Data Processing Assistant for Resting-State fMRI (DPARSF) V2.2

Submitted by YAN Chao-Gan on

Data Processing Assistant for Resting-State fMRI (DPARSF) is a convenient plug-in software based on SPM and REST. You just need to arrange your DICOM files, and click a few buttons to set parameters, DPARSF will then give all the preprocessed (slice timing, realign, normalize, smooth) data, functional connectivity, ReHo, ALFF/fALFF, degree centrality, voxel-mirrored homotopic connectivity (VMHC) results. DPARSF can also create a report for excluding subjects with excessive head motion and generate a set of pictures for easily checking the effect of normalization. You can use DPARSF to extract ROI time courses efficiently if you want to perform small-world analysis. DPARSF basic edition is very easy to use while DPARSF advanced edition (alias: DPARSFA) is much more flexible and powerful. DPARSFA can parallel the computation for each subject, and can be used to reorient your images interactively or define regions of interest interactively. You can skip or combine the processing steps in DPARSF advanced edition freely. Please download a MULTIMEDIA COURSE to know more about how to use this software. Add DPARSF's directory to MATLAB's path and enter "DPARSF" or "DPARSFA" in the command window to enjoy DPARSF basic edition or advanced edition.

The latest release is DPARSF_V2.2_130309

DOWNLOAD 

Multimedia Course: Data Processing of Resting-State fMRI
New features of DPARSF_V2.2_130309:
1. Advanced Edition: Fixed a bug in "Smooth by DARTEL" caused in previous revision (DPARSF_V2.2_130214). (Thanks for the report of Maki Koyama).

New features of DPARSF_V2.2_130303:
1. Advanced Edition: Fixed a bug in processing of multiple sessions - only process the last session in normalization and smooth.
2. Basic Edition: Fixed a bug caused in the previous release (V2.2_130214) - cannot load the correct masks (Thanks for the report of Tao Yang).

New features of DPARSF_V2.2_130224:
1.    This release fixed some minor bugs, but will not affect the results of any data analysis. The bugs appear in uncommon parameter settings and stop the processing in the worst cases.
2.    Updated the DPARSFVersion information in the template parameter files.
3.    DPARSF basic edition will also output the results of ReHo/ALFF/fALFF after Z-standardization (subtract the whole brain mean and divide by the whole brain standard deviation).
4.    Fixed a bug in nuisance covariates regression when CovMat is not defined.
5.    Fixed a bug that can not save the ROI signals correctly in text file if the data is not in double format. (Thanks for the report by H. Baetschmann)
6.    Fixed a bug in creating mean functional image for 4D files. (Thanks for the report and revision by S. Orsolini)
7.    Fixed a bug in displaying ROI Templates in linux system. (Thanks for the report by Han Zhang)

