Abstract #4996
Visualizing 4,230 White Matter Tracts at Once
Bramsh Qamar Chandio1,2, Tamoghna Chattopadhyay3, Conor Owens-Walton3, Julio E. Villalon Reina3, Leila Nabulsi3, Sophia I. Thomopoulos3, Javier Guaje4, Eleftherios Garyfallidis4, and Paul M. Thompson3
1Department of Intelligent SystemsEngineering, School of Informatics, Computing, and Engineering, Indiana University Bloomington, Bloomington, IN, United States, 2Imaging Genetics Center, Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Marina Del Rey, CA, United States, 3Imaging Genetics Center, Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Marina del Rey, CA, United States, 4Department of Intelligent Systems Engineering, School of Informatics, Computing, and Engineering, Indiana University Bloomington, Bloomington, IN, United States
Synopsis
We propose a dimensionality reduction method, based on the bundle-based minimum distance metric and the UMAP technique, to disentangle and visualize clusters in whole-brain tractography. In multishell diffusion MRI data from 141 elderly subjects, 30 tracts were extracted per subject using auto-calibrated RecoBundles in DIPY. A (141x30)x(141x30) bundle distance matrix was calculated and fed into UMAP. Embedding space maps showed that the same bundles were consistently mapped across subjects, making it easier to identify outliers and define clusters for population-based statistical analysis.
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