Department of Psychology, University of Oslo, PO Box 1094 Blindern, 0317 Oslo, Norway; NORMENT, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Oslo, Norway; Sunnaas Rehabilitation Hospital HT, Nesodden, Oslo, Norway.
Department of Psychology, University of Oslo, PO Box 1094 Blindern, 0317 Oslo, Norway; NORMENT, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Oslo, Norway; Department of Psychiatry, University of Oxford, Warneford Hospital, Oxford, United Kingdom.
Neuroimage. 2021 Jan 1;224:117441. doi: 10.1016/j.neuroimage.2020.117441. Epub 2020 Oct 9.
The macro- and microstructural architecture of human brain white matter undergoes substantial alterations throughout development and ageing. Most of our understanding of the spatial and temporal characteristics of these lifespan adaptations come from magnetic resonance imaging (MRI), including diffusion MRI (dMRI), which enables visualisation and quantification of brain white matter with unprecedented sensitivity and detail. However, with some notable exceptions, previous studies have relied on cross-sectional designs, limited age ranges, and diffusion tensor imaging (DTI) based on conventional single-shell dMRI. In this mixed cross-sectional and longitudinal study (mean interval: 15.2 months) including 702 multi-shell dMRI datasets, we combined complementary dMRI models to investigate age trajectories in healthy individuals aged 18 to 94 years (57.12% women). Using linear mixed effect models and machine learning based brain age prediction, we assessed the age-dependence of diffusion metrics, and compared the age prediction accuracy of six different diffusion models, including diffusion tensor (DTI) and kurtosis imaging (DKI), neurite orientation dispersion and density imaging (NODDI), restriction spectrum imaging (RSI), spherical mean technique multi-compartment (SMT-mc), and white matter tract integrity (WMTI). The results showed that the age slopes for conventional DTI metrics (fractional anisotropy [FA], mean diffusivity [MD], axial diffusivity [AD], radial diffusivity [RD]) were largely consistent with previous research, and that the highest performing advanced dMRI models showed comparable age prediction accuracy to conventional DTI. Linear mixed effects models and Wilk's theorem analysis showed that the 'FA fine' metric of the RSI model and 'orientation dispersion' (OD) metric of the NODDI model showed the highest sensitivity to age. The results indicate that advanced diffusion models (DKI, NODDI, RSI, SMT mc, WMTI) provide sensitive measures of age-related microstructural changes of white matter in the brain that complement and extend the contribution of conventional DTI.
人类大脑白质的宏观和微观结构在整个发育和衰老过程中会发生很大的变化。我们对这些寿命适应性的空间和时间特征的大部分了解都来自磁共振成像（MRI），包括扩散 MRI（dMRI），它可以以前所未有的灵敏度和细节可视化和量化大脑白质。然而，除了一些值得注意的例外，以前的研究依赖于横断面设计、有限的年龄范围和基于传统单壳 dMRI 的扩散张量成像（DTI）。在这项混合横断面和纵向研究（平均间隔：15.2 个月）中，我们包括了 702 个多壳 dMRI 数据集，结合了互补的 dMRI 模型，以研究 18 至 94 岁（57.12%为女性）健康个体的年龄轨迹。使用线性混合效应模型和基于机器学习的大脑年龄预测，我们评估了扩散指标的年龄依赖性，并比较了六种不同扩散模型的年龄预测准确性，包括扩散张量（DTI）和峰度成像（DKI）、神经丝取向分散和密度成像（NODDI）、限制谱成像（RSI）、球平均技术多腔室（SMT-mc）和白质束完整性（WMTI）。结果表明，传统 DTI 指标（各向异性分数[FA]、平均弥散度[MD]、轴向弥散度[AD]、径向弥散度[RD]）的年龄斜率与以前的研究基本一致，表现最好的高级 dMRI 模型与传统 DTI 具有相当的年龄预测准确性。线性混合效应模型和威尔克定理分析表明，RSI 模型的“FA 精细”指标和 NODDI 模型的“取向分散”（OD）指标对年龄的敏感性最高。结果表明，高级扩散模型（DKI、NODDI、RSI、SMT-mc、WMTI）提供了对大脑白质与年龄相关的微观结构变化的敏感测量，补充和扩展了传统 DTI 的贡献。