New features of DPARSF_V2.2_121225:
1.    Support parallel computing! If you installed the MATLAB parallel computing toolbox, you can set the number of "Parallel Workers", DPARSFA will distribute the subjects into different CPU cores.
2.    In addressing head motion concerns in resting-state fMRI analyses (Power et al., 2012; Satterthwaite et al., 2012; Van Dijk et al., 2012), we provide Friston 24-parameter correction (See Yan et al., 2013 for a comprehensive assessment of head motion on functional connectomics) as well as voxel-specific head motion calculation and correction (Satterthwaite et al., 2013; Yan et al., 2013). DPARSF also calculate the voxel-specific mean framewise displacement (FD) and volume-level mean FD (Power) (Power et al., 2012) or FD (Jenkinson) (i.e., relative RMS; Jenkinson et al., 2002) for accounting head motion at group-level analysis. The data scrubbing approach is also supported with different methods: 1) model each bad time point as a separate regressor in nuisance covariates regression, 2) delete bad time points, 3) interpolate bad time points with nearest neighbor, linear or cubic spline interpolation. 
3.    According to (Weissenbacher et al., 2009), the nuisance covariate regression could be performed before filtering and at very early stage. Users can also choose Template Parameters: TRADITIONAL order to have the same order as the previous version.
4.    Support .nii.gz in all the steps. No longer need to convert 4D .nii.gz into 3D .img/.hdr. Simply put .nii.gz under FunImg or any starting directory name, DPARSFA will handle the .nii.gz by itself.
5.    If the Number of Time Points is set to 0, then DPARSFA will not check the number of time points.
6.    If TR is set to 0, then DPARSFA will retrieve the TR information from the NIfTI images. Please ensure the TR information in NIfTI images are correct!
7.    If Slice Number is set to 0, then retrieve the slice number from the NIfTI images. The slice order is then assumed as interleaved scanning: [1:2:SliceNumber, 2:2:SliceNumber]. The reference slice is set to the slice acquired at the middle time point, i.e., ceil(SliceNumber/2). SHOULD BE EXTREMELY CAUTIOUS!!!
8.    Support calculating resting-state fMRI metrics in native space and warping by DARTEL. The mask files and/or region of interest (ROI) files in standard space are warped into native space by using the parameters estimated in segmentation or DARTEL.
9.    Spatial normalization and smooth can be performed on the calculated resting-state fMRI derivatives.
10.    Supporting extracting ROI time course using masks with multiple labels. More template ROI definitions are supported: Dosenbach’s 160 functional ROIs (Dosenbach et al., 2010), Andrews-Hanna’s default mode network ROIs (Andrews-Hanna et al., 2010), Craddock’s clustering ROIs (Craddock et al., 2011), AAL atlas and Harvard-Oxford atlas.
11.    More resting-state fMRI metrics are included, e.g., voxel-mirrored homotopic connectivity (VMHC) (Zuo et al., 2010), Degree Centrality (Buckner et al., 2009) and connectome-wide association studies based on multivariate distance matrix regression (Shehzad et al., 2011).
12.    For VMHC analyses, support normalizing the data further to a symmetric template. 1) Get the T1 images in MNI space (e.g., wco*.img or wco*.nii under T1ImgNewSegment or T1ImgSegment) for each subject, and then create a mean T1 image template (averaged across all the subjects). 2) Create a symmetric T1 template by averaging the mean T1 template (created in Step 1) with it's flipped version (flipped over x axis). 3) Normalize the T1 image in MNI space (e.g., wco*.img or wco*.nii under T1ImgNewSegment or T1ImgSegment) for each subject to the symmetric T1 template (created in Step 2), and apply the transformations to the functional data (which have been normalized to MNI space beforehand). Please see a reference from Zuo et al., 2010.
13.    A group analyses function was added as y_GroupAnalysis_Image by scripting call. T tests or F tests could be performed for a given set of regressors.
14.    Template Parameters:
         Calculate in Original Space (warp by DARTEL)
         Calculate in Original Space (warp by information from unified segmentation)
         Calculate in MNI Space (warp by DARTEL)
         Calculate in MNI Space (warp by information from unified segmentation)
         Calculate in MNI Space: TRADITIONAL order [This is the order used in DPARSF basie edition as well as DPARSFA V2.1]
         Intraoperative Processing
         Task fMRI data preprocessing
         VBM (New Segment and DARTEL)
         VBM (unified segmentation)
         Blank

Many thanks to Dr. Chris Rorden for suggesting features 4-7. Many thanks to Dr. Susan Whitfield-Gabrieli for discussing the head motion scrubbing regressors.



