForDPABI.mat to {DPABI}/Templates, for visualizing Yeo and Buckner 7 or 17 networks in DPABI Viewer.
6. Brainnetome Atlas was added to {DPABI}/Templates: BrainnetomeAtlas_BNA_MPM_thr25_1.25mm.nii.gz and BrainnetomeAtlas_BNA_subregions.xlsx. This is the Maximum Probabilistic Map (MPM) of Brainnetome Atlas, including 246 subregions (210 cortical and 36 subcortical subregions). Citation: Fan, L., Li, H., Zhuo, J., Zhang, Y., Wang, J., Chen, L., Yang, Z., Chu, C., Xie, S., Laird, A.R., Fox, P.T., Eickhoff, S.B., Yu, C., Jiang, T., 2016. The Human Brainnetome Atlas: A New Brain Atlas Based on Connectional Architecture. Cereb Cortex 26, 3508-3526.
7. DPABI Viewer. Added a button “Apply a Mask for Additionally Thresholding” under “Cluster”. This function is used for viewing permutation test results from statistical analysis. Please see http://wiki.rfmri.org/PermutationTest for detailed description for performing permutation test and visualizing the results.
 
 
New features of DPABI_V2.2_161201 and DPARSF_V4.2_161201 (download at http://rfmri.org/dpabi):
1. DPARSF V4.2_161201 released.
1.1. 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. 
1.2. 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).
1.3. 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.
1.4. 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.
2. Statistical Analysis.
2.1. Given the recent concerns regarding multiple comparison correction, especially after Eklund et al. 2016 PNAS paper, We have included permutation test in the Statistical Analysis module. The permutation test was achieved by integrating PALM package, with the kind permission by Dr. Anderson M. Winkler. Please click “Permutation test (PALM)” button on the Statistical Analysis panel to use it. Please read http://fsl.fmrib.ox.ac.uk/fsl/fslwiki/PALM/UserGuide for the details of PALM. Please cite Winkler, A.M., Ridgway, G.R., Douaud, G., Nichols, T.E., Smith, S.M., 2016. Faster permutation inference in brain imaging. Neuroimage 141, 502-516 if you used it.
2.2. AlphaSim. For the so-called “bug” — the edge effects within the mask (apply mask and then smooth), DPABI doesn’t have such an issue since DPABI_V1.2_141101. On September 17, 2014, Dr. Katharina Wittfeld reported a bug with AlphaSim for small masks in combination with high smoothness: applying mask before the Gauss filter while the Gauss filter will blur the boundaries of the masked region which will cause problems later (http://rfmri.org/content/alphasim-problem-critical-bug). The code was revised to smooth the whole bounding box first and apply mask later, and the code was distributed with DPABI_V1.2_141101. The estimation of mean and standard deviation is within the whole bounding box as well, which we believe is better than estimating in a small mask (estimation of mean and standard deviation within a small mask might be inaccurate). In addition, Dr. Robert W Cox noted: "Simulations were also repeated with the now infamously "buggy" version of 3dClustSim: the effect of the bug on FPRs was minimal (of order a few percent)." http://biorxiv.org/content/early/2016/07/26/065862.
2.3. AlphaSim. The previous GUI version can only output simulation results for corner connection (Nearest Neighbor 26). Now we output 3 versions: face (NN6), edge (NN18) and corner (NN26) connection.
2.4. Mixed Effect Analysis (within-subject factor by between-subject factor) was added to the Statistical Analysis module. The order of the group images should be: Group1Condition1; Group1Condition2; Group2Condition1; Group2Condition2. You will get: *_ConditionEffect_T.nii - the T values of condition differences (corresponding to the first condition minus the second condition) (WithinSubjectFactor); *_Interaction_F.nii - the F values of interaction (BetweenSubjectFactor by WithinSubjectFactor); *_Group_TwoT.nii - the T values of group differences (corresponding to the first group minus the second group), of note, the two conditions will be averaged first for each subject for Group_TwoT analysis. (BetweenSubjectFactor).
3. A new ICC version based on R was included (y_ICC_Image_LMM_CallR.m), as the previous one (y_ICC_Image_LMM.m) fails to converge in many cases. y_ICC_Image_LMM_CallR.m is based on the R code written by Dr. Ting Xu (R_Cal_ICC.R). Of note, as this one needs the users to configure R environment (install.packages("nlme") and install.packages("R.matlab”)), the DPABI_ICC_TOOL (DPABI->Utilities->Test-Retest Reliability: ICC) GUI is still using y_ICC_Image_LMM.m. The new version (y_ICC_Image_LMM_CallR.m) should be used in command line.
4. DPABI Results Organizer and Intermediate Files Organizer (under “The R-fMRI Maps Project”): revised the parfor loop to prevent errors in case with too many files.
5. y_T1ImgAverager.m (DPABI->Utilities->T1 Images Averager): All the NaN voxels (in some cases) are set to zero now. Previously, the NaN voxels after averaging can induce trouble in bet.
6. MATLAB 2016b compatible.
 
 
New features of DPABI_V2.1_160415:
1. DPARSF V4.1_160415 released.
1.1. 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.
1.2. 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.
1.3. 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.
1.4. 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.
2. DPABI Results Organizer (under “The R-fMRI Maps Project”): also save the text version of ROI signals.
3. Dual Regression added (under Utilities). Define a map, then regress the map on the 4D data for a participant, thus get a time series. Variance-normalize the time series, and then regress on the 4D data, thus get the dual regression map.
4. DPABI Image Calculator (under “Utilities”). Syntax changed, now includes: g1.*To4D((i1>2.3),100) Make a mask (threshold at 2.3 on i1) and then apply to each image in group 1 (group 1 has 100 images).
5. Donsenbach 160 ROIs were merged into a single mask file (Dosenbach_Science_160ROIs_Radius5_Mask.nii).
6. Fixed a bug in y_GroupAnalysis_Image: CovVolume read error.
7. Add "MultiSelect" Mode when user selected "Add Image" for several GUIs.
8. DPABI Image Calculator (under Utilities): Fixed a bug when removing image or directory. Now, when you remove the item, the identifier will be re-ordered.
9. DPABI Viewer: Added a colorbar mode for DPABI_VIEW, when you add "+" or "-" flag at the end in "Add Overlay's Colorbar" entry, DPABI_VIEW will use full colormap to display the overlay.
 
