Abstract #4996

Visualizing 4,230 White Matter Tracts at Once

Bramsh Qamar Chandio^1,2, 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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