References:
Andrews-Hanna, J.R., Reidler, J.S., Sepulcre, J., Poulin, R., Buckner, R.L., 2010. Functional-anatomic fractionation of the brain's default network. Neuron 65, 550-562.
Buckner, R.L., Sepulcre, J., Talukdar, T., Krienen, F.M., Liu, H., Hedden, T., Andrews-Hanna, J.R., Sperling, R.A., Johnson, K.A., 2009. Cortical hubs revealed by intrinsic functional connectivity: mapping, assessment of stability, and relation to Alzheimer's disease. J Neurosci 29, 1860-1873.
Craddock, R.C., James, G.A., Holtzheimer, P.E., 3rd, Hu, X.P., Mayberg, H.S., 2011. A whole brain fMRI atlas generated via spatially constrained spectral clustering. Hum Brain Mapp.
Dosenbach, N.U., Nardos, B., Cohen, A.L., Fair, D.A., Power, J.D., Church, J.A., Nelson, S.M., Wig, G.S., Vogel, A.C., Lessov-Schlaggar, C.N., Barnes, K.A., Dubis, J.W., Feczko, E., Coalson, R.S., Pruett, J.R., Jr., Barch, D.M., Petersen, S.E., Schlaggar, B.L., 2010. Prediction of individual brain maturity using fMRI. Science 329, 1358-1361.
Jenkinson, M., Bannister, P., Brady, M., Smith, S., 2002. Improved optimization for the robust and accurate linear registration and motion correction of brain images. Neuroimage 17, 825-841.
Power, J.D., Barnes, K.A., Snyder, A.Z., Schlaggar, B.L., Petersen, S.E., 2012. Spurious but systematic correlations in functional connectivity MRI networks arise from subject motion. Neuroimage 59, 2142-2154.
Satterthwaite, T.D., Wolf, D.H., Loughead, J., Ruparel, K., Elliott, M.A., Hakonarson, H., Gur, R.C., Gur, R.E., 2012. Impact of in-scanner head motion on multiple measures of functional connectivity: Relevance for studies of neurodevelopment in youth. Neuroimage 60, 623-632.
Satterthwaite, T.D., Elliott, M.A., Gerraty, R.T., Ruparel, K., Loughead, J., Calkins, M.E., Eickhoff, S.B., Hakonarson, H., Gur, R.C., Gur, R.E., Wolf, D.H., 2013. An Improved Framework for Confound Regression and Filtering for Control of Motion Artifact in the Preprocessing of Resting-State Functional Connectivity Data. Neuroimage 64, 240-256.
Shehzad, Z., Reiss, P.T., Adelstein, J., Emerson, J.W., Chabernaud, C., Mennes, M., DiMartino, A., McMahon, K., Copland, D., Castellanos, F.X., Kelly, C., Milham, M.P., 2011. Connectome-Wide Association Studies (CWAS). 17th Annual Meeting of the Organization for Human Brain Mapping, Quebec City.
Van Dijk, K.R., Sabuncu, M.R., Buckner, R.L., 2012. The influence of head motion on intrinsic functional connectivity MRI. Neuroimage 59, 431-438.
Weissenbacher, A., Kasess, C., Gerstl, F., Lanzenberger, R., Moser, E., Windischberger, C., 2009. Correlations and anticorrelations in resting-state functional connectivity MRI: a quantitative comparison of preprocessing strategies. Neuroimage 47, 1408-1416.
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.
Zuo, X.N., Kelly, C., Di Martino, A., Mennes, M., Margulies, D.S., Bangaru, S., Grzadzinski, R., Evans, A.C., Zang, Y.F., Castellanos, F.X., Milham, M.P., 2010. Growing together and growing apart: regional and sex differences in the lifespan developmental trajectories of functional homotopy. J Neurosci 30, 15034-15043.



New features of DPARSF_V2.1_120101:
For DPARSFA (Advanced Edition):
1. Support .nii and .nii.gz 3D or 4D files. For 4D .nii(.gz) functional files, use Checkbox "4D Fun .nii(.gz) to 3D" to convert into 3D files. For T1 3D .nii.gz files, use Checkbox "Unzip T1 .gz" to unzip. Use Checkbox "Crop T1" to Reorient to the nearest orthogonal direction to "canonical space" and remove excess air surrounding the individual as well as parts of the neck below the cerebellum (MRIcroN's dcm2nii).
2. Normalize by DARTEL has been added. Details: (1) "T1 Coreg to Fun": the individual structural T1 image is coregistered to the mean functional image after motion correction. (2) "New Segment + DARTEL": New Segment -- The transformed structural image is then segmented into gray matter, white matter and cerebrospinal fluid by using "New Segment" in SPM8. (3) "New Segment + DARTEL": DARTEL -- Create Template, and DARTEL -- Normalize to MNI space (Many Subjects) for GM, WM, CSF and T1 Images (unmodulated, modulated and smoothed [8 8 8] kernel versions). (4) "Normalize by DARTEL": DARTEL Normalize to MNI space (Few Subjects) for functional images. (5) "Smooth by DARTEL": DARTEL Normalize to MNI space (Few Subjects) for functinal images but with smooth kernel as specified, the smoothing is part of the normalisation to MNI space computes these average intensities from the original data, rather than the warped versions.
3. Reorient functional images and reorient T1 images interactively before coregistration: Checkbox "Reorient Fun*" and Checkbox "Reorient T1*". Interactively reorienting the anatomic images and functional images so that the origin approximated the anterior commissure and the orientation approximated MNI space, this will improve the accuracy in coregistration and segmentation. This step could probably solve the bad normalization problem for some subjects in "normalized by unified segmentation" or "normalized by DARTEL".
4. Multiple functional sessions supported. The directory should be named as FunRaw (or FunImg) for the first session; S2_FunRaw (or S2_FunImg) for the second session; and S3_FunRaw (or S3_FunImg) for the third session... In "Realign", "the sessions are first realigned to each other, by aligning the first scan from each session to the first scan of the first session. Then the images within each session are aligned to the first image of the session." (from SPM Manual).
5. Fixed a bug for calculation error in the second (and 3rd, 4th, ...) subjects in "Calculate in Original Space (Warp by information in unified segmentation)".
6. The calculations of ALFF and fALFF are promoted before filtering. Fixed a previous bug of calculating fALFF after filtering in the previous version of DPARSFA.
7. Mac OS compatible.
8. Template Parameters in DPARSFA:
    8.1. Standard Steps: Normalized by DARTEL
    8.2. Standard Steps: Normalized by DARTEL (Start from .nii.gz files)
    8.3. Standard Steps: Normalized by T1 image unified segmentation
    8.4. Calculate in Original Space (Warp by information in unified segmentation)
    8.5. Intraoperative Processing
    8.6. VBM (New Segment and DARTEL)
    8.7. VBM (unified segmentaition)
    8.8. Blank
  
