DPABI is a GNU/GPL^{*} toolbox for Data Processing & Analysis of Brain Imaging, evolved from DPARSF (Data Processing Assistant for Resting-State fMRI) and contains DPABISurf. 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.

The latest release is **DPABI_V4.0_190305**. **Please cite DPABI as Yan, C.G., Wang, X.D., Zuo, X.N., Zang, Y.F., 2016. DPABI: Data Processing & Analysis for (Resting-State) Brain Imaging. Neuroinformatics 14, 339-351. doi: 10.1007/s12021-016-9299-4**.

You can also use the DPABI/DPABISurf/DPARSF Stand-Alone Version without purchasing a MATLAB license.

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 f^{2} 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 http://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

Programmers: YAN Chao-Gan; WANG Xin-Di; LU Bin

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.

Old Versions: V1.0 V1.1 V1.2 V1.3 V2.0 V2.1 V2.2 V2.3 V3.0 V3.1

Chihhao

Fri, 03/31/2017 - 07:25

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## 您好!

Chihhao

Wed, 04/05/2017 - 08:00

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## 您好!

BrysonR

Fri, 07/21/2017 - 20:17

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## Z standardization for ReHo and Degree Centrality

Dr. Yan,

Are the Z transforms for ReHo and Degree Centrality Fisher Z transforms, or are they some other Z transform? Additionally is there a citation or somewhere in the code where you could point where I could understand exactly how the values are being standardized? Thanks!

-Bryson

YAN Chao-Gan

Fri, 10/13/2017 - 10:41

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## Yan, C.G., Craddock, R.C.,

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.

These Z transformations are minus brain mean and divide by brain STD.

Only VMHC is Fisher's Z transformed.

Peace

Fri, 10/06/2017 - 14:27

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## Multiple Seed Regions/DPARSF

Dear Dr. Yan

Would it possible to designate more than one ROI at one time?

What does the output files look like?

Cheers

Larry

Peace

Mon, 10/09/2017 - 17:03

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## Multiple Seed Regions/DPARSF - Fixed

It worked.

Thanks

hoptman

Wed, 11/21/2018 - 17:40

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## Covariate warning

Hi all,

I am using DPARSFA 4.3, and during the covariate stage, I see the message:

Item 'Covariates', field 'val': Size mismatch (required [1 Inf], present [0 0]).

For each image. The program runs ok, and the covariates seem to have been applied. My covariates are: polynomial trend 2, Firston 24, WM and CSF (from SPM priors), and Compcor.

The results seem to be about the same as in prior work I've done using DPARSFA/DPABI. Is this a warning that can be ignored?

Thanks,

Matt

YAN Chao-Gan

Fri, 11/23/2018 - 07:17

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## Hi Matt,

Hi Matt,

Could you post more warning information? Seems you can just ignore it.

Best,

Chao-Gan

JaneDoc

Thu, 12/06/2018 - 08:57

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## local maxima/sub peaks in cluster needed

Dear all,

I did resting state analysis with dparsf advanced /DPABI (V3.0_171210) and got a big cluster for stronger connectivity with the IPL.

When I look at it with the Viever I can see that it covers several brain regions, but I can only read out one peak of the cluster with "Find peak in this cluster.How can I read out local maxima/subpeaks of different brain areas?

Thanks in advance,

Janine

YAN Chao-Gan

Tue, 12/11/2018 - 10:17

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## We didn't write find local

We didn't write find local maxima/subpeaks.

As for now, you can enhance the threshold until it seperates. Then you can find the local maxima/subpeaks.

JaneDoc

Tue, 12/11/2018 - 10:28

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## Thank you for your reply.

Thank you for your reply.

We already tried the threshold-enhancing method, but wanted to know, if there is a better way.

So, it is not possible to change something in the code of y_ClusterReport.m

to not only write out the amount of voxels for each subcluster, but also the peak of the reported subclusters?

choic12

Wed, 12/12/2018 - 21:31

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## Using DPABI Viewer

Hi Professor Chao-Gan,

First I want to thank you for developing this software. So far, I've been able to successfully view statistical maps, GFR corrected (voxel p<0.0214, cluster p <0.1 two-tailed). However, I would like to show a FC map one before and one after intervention as well. Is there a way to show an average FC map for all participants?

Or would there be a better program to visualize this? I've been trying to use BrainNetViewer with DPABI but was unable to do so. Should it work if the BrainNetViewer file is under the general DPABI folder, or should it be in a specific subfolder?

Please note that I'm very new to RS-fMRI analysis so apologies if my questions have already been answered.

Thank you so much,

Claire

sha.zhiqiang@163.com

Mon, 07/08/2019 - 13:29

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## Questions about CWAS

Dear Dr. Yan,

Thank you for developing such awesome software. I am a big fan of DPABI. Recently, I used y_CWAS.m to perform the CWAS analysis.

[p_Brain, F_Brain, Header] = y_CWAS(DataUpDir, SubID, AResultFilename, AMaskFilename, Regressor, iter, IsNeedDetrend, Band, TR)

I am a little confused of "Regressor". I know CWAS is an approach of identifying the pattern of connectome that relates to the clinical variable.

So, is this "Regressor" my clinical variable or the covariates (e.g. age and gender)?

If I input the clinical variable as "Regressor", where can I input the covariates (e.g. age and gender)?

Looking forward to your reply. Thank you so much. I really appreciate.

Best,

YAN Chao-Gan

Fri, 07/19/2019 - 23:41

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## Clinical Variable, no

Clinical Variable, no covariates.

I haven't maintain this module for a long time. Thus you may need to check Shehzad's paper and see if you need to modify the codes.

## Pages