 
New features of DPABI_V2.0_151201 (together with DPARSF_V4.0_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 processing parameter 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 DPABI_V1.3_150710:
1. SPM12 Compatible.
2. DPARSF_V3.2_150710 released.
3. 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 https://d.rnet.co/DPABI_RatDataProcessing_20150520.mp4.
4. Fixed a bug in generating Voxel Specific Head Motion: missing gmdmp.
5. Fixed a bug in Group Analysis: when CovVolume is not defined.
6. Fixed a bug when calling BrainNet Viewer in DPABI_VIEW.
7. Fixed a bug in Standardization: ‘/‘ is not defined in Windows.
8. Fixed a bug in Image Calculator: output will be split as the input files when calculating group images.
 
New features of DPABI_V1.2_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.
3. DICOM Sorter: Fixed a bug - “Add All” button doesn’t work.
4. DPABI Viewer: Fixed a bug when execute GRF or AlphaSim correction.
5. DPABI ROI Signal Extractor: New module added to DPABI->Utilities.
6. y_AlphaSim: Modified by Katharina Wittfeld -- moved the masking step in the code and also inserted several smaller edits, please click here to see the bug post.
7. Test-Retest Reliability: Intraclass Correlation Coefficient (ICC)New module added to DPABI->Utilities. Three ICC algorithms are supported: ANOVA Model, ReML Model and Linear Mixed Models. The algorithms are based on Dr. Xi-Nian Zuo and his colleague’s work. Please cite Dr. Zuo’s work as detailed in each function.
 
New features of DPABI_V1.1_140827:
1. New modules in DPABI Utilities:
1.1. Multiple T1 Images Averager. If you have multiple T1 runs for each subject, this module will coregister them and make a mean T1 image to put to "T1Img" for following analyses.
1.2. T1 Image Defacer. If you want to openly sharing your data, or simply transfer your data to others, this module will remove the face of the T1 images for privacy concerns. This module coregister the MNI template (mni_icbm152_t1_tal_nlin_asym_09c.nii) and MNI face mask (mni_icbm152_t1_tal_nlin_asym_09c_face_mask.nii) to the individual T1 image, and then remove the face and put to "T1ImgDefaced".
2. Bugs Fixed.
2.1. DPABI Viewer: Cluster report doesn't work correctly after GRF correction. Thanks for Vincent's report!
3. DPABI now can check the latest version and pop up a notice.
 
 
DPABI includes the following components.
 
1.     DPARSF 3.0 Advanced Edition.
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.     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.     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*".
4.     Preprocessing for task fMRI.
Task fMRI data can be preprocessed via DPABI-DPARSF.
5.     VBM.
VBM analyses can be performed via DPABI-DPARSF.
6.     Quality Control.
6.1.  QC Raw T1 images.
6.2.  QC Raw functional images.
6.3.  QC normalization effects. 1) QC on the pictures for checking spatial normalization. 2) Dynamically checking normalized T1, gray matter and functional images.
6.4.  Thresholding QC scores and removing un-qualified subjects.
6.5.  Generating Group masks based on normalized Automasks of each subject.
6.6.  Thresholding EPI coverage.
6.7.  Head motion report.
6.8.  Thresholding head motion.
7.     Standardization. Perform the following standardization according to (Yan et al., 2013).
7.1.  Mean Regression
7.2.  Mean Regression & SD Division
7.3.  Mean Regression & Log SD Regression
7.4.  Z - Standardization
7.5.  Mean Division
7.6.  Mean Subtraction
7.7.  Median-IQR Standardization
7.8.  Rank
7.9.  Quantile Standardization
7.10.               Gaussian Fit
8.     Statistical Analysis.
Smoothness estimation based on the 4D residual is built in regression function – smoothness is written to the NIfTI headers automatically. For AlphaSim and GRF multiple comparison correction, only using smooth kernel applied in preprocessing is NOT sufficient, please use the estimated smoothness instead.
9.     Viewer.
The DPABI_VIEW is based on spm_orthviews, but powered with convenient functions. Please try it out!
10. Utilities.
Utilities including DICOM Sorter, Image Calculator and Image Reslicer.
 
Project Initiator: YAN Chao-Gan
 
 
 
References
Behzadi, Y., Restom, K., Liau, J., Liu, T.T., 2007. A component based noise correction method (CompCor) for BOLD and perfusion based fMRI. Neuroimage 37, 90-101.
Hallquist, M.N., Hwang, K., Luna, B., 2013. The nuisance of nuisance regression: spectral misspecification in a common approach to resting-state fMRI preprocessing reintroduces noise and obscures functional connectivity. Neuroimage 82, 208-225.
McLaren, D.G., Kosmatka, K.J., Kastman, E.K., Bendlin, B.B., Johnson, S.C., 2010. Rhesus macaque brain morphometry: a methodological comparison of voxel-wise approaches. Methods 50, 157-165.
McLaren, D.G., Kosmatka, K.J., Oakes, T.R., Kroenke, C.D., Kohama, S.G., Matochik, J.A., Ingram, D.K., Johnson, S.C., 2009. A population-average MRI-based atlas collection of the rhesus macaque. Neuroimage 45, 52-59.
Yan, C.G., Craddock, R.C., Zuo, X.N., Zang, Y.F., Milham, M.P., 2013. Standardizing the intrinsic brain: towards robust measurement of inter-individual variation in 1000 functional connectomes. Neuroimage 80, 246-262.
 

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hongshengcheng

Tue, 08/19/2014 - 07:17

I have used the new version of DPARSFA in dpabi toolbox to preprocess rest fmri data, and found the order had changed from "Filter->Remove Covariates" to "Remove Covariates -> Filter". Is the latter order better? (or how about the old order?)

