Centre for Medical Image Computing, University College London, London, UK.
Donders Centre for Cognitive Neuroimaging, Donders Institute for Brain, Cognition and Behaviour, Radboud University, Nijmegen, The Netherlands.
Hum Brain Mapp. 2019 Sep;40(13):3982-4000. doi: 10.1002/hbm.24682. Epub 2019 Jun 5.
Longitudinal imaging biomarkers are invaluable for understanding the course of neurodegeneration, promising the ability to track disease progression and to detect disease earlier than cross-sectional biomarkers. To properly realize their potential, biomarker trajectory models must be robust to both under-sampling and measurement errors and should be able to integrate multi-modal information to improve trajectory inference and prediction. Here we present a parametric Bayesian multi-task learning based approach to modeling univariate trajectories across subjects that addresses these criteria. Our approach learns multiple subjects' trajectories within a single model that allows for different types of information sharing, that is, coupling, across subjects. It optimizes a combination of uncoupled, fully coupled and kernel coupled models. Kernel-based coupling allows linking subjects' trajectories based on one or more biomarker measures. We demonstrate this using Alzheimer's Disease Neuroimaging Initiative (ADNI) data, where we model longitudinal trajectories of MRI-derived cortical volumes in neurodegeneration, with coupling based on APOE genotype, cerebrospinal fluid (CSF) and amyloid PET-based biomarkers. In addition to detecting established disease effects, we detect disease related changes within the insula that have not received much attention within the literature. Due to its sensitivity in detecting disease effects, its competitive predictive performance and its ability to learn the optimal parameter covariance from data rather than choosing a specific set of random and fixed effects a priori, we propose that our model can be used in place of or in addition to linear mixed effects models when modeling biomarker trajectories. A software implementation of the method is publicly available.
纵向成像生物标志物对于了解神经退行性变的过程非常有价值，有望能够追踪疾病的进展并比横断面生物标志物更早地发现疾病。为了充分发挥其潜力，生物标志物轨迹模型必须对采样不足和测量误差具有鲁棒性，并且应该能够整合多模态信息以改善轨迹推断和预测。在这里，我们提出了一种基于参数贝叶斯多任务学习的方法，用于对跨受试者的单变量轨迹进行建模，从而满足这些标准。我们的方法在单个模型中学习多个受试者的轨迹，该模型允许在受试者之间进行不同类型的信息共享，即耦合。它优化了非耦合、完全耦合和核耦合模型的组合。基于核的耦合允许根据一个或多个生物标志物测量值来链接受试者的轨迹。我们使用阿尔茨海默病神经影像学倡议 (ADNI) 数据证明了这一点，在该数据中，我们对神经退行性变中 MRI 衍生的皮质体积的纵向轨迹进行建模，基于 APOE 基因型、脑脊液 (CSF) 和淀粉样蛋白 PET 进行耦合基于生物标志物。除了检测已建立的疾病效应外，我们还检测到了文献中尚未受到太多关注的脑岛中的疾病相关变化。由于其在检测疾病效应方面的敏感性、竞争预测性能以及从数据中学习最佳参数协方差而不是预先选择特定的随机和固定效应集的能力，我们提出我们的模型可以替代或补充线性混合效应模型在建模生物标志物轨迹时。该方法的软件实现可公开获得。
