Tract-based morphometry for white matter group analysis.
We introduce an automatic method that we call tract-based morphometry, or TBM, for measurement and analysis of diffusion MRI data along white matter fiber tracts. Using subject-specific tractography bundle segmentations, we generate an arc length parameterization of the bundle with point correspondences across all fibers and all subjects, allowing tract-based measurement and analysis. In this paper we present a quantitative comparison of fiber coordinate systems from the literature and we introduce an improved optimal match method that reduces spatial distortion and improves intra- and inter-subject variability of FA measurements. We propose a method for generating arc length correspondences across hemispheres, enabling a TBM study of interhemispheric diffusion asymmetries in the arcuate fasciculus (AF) and cingulum bundle (CB). The results of this study demonstrate that TBM can detect differences that may not be found by measuring means of scalar invariants in entire tracts, such as the mean diffusivity (MD) differences found in AF. We report TBM results of higher fractional anisotropy (FA) in the left hemisphere in AF (caused primarily by lower lambda(3), the smallest eigenvalue of the diffusion tensor, in the left AF), and higher left hemisphere FA in CB (related to higher lambda(1), the largest eigenvalue of the diffusion tensor, in the left CB). By mapping the significance levels onto the tractography trajectories for each structure, we demonstrate the anatomical locations of the interhemispheric differences. The TBM approach brings analysis of DTI data into the clinically and neuroanatomically relevant framework of the tract anatomy.
O'Donnell
Lauren J
LJ
Department of Neurosurgery, Brigham and Women's Hospital, Harvard Medical School, Boston MA, USA. odonnell@bwh.harvard.edu
Westin
Carl-Fredrik
CF
Golby
Alexandra J
AJ
D001921:Brain / Q000033:anatomy & histology*; D001931:Brain Mapping / Q000379:methods*; D038524:Diffusion Magnetic Resonance Imaging; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods*