Faculty of Human Sciences, Institute III, Department of Sport Science, Otto von Guericke University, Zschokkestraße 32, 39104 Magdeburg, Germany; Department of Neurology, Max Planck Institute for Human Cognitive and Brain Sciences, Stephanstraße 1a, 04103 Leipzig, Germany.
Faculty of Human Sciences, Institute III, Department of Sport Science, Otto von Guericke University, Zschokkestraße 32, 39104 Magdeburg, Germany.
Neuroscience. 2021 Mar 1;457:165-185. doi: 10.1016/j.neuroscience.2021.01.005. Epub 2021 Jan 17.
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 (CV), between-subject coefficient of variation (CV) 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 (CV 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.
弥散加权磁共振成像（DWI）正在不断发展，其目标是开发大脑的体内组织学。最近的方法学进展是神经丝取向分散和密度成像（NODDI），这是一种组织学验证的多室模型，可提供脑组织的微观结构特征，如几何复杂性和神经丝堆积密度，这在成像白质方面特别有用。由于 NODDI 在临床研究以及发育神经科学和神经可塑性等领域越来越受欢迎，因此对其可重复性（或可靠性）进行特征描述非常重要。我们在 4 周的重扫间隔内对 29 名健康年轻受试者进行了多壳 DWI 数据采集，分别评估了受试者内变异系数（CV）、受试者间变异系数（CV）和组内相关系数（ICC）。使用这些指标，我们比较了最常见的图像分析方法（基于束流的空间统计学[TBSS]、具有不同平滑程度的体素分析[VBM 风格]、基于 ROI 的分析）的区域和体素重现性。我们观察到各向异性弥散指数（ODI）的测试-重测重现性很高，而神经丝密度指数（NDI）的结果略差。我们的研究结果还表明，分析方法的选择可能对研究结果产生重大影响。总体而言，具有≥4mm FWHM 高斯平滑核的体素方法和 ROI 平均化在 NDI 和 ODI 图谱中产生了最高的重现性（CV 大多≤3%，ICC 大多≥0.8），而较小的核和 TBSS 的性能始终较差。此外，我们证明图像质量（信噪比[SNR]）是 NODDI 指标重现性的一个重要决定因素。我们讨论了这些结果对神经影像学领域中常用的纵向和横截面研究设计的影响。