By the way, the network really very slow [http://d.rnet.co/Course_DPABI_Chinese_140807.mp4 ], online video will pause after few seconds. If I choose to save the video onto my harddisk, then it will stop at less than 10M (0.0)......,So, will there could be another download link? May dropbox be OK :)

Hi,

For the order, please refer to 2.2. Nuisance Regression (in MNI space) is placed before filtering, according to (Hallquist et al., 2013). Hallquist, M.N., Hwang, K., Luna, B., 2013. The nuisance of nuisance regression: spectral misspecification in a common approach to resting-state fMRI preprocessing reintroduces noise and obscures functional connectivity. Neuroimage 82, 208-225.

For the course, I think that was caused by too many connections to the course at the same time. This should be a bandwidth limitation. I sincerely hope all the R-fMRI nodes can pay some attention (e.g., click, :) ) to the Google Ads at the bottom of each R-fMRI webpage, thus we can use the compensation to enhance our bandwidth for RFMRI.ORG. At the current stage, I have uploaded it to youtube, please click here to visit it on youtube.

Thanks a lot!

Best,

Chao-Gan

 

hongshengcheng

Wed, 08/20/2014 - 05:58

In reply to by YAN Chao-Gan

Thank you for your help :)

Best Regards,

Hongsheng

hongshengcheng

Thu, 08/21/2014 - 01:25

In reply to by YAN Chao-Gan

In original paper--Altered baseline brain activity in children with ADHD revealed by resting-state functional MRI,which comput ALFF in a process: Preprocess --> Bandpass Filter --> Fast Fourier Transform(get the power spectrum) --> calculate ALFF

It seems that in DPASFA3.0 comput the ALFF : Remove Covariance--> ALFF computation --> Bandpass filter (In DPASFA2.3, bandpass filter follwing the ALFF calculation too)

the data process setup is the screenshot in first comment. Is it right to compute ALFF?

OR, should I start from Dir "FunImgARWSD",and set up DPASFA like bellow?

Best Wish,

Hongsheng

Hi Hongsheng,

I believe this order is better "It seems that in DPASFA3.0 compute the ALFF : Remove Covariance--> ALFF computation --> Bandpass filter (In DPASFA2.3, bandpass filter following the ALFF calculation too)".

You don't need to do filter before ALFF. And you can not do filter before fALFF. I guess you want to remove nuisance signals before ALFF/fALFF, at least the head motion effects, right?

Please see Yan, C.G., Cheung, B., Kelly, C., Colcombe, S., Craddock, R.C., Di Martino, A., Li, Q., Zuo, X.N., Castellanos, F.X., Milham, M.P., 2013. A comprehensive assessment of regional variation in the impact of head micromovements on functional connectomics. Neuroimage 76, 183-201. for this order.

Best,

Chao-Gan

 

 

Thanks again :). I do want to remove nuirance siganals before comput ALFF/Reho.

Now I find myself have missed many good papers....

Best Wish,

Hongsheng

Hi! Dr.Yan!

I got some questions about the result files of DPABI

1. Does 'FunImgARCFWS' contrains final files after all of the process I choosen? So I can use them for funcitonal connectivity analysis?

2. Does 'T1ImgNewSegment' contains VBM results that can be directly analysised? To my knowledge, 'smwc*' file are the results of VBM. However, I do not know 8mm,10mm or 12 mm Kernel Smoother was used in this file.

     In addition, what is the meaning of number '1,2,3' in their name? Gray matter, white matter and CSF?

Best Wish

Wei Liu

Hi Wei,

1. Yes, Yes.

2. Yes. 8mm, as you can see in the command window during the processing.

3. Yes.

Best,

Chao-Gan

Hi Chao-Gan,

Is there a new preferred citation for dpabi, or is the older one (2010) your preference?

Best,
Matt

Thanks, Matt!

We are trying to write one manuscript on DPABI. However, at the current stage, please cite the older one (2010).

Best,

Chao-Gan

 

Hi Chao-Gan,

For nonstandardized data, I've always been able to examine pairwise ROI connectivity by looking at the *.mat files in FumImgRCWSF_ROISignals.  What is the easiest way to do that for standardized data, given that there is no new FunImgRCWSF_ROISignals directory when you do the standardization?

Thanks,

Matt

Hi Matt,

I am not sure if I unerstand your question correctly. Here standardization you mean mean regression etc.?

For pair-wise FC, you can use GCOR (Saad, Z., Reynolds, R. C., Jo, H. J., Gotts, S. J., Chen, G., Martin, A., et al. (2013). Correcting brain-wide correlation differences in resting-state fMRI. Brain Connect. 3, 339–352.) or say  Whole brain mean iFC (Yan, C.G., Craddock, R.C., He, Y., Milham, M.P., 2013. Addressing head motion dependencies for small-world topologies in functional connectomics. Front Hum Neurosci 7, 910.) to regress out from the pair-wise FC.

Best,

Chao-Gan

Yes, I mean what you wrote about in your 2013 paper (so mean + ln(stdev)).  The standardization module gives new zROI?FMAP_*.nii files, but not new ROICorrelation_FisherZ_*.mat files.  But I guess I can use GCOR, as you say.

 

From: RFMRI.ORG [mailto:rfmri.org-bounces@rnet.co] On Behalf Of The R-fMRI Network Sent: Friday, February 06, 2015 5:06 PM
To: rfmri.org@rnet.co
Subject: Re: [RFMRI] DPABI: a toolbox for Data Processing & Analysis of Brain Imaging

 

[To post a comment, please reply to rfmri.org@gmail.com ABOVE this line]

 

Commented by YAN Chao-Gan (YAN Chao-Gan)

Hi Matt,

I am not sure if I unerstand your question correctly. Here standardization you mean mean regression etc.?

For pair-wise FC, you can use GCOR (Saad, Z., Reynolds, R. C., Jo, H. J., Gotts, S. J., Chen, G., Martin, A., et al. (2013). Correcting brain-wide correlation differences in resting-state fMRI. Brain Connect. 3, 339–352.) or say  Whole brain mean iFC (Yan, C.G., Craddock, R.C., He, Y., Milham, M.P., 2013. Addressing head motion dependencies for small-world topologies in functional connectomics. Front Hum Neurosci 7, 910.) to regress out from the pair-wise FC.

