UCSF

Traumatic Brain Injury (TBI) is a leading cause of death and disability around the globe. Diffusion tensor imaging (DTI) parameters have been the most commonly used metrics to characterize white matter (WM) microstructures to identify pathology after TBI. More recently, novel metrics like neurite orientation dispersion and density imaging (NODDI) metrics based on multi-shell sequences have provided additional insights to understand WM microstructures. Together with DTI, these metrics predict both short- and long-term impacts of mild TBI (mTBI) on various neural functions, helping to advance mTBI management and treatment. Lateralization analysis based on DTI parameters has also been used to assess neural functions in TBI. When looking at specific brain regions, the region of interest (ROI) analysis based on tract-based spatial statistics (TBSS) with standard space (e.g., mapping the JHU atlas to MNI152 standard T1 space) has been widely applied to study mTBI. However, it is facing significant challenges to study moderate-to-severe TBI due to registration difficulties. Registration challenges come from deformation and lesions in those patients. Lately developed ROI analysis methods based on probabilistic tractography (e.g., FSL XTRACT toolbox) in an individual native diffusion space give promises to fill the gap, but the exact advantages and disadvantages compared to using a standard space have not been well documented. In the present study, the ROI analysis on DTI and NODDI parameters was performed on dMRI of 106 patients (PT), 18 friend controls (FC), and 18 orthopedic controls (OC) collected from two time points, using both standard-space method (“TBSS ROI analysis”) and native-space method (“XTRACT ROI analysis”). The test-retest reliability of these two methods was compared by evaluating the coefficient of variation (CV) at each time point, the Pearson’s correlation (R) between the two time points, the intra-class correlation coefficient (ICC) between the two time points, and lateralization index at each time point. With these statistics, the aim was to determine the precision of the TBSS ROI analysis and the XTRACT ROI analysis quantitatively in the practice of analyzing a particular dataset. ROI analysis based on a standard atlas mapped to skeletonized tracts showed excellent precision and reproducibility, although some regions exhibited site and scanner differences; ROI analysis based on probabilistic tractography in individual diffusion space showed great potentials to classify patients and controls, but with more variability, encouraging further development and exploration of the pipeline to improve precision and reliability. These results could provide a new and general reference for choosing analysis methods in future dMRI studies.