Department of Brain and Cognitive Engineering, Korea University, Seoul 02841, Republic of Korea.
Department of Brain and Cognitive Engineering, Korea University, Seoul 02841, Republic of Korea; Department of Artificial Intelligence, Korea University, Seoul 02841, Republic of Korea.
Neuroimage. 2021 Aug 15;237:118143. doi: 10.1016/j.neuroimage.2021.118143. Epub 2021 May 13.
Alzheimer's disease (AD) is known as one of the major causes of dementia and is characterized by slow progression over several years, with no treatments or available medicines. In this regard, there have been efforts to identify the risk of developing AD in its earliest time. While many of the previous works considered cross-sectional analysis, more recent studies have focused on the diagnosis and prognosis of AD with longitudinal or time series data in a way of disease progression modeling. Under the same problem settings, in this work, we propose a novel computational framework that can predict the phenotypic measurements of MRI biomarkers and trajectories of clinical status along with cognitive scores at multiple future time points. However, in handling time series data, it generally faces many unexpected missing observations. In regard to such an unfavorable situation, we define a secondary problem of estimating those missing values and tackle it in a systematic way by taking account of temporal and multivariate relations inherent in time series data. Concretely, we propose a deep recurrent network that jointly tackles the four problems of (i) missing value imputation, (ii) phenotypic measurements forecasting, (iii) trajectory estimation of a cognitive score, and (iv) clinical status prediction of a subject based on his/her longitudinal imaging biomarkers. Notably, the learnable parameters of all the modules in our predictive models are trained in an end-to-end manner by taking the morphological features and cognitive scores as input, with our circumspectly defined loss function. In our experiments over The Alzheimers Disease Prediction Of Longitudinal Evolution (TADPOLE) challenge cohort, we measured performance for various metrics and compared our method to competing methods in the literature. Exhaustive analyses and ablation studies were also conducted to better confirm the effectiveness of our method.
阿尔茨海默病（AD）是导致痴呆的主要原因之一，其特征是在几年内缓慢进展，目前尚无治疗方法或可用药物。在这方面，人们一直在努力尽早发现 AD 的发病风险。虽然之前的许多研究都考虑了横断面分析，但最近的研究更侧重于使用纵向或时间序列数据来进行疾病进展建模，以对 AD 进行诊断和预后。在相同的问题设置下，在这项工作中，我们提出了一种新的计算框架，可以预测 MRI 生物标志物的表型测量值以及临床状态和认知评分的轨迹，可预测多个未来时间点的情况。然而，在处理时间序列数据时，它通常会面临许多意外的缺失观测值。针对这种不利情况，我们定义了一个估计这些缺失值的二级问题，并通过考虑时间序列数据中固有的时间和多变量关系，以系统的方式解决它。具体来说，我们提出了一个深度递归网络，该网络联合解决了四个问题：（i）缺失值插补，（ii）表型测量预测，（iii）认知评分轨迹估计，以及（iv）基于受试者纵向成像生物标志物的临床状态预测。值得注意的是，我们预测模型中所有模块的可学习参数都是通过将形态特征和认知评分作为输入，采用我们谨慎定义的损失函数，以端到端的方式进行训练的。在我们对阿尔茨海默病纵向演化预测（TADPOLE）挑战赛队列的实验中，我们针对各种指标进行了性能评估，并将我们的方法与文献中的竞争方法进行了比较。我们还进行了详尽的分析和消融研究，以更好地确认我们方法的有效性。
