Abstract
Diffusion tensor imaging (DTI) data differ fundamentally from most
brain imaging data in that values at each voxel are not scalars
but 3 x 3 positive definite matrices also called diffusion tensors.
Frequently, investigators simplify the data analysis by reducing
the tensor to a scalar, such as fractional anisotropy (FA). New
statistical methods are needed for analyzing vector and tensor valued
imaging data. A statistical model is proposed for the principal
eigenvector of the diffusion tensor based on the bipolar Watson
distribution. Methods are presented for computing mean direction
and dispersion of a sample of directions and for testing whether
two samples of directions (e.g., same voxel across two groups of
subjects) have the same mean. False discovery rate theory is used
to identify voxels for which the two-sample test is significant.
These methods are illustrated in a DTI data set collected to study
reading ability. It is shown that comparison of directions reveals
differences in gross anatomic structure that are invisible to FA.
- 15906307
- algorithms,
- anisotropy,
- brain
- child,
- comparative
- diffusion
- dyslexia,
- extramural,
- gov't,
- humans,
- imaging,
- magnetic
- mapping,
- models,
- n.i.h.,
- non-p.h.s.,
- non-u.s.
- p.h.s.,
- research
- resonance
- statistical,
- study,
- support,
- u.s.
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