Best,

Chao-Gan


Online version of this post: http://rfmri.org/comment/3506#comment-3506

 

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Hello,

I get this error using DPARSFA:  

Error using y_alff_falff (line 122)
Subscripted assignment dimension mismatch.
 
Error in DPARSFA_run>(parfor body) (line 3414)
            [ALFFBrain, fALFFBrain, Header] =
            y_alff_falff([AutoDataProcessParameter.DataProcessDir,filesep,FunSessionPrefixSet{iFunSession},AutoDataProcessParameter.StartingDirName,filesep,AutoDataP
            Error in DPARSFA_run (line 3404)
        parfor i=1:AutoDataProcessParameter.SubjectNum
 
Error in DPARSFA>pushbuttonRun_Callback (line 1670)
    [Error]=DPARSFA_run(handles.Cfg);
 
Error in gui_mainfcn (line 96)
        feval(varargin{:});
 
Error in DPARSFA (line 30)
    gui_mainfcn(gui_State, varargin{:});
 
Caused by:
    Subscripted assignment dimension mismatch.
 
Error while evaluating uicontrol Callback
 
The mask gets the suffix "91x109x91", but I checked the actual dimensions and they were "61x73x61".

I tried again replacing 61x73x61 for 91x109x91 in the DPARSFA_run.mat script, but it still doesn't work.

I'd really appreciate any suggestion about how to solve this problem.

Thanks in advance,

mireia

 

Hi Mireia,
What's the resolution of your functional images? Could you post a snapshot of your settings here?

Best,

Chao-Gan

On Tue, Feb 17, 2015 at 9:32 AM, The R-fMRI Network <rfmri.org@gmail.com> wrote:
[To post a comment, please reply to rfmri.org@gmail.com ABOVE this line]

Commented by Mireia Hernandez (mireia)

Hello,

I get this error using DPARSFA:  

Error using y_alff_falff (line 122)
Subscripted assignment dimension mismatch.
 
Error in DPARSFA_run>(parfor body) (line 3414)
            [ALFFBrain, fALFFBrain, Header] =
            y_alff_falff([AutoDataProcessParameter.DataProcessDir,filesep,FunSessionPrefixSet{iFunSession},AutoDataProcessParameter.StartingDirName,filesep,AutoDataP
            Error in DPARSFA_run (line 3404)
        parfor i=1:AutoDataProcessParameter.SubjectNum
 
Error in DPARSFA>pushbuttonRun_Callback (line 1670)
    [Error]=DPARSFA_run(handles.Cfg);
 
Error in gui_mainfcn (line 96)
        feval(varargin{:});
 
Error in DPARSFA (line 30)
    gui_mainfcn(gui_State, varargin{:});
 
Caused by:
    Subscripted assignment dimension mismatch.
 
Error while evaluating uicontrol Callback
 
The mask gets the suffix "91x109x91", but I checked the actual dimensions and they were "61x73x61".

I tried again replacing 61x73x61 for 91x109x91 in the DPARSFA_run.mat script, but it still doesn't work.

I'd really appreciate any suggestion about how to solve this problem.

Thanks in advance,

mireia

 


Online version of this post: http://rfmri.org/comment/3527#comment-3527


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Hi Chao-Gan,

Here I paste the info. Please, let me know if you need to know anything else.

Thanks,

mireia

Funcional image without normalization (folder FunImg)

 

Funcional 4D image with normalization (folder FunImgARCWS)

 

Mask --> AllResampled_BrainMask_05_91x109x91.nii (folder mask)

 

Hi mireia,

I can not see the figures you posted. Please post using the "image" button on the tools line.

I guess you are calculating ALFF for images without normalization? Then you should not use AllResampled_BrainMask_05_91x109x91.nii. In such a case, choose the default setting for "Calculate in Original Space".

Best,

Chao-Gan

Hi Chao-Gan,
I had trouble pasting the images in the forum. So, I attach the info about the ressolution of the functional images in a Word file here.

Brw, I do normalize data. 

As a reminder of the error I get:
Error using y_alff_falff (line 122)
Subscripted assignment dimension mismatch.

Error in DPARSFA_run>(parfor body) (line 3414)
            [ALFFBrain, fALFFBrain, Header] =
            y_alff_falff([AutoDataProcessParameter.DataProcessDir,filesep,FunSessionPrefixSet{iFunSession},AutoDataProcessParameter.StartingDirName,filesep,AutoDataP
            Error in DPARSFA_run (line 3404)
        parfor i=1:AutoDataProcessParameter.SubjectNum

Error in DPARSFA>pushbuttonRun_Callback (line 1670)
    [Error]=DPARSFA_run(handles.Cfg);

Error in gui_mainfcn (line 96)
        feval(varargin{:});

Error in DPARSFA (line 30)
    gui_mainfcn(gui_State, varargin{:});

Caused by:
    Subscripted assignment dimension mismatch.
 
Error while evaluating uicontrol Callback

The mask gets the suffix "91x109x91", but I checked the actual dimensions and they were "61x73x61".

I tried again replacing 61x73x61 for 91x109x91 in the DPARSFA_run.mat script, but it still doesn't work.


Thank you!

mireia




On Tue, Feb 17, 2015 at 4:34 PM, The R-fMRI Network <rfmri.org@gmail.com> wrote:
[To post a comment, please reply to rfmri.org@gmail.com ABOVE this line]

Commented by YAN Chao-Gan (YAN Chao-Gan)

Hi mireia,

I can not see the figures you posted. Please post using the "image" button on the tools line.

I guess you are calculating ALFF for images without normalization? Then you should not use AllResampled_BrainMask_05_91x109x91.nii. In such a case, choose the default setting for "Calculate in Original Space".

Best,

Chao-Gan


Online version of this post: http://www.rfmri.org/comment/3530#comment-3530


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Hi Dear Developers,

Thanks for the great work! Could you please refresh the download link, as it does not seem to work.

