Predicting whether subjects with mild cognitive impairment (MCI) will convert to Alzheimer's disease is a significant clinical challenge. Longitudinal variations and complementary information inherent in longitudinal and multimodal data are crucial for MCI conversion prediction, but persistent issue of missing data in these data may hinder their effective application. Additionally, conversion prediction should be achieved in the early stages of disease progression in clinical practice, specifically at baseline visit (BL). Therefore, longitudinal data should only be incorporated during training to capture disease progression information. To address these challenges, a multi-view imputation and cross-attention network (MCNet) was proposed to integrate data imputation and MCI conversion prediction in a unified framework. First, a multi-view imputation method combined with adversarial learning was presented to handle various missing data scenarios and reduce imputation errors. Second, two cross-attention blocks were introduced to exploit the potential associations in longitudinal and multimodal data. Finally, a multi-task learning model was established for data imputation, longitudinal classification, and conversion prediction tasks. When the model was appropriately trained, the disease progression information learned from longitudinal data can be leveraged by BL data to improve MCI conversion prediction at BL. MCNet was tested on two independent testing sets and single-modal BL data to verify its effectiveness and flexibility in MCI conversion prediction. Results showed that MCNet outperformed several competitive methods. Moreover, the interpretability of MCNet was demonstrated. Thus, our MCNet may be a valuable tool in longitudinal and multimodal data analysis for MCI conversion prediction. Codes are available at https://github.com/Meiyan88/MCNET.

翻译：预测轻度认知障碍（MCI）患者是否会转化为阿尔茨海默病是一项重要的临床挑战。纵向和多模态数据中固有的纵向变化及互补信息对MCI转化预测至关重要，但这些数据中普遍存在的数据缺失问题可能阻碍其有效应用。此外，临床实践中需在疾病进展早期阶段（特别是基线访视，即BL）实现转化预测。因此，纵向数据应仅在训练阶段纳入以捕获疾病进展信息。为应对这些挑战，本文提出了一种多视图插补与交叉注意力网络（MCNet），将数据插补与MCI转化预测整合于统一框架中。首先，设计了一种结合对抗学习的多视图插补方法，用于处理多种数据缺失场景并降低插补误差。其次，引入两个交叉注意力模块以挖掘纵向与多模态数据中的潜在关联。最后，建立多任务学习模型，同时执行数据插补、纵向分类及转化预测任务。当模型经过适当训练后，BL数据可利用从纵向数据中习得的疾病进展信息，提升基线访视时的MCI转化预测性能。MCNet在两个独立测试集及单模态BL数据上进行了验证以评估其在MCI转化预测中的有效性与灵活性。结果表明，MCNet优于多种竞争方法，且其可解释性得到证实。因此，所提MCNet有望成为纵向与多模态数据分析中用于MCI转化预测的有力工具。代码已开源：https://github.com/Meiyan88/MCNET。