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Create a connectometry database

This documentation introduces the steps to create a connectometry database. A connectometry database contains information of the subjects' diffusion profile that allows further connectometry analysis.

Step C1: Reconstruct SRC Files for Connectometry

Before this step, please acquire SRC files for your study subjects (see Batch Processing using GUI) and place all SRC files in a folder.

Click [Step C1: Reconstruct SRC Files for Connectometry] and select the folder to reconstruct all SRC files. 

If you would like to use study specific anisotropy template instead of the built-in HCP1021 template, then you may need to reconstruct each SRC file by QSDR in [Step T2: Reconstruction]. Please check [ODFs] in the output option, and assign your study-specific template under the QSDR reconstruction tab.

Step C1a: Post-reconstruction quality check

Get a list of the FIB files generated in the subjects' folders (search *.fib.gz file). Each FIB file generated from the previous step will has a file name such as *.odf8.f5rec.cdm.qsdr.1.25.2mm.R67.fib.gz. The R67 indicates that the R-sqaured value between the subject and the template's QA map is 0.67. 

**An R-squared value lower than 0.4 may require further inspection to confirm whether it is due to registration error**.

The most common cause for a low R2 is an inverse order of the axial slices, which can be corrected in the reconstruction step to flip the image volume at the Z direction. The second common cause is a prominent artifact in the background, which may be handled by introducing a brain mask and use [Edit][Trim Image] in the reconstruction step to clean up the background. 

If you still cannot solve the low R2 value problem, please feel free to send the data to me (uploaded provided in the webpage of the Discussion forum), and I will figure out a solution for you.

Step C2: Create a connectometry database

A connectometry database requires the ODF-containing fib files for all subjects and an atlas that serves as the sampling skeleton.

1) In the [Diffusion MRI Connectometry] tab, click "Create a connectometry database".

This brings up the following dialog.

2) Click the [Search in Directory] and select the study folder. You may also use the "Add" button to select individual FIB file. You can change the order of the files by the "up", "down", "sort" button. You can save/load a list of the SRC files.

*If you are going to study the change in a longitudinal study, make sure that you place baseline and followup scans of the same subject together (e.g. base of s#1, followup of #1, base of s#2, followup of s#2...etc.)

3) Assign the atlas file (skip if a default file is assigned). I recommend using the latest HCP 842 atlas, which is available at Atlas and sample images. The latest version of DSI Studio has it shipped with the package and will load it as the default. Using the default HCP842 template meaning that the HCP subjects (young aged adult) can be representative to your subject pool. If this is not true, you should consider create your own atlas. The choice depends entirely on your study and subject pool. 

4) Assign "Index of interest" (skip if using SDF as the default): You can also study other diffusion measures such as FA, AD, RD, MD, RDI, NRDI (see here for explanation for each index) using connectometry. If you only see SDF in the selection, please update DSI Studio and re-run STEP1d of this page to reconstruct SRC files again. (or feel free to contact me if still not solved).

4) Specify the output file name.

    Please assign the file extension as *.db.fib.gz

5) Click the "Create Database" button to create the connectometry database as a db.fib.gz file. You can add or remove subjects from the database using [Diffusion MRI Connectometry][Edit Connectometry Database]

6) Once you have the connectometry database, you can run group connectometry.

At the end of this step, you will have a connectometry database, which is a file name with extension *.db.fib.gz

TIP: You can add more subjects or remove subjects from the database later using [Tools][Open a Connecometry Database] from the main window tab.

TIP: If you are going to study the change in a longitudinal study, use [Tools][Open a Connecometry Database] to calculate the difference between baseline scan and followup scan for each subject (e.g. base of s#1, followup of #1, base of s#2, followup of s#2...etc.)

Optional: Create a population average template

The FIB files created from Step C1 can be averaged into a population average template using the following steps:

1) Click [Tools][O8: Create template/skeleton]

2) Add in all FIB files and specify the output file name. This dialog outputs two fib files, one without and one with the averaged ODFs. The one without the ODFs can be used as the template for creating a connectometry database.


[1] Yeh, F.C., Tseng, W.Y., 2011. NTU-90: A high angular resolution brain atlas constructed by q-space diffeomorphic reconstruction. Neuroimage 58, 91-99. (pdf)

[2] Yeh, F.C., Wedeen, V.J., Tseng, W.Y., 2011. Estimation of fiber orientation and spin density distribution by diffusion deconvolution. Neuroimage 55, 1054-1062. (pdf)

[3] Yeh, F.C., Tseng, W.Y.Sparse Solution of Fiber Orientation Distribution Function by Diffusion Decomposition”, PLoS One. 2013 Oct 11;8(10):e75747. doi: 

Optional:Output local connectome fingerprint

What is local connectome fingerprint?

DSI Studio provides a way to sample white matter characteristics as a "fingerprint", an approach called "local connectome fingerprinting" (LCF) [1]. The method uses on q-space diffeomorphic reconstruction (see Reconstruction(DTI, QBI, DSI, GQI, QSDR) to calculate the density of diffusing water along major fiber bundles from diffusion MRI. LCF provides a vector measurement characterizing white matter structures at the voxel level for each dMRI scan. LCP was found to be highly unique for each individual [1], making it a tangible metric of connectomic differences. The variability in LCF was also found to be partially determined by genetic factors, but largely plastic across time. LCF potentially opens a new door for probing the influence of pathological, genetic, social, or environmental factors on the unique configuration of the human connectome. The density sampled exhibits high individuality that allows us reliably identify individuals or quantify similarity between two connectome architectures.

Output local connectome fingerprint

Click [Tools][O1: Open a Connectometry Database] to bring up the connectometry database content. Then click on [Fingerprinting][Save fingerprints] to export the fingerprints of the whole brain. There are several options:

  • If you would like to output the fingerprint of a specific region, load a ROI NIFTI file using [Fingerprinting][Load fingerprint mask...] to assign an ROI as a mask, and then save the fingerprint using [Fingerprinting][Save fingerprints]. The exported fingerprint will use the mask to extract the values.
  • Fiber threshold is a value used to filter out gray matter region. The default is 0.6 otsu's threshold.
  • Normalize FP will scale the fingerprint values so that the standard deviation of a fingerprint vector is scaled to one.
  • The fingerprints will be stored in a MATLAB mat file with the following structures.

dimension: the dimension of the template space.
'subject0' to 'subjectN': a total of N subjects' fingerprint vectors
subject_names: the name for each subject. It is a 1xN vector of number and can be converted to named by using the command, ID = textscan(char(subject_names),'%s');
voxel_location: an 1-by-N matrix storing an index for each fingerprint entry.
mni_location: a 3-by-N matrix the MNI coordinate for each fingerprint entry
fiber_direction: a 3-by-N matrix storing the associated fiber direction for each fingerprint entry.


[1] Yeh F-C, Vettel JM, Singh A, Poczos B, Grafton ST, Erickson KI, et al. (2016) Quantifying Differences and Similarities in Whole-Brain White Matter Architecture Using Local Connectome Fingerprints. PLoS Comput Biol 12(11): e1005203. doi:10.1371/journal.pcbi.1005203 (link)