===

Regards,

Alex

Dear Alex,

Thank you very much for your report!

The problem has been fixed. Please go ahead to download it. (As we are collecting feedback from the users, please kindly spend 2 minutes to finish a very brief survey, thanks!)

Please let us know if you encounter any problems or get any bugs.

Best,

Chao-Gan

 

Hello 

 

I posted this to the mailing list :

 

"Dear Yan,

 
I did download now the demo but still it does not recognise it. I re-arranged my files as into a folder called 
FunImg/Sub_001/S1.nii
FunImg/Sub_002/S2.nii
 
But still as well when I point to the directory the white box under the directory where it says participants is empty.
 
Can you please help?
 
Thanks
 
On Fri, Oct 23, 2015 at 7:09 PM, Aser A <fmri2013@gmail.com> wrote:
Dear Yan I hope you can help with this. I tried to look at the examples and the website but could not really understand how I should arrange my data. I explained it above the arrangement that I have now. 
 
Then I would like to use two ROIs I have them already. 
 
Thank you 
 
Aser
 
On Fri, Oct 23, 2015 at 6:56 PM, Aser A <fmri2013@gmail.com> wrote:
Hi all, 
 
I have just decided to analysis my rsfMRI data using dpabi. However I am struggling of how to run it. 
 
Basically, I have two groups: patients and healthy. I have arranged my data as the following and all the data are in nii (nifti).
 
1- rsfMRI/patients/rsfMRI/S1.nii;S2.nii;S3.nii...etc. So all the imaging files are in one folder and named S1,S2..etc.
 
2- rsfMRI/patients/T1/S1.nii;S2.nii;S3.nii..etc. So again similar to the above.
 
I also have two other similar folders for the second group. 
 
However when I want to direct dpabi to the folder it does not recognize it. I am not sure what to so please can anyone help how to run it from the beginning ?
 
My aim is to analyse the two groups using two ROIs (ROI A and B) as seeds. and then see the different connection between the two. 
 
So I hope some one can guide me with this.
 
Thank you 
 
Aser"
 
 
 
>>I got a replay by saying that I should look t the structure of the demo.
however it is still no recognaized . 
 
I looked at the videos and followed all the steps but did not work.
 
can please someone give an example at least of what I should do ?
 
thank
 

Note pleas that I tried also the demo and watched the videos and organized the data ( NIFITI) in the same ibstructed way. The demo was not recognized as well. i cannot see the folders of the subjects.

can you please help by providing at least an example of what to do?

Hi all,

 

I got this error message and I am not sure why and where to solve it.  See below please:

 

Also Do I have once this is fixed to repat the whole process again ?

 

/bin/bash: bet: command not found

/bin/bash: bet: command not found
/bin/bash: bet: command not found
/bin/bash: bet: command not found
/bin/bash: bet: command not found
/bin/bash: bet: command not found
/bin/bash: bet: command not found
/bin/bash: bet: command not found
/bin/bash: bet: command not found
/bin/bash: bet: command not found
/bin/bash: bet: command not found
/bin/bash: bet: command not found
/bin/bash: bet: command not found
/bin/bash: bet: command not found
/bin/bash: bet: command not found
/bin/bash: bet: command not found
/bin/bash: bet: command not found
/bin/bash: bet: command not found
Bet finished
Copying T1 image Files:Sub_003 OKCopying T1 image Files:Sub_002 OKCopying T1 image Files:Sub_001 OKCopying T1 image Files:Sub_009 OK
Copying T1 image Files:Sub_006 OKCopying T1 image Files:Sub_005 OKCopying T1 image Files:Sub_004 OKCopying T1 image Files:Sub_008 OKCopying T1 image Files:Sub_007 OK
 
Error using DPARSFA_run>(parfor body) (line 1608)
Index exceeds matrix dimensions.
 
Error in DPARSFA_run (line 1551)
    parfor i=1:AutoDataProcessParameter.SubjectNum
 
 
Error in DPARSF_run (line 113)
[Error] = DPARSFA_run(Cfg);
 
 
Error in DPARSF>pushbuttonRun_Callback (line 1122)
    [Error]=DPARSF_run(handles.Cfg);
 
 
Error in gui_mainfcn (line 95)
        feval(varargin{:});

echoqiu77

Fri, 11/13/2015 - 19:56

严老师:

您好!

我现在匹兹堡医学院的一个实验室做核磁的一些数据分析,但在这里我的电脑上不能用Matlab,必须通过Putty和VNC连接到主机上使用。 然而这种远程连接十分麻烦,一用DPABI就报错,请您帮忙看看。

十分感谢!

Warning: File: DPARSFA_run.m Line: 673 Column: 66
The temporary variable SliceOrder will be cleared at the
beginning of each iteration of the parfor loop.
Any value assigned to it before the loop will be lost.  If
SliceOrder is used before it is assigned in the parfor loop, a
runtime error will occur.
See Parallel for Loops in MATLAB, "Temporary Variables". 
> In DPARSF_run at 113
  In DPARSF>pushbuttonRun_Callback at 1122
  In gui_mainfcn at 96
  In DPARSF at 51 
Warning: File: DPARSFA_run.m Line: 928 Column: 83
The temporary variable RefFile will be cleared at the
beginning of each iteration of the parfor loop.
Any value assigned to it before the loop will be lost.  If
RefFile is used before it is assigned in the parfor loop, a
runtime error will occur.
See Parallel for Loops in MATLAB, "Temporary Variables". 
> In DPARSF_run at 113
  In DPARSF>pushbuttonRun_Callback at 1122
  In gui_mainfcn at 96
  In DPARSF at 51 
Warning: File: DPARSFA_run.m Line: 2632 Column: 24
The temporary variable DirImg will be cleared at the beginning
of each iteration of the parfor loop.
Any value assigned to it before the loop will be lost.  If
DirImg is used before it is assigned in the parfor loop, a
runtime error will occur.
See Parallel for Loops in MATLAB, "Temporary Variables". 
> In DPARSF_run at 113
  In DPARSF>pushbuttonRun_Callback at 1122
  In gui_mainfcn at 96
  In DPARSF at 51 
Warning: File: DPARSFA_run.m Line: 4201 Column: 32
The temporary variable DirImg will be cleared at the beginning
of each iteration of the parfor loop.
Any value assigned to it before the loop will be lost.  If
DirImg is used before it is assigned in the parfor loop, a
runtime error will occur.
See Parallel for Loops in MATLAB, "Temporary Variables". 
> In DPARSF_run at 113
  In DPARSF>pushbuttonRun_Callback at 1122
  In gui_mainfcn at 96
  In DPARSF at 51 


Error using cd
Cannot CD to /data/home/qiuh/data/Analysis/FunImg (Name is
nonexistent or not a directory).

