Multi-Scale Attention-Based Deep Neural Network for Brain Disease Diagnosis

Abstract

Whole brain functional connectivity (FC) patterns obtained from resting-state functional magnetic resonance imaging (rs-fMRI) have been widely used in the diagnosis of brain disorders such as autism spectrum disorder (ASD). Recently, an increasing number of studies have focused on employing deep learning techniques to analyze FC patterns for brain disease classification. However, the high dimensionality of the FC features and the interpretation of deep learning results are issues that need to be addressed in the FC-based brain disease classification. In this paper, we proposed a multi-scale attention-based deep neural network (MSA-DNN) model to classify FC patterns for the ASD diagnosis. The model was implemented by adding a flexible multi-scale attention (MSA) module to the auto-encoder based backbone DNN, which can extract multi-scale features of the FC patterns and change the level of attention for different FCs by continuous learning. Our model will reinforce the weights of important FC features while suppress the unimportant FCs to ensure the sparsity of the model weights and enhance the model interpretability. We performed systematic experiments on the large multi-sites ASD dataset with both ten-fold and leave-one-site-out cross-validations. Results showed that our model outperformed classical methods in brain disease classification and revealed robust inter-site prediction performance. We also localized important FC features and brain regions associated with ASD classification. Overall, our study further promotes the biomarker detection and computer-aided classification for ASD diagnosis, and the proposed MSA module is flexible and easy to implement in other classification networks.