Longitudinal Reproducibility of Neurite Orientation Dispersion and Density Imaging (NODDI) Derived Metrics in the White Matter. (1st March 2021)

• Record Type: Journal Article
• Authors: Lehmann, Nico; Aye, Norman; Kaufmann, Jörn; Heinze, Hans-Jochen; Düzel, Emrah; Ziegler, Gabriel; Taubert, Marco
• Is Part Of: Neuroscience. Volume 457(2021)
• Page Start: 165
• Page End: 185
• Publication Date: 2021-03-01
• Subjects: BBR boundary-based registration -- BMI body mass index -- CI confidence interval -- CSF cerebrospinal fluid -- CV coefficient of variation -- CVBS between-subject variation expressed as a CV -- CVWS within-subject variation expressed as a CV -- DW diffusion-weighted -- DWI diffusion-weighted imaging -- EPI echo-planar imaging -- FA fractional anisotropy -- FWHM Full width at half maximum -- FSL FMRIB Software Library -- GM gray matter -- ICC intraclass correlation -- ISO isotropic volume fraction -- MD mean diffusivity -- MRI magnetic resonance imaging -- NDI neurite density index (aka intra-cellular volume fraction) -- NODDI Neurite Orientation Dispersion and Density Imaging -- ODI orientation dispersion index -- RM-ANOVA Repeated Measures Analysis of Variance -- ROI region of interest -- SD standard deviation -- SNR signal-to-noise ratio -- TBSS Tract-Based Spatial Statistics -- T1w T1-weighted -- WM white matter; diffusion-weighted imaging -- Neurite Orientation Dispersion and Density Imaging (NODDI) -- Reproducibility -- Reliability -- Precision; Neurochemistry -- Periodicals; Neurophysiology -- Periodicals; Neurology -- Periodicals; Neurochimie -- Périodiques; Neurophysiologie -- Périodiques; Neurochemistry; Neurophysiology; Electronic journals; Periodicals; Electronic journals; 612.8
• DOI: 10.1016/j.neuroscience.2021.01.005
• Languages: English
• ISSNs: 0306-4522

Abstract

Highlights: We analyzed absolute (CV) and relative (ICC) reproducibility of NODDI metrics. Reproducibility of ROI-based, "VBM-style" and TBSS analysis was compared. Reproducibility of NODDI metrics was generally high. Reproducibility of NODDI metrics depends on SNR and choice of analysis approach. Implications of these results for common research designs are discussed. Abstract: Diffusion-weighted magnetic resonance imaging (DWI) is undergoing constant evolution with the ambitious goal of developing in-vivo histology of the brain. A recent methodological advancement is Neurite Orientation Dispersion and Density Imaging (NODDI), a histologically validated multi-compartment model to yield microstructural features of brain tissue such as geometric complexity and neurite packing density, which are especially useful in imaging the white matter. Since NODDI is increasingly popular in clinical research and fields such as developmental neuroscience and neuroplasticity, it is of vast importance to characterize its reproducibility (or reliability). We acquired multi-shell DWI data in 29 healthy young subjects twice over a rescan interval of 4 weeks to assess the within-subject coefficient of variation (CVWS ), between-subject coefficient of variation (CVBS ) and the intraclass correlation coefficient (ICC), respectively. Using these metrics, we compared regional and voxel-by-voxel reproducibility of the most common image analysis approaches (tract-based spatial statistics [TBSS], voxel-based analysis with different extents of smoothing ["VBM-style"], ROI-based analysis). We observed high test–retest reproducibility for the orientation dispersion index (ODI) and slightly worse results for the neurite density index (NDI). Our findings also suggest that the choice of analysis approach might have significant consequences for the results of a study. Collectively, the voxel-based approach with Gaussian smoothing kernels of ≥4 mm FWHM and ROI-averaging yielded the highest reproducibility across NDI and ODI maps (CVWS mostly ≤3%, ICC mostly ≥0.8), respectively, whilst smaller kernels and TBSS performed consistently worse. Furthermore, we demonstrate that image quality (signal-to-noise ratio [SNR]) is an important determinant of NODDI metric reproducibility. We discuss the implications of these results for longitudinal and cross-sectional research designs commonly employed in the neuroimaging field.