Error in DPARSFA_run (line 542)
        cd([AutoDataProcessParameter.DataProcessDir,filesep,FunSessionPrefixSet{iFunSession},AutoDataProcessParameter.StartingDirName]);
        
Error in DPARSF_run (line 113)
[Error] = DPARSFA_run(Cfg);

Error in DPARSF>pushbuttonRun_Callback (line 1122)
    [Error]=DPARSF_run(handles.Cfg);

Error in gui_mainfcn (line 96)
        feval(varargin{:});

Error in DPARSF (line 51)
    gui_mainfcn(gui_State, varargin{:});
 
Error while evaluating uicontrol Callback

Error using load
Argument must contain a string.

Error in DPARSF>pushbuttonLoad_Callback (line 1057)
    load([pathname,filename]);

Error in gui_mainfcn (line 96)
        feval(varargin{:});

Error in DPARSF (line 51)
    gui_mainfcn(gui_State, varargin{:});
 
Error while evaluating uicontrol Callback

>> 

你好!

Error using cd
Cannot CD to /data/home/qiuh/data/Analysis/FunImg (Name is
nonexistent or not a directory).

看起来是数据整理的问题。你的处理参数是什么样子的呢?

祝一切顺利!

超赣

我只是用了6个人做练习,这些数据都是以前用dparsf跑出来过的啊。只是这次用的是被试自己的T1像做配准,其他没有什么特别的。 有没有可能是主机不让我读取或者储存数据呢?

还有前面那部分报错是什么意思呢?

非常感谢严老师!

严老师:

您好!

我找这边的技术人员看了一下,好像当使用VNC 连接的情况下,Dpabi 是不能正常运行,后来改为用MobaXterm 就好多了。

现在我跑数据又出现以下报错,请问下一这是怎么回事,需如何处理?

Error using file_array/subsref>subfun (line 80)

An UndefinedFunction error was thrown on the workers for 'file2mat'.  This

might be because the file containing 'file2mat' is not accessible on the

workers.  Use addAttachedFiles(pool, files) to specify the required files to

be attached.  See the documentation for 'parallel.Pool/addAttachedFiles' for

more details.

 

Error in file_array/subsref (line 60)

    t = subfun(sobj,args{:});

 

Error in nifti/subsref>rec (line 219)

            t = subsref(t,subs(2:end));

 

Error in nifti/subsref (line 45)

varargout = rec(opt,subs);

 

Error in DPARSFA_run>(parfor body) (line 575)

                    y_Write(Nii.dat(:,:,:,AutoDataProcessParameter.RemoveFirstTimePoints+1:end),Nii,DirImg(1).name);

                   

Error in DPARSFA_run (line 543)

        parfor i=1:AutoDataProcessParameter.SubjectNum

 

Error in DPARSF_run (line 113)

[Error] = DPARSFA_run(Cfg);

 

Error in DPARSF>pushbuttonRun_Callback (line 1122)

    [Error]=DPARSF_run(handles.Cfg);

 

Error in gui_mainfcn (line 96)

        feval(varargin{:});

 

Error in DPARSF (line 51)

    gui_mainfcn(gui_State, varargin{:});

 

Caused by:

    Undefined function 'file2mat' for input arguments of type 'struct'.

 

Error while evaluating uicontrol Callback

非常感谢!

邱海棠

通常这个错误是因为你的spm8没有安装update包或者你的matlab的搜索路径中 有REST1.8,REST1.8内置了一部分spm5的命令,所以可能matlab在搜索时候会使用 REST中的spm函数,导致版本不兼容而报错,可以在使用DPABI的时候把REST从工作 路径中移除。 祝好, 鑫迪 [To post a comment, please reply to rfmri.org@gmail.com ABOVE this line] On Mon, Nov 30, 2015 at 04:11:27PM +0000, The R-fMRI Network wrote: > > Commented by 邱海棠 (echoqiu77)

鑫迪,

您好!

谢谢您的回复,问题得到完美解决,我的数据已经能够畅快地运行,真是太好了!

我现在尝试用DPABI 处理nii格式的文件,因为这边实验室对这个软件不熟悉,希望我先用他们跑过的数据再跑一下看看结果如何,所以我只能按照他们的要求来跑。我也很希望能够得到不错的结果,这样我以后的工作可以轻松很多,也可以给他们介绍这个强大又方便的软件。

他们让我略过slice timing这一步,所以我在basic dparsf里就没有勾选从DICOM 转成NII 这一步,也没有勾选slice timing,结果报错如下:麻烦您帮我看下又是哪里出了问题, 非常感谢!

Error using DPARSFA_run>(parfor body) (line 544)

Cannot CD to /data/home/qiuh/data/Analysis/FunRaw/CU0009_1R1_bold_resting2.nii

(Directory permission denied).%%%是因为nii格式的文件不能直接读取的意思吗?

 

Error in DPARSFA_run (line 543)

        parfor i=1:AutoDataProcessParameter.SubjectNum

 

Error in DPARSF_run (line 113)

[Error] = DPARSFA_run(Cfg);

 

Error in DPARSF>pushbuttonRun_Callback (line 1122)

    [Error]=DPARSF_run(handles.Cfg);

 

Error in gui_mainfcn (line 96)

        feval(varargin{:});

 

Error in DPARSF (line 51)

    gui_mainfcn(gui_State, varargin{:});

 

Caused by:

    Error using cd

    Cannot CD to /data/home/qiuh/data/Analysis/FunRaw/CU0009_1R1_bold_resting2.nii

    (Directory permission denied).

