This method finds corresponding clusters across subjects. Currently, only one-to-one cluster's correspondences are available using the Hungarian algorithm. Our implementation uses our anatomical similarity metric which allows us to find correspondences without the need of registration, comparing clusters of streamlines in each subject's native space.
The Hungarian algorithm
The Hungarian algorithm finds corresponding clusters between two subjects.
bash /space/erebus/2/users/vsiless/code/freesurfer/anatomicuts/dmri_ac.sh preGA SUBJECT TARGET_SUBJECT MIN_STREMLINE_LENGHT STD_CLUSTER_CLEANING bash /space/erebus/2/users/vsiless/code/freesurfer/anatomicuts/dmri_ac.sh forAll preGA TARGET_SUBJECT MIN_STREMLINE_LENGHT STD_CLUSTER_CLEANING - "pbsubmit_-n_1_-c_" AnatomiCuts_correspondences -s1 segmentation1.nii.gz -s2 segmentation2.nii.gz -c numClusters -h1 clusteringPath1 -h2 clusteringPath2 -m metric -o output.csv
-s1 the segmentation to be used for anatomical similarity in subject one.
-s2 the segmentation to be used for anatomical similarity in subject two.
-h1 the path to the AnatomiCuts folder to be used for subject one.
-h2 the path to the AnatomiCuts folder to be used for subject two.
-m metric to be used: labels (anatomical similarity) or euclid (euclidean similarity).
-sym (under development) this flag will mirror the segmentation in subject two to find between hemisphere correspondences.
-o output csv file
The output will be a csv file:
Subject one, Subject two 100000,11111 1010101, 100010
Where cluster 100000.trk from Subject one corresponds to cluster 11111.trk from Subject two
V. Siless, J. Y. Davidow, J. Nielsen, Q. Fan, T. Hedden, M. Hollinshead, C. V. Bustamante, M. K. Drews, K. R. A. Van Dijk, M.A. Sheridan, R. L. Buckner, B. Fischl, L. Somerville, and A. Yendiki. 2017. “Registration-free analysis of diffusion MRI tractography data across subjects through the human lifespan.”