For DPARSF (Basic Edition)
1. Normalize by DARTEL has been added. By checking "Normalized by using.. DARTEL", the processing details are the same as in DPARSFA: (1) "T1 Coreg to Fun": the individual structural T1 image is coregistered to the mean functional image after motion correction. (2) "New Segment + DARTEL": New Segment -- The transformed structural image is then segmented into gray matter, white matter and cerebrospinal fluid by using "New Segment" in SPM8. (3) "New Segment + DARTEL": DARTEL -- Create Template, and DARTEL -- Normalize to MNI space (Many Subjects) for GM, WM, CSF and T1 Images (unmodulated, modulated and smoothed [8 8 8] kernel versions). (4) "Normalize by DARTEL": DARTEL Normalize to MNI space (Few Subjects) for functional images. (5) "Smooth by DARTEL": DARTEL Normalize to MNI space (Few Subjects) for functinal images but with smooth kernel as specified, the smoothing is part of the normalisation to MNI space computes these average intensities from the original data, rather than the warped versions.

Hope to finish a video course for the new features in soon.


New features of DPARSF_V2.0_110505:
1. Fixed an error in the future MATLAB version in "[pathstr, name, ext, versn] = fileparts...".
New features of DPARSF_V2.0_101025:
1. DPARSF advanced edition (alias: DPARSFA) is added with the following new features:
1.1. The processing steps can be freely skipped or combined.
1.2. The processing can be start with any Starting Directory Name.
1.3. Support ReHo, ALFF/fALFF and Functional Connectivity calculation in individual space.
1.4. The masks or ROI files would be resampled automatically if the dimension mismatched the functional images.
1.5. The masks or ROI files in standard space can be warped into individual space by using the parameters estimated in unified segmentaion.
1.6. Support VBM analysis by checking "Segment" only.
1.7. Support reorientation interactively if the images in a bad orientation.
1.8. Support define regions of interest interactively based on the participant's T1 image in individual space.

2. DPARSF basic edition is preserved with the same operation style with DPARSF V1.0. DPARSF basic edition has the following new features:
2.1. Fixed a bug in copying "*.ps" files.
2.2. Will not check "wra*" prefix in "FunImgNormalized" directory.
2.3. Fixed a bug while regress out head motion parameters only.

The multimedia course for DPARSF advanced edition is estimated to be released in this November, thanks for your patience.

New features of DPARSF_V1.0_100510:
1. Added a right-click menu to delete all the participants' ID.
2. Fixed a bug in converting DICOM files to NIfTI in Windows 7, thanks to Prof. Chris Rorden's new dcm2nii.
3. Now will detect if co* T1 image (T1 image which is reoriented to the nearest orthogonal direction to 'canonical space' and removed excess air surrounding the individual as well as parts of the neck below the cerebellum) exists before normalization by using T1 image unified segmentation. T1 image without 'co' is also allowed in the analysis now.

New features of DPARSF_V1.0_100420:
1. After extracting ROI time courses, not just functional connectivity will be calculated, but also transform the r values to z values by Fisher's z transformation.
2. Fixed a bug in generating pictures for checking normalization when the bounding box is not [-90 -126 -72;90 90 108].

New features of DPARSF_V1.0_100201:
1. Save the configuration parameters automatically.
2. Fixed the bug in converting DICOM files to NIfTI files when DPARSF stored under C:\Program Files\Matlab\Toolbox.
3. Fixed the bug in converting DICOM files to NIfTI files when the filename without extension.

