Large-Scale Brain Functional Network Integration for Discrimination of Autism Using a 3-D Deep Learning Model

GOAL: Brain functional networks (BFNs) constructed using resting-state functional magnetic resonance imaging (fMRI) have proven to be an effective way to understand aberrant functional connectivity in autism spectrum disorder (ASD) patients. It is still challenging to utilize these features as potential biomarkers for discrimination of ASD. The purpose of this work is to classify ASD and normal controls (NCs) using BFNs derived from rs-fMRI. METHODS: A deep learning framework was proposed that integrated convolutional neural network (CNN) and channel-wise attention mechanism to model both intra- and inter-BFN associations simultaneously for ASD diagnosis. We investigate the effects of each BFN on performance and performed inter-network connectivity analysis between each pair of BFNs. We compared the performance of our CNN model with some state-of-the-art algorithms using functional connectivity features. RESULTS: We collected 79 ASD patients and 105 NCs from the ABIDE-I dataset. The mean accuracy of our classification algorithm was 77.74% for classification of ASD versus NCs. CONCLUSION: The proposed model is able to integrate information from multiple BFNs to improve detection accuracy of ASD. SIGNIFICANCE: These findings suggest that large-scale BFNs is promising to serve as reliable biomarkers for diagnosis of ASD.