White Matter Injury Susceptibility via Fiber Strain Evaluation Using Whole-Brain Tractography

Abstract

Microscale brain injury studies suggest axonal elongation as a potential mechanism for diffuse axonal injury (DAI). Recent studies have begun to incorporate white matter (WM) structural anisotropy in injury analysis, with initial evidence suggesting improved injury prediction performance. In this study, we further develop a tractography-based approach to analyze fiber strains along the entire lengths of fibers from voxel-or anatomically constrained whole-brain tractography. This technique potentially extends previous element-or voxel-based methods that instead utilize WM fiber orientations averaged from typically coarse elements or voxels. Perhaps more importantly, incorporating tractography-based axonal structural information enables assessment of the overall injury risks to functionally important neural pathways and the anatomical regions they connect, which is not possible with previous methods. A DAI susceptibility index was also established to quantify voxel-wise WM local structural integrity and tract-wise damage of individual neural pathways. This ''graded'' injury susceptibility potentially extends the commonly employed treatment of injury as a simple binary condition. As an illustration, we evaluate the DAI susceptibilities of WM voxels and transcallosal fiber tracts in three idealized head impacts. Findings suggest the potential importance of the tractography-based approach for injury prediction. These efforts may enable future studies to correlate WM mechanical responses with neuroimaging, cognitive alteration, and concussion, and to reveal the relative vulnerabilities of neural pathways and identify the most vulnerable ones in real-world head impacts.

Injury prediction and vulnerability assessment using strain and susceptibility measures of the deep white matter

“…This required transforming all image volumes, their corresponding geometrical entities, and the head FE model into a common coordinate system. For convenience, we have chosen the coordinate system of the Worcester Head Injury Model (WHIM; formerly known as the Dartmouth Head Injury Model or DHIM; (Ji et al 2015; Zhao et al 2016) as a common reference. The WHIM was created based on high-resolution T1-weighted MRI (at an isotropic resolution of 1 mm 3 ) of an individual.…”

“…2), including model creation, mesh quality, assignment of material properties and boundary conditions, and validation performances have been reported extensively in recent publications (Ji et al 2015; Ji and Zhao 2015; Zhao et al 2016; Zhao and Ji 2016). Therefore, they are not repeated here.…”

“…Whole-brain tractography was generated using the DTI of the same individual selected to develop the baseline WHIM, as previously reported (Zhao et al 2016). This led to ~35 k fibers and ~3.3 million sampling points in total.…”

“…1) as anatomical constraints, the 50 deep WM ROIs (see Appendix A) and their corresponding neural tracts were identified within the WHIM (illustrated in Fig. 2c and d; (Zhao et al 2016)).…”

“…This is a popular cross-validation technique (Arlot and Celisse 2010), but does not appear to have been applied in model-based TBI studies to date. Further, we analyzed injury prediction performances and vulnerabilities of the entire deep WM ROIs as well as ROI-constrained neural tracts from whole-brain tractography (Zhao et al 2016). Instead of relying on peak responses from a single element from a predefined ROI, we used data sampling across all of the deep WM ROIs/neural tracts.…”