New features of DPARSF_V1.0_091215:
1. Also can regress out other kind of covariates other than head motion parameters, Global mean signal, White matter signal and Cerebrospinal fluid signal.

New features of DPARSF_V1.0_091201:
1. Added an option to choose different Affine Regularisation in Segmentation: East Asian brains (eastern) or European brains (mni). The interpretation of this option from SPM is: “If you can approximately align your images prior to running Segment, then this will increase the robustness of segmentation. Another thing that may help would be to change the regularisation of the initial affine registration, via Segment->Custom->Affine Regularisation. If you set this to "ICBM space template - East Asian brains (or European brains)", then the algorithm will make use of knowledge about the approximate variability to expect among the width/length etc of the brains of the population.” “The prior probability distribution for affine registration of East-Asian brains to MNI space was derived from 65 seg_inv_sn.mat files from Singapore. The distribution of affine transforms of European brains was estimated from: Incorporating Prior Knowledge into Image Registration NeuroImage, Volume 6, Issue 4, November 1997, Pages 344-352 J. Ashburner, P. Neelin, D. L. Collins, A. Evans, K. Friston.”
2. Added a Utility: change the Prefix of Images since DPARSF need some special prefixes in some cases. For example, if you do not have T1 DICOM files and your T1 NIFTI files are not initiated with “co”, then you can use this utility to add the “co” prefix to let DPARSF perform normalization based on segmentation of T1 images.
3. Added a popup menu to delete selected subject by right click.
4. Added a checkbox for removing first time points.
5. Added a function to close wait bar when program finished.
 
New features of DPARSF_V1.0Beta_091001:

1. SPM8 compatible.
2. Generate the pictures (output in {Working Directory}\PicturesForChkNormalization\) for checking normalization.

New features of DPARSF_V1.0Beta_090911:
1. Fixed the bug of setting user's defined mask.

New features of DPARSF_V1.0Beta_090901:
1. Fixed the bug of setting FWHM kernel of smooth.
2. Smooth the mReHo results.
3. Remove any number of the first time points.

New features of DPARSF_V1.0Beta_090713:
1. mReHo - 1, mALFF - 1, mfALFF - 1 function.
2. Creating report for excessive head motion subjects excluding.

New features of DPARSF_V1.0Beta_090701:
1. Linux compatible.

DPARSF's standard processing steps:
1. Convert DICOM files to NIFTI images.
2. Remove First 10 (more or less) Time Points.
3. Slice Timing.
4. Realign.
5. Normalize.
6. Smooth (optional).
7. Detrend.
8. Filter.
9. Calculate ReHo, ALFF, fALFF (optional).
10. Regress out the Covariables (optional).
11. Calculate Functional Connectivity (optional).
12. Extract AAL or ROI time courses for further analysis (optional).

-----------------------------------------------------------
Citing Information:
If you think DPARSFA is useful for your work, citing it in your paper would be greatly appreciated.
Something like "... The preprocessing was carried out by using Data Processing Assistant for Resting-State fMRI (DPARSF) (Yan & Zang, 2010, http://www.restfmri.net) which is based on Statistical Parametric Mapping (SPM8) (http://www.fil.ion.ucl.ac.uk/spm) and Resting-State fMRI Data Analysis Toolkit (REST, Song et al., 2011. http://www.restfmri.net)..."
Reference: Yan C and Zang Y (2010) DPARSF: a MATLAB toolbox for "pipeline" data analysis of resting-state fMRI. Front. Syst. Neurosci. 4:13. doi:10.3389/fnsys.2010.00013;     Song, X.W., Dong, Z.Y., Long, X.Y., Li, S.F., Zuo, X.N., Zhu, C.Z., He, Y., Yan, C.G., Zang, Y.F., 2011. REST: A Toolkit for Resting-State Functional Magnetic Resonance Imaging Data Processing. PLoS ONE 6, e25031.