 

Error while evaluating uicontrol Callback

另外还想问一个问题,我在跑另一组数据进行中没有问题,但是我是用被试自己的T1象进行配准的,那么当出来图像,调整选项里有一个up 我不知道是调节哪个方向的?

非常感谢!

这个看起来是该路径权限错误,具体得问超赣了,我并没有遇到过这个情况。 [To post a comment, please reply to rfmri.org@gmail.com ABOVE this l

看起来你这个问题是关于数据整理的。建议再过一遍http://rfmri.org/course#RfMRICourseV2_zh
超赣

On Thu, Dec 3, 2015 at 6:20 PM, The R-fMRI Network <rfmri.org@gmail.com> wrote:
[To post a comment, please reply to rfmri.org@gmail.com ABOVE this line]

Commented by Xin-di Wang (sandywang)
这个看起来是该路径权限错误,具体得问超赣了,我并没有遇到过这个情况。 [To post a comment, please reply to rfmri.org@gmail.com ABOVE this l
Online version of this post: http://rfmri.org/comment/4021#comment-4021


Many a little makes a mickle -- your kind contributions shall make our efforts not perish from the earth. Please help The R-fMRI Network at http://rfmri.org/#overlay=HelpUs
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--
Chao-Gan YAN, Ph.D.
Principal Investigator
Institute of Psychology, Chinese Academy of Sciences
16 Lincui Road, Chaoyang District, Beijing 100101, China
-

hello YAN please i have this error

 Extracting ROI signals finished, elapsed time: 21.0938 seconds.
You do not have sufficient privilege to perform this operation. 
 
Extracting ROI signals...??? Error using ==> y_ReadAll at 60
The input name is not supported by y_ReadAll:
F:\aa\ProcessingDemoData\FunImgARglobal\Sub_003
 
Error in ==> y_ExtractROISignal at 56
    [AllVolume,VoxelSize,theImgFileList, Header]
    =y_ReadAll(AllVolume);
 
Error in ==> DPARSFA_run>(parfor body) at 2598
                y_ExtractROISignal([AutoDataProcessParameter.DataProcessDir,filesep,FunSessionPrefixSet{iFunSession},AutoDataProcessParameter.StartingDirName,filesep,AutoDataProcessParameter.SubjectI
                Error in ==> parallel_function at 473
            F(base, limit);
 
Error in ==> DPARSFA_run at 2453
        parfor i=1:AutoDataProcessParameter.SubjectNum
 
Error in ==> DPARSFA_RerunWithGSR at 87
[Error]=DPARSFA_run(Cfg);
 
Error in ==> DPARSFA>pushbuttonRun_Callback at 1764
        [Error]=DPARSFA_RerunWithGSR(handles.Cfg);
 
Error in ==> gui_mainfcn at 96
        feval(varargin{:});
 
Error in ==> DPARSFA at 30
    gui_mainfcn(gui_State, varargin{:});
 
??? Error while evaluating uicontrol Callback
 
 

 Dear YAN i have this error
I checked the FunImgARglobal\Sub_003 there is no file
Extracting ROI signals finished, elapsed time: 14.1406 seconds.
You do not have sufficient privilege to perform this operation. 
 
Extracting ROI signals...??? Error using ==> y_ReadAll at 60
The input name is not supported by y_ReadAll: F:\bb\ProcessingDemoData\FunImgARglobal\Sub_003
 
Error in ==> y_ExtractROISignal at 56
    [AllVolume,VoxelSize,theImgFileList, Header] =y_ReadAll(AllVolume);
 
Error in ==> DPARSFA_run>(parfor body) at 2598
                y_ExtractROISignal([AutoDataProcessParameter.DataProcessDir,filesep,FunSessionPrefixSet{iFunSession},AutoDataProcessParameter.StartingDirName,filesep,AutoDataProcessParameter.SubjectI
                Error in ==> parallel_function at 473
            F(base, limit);
 
Error in ==> DPARSFA_run at 2453
        parfor i=1:AutoDataProcessParameter.SubjectNum
 
Error in ==> DPARSFA_RerunWithGSR at 87
[Error]=DPARSFA_run(Cfg);
 
Error in ==> DPARSFA>pushbuttonRun_Callback at 1764
        [Error]=DPARSFA_RerunWithGSR(handles.Cfg);
 
Error in ==> gui_mainfcn at 96
        feval(varargin{:});
 
Error in ==> DPARSFA at 30
    gui_mainfcn(gui_State, varargin{:});
 
??? Error while evaluating uicontrol Callback

Hi,

I guess the symbolic link of FunImgARglobal is not successfully created.

You can simply copy FunImgAR to FunImgARglobal to fix this problem.

Best,

Chao-Gan

Hi everyone, a basic question: Are the processed images in folder FunImgARWSDFC in the MNI space? I believe this is the case but wish to double check. Thanks!

Hi,
Yes. W means normalized into MNI space.

Best,

Chao-Gan

On Sun, Feb 28, 2016 at 1:03 AM, The R-fMRI Network <rfmri.org@gmail.com> wrote:
[To post a comment, please reply to rfmri.org@gmail.com ABOVE this line]

Commented by Farras Abdelnour (farras)

Hi everyone, a basic question: Are the processed images in folder FunImgARWSDFC in the MNI space? I believe this is the case but wish to double check. Thanks!


Online version of this post: http://rfmri.org/comment/4158#comment-4158


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--
Chao-Gan YAN, Ph.D.
Principal Investigator
Institute of Psychology, Chinese Academy of Sciences
16 Lincui Road, Chaoyang District, Beijing 100101, China
-

Thank you, Chao-Gan.
On 2/27/16 20:20, The R-fMRI Network wrote:
[To post a comment, please reply to rfmri.org@gmail.com ABOVE this line]

Commented by YAN Chao-Gan (YAN Chao-Gan)
Hi,
Yes. W means normalized into MNI space.

