PANDA: a pipeline tool for diffusion MRI

PANDA (Pipeline for Analyzing braiN Diffusion imAges) is a matlab toolbox for pipeline processing of diffusion MRI images. For each subject, PANDA can provide outputs in 2 types: i) diffusion parameter data that is ready for statistical analysis; ii) brain anatomical networks constructed by using diffusion tractography. Particularly, there are 3 types of resultant diffusion parameter data: WM atlas-level, voxel-level and TBSS-level. The brain network generated by PANDA has various edge definitions, e.g. fiber number, length, FA or connectivity probability-weighted.

The key advantages of PANDA are as follows:
1.) fully-automatic processing from raw DICOM/NIFTI to final outputs;
2.) Supporting both sequential and parallel computation. The parallel environment can be a single desktop with multiple-cores or a computing cluster with a SGE system;
3.) A very friendly GUI (graphical user interface).


SPM – Statistical Parametric Mapping

Statistical Parametric Mapping refers to the construction and assessment of spatially extended statistical processes used to test hypotheses about functional imaging data. These ideas have been instantiated in software that is called SPM.

The SPM software package has been designed for the analysis of brain imaging data sequences. The sequences can be a series of images from different cohorts, or time-series from the same subject. The current release is designed for the analysis of fMRI, PET, SPECT, EEG and MEG


FSL-FMRIB Software Library

FSL is a comprehensive library of analysis tools for FMRI, MRI and DTI brain imaging data. It runs on Apple and PCs (both Linux, and Windows via a Virtual Machine), and is very easy to install. Most of the tools can be run both from the command line and as GUIs (“point-and-click” graphical user interfaces).



AFNI is a set of C programs for processing, analyzing, and displaying functional MRI (FMRI) data – a technique for mapping human brain activity. It runs on Unix+X11+Motif systems, including SGI, Solaris, Linux, and Mac OS X. It is available free (in C source code format, and some precompiled binaries) for research purposes.


Resting-State fMRI Data Analysis Toolkit V1.8

Resting-State fMRI Data Analysis Toolkit (REST) is a convenient toolkit to calculate Functional Connectivity (FC), Regional Homogeneity (ReHo), Amplitude of Low-Frequency Fluctuation (ALFF), Fractional ALFF (fALFF), Gragner causality, degree centrality, voxel-mirrored homotopic connectivity (VMHC) and perform statistical analysis. You also can use REST to view your data, perform Monte Carlo simulation similar to AlphaSim in AFNI, perform Gaussian random field theory multiple comparison correction like easythresh in FSL, calculate your images, regress out covariates, extract ROI time courses, reslice images, and sort DICOM files. Download a MULTIMEDIA COURSE would be helpful for knowing more about how to use this software. Add REST’s directory to MATLAB’s path and enter “REST” in the command window of MATLAB to enjoy it.


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 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.



FreeSurfer is a set of tools for analysis and visualization of structural and functional brain imaging data. FreeSurfer contains a fully automatic structural imaging stream for processing cross sectional and longitudinal data.

FreeSurfer provides many anatomical analysis tools, including: representation of the cortical surface between white and gray matter, representation of the pial surface, segmentation of white matter from the rest of the brain, skull stripping, B1 bias field correction, nonlinear registration of the cortical surface of an individual with a stereotaxic atlas, labeling of regions of the cortical surface, statistical analysis of group morphometry differences, and labeling of subcortical brain structures and much more .



MRIcron is a cross-platform NIfTI format image viewer. It can load multiple layers of images, generate volume renderings and draw volumes of interest. It also provides dcm2nii for converting DICOM images to NIfTI format and NPM for statistics. MRIcron is a mature and useful tool, however you may want to consider the more recent MRIcroGL as an alternative.



TrackVis is a software tool that can visualize and analyze fiber track data from diffusion MR imaging (DTI/DSI/HARDI/Q-Ball) tractography.


Diffusion Toolkit

Diffusion Toolkit is a set of command-line tools with a GUI frontend that performs data reconstruction and fiber tracking on diffusion MR images. Basically, it does the preparation work for TrackVis.



MRtrix provides a set of tools to perform diffusion-weighted MR white-matter tractography in a manner robust to crossing fibres, using constrained spherical deconvolution (CSD) and probabilistic streamlines.

In-house code


HBM Dyslexia Classification Code

Code for nested linear SVM & logistic classifcation in HBM paper (Inner cross validation for P threshold selection, outer cross validation for classifier evaluation)

Nested_Ttest_LR.tar  ,  Nested_Ttest_LSVM.tar


OHBM Abstract

Qing, Z., He, Y., Gong, GL., 2014. Partial volume effect has an impact on individual differences of ALFF from resting-state fMRI. 20th Annual Meeting of OHBM, Jun.8-12. 2014, Hamburg, Germany. Poster No. MT 1711



CC Post-processing Images

The specific DARTEL template and GM mask based on all S500  subjects.




Functions and scripts for our CC paper (link will be added soon). The functions contain elastic-net prediction (both 3F-CV and A-Predict-B) with inner 3F-CVs for parameter selection.

In this paper, scikit-learn ( 0.16.1 and python 3.4.3 were used to implement the elastic-net algorithm.

Also, the models construstruted using S500 dataset in our work was released to facilitate the full reproducibility of the paper. The ORRT S500 model was in the folder ‘ORRT_S500_All_Model’ and the PVT S500 model was in the folder ‘PVT_S500_All_Model’. To use this ORRT model, you should have testing data matrix of m rows and 174947 columns. If this variable is named testing_data, then:

from sklearn.externals import joblib



testing_data_scaled = ss.transform(testing_data)

Prediction_Scores = mm.predict(testing_data_scaled)

To acquire the data with 174947 features, you should use our template for registration and our mask to extract the brain GMV voxels (