Differential tractography for longitudinal data


Figure: conventional tractography compared with differential tractography. Conventional tractography tracks "existence", whereas differential tractography tracks "differences".

Tracking differences instead of tracking existence
Differential tractography is a new type of tractography that compares repeat scans of the same individuals to capture neuronal injury reflected by a decrease of anisotropy. It is realized by adding a criterion to track along trajectories only if a decrease of anisotropy is found between repeat scans. Integrating this “tracking-the-difference” paradigm into the fiber tracking process results in a new tractography modality called differential tractography. It will track the exact portion of pathways exhibiting substantial differences in anisotropy. The additional criterion ignores unaffected regions and enhances meaningful findings related to neuronal injury. 

Conventional fiber tracking, in comparison, is based on a “tracking-the-existence” paradigm. It only considers anisotropy from one MRI scan and thus will include all existing pathways regardless of whether they have an injury. 

The following are steps to reproduce the result published in the differential tractography paper. 

*This feature is only available after 8/23/2019 version. Please update DSI Studio before you start.

Step 1: Experiment design

You will need to have "longitudinal data" (one baseline scan and one follow-up scan) of the same subject to run differential tractography. The scanning interval has to be more than one month. The optimal is more than 3 months because structural change needs time to build up. For estimating FDR, the optimal experiment setting include additional "sham" scan, which is an additional baseline scan one or two days after the baseline scan. This additional baseline scan will be used in estimating the false discovery rate. If there is no sham scan, then an "alternative sham" approach can be used.

Step 2: Diffusion MRI acquisition

Differential tractography can be applied to any diffusion data set, including DTI data, multi-shell data, and DSI data. But, we found that higher b-value signals will be more sensitive to early-stage neuronal changes, whereas low b-value signal may include a lot of physiological fluctuations. If your data are acquired at low b-value (e.g., < 3000), then you can expect to have a higher false discovery rate (FDR). In the original study, we used 256-direction grid sampling with b-max=7000 to get excellent FDR values lower than 0.05. Using DTI data may increase FDR to 0.2

The recommend b-table and acquisition setting can be found here. The setting in the link has b-max=4000. For differential tractography, you may need to increase both the voxel size to 2.5 mm cubic and b-value to 7000.

Step 3: Quality Control

Quality control is critical for differential tractography because the signal dropout will also be captured by differential tractography. After you collected the data, first convert DICOM/NIFTI to SRC file. The webpage also has a quality check section. Make sure that all your SRC data are created and pass the quality control.

Step 4: Compare differences 

Open the baseline SRC file in [Step T2 Reconstruction], make sure that the mask at Step T2(a) is okay, and in Step T2b(1) select [GQI] and click the [Compare SRC...] button in T2b(2) to assign the SRC file of the follow-up scan. The other setting follows the figure:

Then click [Run reconstruction]. 

DSI Studio will generate a fib.gz file with a file name such as baseline.src.gz.odf8.f5.df.follow_up.R96.rdi.gqi.1.25.fib.gz

Here R96 means the R squared value is 0.96, which is very good. If the value is lower than R80, then you will need to check the data quality or email Frank to see if there is any problem in the data registration. 

*If you have a large amount of SRC files to process, you can place all SRC files together in the same directory and select all baseline SRC files when clicking on [Step T2 Reconstruction]. DSI Studio will load the first SRC file, and please select its matching SRC file. For the rest SRC files, DSI Studio will then do the same by findings the best matching follow-up scans.

Step 5: Initial tractography test

Before tracking the differences, we will need to make sure that regular fiber tracking produces good quality of whole brain tracks following the steps:

a. Open the generated FIB file in [Step T3 Fiber Tracking] 
b. In the right upper corner [Step T3c Option] window, expand the [Tracking Parameters]
c. At [Tracking Parameters][Terminate If], assign 50,000 "seed"
d. Click on the  [Step T3d Tracts][Fiber Tracking] button to generate whole brain track. A good quality of whole brain tracks should look like this 
e. Examples of poor-quality whole brain tracks:

[Step T3c Options][Tracking Parameters][Tracking Threshold] too high/low. 
Solution: adjust the threshold

Uncorrected EPI distortion
Solution: (1) correct distortion using opposite phase encoding data, or (2) assign a seed region what excludes anterior frontal love

Step 6: Tracking differences 

Using the parameters from Step 5, we can now track the differences:

a. At [Tracking Parameters][Differential Tracking Index], assign "dec_qa" to track decrease of "qa". You may use "dec_fa" to track decrease of "fa". The increase anisotropy can be tracked by "inc_qa" or "inc_fa". 
b. At [Tracking Parameters][Differential Tracking Threshold], assign 0.2 to track anisotropy decrease larger than 20%. The recommended value for [Differential Tracking Threshold] is 0.2~0.5, depending on the condition. 20% is good for early demyelination. 50%is good for axonal loss.
c. (Optional) The sensitivity and specificity can be adjusted by [Tracking Parameters][Min Length (mm)]. Lower value like 20 mm is more sensitive, whereas 40 mm is more specific. See figure below for an example.
d. (Optional) You can add ROI from an atlas to limit the findings to a specific region or use automatic fiber tracking to limit one specific pathway (see Diffusion MRI Fiber Tracking in DSI Studio)

Step 7: Estimating FDR 

A sham setting is needed to estimate the FDR, and the procedure is shown as follows:

The paradigm is to replace the follow-up scan by a sham scan and repeat the analysis. The FDR is calculated as (number of tracks in the sham scan) by (number tracks in the follow-up scans). If there is no sham scan, you can change [Differential Tracking Index] to "inc_qa" (or "inc_fa" for DTI data) in the previous step as the "alternative sham".

If the FDR value is lower than 0.2, then it is worth reporting. FDR < 0.05 is confirmative of the findings.

There are two ways to design experiments for sham scans.

Approach 1 (Recommended)

Acquire baseline scans for patients and follow-up scans at 3 months (or longer). The sham scans can be acquired on the same patients on the same days as the baseline scans or a few days later. Do not scan sham scans right after baseline scans because this may falsely reduce FDR.

FDR= (total number of differential tracks count from sham scans) / (total number of differential tracks count from the follow-up scans)

Approach 2
Acquire baseline scans and follow-up scans for patients. Recruit healthy control subjects and do the same. The age/sex and all other settings should be matched between the patient and the healthy control group. 

FDR= (averaged number of differential tracks count from healthy controls) / (averaged number of differential tracks count from the patient groups)


1. Can I apply differential tractography to DTI dataset?
Ans: Yes, the exact procedure can be applied to DTI data, but the FDR will not be as good as the recommended acquisition (e.g., Diffusion MRI acqusition)

2. Can I apply differential tractography to study increased connectivity? 
Ans: Yes, but you may need a control subject or a sham scan to estimate FDR. You cannot use "alternative sham" approach (see the original paper for details).

3. Can I combine differential tractography with ROI?
Ans: Yes, differential tractography can be combined with an ROI to limit the findings to a region.


Yeh FC, Zaydan IM, Suski VR, Lacomis D, Richardson RM, Maroon J, Barrios-Martinez J. Differential Tractography as a Track-Based Biomarker for Neuronal Injury. Neuroimage. 2019.