Multivariate Linear Discriminant Analysis of DTI Data Improves the Detection of Microstructural Damage in Young Professional Boxers
نویسندگان
چکیده
M. H. Chappell, J. A. Brown, J. C. Dalrymple-Alford, A. M. Ulug, and R. Watts Department of Physics and Astronomy, University of Canterbury, Christchurch, Canterbury, New Zealand, Van der Veer Institute for Parkinson's and Brain Research, Christchurch, Canterbury, New Zealand, Department of Mathematics and Statistics, University of Canterbury, Christchurch, Canterbury, New Zealand, Department of Psychology, University of Canterbury, Christchurch, Canterbury, New Zealand, Department of Radiology, Weill Medical College of Cornell University, New York, New York, United States
منابع مشابه
Multivariate analysis of diffusion tensor imaging data improves the detection of microstructural damage in young professional boxers.
In this study, we present two different methods of multivariate analysis of voxel-based diffusion tensor imaging (DTI) data, using as an example data derived from 59 professional boxers and 12 age-matched controls. Conventional univariate analysis ignores much of the diffusion information contained in the tensor. Our first multivariate method uses the Hotelling's T(2) statistic and the second u...
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تاریخ انتشار 2007