Probabilistic learning approaches for complex disease progression based on high-dimensional MRI data

本项目提出了基于磁共振成像（MRI）获得的高维医学数据来揭示病理轨迹的知情、数据驱动的方法，这些数据作为回归方程的输入和输出都是相关的，可以充分地进行早期诊断，并建模、理解和预测实际和未来的疾病进展。为此，我们将深度学习（DL）方法与贝叶斯统计相融合，以：(1)基于MRI数据和进一步的混杂因素和协变量（如临床或人口变量）准确预测个体患者的完整结果分布，以充分量化预测中的不确定性，而不是提供任何置信度的点预测(2)在生物医学患者数据中建立时间动态模型。关于(1)，我们将为图像输入开发深度分布回归模型，以准确预测不同疾病评分（例如症状严重程度）的整体分布，这些评分可以是多变量的，通常是非正态分布的。关于(2)，我们将通过开发基于dl的状态空间模型来模拟神经系统疾病的复杂时间演化。这两种模型都不是针对特定疾病量身定制的，但两种模型都将作为示范性的开发和测试两种神经系统疾病，即阿尔茨海默病（AD）和多发性硬化症（MS），根据它们不同的疾病进展概况进行选择。

This project proposes informed, data-driven methods to reveal pathological trajectories based on high-dimensional medical data obtained from magnetic resonance imaging (MRI), which are relevant as both inputs and outputs in regression equations to adequately perform early diagnosis and to model, understand, and predict actual and future disease progression. For this, we will fuse deep learning (DL) methods with Bayesian statistics to (1) accurately predict the complete outcome distributions of individual patients based on MRI data and further confounders and covariates (such as clinical or demographical variables) to adequately quantify uncertainty in predictions in contrast to point predictions not delivering any measures of confidence (2) model temporal dynamics in biomedical patient data. Regarding (1), we will develop deep distributional regression models for image inputs to accurately predict the entire distributions of the different disease scores (e.g. symptom severity), which can be multivariate and are typically highly non-normally distributed. Regarding (2), we will model the complex temporal evolution in neurological diseases by developing DL-based state-space models. Neither model is tailored to a specific disease, but both will be exemplary developed and tested for two neurological diseases, namely Alzheimer’s disease (AD) and multiple sclerosis (MS), chosen for their different disease progression profiles.