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 Drews^1,2, Koene R.A. Van Dijk¹, Margaret A Sheridan³, Randy L Buckner^1,2, Bruce Fischl^1,4, 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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