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.

Functional-structural large-scale brain networks are correlated with neurocognitive impairment in acute mild traumatic brain injury

“…MTBI is associated with neurocognitive dysfunction at the acute stage, and more than 20% of cases continue to report longterm neurocognitive complications years after the injury (4). With the rising prevalence of mTBI, cognitive problems followed by mTBI have attracted increasing attention (5)(6)(7). However, in the acute stage, mTBI patients with cognitive impairment lack brain abnormalities on conventional imaging modalities.…”

Differential and correlational tractography as tract‐based biomarkers in mild traumatic brain injury: A longitudinal MRI study

“…To compare traditional voxel‐wise statistics and differential and correlational tractography, traditional DTI metrics (i.e., FA, AD, RD, and MD) were calculated. Next, TBSS was performed using the FSL toolbox, and TBSS and voxelwise statistical analyses were conducted as carried out in our previous paper 44 . FSL randomize was conducted among patients and healthy controls.…”

“…Next, TBSS was performed using the FSL toolbox, and TBSS and voxelwise statistical analyses were conducted as carried out in our previous paper. 44 FSL randomize was conducted among patients and healthy controls. Cross-sectional comparisons (acute mTBI patients vs. healthy controls and chronic mTBI patients vs. healthy controls) involved a matrix design of an unpaired two-sample t-test and longitudinal comparison (acute mTBI vs. chronic mTBI patients) involved a matrix design of a paired two-sample t-test using FSL.…”

Altered isotropic volume fraction in gray matter after sleep deprivation and its association with visuospatial memory: A neurite orientation dispersion and density imaging study

“…For example, DTI assumes that water diffusion is Gaussian distribution, so it is not able to completely characterize tissue microstructure ( Steven et al, 2014 ). In addition, DTI extracts information from dMRI data via “signal representations” approach, which lacks specificity and remains an indirect characterization of microstructure ( Huang S. et al, 2022 ). Furthermore, due to its assumption of a single tissue compartment, DTI cannot distinguish microstructural properties between intra-cellular and extra-cellular spaces ( Beaulieu, 2002 ).…”