An Interpretable Brain Network Atlas-Based Hybrid Model for Mild Cognitive Impairment Progression Prediction

Abstract

The process of Alzheimer’s disease (AD) is irreversible, but reasonable medical intervention for preclinical AD can delay AD’s onset. Progressive mild cognitive impairment (pMCI) is the most critical stage for AD preclinical intervention. Therefore, accurate identification of pMCI will significantly improve patient benefits. Functional MRI is a neuroimaging modality that has been widely utilized to study brain activity related to AD. However, it is challenging to obtain functional MRI data, and a small amount of data will easily lead to the overfitting of the identification model. In addition, the current pMCI identification model lack interpretability leads to difficulty in acceptance by clinicians. In this work, we propose an interpretable hybrid model based on a brain network atlas to identify pMCI subjects. First, the hybrid model utilizes multi-layer perceptron to obtain categorical global features to help graph neural networks reduce overfitting. Second, the attention mechanism is introduced into the model to explain the recognition behavior of the model. The results show that our model outperforms the comparison models on multiple metrics.