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Abstract #1271

Registration-free analysis of diffusion MRI tractography data across subjects through the human lifespan

Viviana Siless1, Juliet Y Davidow2, Jared Nielsen2, Qiuyun Fan1, Trey Hedden1, Marisa Hollinshead1, Constanza Vidal Bustamante2, Michelle K Drews1,2, Koene R.A. Van Dijk1, Margaret A Sheridan3, Randy L Buckner1,2, Bruce Fischl1,4, Leah Somerville2, and Anastasia Yendiki1

1Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, United States, 2Department of Psychology and Center for Brain Science, Harvard University, MA, United States, 3Department of Psychology, University of North Carolina at Chapel Hill, NC, United States, 4MIT Artificial Intelligence Laboratory, MIT, MA, United States

Diffusion MRI tractography produces massive sets of streamlines that need to be clustered into anatomically meaningful bundles. Conventional clustering techniques group streamlines based on their proximity in Euclidean space. We have developed an unsupervised method for clustering tractography streamlines based on their neighboring anatomical structures, rather than their coordinates in Euclidean space. In this work, we show how this approach can be extended to find corresponding clusters across subjects without inter-subject registration. We evaluate the approach on data from the MGH-Harvard-USC Lifespan Human Connectome Project, showing improved correspondence in tract clusters across subjects aged 8-90, without the need for registration.

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