Individual connectometry (link) 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.
You need to create an SRC file first from the diffusion MRI data (see Read DICOM, NIFTI, Bruker 2dseq, or Varian FDF Files). Open the SRC file in STEP2: Reconstruction. Skip the mask selection and switch to the last tab as follows:
1. Choose QSDR as the reconstruction method so that the subject data will be reconstructed to the MNI space.
2. You may increase the order the registration method. 7-9-7 is good already, but higher values give higher registration accuracy (This requires a lot of memory. It may crash your computer)
3. The output resolution should match your normative data. 2 mm resolution is the default setting.
4. No ODF sharpening.
5. Check ODFs as the output information.
6. The ODF tessellation =8 and number of fiber resolved = 5.
7. Half-sphere DSI: Can be checked or unchecked. DSI Studio will choose for you.
8. Scheme balance: if you use DTI data with a number of sampling direction lower than 60, check this to avoid directional bias.
Click on "run reconstruction" to create a FIB file that contains ODF information of the subjects in the MNI space.
Open DSI Studio and select individual connectometry in the connectometry tab.
DSI Studio will show a dialog for connectometry analysis:
For longitudinal studies, please load the FIB files of the baseline scan and study scans. DSI Studio will compare the study scan with the baseline scan.
For individuals versus a template or a group of subject, you may also assign the template fib.fz file or a connectometry db.fib.gz file as the baseline for comparison. This comparison with a template requires additional normalization, and a "maximum normalized to one" option needs to be checked. (see the advance options section below).
The template file will be used as the framework for comparison. A default 2-mm template will be loaded by DSI Studio.
You may also provide your own template.
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 a normalization is thus requires 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 assume 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.
To track pathways with decreased SDF, select "dec" (or "<%" if compared with template) as the termination index in the right-hand window under tracking parameters (see figure). A threshold of 0.05 means that connectometry will track pathways with more than 0.05 decrease in SDF (if "<%" is selected, 0.05 means 5% decrease). You may need to assign a minimal track length (e.g. 40 mm) to filter out false positive findings. Click on "Run tracking" button at the right bottom window to start tracking. To track pathways with increased SDF, select "inc" as the termination index.
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 internal capsule by assigning 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 connectometry analysis.
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.
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 has "longer" affected pathways than controls.
 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.