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Abstract #2019

Effect of Step Size on Probabilistic Streamlines: Implications for the Interpretation of Connectivity Analyses.

J-Donald Tournier1,2, Fernando Calamante1,2, Alan Connelly1,2

1Brain Research Institute, Florey Neuroscience Institutes, Melbourne, Victoria, Australia; 2Department of Medicine, University of Melbourne, Melbourne, Victoria, Australia

Probabilistic streamlines algorithms aim to provide an estimate of the uncertainty of the path of fibre tracks. In this study we investigate the effect of step size using theory, simulations, and in vivo data. Our results show that the estimate of tracking uncertainty provided by many implementations is strongly dependent on the step size specified by the user (in contrast to the equivalent deterministic streamlines case). This has strong implications for the interpretation of such results, since a small step size will give a grossly misleading representation of the probability of a connection.