NODDI-DTI: Estimating Neurite Orientation and Dispersion Parameters from a Diffusion Tensor in Healthy White Matter.

Luke J Edwards,Kerrin J Pine,Isabel Ellerbrock,Nikolaus Weiskopf,Siawoosh Mohammadi

doi:10.3389/fnins.2017.00720

Abstract

The NODDI-DTI signal model is a modification of the NODDI signal model that formally allows interpretation of standard single-shell DTI data in terms of biophysical parameters in healthy human white matter (WM). The NODDI-DTI signal model contains no CSF compartment, restricting application to voxels without CSF partial-volume contamination. This modification allowed derivation of analytical relations between parameters representing axon density and dispersion, and DTI invariants (MD and FA) from the NODDI-DTI signal model. These relations formally allow extraction of biophysical parameters from DTI data. NODDI-DTI parameters were estimated by applying the proposed analytical relations to DTI parameters estimated from the first shell of data, and compared to parameters estimated by fitting the NODDI-DTI model to both shells of data (reference dataset) in the WM of 14 in vivo diffusion datasets recorded with two different protocols, and in simulated data. The first two datasets were also fit to the NODDI-DTI model using only the first shell (as for DTI) of data. NODDI-DTI parameters estimated from DTI, and NODDI-DTI parameters estimated by fitting the model to the first shell of data gave similar errors compared to two-shell NODDI-DTI estimates. The simulations showed the NODDI-DTI method to be more noise-robust than the two-shell fitting procedure. The NODDI-DTI method gave unphysical parameter estimates in a small percentage of voxels, reflecting voxelwise DTI estimation error or NODDI-DTI model invalidity. In the course of evaluating the NODDI-DTI model, it was found that diffusional kurtosis strongly biased DTI-based MD values, and so, making assumptions based on healthy WM, a novel heuristic correction requiring only DTI data was derived and used to mitigate this bias. Since validations were only performed on healthy WM, application to grey matter or pathological WM would require further validation. Our results demonstrate NODDI-DTI to be a promising model and technique to interpret restricted datasets acquired for DTI analysis in healthy white matter with greater biophysical specificity, though its limitations must be borne in mind.

Highlights

The white matter (WM) of the human brain consists of dense bundles of neuronal axons connecting the brain’s functional areas
The effect of this bias on NODDI-Diffusion tensor imaging (DTI) estimates is evidenced in Figure 2, where the main bulk of the mean diffusivity” (MD) values lies below the red line representing the ν vs. MD prediction of Equation (2)
The effect of diffusional kurtosis is much less pronounced for fractional anisotropy” (FA) in Figure 2, as expected based upon Veraart et al (2011), implying that we are justified in using Equation (3) to estimate τ

Summary

Introduction

The white matter (WM) of the human brain consists of dense bundles of neuronal axons connecting the brain’s functional areas. Diffusion tensor imaging (DTI; Basser et al, 1994; Jones, 2014) is, at present, the most commonly used method to observe WM changes in-vivo (Scholz et al, 2009; Fields, 2010; Zatorre et al, 2012) This is because DTI is implemented and time efficient while allowing robust estimation of complementary parameters [e.g., “fractional anisotropy” (FA) and “mean diffusivity” (MD), Pierpaoli et al, 1996] sensitive to microstructural WM changes (Beaulieu, 2014), even in clinical contexts (see e.g., Meinzer et al, 2010; Freund et al, 2013a). Numerous studies show MD and FA change in white matter [e.g., due to learning a new skill (Scholz et al, 2009) or the pathology of Alzheimer’s disease (Acosta-Cabronero et al, 2010)], but cannot, in the absence of further information, distinguish e.g., changes in axon density from changes in axon arrangement

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Discussion

Conclusion