DPARSF is based on MRIcroN' dcm2nii, SPM and REST, if you used the related modules, the following software may need to be cited:
Step 1: MRIcroN software (by Chris Rorden, http://www.mricro.com).
Step 3 - Step 6: Statistical Parametric Mapping (SPM8, http://www.fil.ion.ucl.ac.uk/spm).
Step 7 - Step 11: Resting-State fMRI Data Analysis Toolkit (REST, Song et al., 2011. http://www.restfmri.net)


Licence: GNU General Public Licence (GPL)


Old versions:
V1.0 V2.0 V2.1
 

Dear Chao-gan,
     Thanks for your great work! It's really wonderful! I tried the DPARSF and REST, but several  errors occurred, some like these:


>> DPARSF
Welcome: dell, 2012-09-05 20:28
Data Processing Assistant for Resting-State fMRI (DPARSF) Advanced Edition (alias: DPARSFA).
Release=V2.2_120901Beta
Copyright(c) 2009~2013
The Nathan Kline Institute for Psychiatric Research, 140 Old Orangeburg Road, Orangeburg, NY 10962; Child Mind Institute, 445 Park Avenue, New York, NY 10022; The Phyllis Green and Randolph Cowen Institute for Pediatric Neuroscience, New York University Child Study Center, New York, NY 10016
State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, China
Mail to Author: YAN Chao-Gan
http://www.restfmri.net
-----------------------------------------------------------
Citing Information:
If you think DPARSFA is useful for your work, citing it in your paper would be greatly appreciated.
Something like "... The data processing was carried out by using Data Processing Assistant for Resting-State fMRI (DPARSF) (Yan & Zang, 2010, http://www.restfmri.net)..."
Reference: Yan C and Zang Y (2010) DPARSF: a MATLAB toolbox for "pipeline" data analysis of resting-state fMRI. Front. Syst. Neurosci. 4:13. doi:10.3389/fnsys.2010.00013
??? Error using ==> rest_misc>ViewROI at 307
REST doesn't support the selected ROI definition now, Please check:
D:\SOFTWARE\toolbox\spm8\toolbox\DPARSF_V2.2PRE_120905\Templates\aal.nii

Error in ==> rest_misc at 196
ViewROI(AROIDef);

Error in ==> rest_ROIList_gui>btnView_Callback at 155
rest_misc( 'ViewROI', AROIDef);

Error in ==> gui_mainfcn at 96
feval(varargin{:});

Error in ==> rest_ROIList_gui at 28
gui_mainfcn(gui_State, varargin{:});

??? Error using ==> waitfor
Error while evaluating uicontrol Callback

Best wishes!
                                                                        Yours Lei.

mikolas@pcp.lf…

Wed, 09/12/2012 - 08:37

Dear Yan,

thank you for providing your useful tool and comprehensive tutorial. I encountered a problem with continuing an interrupted process in DPARSFA 2.2. I started with *.ima dicom files and the process was interrupted during Segmentation with DARTEL. I start fromprevious step (Segmentation+DARTEL), but I get an error message message "Too many .nii.gz files in each subject's directory, should keep one 4D .nii.gz file." However, there are no .nii.gz files, all "Fun" and "T1" directories contain multiple img+hdr files.
Also, the DPARSFA GUI does not read my subjects in the working directory, although DPARSF does. It loads the names after it performs some steps though.
I use REST 1.8, MATLAB 7.12.0, SPM8.

Thank you for your help. Best regards.

Hi!

Thank you very much for your testing!

I don't know why the initial New Segment + DARTEL failed, but they will generate lots of intermediate files. This will confuse the program which file should choose. One way is you can delete the T1ImgNewSegment directory, and retry it. DAPRSFA will start from the directory of T1ImgCoreg or T1Img.

For DPARSFA GUI, I will only detect the subject list at the last step, after you specifying the Starting Directory.  DPARSF will check always, thus it's not so flexible. :)

Best,

Chao-Gan

YAN Chao-Gan

Wed, 09/19/2012 - 17:56

Fixed a bug when calculating with "no mask". Thank Mikolas Pavol for the report.

yuanbinke

Wed, 11/07/2012 - 08:18

严老师:
 您好!最新版的DPARSF做ReHo计算时不能生成平滑的smReHo map,我看代码里做平滑时是找mReHo里的img文件,但是新版的已经默认的是nii文件了。还有就是DPARSFA计算ReHo时生成zReHo map,那这个文件做统计时是不是还要平滑一下?
祝好!
彬科

彬科:

你好!

新版的DPARSF在最后有一个Smooth derivatives的选项,可以在计算出各种指标之后进行平滑。计算ReHo,可以选上这步,在最后进行平滑。

mReHo和zReHo,都应该进行平滑一下再做统计。最近我也想着做一个简单的小研究,比较一下这些不同的变换,更好地统一起来。

祝一切顺利!

超赣

报错信息如下:

Converting T1 Images:001 OK
??? Index exceeds matrix dimensions.

