Differential tractography for cross sectional data

Introduction


Here I will introduce differential tractography for a cross-sectional comparison, including

    (DT-C1) comparing one subject with another subject.

    (DT-C2) comparing one subject with a group average. 

    (DT-C3) comparing a group average with another group.

The methodology can be summarized as "tracking the difference", which allows for mapping the differences in the pathways.

The following is a step-by-step instruction to reproduce the result.

Step 1: Reconstruct subject data


For DT-C1 and DT-C2, the individual data has to be converted to *.SRC.GZ (see Read DICOM, NIFTI, Bruker 2dseq, or Varian FDF Files or Batch Processing using GUI) and make sure that the quality is good (see quality control procedures here). Once the *.SRC.GZ files are generated, click on [Step C1: Reconstruct SRC files for connectometry] to select the folder that contains the SRC files (or a root folder with subfolders containing the SRC files). DSI Studio will reconstruct the SRC files and generate a FIB file for each of them in the same folder. 


For DT-C2 and DT-C4, the group average data can be created by Create dMRI TemplateThe procedure will create *.mean.fib.gz and *.mean.odf.fib.gz. We will use the *.odf.fib.gz file in the following steps. Alternatively, you can use HCP1021.2mm.fib.gz (included in DSI Studio package) or a connectometry database (*.db.fib.gz) as the group average data. 

Step 2: Compare Data


Click [Tools][O2: Individual Connectometry analysis] from the main window. 


DSI Studio will show a dialog for the analysis:


For DT-C1 (comparing one subject with another subject), assign the fib.gz files of two individuals at [Base line FIB] and [Study FIB].

For DT-C2 (comparing one subject with a group average template), assign the group average to [Base line FIB] and individuals FIB file to [Study FIB].

For DT-C3 (comparing a group average with another group), assign the group average (e.g. healthy or control group) to [Base line FIB] and another group average (e.g., study group of patient group) to [Study FIB].

A default 2-mm template (HCP1021.2mm.fib.gz) will be assigned to [Template] by DSI Studio. You may also provide your own template. The template file will be used as the framework for comparison. 

Advanced options

In the [Advanced Options...], you may try different options and see which fits the condition best. There are four types of SDF normalization methods available. The purpose of SDF normalization is to handle the arbitrary unit of the diffusion MRI signals (the raw signals cannot be compared between scans due to different receiver gain). Consequently, SDF cannot be compared across scan, and normalization is thus required to match the signals. You may try different normalization approaches and see which approach fits the best with your data. 

(1) None: The SDFs in the FIB files are already calibrated by the signals in the ventricles. Selecting none will use the SDFs values calibrated by free water diffusion in the ventricles.

(2) Maximum normalized to one: The maximum SDF value will be normalized to one. This assumes that the maximum SDF are the same across subjects and scans.

(3) Least squared difference: The normalization will scale the SDFs so that two scans have the minimum squared difference.

(4) Variance normalized to one: The normalization will scale the SDFs so that the variance of the SDFs is 1.

Click on the "Compare" button to proceed with the comparison.

This step will take a while until a new tracking window appears.

Step 3: Tracking the difference


DSI Studio will bring up a new fiber tracking window to map the differential tractography. 

Please follow the steps to map tracks with differences.

1. Quality check:

    In the tracking parameters (right upper window), set [Min length] to "40 mm",  [Terminate if] to "100,000 seeds", [Differential Tracking Index] to "none." Make sure to use a fixed "seeding count" (not track count!) in the connectometry analysis.

    Click "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 [Threshold] or [Min length].

    If missing too many branches, considers lowering the [Threshold] or [Min length].

    You may also need to adjust other parameters (check out Diffusion MRI Fiber Tracking in DSI Studio) until you get a good quality of whole brain track

2. Tracking difference:

    Clean up all tracks from the track list on the right lower window. (e.g. [Tracts][Delete Tracts][Delete all]

    In the tracking parameters, set [Differential Tracking Index] to "dec_qa" for finding tracks with decreased connectivity or "inc_qa" for finding tracks with increased connectivity

    Adjust [Differential Tracking Threshold], 0.1 means 10% increase or decrease, 0.2 means 20%. I would suggest starting with 20% difference (i.e. set [Differential Tracking Threshold]=0.2) and explore different values (0.1, 0.3, 0.4...etc.)

    If you have a target track to study (e.g., optic radiation), you may add ROIs to limit findings (see Fiber Tracking). For example, you can map the affected pathways that pass through the internal capsule by assigning the internal capsule as the ROI. The number and length of tracks can be compared between patients if other tracking parameters are fixed (be sure to fix the seed count).  

    Click on fiber tracking to map the differential stratigraphy. The next step is to test the reliability of the findings.

Notes:

*Different scans may have different coverage at the cerebellum, and it is recommended to remove findings at the bottom slices.
*To know the anatomical structure of the findings, you can use findings as ROIs by [Tracts][Tracts to ROI] to run fiber tracking and then use [Tracts][Miscellaneous][Recognize Track] to determine the anatomical structures.

 

Step 4: False discovery rate and statistical testing (Optional)


One key question for individual connectometry is the significance of the findings. For example, if we observe a lot of tracks showing up in individual connectometry, how many of them are false positive? A way to quantifying this reliability is 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

1. Acquire pre-treatment and post-treatment scans for 5 patients and 5 controls.

2. Repeat individual connectometry for each patient and control (either individual v.s. individual or individual v.s. normative atlas). When tracking the difference, use the same number of seeds (e.g. 10,000) and same length for the minimum length threshold (e.g. 40 mm, longer length increases the specificity, whereas lower length threshold increases sensitivity) for all subjects.

3. Calculate false discovery rate: 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%. You can then show the tracks in patients and report the false discovery rate.

note: You can stratify the analysis by using different "dec" or "inc" threshold. For example, using a "dec" of 0.05 means you are mapping affected tracks with more than 0.05 difference in SDF. Repeat the analysis using "dec" of 0.05, 0.10, 0.15 ... to show the overview of the affected brain networks. 

Example 2: test whether patients have longer affected pathways than controls.


1. 2. The same as the previous example.

3. For each patient and control, use [Tract][Statistics] to get the average length of the tracks. Then you can run a t-test to test whether the patients have "longer" affected pathways than controls.

[1] Yeh, Fang-Cheng, Pei-Fang Tang, and Wen-Yih Isaac Tseng. "Diffusion MRI connectometry automatically reveals affected fiber pathways in individuals with chronic stroke." NeuroImage: Clinical 2 (2013): 912-921.
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