Group analysis of DTI fiber tract statistics with application to neurodevelopment.
Diffusion tensor imaging (DTI) provides a unique source of information about the underlying tissue structure of brain white matter in vivo including both the geometry of major fiber bundles as well as quantitative information about tissue properties represented by derived tensor measures. This paper presents a method for statistical comparison of fiber bundle diffusion properties between populations of diffusion tensor images. Unbiased diffeomorphic atlas building is used to compute a normalized coordinate system for populations of diffusion images. The diffeomorphic transformations between each subject and the atlas provide spatial normalization for the comparison of tract statistics. Diffusion properties, such as fractional anisotropy (FA) and tensor norm, along fiber tracts are modeled as multivariate functions of arc length. Hypothesis testing is performed non-parametrically using permutation testing based on the Hotelling T(2) statistic. The linear discriminant embedded in the T(2) metric provides an intuitive, localized interpretation of detected differences. The proposed methodology was tested on two clinical studies of neurodevelopment. In a study of 1 and 2 year old subjects, a significant increase in FA and a correlated decrease in Frobenius norm was found in several tracts. Significant differences in neonates were found in the splenium tract between controls and subjects with isolated mild ventriculomegaly (MVM) demonstrating the potential of this method for clinical studies.
Goodlett
Casey B
CB
Scientific Computing and Imaging Institute, School of Computing, University of Utah, Salt Lake City, Utah 84112, USA. gcasey@sci.utah.edu
Fletcher
P Thomas
PT
Gilmore
John H
JH
Gerig
Guido
G
D001921:Brain / Q000254:growth & development*; D002675:Child, Preschool; D038524:Diffusion Magnetic Resonance Imaging; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D007223:Infant; D015233:Models, Statistical; D009434:Neural Pathways / Q000254:growth & development*