Tensor‐based morphometry using scalar and directional information of diffusion tensor MRI data (DTBM): Application to hereditary spastic paraplegia

Abstract

Tensor‐based morphometry (TBM) performed using T1‐weighted images (T1WIs) is a well‐established method for analyzing local morphological changes occurring in the brain due to normal aging and disease. However, in white matter regions that appear homogeneous on T1WIs, T1W‐TBM may be inadequate for detecting changes that affect specific pathways. In these regions, diffusion tensor MRI (DTI) can identify white matter pathways on the basis of their different anisotropy and orientation. In this study, we propose performing TBM using deformation fields constructed using all scalar and directional information provided by the diffusion tensor (DTBM) with the goal of increasing sensitivity in detecting morphological abnormalities of specific white matter pathways. Previously, mostly fractional anisotropy (FA) has been used to drive registration in diffusion MRI‐based TBM (FA‐TBM). However, FA does not have the directional information that the tensors contain, therefore, the registration based on tensors provides better alignment of brain structures and better localization of volume change. We compare our DTBM method to both T1W‐TBM and FA‐TBM in investigating differences in brain morphology between patients with complicated hereditary spastic paraplegia of type 11 (SPG11) and a group of healthy controls. Effect size maps of T1W‐TBM of SPG11 patients showed diffuse atrophy of white matter. However, DTBM indicated that atrophy was more localized, predominantly affecting several long‐range pathways. The results of our study suggest that DTBM could be a powerful tool for detecting morphological changes of specific white matter pathways in normal brain development and aging, as well as in degenerative disorders.

The effect of Zika virus infection in the ferret

“…There were no significant differences between groups These values were subject to statistical analysis using the Mann-Whitney U-test. Finally, DTI-driven tensor based morphometry was performed to generate LogJ maps reporting local volume differences between each brain and the template, where LogJ is the tensor based morphometry (TBM) metric of the log of the determinant of the Jacobian of the deformations field (Ashburner & Friston, 2001;Davatzikos et al, 1996;Sadeghi et al, 2018) and is positive/negative when a voxel is larger/smaller in the warped brain than the template. The LogJ maps were also used for voxelwise analysis of effect size.…”

Detection and Distinction of Mild Brain Injury Effects in a Ferret Model Using Diffusion Tensor MRI (DTI) and DTI-Driven Tensor-Based Morphometry (D-TBM)

“…In order to detect local volume differences within the whole brain in an operator-independent manner, TBM methods ( Davatzikos et al, 1996 ; Ashburner and Friston, 2000 ) were applied using both standard structural MRI-based TBM as well as a novel DTI-driven TBM (D-TBM) ( Sadeghi et al, 2016 ; Nayak et al, 2017 ). TBM is a well-known MRI analysis approach that measures the determinant of the Jacobian (J) of the transformation to morph one brain volume to a template volume.…”

“…D-TBM in this study was performed using the deformation fields generated from the DRTAMAS registration described above of individual tensor volumes to the group averaged DT of the controls. D-TBM, which uses the registration of diffusion MRI data rather than anatomical images, takes advantage of the unique ability of diffusion MRI to depict individual WM pathways in regions that appear homogeneous in anatomical MRIs ( Sadeghi et al, 2016 ). For both TBM and D-TBM, group LogJ maps were made by averaging the individual LogJ maps for all brains in each group.…”

In vivoassessment of neurodegeneration in Spinocerebellar Ataxia type 7

“…The transformation from each subject's space to the standard space was also used to perform DTBM, which allows for quantification of morphological differences between two groups on a voxel-wise basis 26 . Since these transformations were computed using information from the full diffusion tensor, they contain accurate information about how all tissue types must be deformed to match the population template.…”

“…The copyright holder for this preprint this version posted October 7, 2020. ; https://doi.org/10.1101/2020.10.05.20207340 doi: medRxiv preprint abnormalities through diffusion tensor-based morphometry (DTBM), which uses the warps from subject to template space to determine which areas were larger or smaller relative to the template 26 .…”