Best,

Chao-Gan

On Sun, Feb 28, 2016 at 1:03 AM, The R-fMRI Network <rfmri.org@gmail.com> wrote:
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Commented by Farras Abdelnour (farras)

Hi everyone, a basic question: Are the processed images in folder FunImgARWSDFC in the MNI space? I believe this is the case but wish to double check. Thanks!


Online version of this post: http://rfmri.org/comment/4158#comment-4158


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--
Chao-Gan YAN, Ph.D.
Principal Investigator
Institute of Psychology, Chinese Academy of Sciences
16 Lincui Road, Chaoyang District, Beijing 100101, China
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Online version of this post: http://rfmri.org/comment/4159#comment-4159



-- 
Farras Abdelnour
http://sacklerinstitute.academia.edu/FarrasAbdelnour 

Good evening,

I have used the EPI template for the normalization of resting-state data in DPARSF. However, I am wondering whether there is any way that I could take a look at the EPI template which is then generated by DPARSF over all subjects. With the DPARSF-Viewer I can only find the individual normalized brains.

With kind regards,

Markus Loose

 

Hello,

My matlab reported the following error, while using dpabi's paired t test tool. Really need help. Thank you!

My matlab version is: R2012b; the package of: rest version: REST_V1.8_130615; spm 12, have been setpath.

The error was reported as: 

______________________________________________
Reading images from "I:\XXX.nii" etc.
Undefined function 'file2mat' for input arguments of type 'struct'.
 
Error in file_array/subsref>subfun (line 80)
t = file2mat(sobj,varargin{:});
 
Error in file_array/subsref (line 60)
    t = subfun(sobj,args{:});
 
Error in file_array/numeric (line 13)
out = subsref(fa,struct('type','()','subs',{vo}));
 
Error in file_array/double (line 11)
out = double(numeric(fa));
 
Error in y_Read (line 71)
    Data = double(Nii.dat);
 
Error in y_ReadRPI (line 41)
[Data,Header] = y_Read(FileName,VolumeIndex);
 
Error in y_ReadAll (line 70)
    [Data, VoxelSize, Header] = y_ReadRPI(FileList{1});
 
Error in y_TTestPaired_Image (line 29)
    [AllVolume,VoxelSize,theImgFileList, Header] = y_ReadAll(DependentDirs{i});
 
Error in DPABI_STAT_TOOL>ComputeButton_Callback (line 500)
        y_TTestPaired_Image(S, OutputName, MaskFile, ImageCell, TextCell);
 
Error in gui_mainfcn (line 96)
        feval(varargin{:});
 
Error in DPABI_STAT_TOOL (line 42)
    gui_mainfcn(gui_State, varargin{:});
 
Error in
@(hObject,eventdata)DPABI_STAT_TOOL('ComputeButton_Callback',hObject,eventdata,guidata(hObject))
 
 
Error while evaluating uicontrol Callback
________________________________________________________
I have tried the one/two sample t test, the same error occurred every time.
 
Thank you!
Best,
Qing
>> 

szzhaoxin

Sat, 11/26/2016 - 08:36

严老师:

         您好!按视频中使用DPARSF时,输入工作路径后,参与者一栏没有显示Sub__001,Sub__002...,这是为什么呢?

          这样点击RUN,运行以后,没有FunImg文件,也无法生成XX.hdr 和XX.img,只是在路径中生成了DPARS_AutoSave_XX.mat文件。使用的是SPM8,REST1.8,最新的DPARS。

         求老师解答!谢谢!

您好!
请检查您的数据是否严格按照课程中的要求整理完毕,文件夹名称严格命名为Funraw、T1raw,路径中不要出现任何中文以及空格。
祝好!


-----原始邮件-----
发件人: "The R-fMRI Network" <rfmri.org@gmail.com>
发送时间: 2016年11月26日 星期六
收件人: rfmri.org@rnet.co
抄送:
主题: Re: [RFMRI] DPABI: a toolbox for Data Processing &amp; Analysis for Brain Imaging

[To post a comment, please reply to rfmri.org@gmail.com ABOVE this line]

Commented by zhaoxin (szzhaoxin)

严老师:

         您好!按视频中使用DPARSF时,输入工作路径后,参与者一栏没有显示Sub__001,Sub__002...,这是为什么呢?

          这样点击RUN,运行以后,没有FunImg文件,也无法生成XX.hdr 和XX.img,只是在路径中生成了DPARS_AutoSave_XX.mat文件。使用的是SPM8,REST1.8,最新的DPARS。

         求老师解答!谢谢!


Online version of this post: http://rfmri.org/comment/4702#comment-4702






您好!

建议您按照如下的步骤进行操作:

1. 选择数据处理模板,如V4,Calculate in MNI space 等;

2. 选择工作路径;

3. 建议检查工作路径中,被试的影像格式是否为DICOM,如果不是请取消勾选EPI DICOM to NIFTI的选项,否则被试列表无法读出;

4. 输入starting Directory Name,区分大小写;回车后应当会出现被试列表。

 

祝好!

鲁彬

您好,
 
我是DPABI的新使用者,有几个不太确定的问题想请教(我的设置如附图)。
 
我分别以两个coordinate为中心,分别画了radius = 9, 18, 27的sphere ROI,一共有6个ROI
 
在完成处理後,我在Result folder中得到了3个folder,分别是: "FC_FunImgARCFWS", "ROISignals_FunImgARCFWS", "VMHC_FunImgARCFWSsym"。
 
我分别读取了同一位参与者的ROI_FCMap_XXX.mat以及ROISignals_XXX.mat,我发现这两个档案中,前两行(radius = 9 的两个ROI)的参数完全相同,但另外四个ROI则有所差异。
 
我对於这点很困惑,想请问一下关於这两个.mat file之间差异,谢谢。
 
Chih-Hao

您好!

ROI_FCMap_XXX.mat里面包含的应该是您选取的ROI与全脑所有体素之间的相关系数,ROISignals_XXX.mat包含的是您选区的所有ROI的时间序列,应当是一个nxm的矩阵,n是您数据的时间序列的时间点数量,m是您的ROI数。