White Matter Abnormalities and Cognitive Deficit After Mild Traumatic Brain Injury: Comparing DTI, DKI, and NODDI

Abstract

White matter (WM) disruption is an important determinant of cognitive impairment after mild traumatic brain injury (mTBI), but traditional diffusion tensor imaging (DTI) shows some limitations in assessing WM damage. Diffusion kurtosis imaging (DKI) and neurite orientation dispersion and density imaging (NODDI) show advantages over DTI in this respect. Therefore, we used these three diffusion models to investigate complex WM changes in the acute stage after mTBI. From 32 mTBI patients and 31 age-, sex-, and education-matched healthy controls, we calculated eight diffusion metrics based on DTI (fractional anisotropy, axial diffusivity, radial diffusivity, and mean diffusivity), DKI (mean kurtosis), and NODDI (orientation dispersion index, volume fraction of intracellular water (Vic), and volume fraction of the isotropic diffusion compartment). We used tract-based spatial statistics to identify group differences at the voxel level, and we then assessed the correlation between diffusion metrics and cognitive function. We also performed subgroup comparisons based on loss of consciousness. Patients showed WM abnormalities and cognitive deficit. And these two changes showed positive correlation. The correlation between Vic of the splenium of the corpus callosum and Digit Symbol Substitution Test scores showed the smallest p-value (p = 0.000, r = 0.481). We concluded that WM changes, especially in the splenium of the corpus callosum, correlate to cognitive deficit in this study. Furthermore, the high voxel count of NODDI results and the consistency of mean kurtosis and the volume fraction of intracellular water in previous studies and our study showed the functional complementarity of DKI and NODDI to DTI.

Keywords: DKI; DTI; NODDI; cognitive function; loss of concussion; mild traumatic brain injury.

Figure 1

Tract-based spatial statistics (TBSS) results of diffusion metrics. The TBSS analyses revealed increased FA, AD, MD, and decreased MK, ODI, Vic, and Viso in patients compared with controls. Green represents White Matter skeleton. Orange-yellow represents areas of significant differences. Orange represents lower FA, AD, MD, higher MK, ODI, Vic, and Viso, and yellow represents higher FA, AD, MD, lower MK, ODI, Vic, and Viso. FA, fractional anisotropy; AD, axial diffusivity; MD, mean diffusivity; MK, mean kurtosis; ODI, orientation dispersion index; Vic, the volume fraction of intra-cellular water; Viso, the volume fraction of the isotropic diffusion compartment.

Figure 2

Post-hoc region-of-interest (ROI) analysis results. Clusters are significant tracts in Tract-based spatial statistics (TBSS). Different boxplot represent different diffusion metrics. Orange box represent healthy controls (HCs), and blue box represent mild traumatic brain injury (mTBI) group. FA, fractional anisotropy; AD, axial diffusivity; MD, mean diffusivity; MK, mean kurtosis; ODI, orientation dispersion index; Vic, the volume fraction of intra-cellular water; Viso, the volume fraction of the isotropic diffusion compartment.

Figure 3

Partial correlations between diffusion metrics and cognitive function. Clusters are significant tracts in Tract-based spatial statistics (TBSS). FA in cluster 1 and 2, AD in cluster 1 and 2 and MD in cluster 1 correlated negatively with the Digital Symbol Substitution Test (DSST) scores, and MK in cluster 1, ODI in cluster 1 and Vic in cluster 1 and 2 correlated positively with DSST scores. FA, fractional anisotropy; AD, axial diffusivity; MD, mean diffusivity; MK, mean kurtosis; ODI, orientation dispersion index; Vic, the volume fraction of intra-cellular water.

Figure 4

Spearman correlations between Vic and DSST in mTBI group. Vic in cluster 2 correlated positively with DSST scores. Vic, the volume fraction of intra-cellular water.

Figure 5

Receiver operating characteristic (ROC) curve of orientation dispersion index (ODI). Areas under the ROC curve (AUC) is 0.728. The cut-off value was ODI = 0.168 with a sensitivity of 50.0%, a specificity of 90.3%.