Diagnosis of Autism Spectrum Disorder Based on Functional Brain Networks with Deep Learning

Journal of computational biology

article

Wutao Yin¹  Sakib Mostafa²  Fang-xiang Wu³ 

[1] Division of Biomedical Engineering, University of Saskatchewan;Department of Computer Science, University of Saskatchewan;Division of Biomedical Engineering, Department of Mechanical Engineering, Department of Computer Science, University of Saskatchewan

关键词: autism spectrum disorder;    autoencoder;    deep learning;    functional brain network;    functional magnetic resonance imaging.;   

DOI  :  10.1089/cmb.2020.0252

来源: Mary Ann Liebert, Inc. Publishers

【 摘 要 】

Autism spectrum disorder (ASD) is a neurological and developmental disorder. Traditional diagnosis of ASD is typically performed through the observation of behaviors and interview of a patient. However, these diagnosis methods are time-consuming and can be misleading sometimes. Integrating machine learning algorithms with neuroimages, a diagnosis method, can possibly be established to detect ASD subjects from typical control subjects. In this study, we develop deep learning methods for diagnosis of ASD from functional brain networks constructed with brain functional magnetic resonance imaging (fMRI) data. The entire Autism Brain Imaging Data Exchange 1 (ABIDE 1) data set is utilized to investigate the performance of our proposed methods. First, we construct the brain networks from brain fMRI images and define the raw features based on such brain networks. Second, we employ an autoencoder (AE) to learn the advanced features from the raw features. Third, we train a deep neural network (DNN) with the advanced features, which achieves the classification accuracy of 76.2% and the receiving operating characteristic curve (AUC) of 79.7%. As a comparison, we also apply the same advanced features to train several traditional machine learning algorithms to benchmark the classification performance. Finally, we combine the DNN with the pretrained AE and train it with the raw features, which achieves the classification accuracy of 79.2% and the AUC of 82.4%. These results show that our proposed deep learning methods outperform the state-of-the-art methods.

【 授权许可】

Unknown