  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.
