Neuroimaging techniques have been increasingly used to understand the neural biology of aging brains. The neuroimaging variables from distinct brain locations and modalities can exhibit age-related patterns that reflect localized neural decline. However, it is a challenge to identify the impacts of risk factors (eg, mental disorders) on multivariate imaging variables while simultaneously accounting for the dependence structure and nonlinear age trajectories using existing tools. We propose a mixed-effects model to address this challenge by building random effects based on the latent brain aging status. We develop computationally efficient algorithms to estimate the parameters of new random effects. The simulations show that our approach provides accurate parameter estimates, improves the inference efficiency, and reduces the root mean square error compared to existing methods. We further apply this method to the UK Biobank data to investigate the effects of tobacco smoking on the white matter integrity of the entire brain during aging and identify the adverse effects on white matter integrity with multiple fiber tracts.

中文翻译：

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对具有依赖性的多变量年龄相关成像变量进行建模

神经影像技术越来越多地用于了解衰老大脑的神经生物学。来自不同大脑位置和模式的神经影像变量可以表现出反映局部神经衰退的年龄相关模式。然而，确定风险因素（例如精神障碍）对多变量成像变量的影响，同时使用现有工具解释依赖性结构和非线性年龄轨迹是一个挑战。我们提出了一种混合效应模型，通过根据潜在的大脑老化状态建立随机效应来应对这一挑战。我们开发计算高效的算法来估计新随机效应的参数。模拟表明，与现有方法相比，我们的方法提供了准确的参数估计，提高了推理效率，并降低了均方根误差。我们进一步将这种方法应用于英国生物银行的数据，以研究吸烟对衰老过程中整个大脑白质完整性的影响，并确定多纤维束对白质完整性的不利影响。