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Individual Connectometry

Introduction


Individual connectometry (link)[1] aims at mapping the deviant (e.g., abnormal) pathways of an individual by comparing subject's scans with (a) previous scans, (b) a template (an FIB.gz file provided in DSI Studio), or (c) a group of subjects (requires building a connectometry db first). The methodology can be summarized as "tracking the difference", which allows for mapping the affected pathways of a patient.

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

STEP 1: Reconstruct subject data

You need to create an SRC file for each subject's diffusion MRI scan (see Read DICOM, NIFTI, Bruker 2dseq, or Varian FDF Files) and make sure that the quality is good (see quality control procedures here).

Click [Diffusion MRI Connectometry][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 an FIB file for each of them in the same folder. 

STEP 2: Compare Data


Open DSI Studio and select individual connectometry in the connectometry tab.


DSI Studio will show a dialog for connectometry analysis:



To compare repeat scans of the same individuals, please assign the subject's baseline FIB and study FIB to [Baseline FIB] and [Study FIB]. In the advanced option, assign "none". The other options can also be tested.

To compare individuals with a template or a group of subject, assign the HCP1021.2mm.fib.gz (included in DSI Studio package) or a connectometry db.fib.gz file to the [Baseline FIB]. The individuals FIB file is assigned to [Study FIB]. Since the signal pattern could be different, in the advanced option, please use "Least squared differences" or other normalization methods (do not use "none");

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. 

Advance options

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.

STEP 3: Tracking the difference


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'

    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.)

    
Notes:

*Different slices coverage may produce false results at the cerebellum, and it is recommended to remove any findings at the bottom slices.
    
*Individual connectometry can be combined with ROI/ROA/End and any tracking options (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).

*Make sure to use a fixed "seeding count" (not track count!) in the connectometry analysis.

STEP 4: False discovery rate and statistical testing


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 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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