Characterization of ten white matter tracts in a representative sample of Cuban population

BACKGROUND The diffusion tensor imaging technique (DTI) combined with tractography methods, has achieved the tridimensional reconstruction of white matter tracts in the brain. It allows their characterization in vivo in a non-invasive way. However, one of the largest sources of variability originates from the location of regions of interest, is therefore necessary schemes which make it possible to establish a protocol to be insensitive to variations in drawing thereof. The purpose of this paper is to stablish a reliable protocol to reconstruct ten prominent tracts of white matter and characterize them according to volume, fractional anisotropy and mean diffusivity. Also we explored the relationship among these factors with gender and hemispheric symmetry. METHODS This study aims to characterize ten prominent tracts of white matter in a representative sample of Cuban population using this technique, including 84 healthy subjects. Diffusion tensors and subsequently fractional anisotropy and mean diffusivity maps were calculated from each subject's DTI scans. The trajectory of ten brain tracts was estimated by using deterministic tractography methods of fiber tracking. In such tracts, the volume, the FA and MD were calculated, creating a reference for their study in the Cuban population. The interactions between these variables with age, cerebral hemispheres and gender factors were explored using Repeated Measure Analysis of Variance. RESULTS The volume values showed that a most part of tracts have bigger volume in left hemisphere. Also, the data showed bigger values of MD for males than females in all the tracts, an inverse behavior than FA values. CONCLUSIONS This work showed that is possible reconstruct white matter tracts using a unique region of interest scheme defined from standard to native space. Also, this study indicates differing developmental trajectories in white matter for males and females and the importance of taking gender into account in developmental DTI studies and in underlie gender-related cognitive differences.