Error in ==> DPARSFA_run>(parfor body) at 275
                Nii  = nifti(DirImg(1).name);

Error in ==> parallel_function at 471
            consume(base, limit, F(base, limit));

Error in ==> DPARSFA_run at 264
            parfor i=1:AutoDataProcessParameter.SubjectNum

Error in ==> DPARSFA>pushbuttonRun_Callback at 1490
    [Error]=DPARSFA_run(handles.Cfg);

Error in ==> gui_mainfcn at 96
        feval(varargin{:});

Error in ==> DPARSFA at 33
    gui_mainfcn(gui_State, varargin{:});
 
??? Error while evaluating uicontrol Callback


另外VBM分析分割后产生的mask是错误的,但是这里无法贴图,不知道是不是我电脑的问题,点击插入图片浏览器就死掉.


王珏

你是只做VBM吗?因为TR设为0了,DPARRSFA试图去功能图像的头文件中读取TR信息,但是找不到相应的文件。

下一个版本加一个设定吧,如果没有功能图像,就不去读头文件的TR信息了。

分割后的mask你是指哪个mask?

超赣

yuanbinke

Fri, 12/14/2012 - 07:34

严老师:
我用最先版的DPARSFA做数据预处理,有以下报错信息,你看看怎么解决:
Sending a stop signal to all the labs ... stopped.

??? Undefined function or method 'file2mat' for input arguments
of type 'struct'.

 

Error in ==> file_array.subsref>subfun at 80

t = file2mat(sobj,varargin{:});

 

Error in ==> file_array.subsref at 60
   
t = subfun(sobj,args{:});

 

Error in ==> nifti.subsref>rec at 219
           
t = subsref(t,subs(2:end));

 

Error in ==> nifti.subsref at 45
varargout = rec(opt,subs);

 

Error in ==> DPARSFA_run>(parfor body) at 322
                   
y_Write4DNIfTI(Nii.dat(:,:,:,AutoDataProcessParameter.RemoveFirstTimePoints+1:end),Nii,DirImg(1).name);

                   

Error in ==> parallel_function at 491
           
S = F(base, limit);

 

Error in ==> DPARSFA_run at 290
       
parfor i=1:AutoDataProcessParameter.SubjectNum

 

Error in ==> DPARSFA>pushbuttonRun_Callback at 1490

   
[Error]=DPARSFA_run(handles.Cfg);


Error in ==> gui_mainfcn at 96
       
feval(varargin{:});

 

Error in ==> DPARSFA at 33
    gui_mainfcn(gui_State, varargin{:});
 

??? Error while evaluating uicontrol Callback

彬科
 

YAN Chao-Gan

Wed, 12/26/2012 - 07:35

Dear all,
 
Merry Christmas and Happy New Year!
 
It’s said that December 21, 2012 is the end of the world, thus it marks the beginning of a new era. Here, we celebrate the Christmas and New Year in the new era, and are pleased to release the stable version of REST V1.8 and DPARSF V2.2. :)
 
To facilitate parallel computing and lots of new features, many functions were re-structured, especially for DPARSF advanced version. Thus, we released the pre-release of REST V1.8 and DPARSF V2.2 on September 5, 2012 and invited users to help us to refine them. Many thanks to our users, for testing, using and reporting bugs or problems encountered, we kept updating the pre-release and now believe REST V1.8 (REST_V1.8_121225) and DPARSF V2.2 (DPARSF_V2.2_121225) have reached a more stable stage (probably there still will be bugs, we will fix them soon and make a next stable release upon receiving reports).
 
We encourage the users to re-perform the analyses with the stable version if you used the earlier pre-releases of REST V1.8 and DPARSF V2.2. We expect most of the results will keep the same, but there are several changes in the stable version.
 
Of note, the default parameter in the pre-release is set to “Calculate in Original Space” which calculates the R-fMRI measures on the data before spatial normalization and resampling. This way was suggested to maintain the intrinsic fidelity of spontaneous fluctuations, and thus be beneficial in revealing changes in subtle small brain areas (Wu et al., 2011. Empirical Evaluations of Slice-Timing, Smoothing, and Normalization Effects in Seed-Based, Resting-State Functional Magnetic Resonance Imaging Analyses. Brain Connect 1, 401-410). We set the default parameter in the pre-release to promote the capability of calculating in original space with DPARSF. However, the same study (Wu et al., 2011) found no significance between strategies of calculating in original space or MNI space, and they suggested using the latter way when targeting large and robust functional networks by providing consistent spatial extent. In the stable release of DAPRSF (DPARSF_V2.2_121225), the default parameter is conservatively set back to “Calculate in MNI Space” for a better comparison with most of the previous studies. Another consideration is that if data of multiple sites were involved (e.g., the FCP 1000 data), ReHo and degree centrality need to be performed in MNI space to address the issue with difference in voxel size. Nonetheless, users could choose either way by selecting from “Template Parameters” based upon their objectives.
 
