Differential Tractography


Differential tractography compares scans to capture neuronal change reflected by a decrease of anisotropy. Compared with conventional tractography, differential integrates the “tracking-the-difference” paradigm as opposed to “tracking-the-existence” one used in the conventional setting. It is realized by adding one criterion to track along trajectories only if a decrease of anisotropy is found between repeat scans. This approach greatly increase the sensitivity and specificity of the diffusion metrics by aggregating results along white matter pathways.

Differential tractography can be applied to 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 signals may include a lot of physiological fluctuations. If your data are acquired at a low b-value (e.g., < 3000), then you may expect to have a higher false discovery rate (FDR). In the original study, we used 256-direction grid sampling with b-max=4000 or even up to 7000 to get excellent FDR values lower than 0.05. Using DTI data may increase FDR to 0.2.

Open the main window and click on [Step T3: Fiber Tracking] to select a GQI-reconstructed FIB file from Step T1 and Step T2.

0. Quality check using whole-brain tracking

In the tracking parameters (right upper window), set [Step T3c: Options][Tracking Parameters][Min length] to “30 mm”,  [Terminate if] to 100,000 seeds

Click the [Step T3d: Tracts][Fiber Tracking] button to see if you can get a good quality of whole-brain fiber tracking.

If there are too many noisy fibers, consider increasing the [Step T3c: Options][Tracking Parameters][Threshold] or [Min length].

If missing too many branches, considers lowering the [Step T3c: Options][Tracking Parameters][Threshold] or [Min length].

You may also need to adjust other parameters (check out Step T3 Whole brain fiber tracking ) until you get a good quality of the whole-brain track.

1. Load comparison data

We can compare any voxel-based metrics, including those from other modalities. My recommended list for dMRI metrics includes qa, fa, iso, rdi.

The FIB file may include metrics from one scan, and we may need to load metrics from scans in the nifti file format, which can be exported from a FIB file using [Step T3][Export].

The followings are steps for cross-sectional and longitudinal studies, respectively.

2. Compare Metrics

Assuming that the NIFTI file you added from previous step generated a new qa metrics named follow_up_qa and the FIB file already has a qa metrics, which is the baseline qa map.

A new tracking metric can be created using [Analysis][Add Tracking Metrics] and input qa-follow_up_qa for mapping decrease of QA in the follow-up. For mapping the increase of QA in the follow-up, input input follow_up_qa-qa.

We can compare any inserted NIFTI metrics (e.g, for DKI_base.nii.gz and DKI_followup.nii.gz, the comparison metric due mapping decreased DKI is ‘DKI_base-DKI_followup’).

A new differential tracking metrics will be added to the [Step T3c: Options][Tracking Parameters][Differential Tracking][Metrics]

3. Tracking the difference

Clear all tracks from the tract list using [Tracts][Delete Tracts][Delete all].

In the tracking parameters, set [Step T3c: Options][Tracking Parameters][Differential Tracking][Metrics] from none to the newly added comparison metric.

  1. Set [Differential Tracking][Threshold]=0.1,

    0.1 means 10% increase or decrease, 0.2 means 20%.

  2. Set [Tracking Parameters][Min Length (mm)]=20

    Lower value like 20 mm is more sensitive, whereas 40 mm is more specific.

  3. Click [Step T3d][Fiber Tracking] to map the differential stratigraphy.

  4. Test different [Differential Tracking][Threshold] (e.g. 0.05, 0.10, … 0.25) and [Tracking Parameters][Min Length (mm)] (20, 25, 30, 35, 40, 45)

    The goal is to maximize sensitivity while retaining specificity. The following example suggest [Min Length (mm)]=40 has the best trade-off.



4. False discovery rate and statistical testing

One key question for differential tractography is the significance of the findings. For example, if we observe a lot of tracks showing up in differential tractography, how many of them are false positive? A way to quantifying this reliability is by calculating the false discovery rate from a group of patients and a group of control subjects. The following steps illustrate how this can be carried out in DSI Studio.

Example 1: calculate the false discovery rate using control subjects

  1. Apply identical steps and parameters for N patients and N matched controls, respectively.

  2. Calculate false discovery rate (FDR)

    if you can get a total of 1000 tracks from 5 patients and 10 tracks from 5 controls, then the false discovery rate of the findings in patients is 10/100 = 10%. An FDR lower than 0.05 can be considered as significant.

Example 2: calculate the false discovery rate without control subjects

This is the alternative sham approach described in the original work in Yeh, Neuroimage, 2019.

  1. Apply identical steps to all subjects.

  2. Apply identical steps to all subjects but with an opposite comparison metrics (e.g. for follow_up_qa-qa, the opposite is qa-follow_up_qa )

  3. Calculate false discovery rate (FDR)

    if you can get a total of 1000 tracks from 1. patients and 10 tracks from 2., then the false discovery rate of the findings is 10/100 = 10%. An FDR lower than 0.05 can be considered as significant.