Abstract #0517

Automated Superstructure-based Segmentation of Ascending Arousal Network Nuclei for Diffusion Tractography

Mark D Olchanyi^1,2,3, Brian L Edlow^1,4, Emery N Brown^2,3,5,6, and Juan E Iglesias ^4,7,8

¹Center for Neurotechnology and Neurorecovery, Department of Neurology, Massachusetts General Hospital, Boston, MA, United States, ²Neurostatistics Research Laboratory, Massachusetts Institute of Technology, Cambridge, MA, United States, ³Institute for Medical Engineering and Science, Massachusetts Institute of Technology, Cambridge, MA, United States, ⁴Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital and Harvard Medical School, Charlestown, MA, United States, ⁵Picower Institute, Massachusetts Institute of Technology, Cambridge, MA, United States, ⁶Department of Anesthesia, Critical Care and Pain Medicine, Massachusetts General Hospital and Harvard Medical School, Boston, MA, United States, ⁷Centre for Medical Image Computing, University College London, London, United Kingdom, ⁸Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, MA, United States

Synopsis

Traumatic brain injury that causes sheering of white matter pathways that connect ascending arousal network (AAN) nuclei in the rostral brainstem to cortical and subcortical targets leads to disorders of consciousness. Connectivity studies involving these nuclei are limited due to their small size, vague boundaries, and lack of reliable annotations. We present a method to automatically segment AAN nuclei from diffusion MR volumes using image registration constrained by brainstem structures with definable contrast boundaries. We test AAN segmentation robustness with probabilistic tractography and provide new insights into connectivity between AAN nuclei and the thalamus.

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