Transfer Learning for Predicting Conversion from Mild Cognitive Impairment to Dementia of Alzheimer Type based on 3D-Convolutional Neural Network

Abstract

Dementia of Alzheimers Type (DAT) is associated with a devastating and irreversible cognitive decline. As a pharmacological intervention has not yet been developed to reverse disease progression, preventive medicine will play a crucial role for patient care and treatment planning. However, predicting which patients will progress to DAT is difficult as patients with Mild Cognitive Impairment (MCI) could either convert to DAT (MCI-C) or not (MCI-NC). In this paper, we develop a deep learning model to address the heterogeneous nature of DAT development. Structural magnetic resonance imaging was utilized as a single biomarker, and a three-dimensional convolutional neural network (3D-CNN) was developed. The 3D-CNN was trained using transfer learning from the classification of Normal Control and DAT scans at the source task. This was applied to the target task of classifying MCI-C and MCI-NC scans. The model results in 82.4% classification accuracy, which outperforms current models in the field. Furthermore, by implementing an occlusion map approach, we visualize key brain regions that significantly contribute to the prediction of MCI-C and MCI-NC. Results show the hippocampus, amygdala, cerebellum, and pons regions as significant to prediction, which are consistent with current understanding of disease. Finally, the models prediction value is significantly correlated with rates of change in clinical assessment scores, indicating the model is able to predict an individual patients future cognitive decline. This information, in conjunction with the identified anatomical features, will aid in building a personalized therapeutic strategy for individuals with MCI. This model could also be useful for selection of participants for clinical trials.