The easiest version of Matlab for testing DPABI is Matlab2010b, if you
have any error message dumped when you execute on a earlier version
Matlab, you can send a e-mail to me. It should be OK for Mablab2009a.
Best,
Sandy
From my windowsphone.
-----原始邮件-----
发件人: "The R-fMRI Network"
发送时间: 2014/8/18 18:49
收件人: "rfmri.org@rnet.co"
主题: [RFMRI] DPABI: a toolbox for Data Processing & Analysis of
Brain Imaging
The easiest version of Matlab for testing DPABI is Matlab2010b, if you
have any error message dumped when you execute on a earlier version
Matlab, you can send a e-mail to me. It should be OK for Mablab2009a.
Best,
Sandy
From my windowsphone.
-----原始邮件-----
发件人: "The R-fMRI Network"
发送时间: 2014/8/18 18:49
收件人: "rfmri.org@rnet.co"
主题: [RFMRI] DPABI: a toolbox for Data Processing & Analysis of
Brain Imaging
By YAN Chao-Gan (YAN Chao-Gan)
DOWNLOAD
DPABI: a toolbox for Data Processing & Analysis of Brain Imaging
DPABI is a GNU/GPL* toolbox for Data Processing & Analysis of Brain
Imaging, evolved from DPARSF (Data Processing Assistant for
Resting-State fMRI). Please refer to a MULTIMEDIA 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_V1.0_140815_Beta.
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
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.
*Some codes from REST V1.0 and V1.1 that are not complying with GNU
GPL have been re-written here to comply GNU GPL. Some GNU GPL
functions of REST V1.2 ~ V1.8 have been modified to be integrated here
under GNU GPL.
Online version of this post: http://rfmri.org/dpabi
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