A transformer-based multi-features fusion model for prediction of conversion in mild cognitive impairment

Mild cognitive impairment (MCI) is usually considered the early stage of Alzheimer's disease (AD). Therefore, the accurate identification of MCI individuals with high risk in converting to AD is essential for the potential prevention and treatment of AD. Recently, the great success of deep learning has sparked interest in applying deep learning to neuroimaging field. However, deep learning techniques are prone to overfitting since available neuroimaging datasets are not sufficiently large. Therefore, we proposed a deep learning model fusing cortical features to address the issue of fusion and classification blocks. To validate the effectiveness of the proposed model, we compared seven different models on the same dataset in the literature. The results show that our proposed model outperformed the competing models in the prediction of MCI conversion with an accuracy of 83.3% in the testing dataset. Subsequently, we used deep learning to characterize the contribution of brain regions and different cortical features to MCI progression. The results revealed that the caudal anterior cingulate and pars orbitalis contributed most to the classification task, and our model pays more attention to volume features and cortical thickness features.