In addition, before VMHC calculation, DPARSF_V2.2_121225 now can create a symmetric mean T1 template from all the participants, and will normalize the T1 image for each subject to the symmetric T1 template (created in Step 2), and apply the transformations to the functional data. In addition, REST-GCA could handle multiple ROIs (other than 2) in ROI-wise GCA in REST V1.8 pre-release, but the outputs were flipped. We fixed this bug in the REST_V1.8_121225, now the outputs were put in according to the description in REST-GCA readme.
 
We hope REST_V1.8_121225 and DPARSF_V2.2_121225 can continually help our users in your research and applications and thus promote the resting-state fMRI studies.
 
On behalf of the REST Team, I wish you a happy, healthy and prosperous 2013!
 
Best,
 
Chao-Gan
 

 严老师,
        您好!我有两个问题:
        第一个是 在计算 ALFF/fALFF 时需要做 nuisance covariates regression (6 rigid-body parameters, CSF signals, white matter signal) 吗?
       您在 “The motion crisis in functional connectomics: damage assessemtn and control for resting-state fMRI" poster 中提到 头动 也会对 ALFF/fALFF 有影响。具体是什么样的影响呢? 在计算ALFF/fALFF 是否要做nuisance covariates regression (6 rigid-body parameters, CSF and white matter signal)?
        第二个问题是关于DPARSFA_2.2 中新增了处理头动的方法:voxel-specific head motion, Friston 24, voxel-specific 12, head motion scrubbing regressors, derivarive 12. 到底怎样选择呢?根据您的 poster 内容,是否选择”voxel-specific head motion" + "polynomial trend 3 (32)"+ "voxel-specific 12" 更好呢?同样在计算ALFF/fALFF 也做这样的处理呢?
        谢谢!
陈杰

cheungmen

Sat, 01/26/2013 - 02:20

 if I compute connectivity between the ROI and the whole brain, whether a correlation matrix can be created in the Results or not?
I hope to get the correlation matrix of a ROI with the whole brain, but I don't how to do?
I choose Functional Connectivity in the GUI, and click Define ROI, then click Run. but in the Results file, no correlation matrix can be found.


Remko van Lutterveld

Tue, 05/28/2013 - 13:04

Dear Chao-gan,
    
Thank you for your great program!

I hope you can help with the following:

I am trying to regress out covariates (CSF, average signal, white matter) from data that have already been preprocessed up to normalization using SPM8, and I get the following error message:

(DPARSFA version: DPARSF_V2.2_130309
REST version: REST_V1.8_130303/
Matlab version: 7.12.0 (R2011a)

Extracting ROI signals...
Read 3D EPI functional images: "/mnt/data/fmri/remko/AVH+vsAVH-/test/FunImgARW/Subject001".
??? Error using ==> y_ExtractROISignal at 188
Wrong ROI definition, please check:
/mnt/data/fmri/remko/AVH+vsAVH-/test/Masks/Subject001_BrainMask_05_91x109x91.nii
 
Error in ==> DPARSFA_run>(parfor body) at 1852
                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 1753
        parfor i=1:AutoDataProcessParameter.SubjectNum
 
Error in ==> DPARSFA>pushbuttonRun_Callback at 1579
    [Error]=DPARSFA_run(handles.Cfg);
 
Error in ==> gui_mainfcn at 96
        feval(varargin{:});
 
Error in ==> DPARSFA at 33
    gui_mainfcn(gui_State, varargin{:});
 
??? Error while evaluating uicontrol Callback

 Dear DPARSF experts,

I got an error message when running DPARSFA with the default parameters:

'Reference to non-existent field 'ROIDefForEachSubject'
Error in DPARSFA_run>(parfor body)
 ............................................................

I would appreciate it if you can let me know what's wrong and how to fix it.
Thanks in advance !!

Sincerely,
Yi-Yu Chou

Forums
Predefined Types