title: Ensemble Tractography.
abstract: Tractography uses diffusion MRI to estimate the trajectory and cortical projection zones of white matter fascicles in the living human brain. There are many different tractography algorithms and each requires the user to set several parameters, such as curvature threshold. Choosing a single algorithm with specific parameters poses two challenges. First, different algorithms and parameter values produce different results. Second, the optimal choice of algorithm and parameter value may differ between different white matter regions or different fascicles, subjects, and acquisition parameters. We propose using ensemble methods to reduce algorithm and parameter dependencies. To do so we separate the processes of fascicle generation and evaluation. Specifically, we analyze the value of creating optimized connectomes by systematically combining candidate streamlines from an ensemble of algorithms (deterministic and probabilistic) and systematically varying parameters (curvature and stopping criterion). The ensemble approach leads to optimized connectomes that provide better cross-validated prediction error of the diffusion MRI data than optimized connectomes generated using a single-algorithm or parameter set. Furthermore, the ensemble approach produces connectomes that contain both short- and long-range fascicles, whereas single-parameter connectomes are biased towards one or the other. In summary, a systematic ensemble tractography approach can produce connectomes that are superior to standard single parameter estimates both for predicting the diffusion measurements and estimating white matter fascicles.
authors: Center for Information and Neural Networks (CiNet), National Institute of Information and Communications Technology, and Osaka University, Suita, Japan.|The Japan Society for the Promotion of Science, Tokyo, Japan.|Graduate School of Frontier Biosciences, Osaka University, Suita, Japan.|Department of Psychology, Stanford University, Stanford, California, United States of America.
authors: Hiromasa
authors: http://orcid.org/0000-0002-2096-2384
authors: H
authors: Takemura
authors: Instituto Argentino de Radioastronomía (IAR)-CCT La Plata-CONICET, Villa Elisa, Buenos Aires, Argentina.
authors: Cesar F
authors: http://orcid.org/0000-0001-5437-6095
authors: CF
authors: Caiafa
authors: Department of Psychology, Stanford University, Stanford, California, United States of America.
authors: Brian A
authors: BA
authors: Wandell
authors: Department of Psychological and Brain Sciences, Indiana University, Bloomington, Indiana, United States of America.|Programs in Neuroscience and Cognitive Science, Indiana University Network Science Institute, Indiana University, Bloomington, Indiana, United States of America.
authors: Franco
authors: http://orcid.org/0000-0002-2469-0494
authors: F
authors: Pestilli
mesh_terms: D000328:Adult; D001921:Brain / Q000502:physiology*; D019295:Computational Biology; D063132:Connectome / Q000379:methods*; D056324:Diffusion Tensor Imaging / Q000379:methods*; D006801:Humans; D008279:Magnetic Resonance Imaging / Q000379:methods*; D008297:Male; D009415:Nerve Net / Q000502:physiology