ZangYF Group's CVs

Data Processing Assistant for Resting-State fMRI (DPARSF) V1.0


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, nomalize, smooth) data, FC, ReHo, ALFF and fALFF 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 AAL or ROI time courses (or extract Gray Matter Volume of AAL regions, command line only) efficiently if you want to perform small-world analysis. This software is very easy to use, just click on buttons if you are not sure what it means, popup tips would tell you what you need to do. You also can download a MULTIMEDIA COURSE to know more about how to use this software. Add DPARSF's directory to MATLAB's path and enter "DPARSF" in the command window of MATLAB to enjoy it.

The latest release is DPARSF_V1.0_100510

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Multimedia Course: Data Processing of Resting-State fMRI

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].

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Taxonomy upgrade extras: 

Stat maps

 Hi all,
This may seem like a basic question, but I just wanted to check whether entering the single-subject FC or zFCmaps produced by REST  into a one-sample t-test in spm is an appropriate way to generate a group-averaged stat map.
Thanks,
Alex 

Detrending error in LINUX system

Dear REST experts

Recently , we moved our analysis platform from windows to 64 bits centos 5.3 system with Matlab R2009a

All the resting fmri data were processed by using DPARSF_V1.0_Beta_090701

when enter into the stage of remove linear trend  , we meet the following error

could someone can help us to slove the problem ?

===========================================================================================
Removing the linear trend:

ROI wise comparison text files

 Hello, 
I'm trying to run functional connectivity using ROI wise comparison between regions.  After detrending, filtering, choosing ROIs, inputting covariables and performing the functional connectivity, the program gave me the expected text files. Does the ROI_FCMap_  file represent the time course before or after the covariable have been regressed out? If it is before the covariables have been accounted for, is there a way to acquire the time course after the covariables have been regressed out?

Thanks for your help in this matter

Writing covariable-corrected images

 Hi again,

I am trying to use the rest_Y_RegressOutCovariables function to write our covariable-corrected image files. I get the error below, which I was hoping you could help me with. I've double-checked, and the timeseries vectors are the correct length.
Thanks for your help,
Alex

 

 

Fwd: What's new for '+"resting" +"fMRI"' in PubMed

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Sender's message: Search: "resting"[All Fields] AND "fMRI"[All Fields]

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Linear trend removal

 Hi all,
Sorry to post this issue again, but I was hoping for some clarification so I can progress with my analysis.

I just have a question about the detrending and filtering. I have been looking at ROI time courses at various stages of detrending and filtering. 
I have noticed that after detrending, the linear trend does indeed seem to be removed, as is expected.
After filtering (.008 to .08 Hz) only (without detrending), I can clearly see the slower oscillations, although the linear trend is still present, as would be expected.