AbstractProbabilistic MRI diffusion tractography is a sophisticated technique to investigate structural connectomes, but its steep computational cost prevents application to broader research and clinical settings. Major speedup can be achieved by reducing the number of tractography streamlines. To ensure this does not degrade connectome quality, we calculate the identifiability of connectomes between test and retest MRI as a proxy for information content. We find that reducing streamline count by up to two orders of magnitude from prevailing levels in literature has no significant impact on identifiability. Incidentally, we also observe that Jaccard similarity is more effective than Pearson correlation in achieving identifiability.This document was prepared as an account of work sponsored by an agency of the United States government. Neither the United States government nor Lawrence Livermore National Security, LLC, nor any of their employees makes any warranty, expressed or implied, or assumes any legal liability or responsibility for the accuracy, completeness, or usefulness of any information, apparatus, product, or process disclosed, or represents that its use would not infringe privately owned rights. Reference herein to any specific commercial product, process, or service by trade name, trademark, manufacturer, or otherwise does not necessarily constitute or imply its endorsement, recommendation, or favoring by the United States government or Lawrence Livermore National Security, LLC. The views and opinions of authors expressed herein do not necessarily state or reflect those of the United States government or Lawrence Livermore National Security, LLC, and shall not be used for advertising or product endorsement purposes.
Architectures for Shared Control of Vehicle Steering**The authors wish to acknowledge the financial support of the Automotive Research Center (ARC) under Cooperative Agreement W56HZV-04-2-0001 with the U.S. Army Tank Automotive Research, Development and Engineering Center (TARDEC) Warren, MI. Disclaimer: Reference herein to any specific commercial company, product, process, or service by trade name, trademark, manufacturer, or otherwise, does not necessarily constitute or imply its endorsement, recommendation, or favoring by the United States Government or the Department of the Army (DoA). The opinions of the authors expressed herein do not necessarily state or reect those of the United States Government or the DoA, and shall not be used for advertising or product endorsement purposes. UNCLASSIFIED: Distribution Statement A. Approved for public release; distribution is unlimited. #27406.
