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Predicting whether subjects with mild cognitive impairment (MCI) will convertto Alzheimer's disease is a significant clinical challenge. Longitudinalvariations and complementary information inherent in longitudinal andmultimodal data are crucial for MCI conversion prediction, but persistent issueof missing data in these data may hinder their effective application.Additionally, conversion prediction should be achieved in the early stages ofdisease progression in clinical practice, specifically at baseline visit (BL).Therefore, longitudinal data should only be incorporated during training tocapture disease progression information. To address these challenges, amulti-view imputation and cross-attention network (MCNet) was proposed tointegrate data imputation and MCI conversion prediction in a unified framework.First, a multi-view imputation method combined with adversarial learning waspresented to handle various missing data scenarios and reduce imputationerrors. Second, two cross-attention blocks were introduced to exploit thepotential associations in longitudinal and multimodal data. Finally, amulti-task learning model was established for data imputation, longitudinalclassification, and conversion prediction tasks. When the model wasappropriately trained, the disease progression information learned fromlongitudinal data can be leveraged by BL data to improve MCI conversionprediction at BL. MCNet was tested on two independent testing sets andsingle-modal BL data to verify its effectiveness and flexibility in MCIconversion prediction. Results showed that MCNet outperformed severalcompetitive methods. Moreover, the interpretability of MCNet was demonstrated.Thus, our MCNet may be a valuable tool in longitudinal and multimodal dataanalysis for MCI conversion prediction. Codes are available athttps://github.com/Meiyan88/MCNET.

Multi-View Imputation and Cross-Attention Network Based on Incomplete  Longitudinal and Multimodal Data for Conversion Prediction of Mild Cognitive  Impairment