Documentation‎ > ‎

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.

Figure: local connectome fingerprinting

The following is a list of steps to get the fingerprint data using DSI Studio.

How to get the local connectome fingerprint?

The local connectome fingerprint can be calculated from any diffusion MRI data, including DTI, DSI, and multi-shell data.

STEP1 Construct a connectome db

You may either get an existing one (see connectome db at Atlas and sample images) or create one using your data (see instructions in Create a connectometry database)

STEP2 Open the connectometry db

Open the .db.fib.gz file in "STEP2 Connectometry analysis" under the Diffusion MRI Connectometry tab.

STEP3 Output fingerprint 

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)