Deep discriminative learning for autism spectrum disorder classification

Abstract

Autism spectrum disorder (ASD) is a complex neurodevelopmental disorder characterized by deficiencies in social, communication and repetitive behaviors. We propose imaging-based ASD biomarkers to find the neural patterns related ASD as the primary goal of identifying ASD. The secondary goal is to investigate the impact of imaging-patterns for ASD. In this paper, we model and explore the identification of ASD by learning a representation of the T1 MRI and fMRI by fusioning a discriminative learning (DL) approach and deep convolutional neural network. Specifically, a class-wise analysis dictionary to generate non-negative low-rank encoding coefficients with the multi-model data, and an orthogonal synthesis dictionary to reconstruct the data. Then, we map the reconstructed data with the original multi-modal data as input of the deep learning model. Finally, the learned priors from both model are returned to the fusion framework to perform classification. The effectiveness of the proposed approach was tested on a world-wide cross-site (34) database of 1127 subjects, experiments show competitive results of the proposed approach. Furthermore, we were able to capture the status of brain neural patterns with the known input of the same modality.