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Connectome fingerprint

Introduction
DSI Studio provides a way to sample white matter characteristics as a "fingerprint", an approach called "local connectome fingerprinting" [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. 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 connectome fingerprint?


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 


Switch to the "Source data" tab and click on a button labeled "save all fingerprint" to export fingerprint as a mat file. The mat file will contain the following matrix:

1) dimension: the dimension of the template space.
2) 'subject0' to 'subjectXXX': the fingerprint vector for each subject
3) 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');
4) voxel_location: the voxel index for each fingerprint entry
5) mni_location: the MNI coordinate for each fingerprint entry


The "load mask" button allows you to specify the regions for sampling the connectome fingerprint, whereas the "save mask" button can output the regions where the fingerprints are sampled from.

Reference


[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)



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