Group analysis of DTI fiber tract statistics with application to neurodevelopment

Abstract

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 T2 statistic. The linear discriminant embedded in the T2 metric provides an intuitive, localized interpretation of detected differences. The proposed methodology was tested on two clinical studies of neurodevelopment. In a study of one and two 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.

A Tract-Specific Framework for White Matter Morphometry Combining Macroscopic and Microscopic Tract Features

“…The potential of the framework was illustrated with an application to assess WM atrophy in ALS. In the future, we plan to explore extending the novel multivariate analysis framework for tubular WM tracts proposed recently by Goodlett et al [3] to sheet-like tracts. This should enhance our ability to capture additional patterns of morphological differences in the spatial domain.…”

“…We adopt the atlas-based segmentation strategy that has been successfully applied in the literature [7,6,3]. It involves WM parcellation in a populationaveraged DTI template using fiber tractography.…”

“…As identified in [6], six major tracts fit into this category: corpus callosum (CC), corticospinal tracts (CST), inferior fronto-occipital tracts (IFO), inferior longitudinal tracts (ILF), superior longitudinal tracts (SLF), and uncinates (UNC). White matter tracts that are more appropriately represented by tubular models have been extensively studied in the literature [3,4,5] and are not considered here. After fiber tractography, binary 3D segmentations of individual tracts are generated by labeling voxels in the template through which at least one fiber passes.…”

“…However, the whole-brain approach of the TBSS fundamentally limits its anatomical specificity. Recognizing the importance of tract-specific analysis, many groups have recently developed innovative techniques for analyzing individual WM tracts with either tubular geometry [3,4,5] or sheet-like appearance [6].…”

Morphological changes in the corpus callosum: a study using joint Riemannian feature spaces

“…Correspondence and averaging methods have been applied to preserve the FA function for bundles and atlases of prominent CC tracts like the genu and splenium. 16,17 Classification based on the geometrical shapes of fiber tracts has received less attention.…”