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Diffusion MRI Analysis Protocol


     This short tutorial is a summary of the review paper I coauthored [1]. Started from the simplest approach to more recent connectome analysis, I am listing a step-by-step workflow in the following sections. I would also recommend checking out the citation page to see how other studies have used DSI Studio to do their analysis.

Region-based analysis

     The simplest way to analyze diffusion data is to assign a region in the brain (either manual drawing or from an atlas) and get statistics on its diffusion indices. The following is the steps for ROI-based analysis.

     2. Reconstruct each SRC file using a reconstruction method (DTI or GQI) to get a FIB file. 

         The method of choice depends on what diffusion indices you are interested in. The most popular method is DTI reconstruction, which offers you FA (related to tracking integrity), radial diffusivity (related to demyelination), and mean diffusivity (related to demyelination, cell density, ..etc., see Diffusion MRI biophysics). You may also use GQI reconstruction, which offers you QA [2] (related to tract compactness), and iso (related to free water diffusion), and also RDI (restricted diffusion imaging) which quantifies the amount of restricted diffusion and is shown to reveal cellularity.   

     3. Load the fib file. There are two ways to assign a region. You can manually draw a regionload a region from a file, or load the region from an atlas. Here I recommend loading a set of regions from the tractography atlas from

     4. [Optional]  If you would like to analysis other measures (e.g. T1W, T12W signals, or PET) along with the diffusion indices, you would need to have those measures saved in the NIFTI format, and insert them using [Slices][Insert T1W/T2W images]. DSI Studio will register newly added image to the diffusion data set.

     5. Once you assign a region or a set of regions, you can get the average value of the diffusion indices (e.g., FA) and newly added measures from the main menu [Region][Statistics]. Click on "show details" button to see the results. You may load the results in Excel to do further analysis. Each subject should have a diffusion index value calculated from a region.
     6. On other tool or software (Excel) calculate the correlation of the indices with the study variables.

Track-specific analysis

     Example studies:

     Tractography based analysis uses diffusion fiber tracking to define the trajectories of fiber pathways, and the diffusion indices along the pathways can be averaged and analyzed. The advantage of tractography based analysis is that it is fiber specific, whereas region based analysis does not specify which fiber pathways to be analyzed. The following list is the steps for tractography-based analysis.

     2. Reconstruct each SRC file using a reconstruction method to get a FIB file. You may use DTI reconstruction, but it is not able to resolve fiber crossing. Other options include GQI reconstruction or QSDR (reconstructs data in the MNI space).

     3. Load the fib file and do fiber tracking You may need to use a combination of SEED/ROI/ROA to obtain the exact fiber pathways you are interested in. You may also need to use track editing to get the exact segments or branches of the fiber pathways.

     4. [Optional]  If you would like to analysis other measures (e.g. T1W, T12W signals, or PET) along with the diffusion indices, you would need to have those measures saved in the NIFTI format, and insert them using [Slices][Insert T1W/T2W images]. DSI Studio will register newly added image to the diffusion data set.
     5. Use main menu [Tracts][Statistics] to get the average diffusion indices along the fiber pathways. Click on "show details" to get information and copy it to Excel for further analysis.

     Note: the anisotropy threshold is used as a mask to filter out background voxels, and its value will determine how DSI Studio samples along track anisotropy. To avoid this inconsistency issue, you may need to keep a consistent anisotropy threshold as you save the along track anisotropy.

     6. Alternatively, you can use track profile to get the distributed pattern of a diffusion index along the fiber pathways. 

Connectivity matrix and graph theoretical analysis

     Connectivity matrix uses an atlas for partitioning the cortex into several regions and use a number of connecting tracks as the matrix entry. A connectivity matrix can be generated for each subject, and the matrices of a group of subjects can be compared with those from another group of subjects to study the differences.

     The following list is the steps for generating a connectivity matrix.

     2. Reconstruct each SRC file using QSDR reconstruction. 


Connectometry analysis

     Two types of connectometry analysis are available in DSI Studio: group connectometry and individual connectometry. 

     Group connectometry aims to find group difference or correlate group variation to a study variable. It is an atlas-based analysis that tracks the association between track connectivity and any study variable. The method allows you to identify the exact segment/subcomponent/branches of tracks that are correlated to group difference or any study variable (age, sex, ...etc). 

     Individual connectometry aims to find the difference between the longitudinal study of a single subject. The method tracks the deviate pathways of an individual by comparing the subject with a normal population, an atlas, or the subject's previous scan. It is a power tool to map the tracks that are damaged. The steps to get group or individual connectometry is described in the following documentation:

The following is the steps for doing connectometry analysis.

     For group connectometry see:

     1. See Create a connectometry database to create a connectometry database

     For individual connectometry see:


[1] Abhinav, K., et al., Advanced diffusion MRI fiber tracking in neurosurgical and neurodegenerative disorders and neuroanatomical studies: A review. Biochim Biophys Acta, 2014. 1842(11): p. 2286-2297.
[2] Yeh, F.C., et al., Deterministic diffusion fiber tracking improved by quantitative anisotropy. PLoS ONE, 2013. 8(11): p. e80713.