title: Confidence mapping in diffusion tensor magnetic resonance imaging tractography using a bootstrap approach.
abstract: The bootstrap technique is an extremely powerful nonparametric statistical procedure for determining the uncertainty in a given statistic. However, its use in diffusion tensor MRI tractography remains virtually unexplored. This work shows how the bootstrap can be used to assign confidence to results obtained with deterministic tracking algorithms. By invoking the concept of a "tract-propagator," it also underlines the important effect of local fiber architecture or architectural milieu on tracking reproducibility. Finally, the practical advantages and limitations of the technique are discussed. Not only does the bootstrap allow any deterministic tractography algorithm to be used in a probabilistic fashion, but also its model-free inclusion of all sources of variability (including those that cannot be modeled) means that it provides the most realistic approach to probabilistic tractography.
authors.affiliation: Section on Tissue Biophysics and Biomimetics, Laboratory of Integrative Medicine and Biophysics, National Institute of Child Health and Human Development, National Institutes of Health, Bethesda, Maryland, USA. d.jones@iop.kcl.ac.uk
authors.forename: Derek K
authors.initials: DK
authors.lastname: Jones
authors.forename: Carlo
authors.initials: C
authors.lastname: Pierpaoli
mesh_terms: D000465:Algorithms; D001931:Brain Mapping / Q000379:methods*; D038524:Diffusion Magnetic Resonance Imaging / Q000379:methods*; D006801:Humans; D015233:Models, Statistical; D015203:Reproducibility of Results; D018709:Statistics, Nonparametric