AIMAFE: Autism spectrum disorder identification with multi-atlas deep feature representation and ensemble learning - Wang, Yufei; Wang, Jianxin; Wu, Fang-Xiang; Hayrat, Rahmatjan; Liu, Jin (Journal of neuroscience methods, 2020)
•Multi-atlas functional connectivity is calculated as the original feature representation.•Multi-atlas deep feature representation is extracted by a deep learning method.•An ensemble learning strategy is proposed to perform the final ASD identification task.•Our proposed method achieves classification accuracy of 74.52%. Autism spectrum disorder (ASD) is a neurodevelopmental disorder that could cause problems in social communications. Clinically, diagnosing ASD mainly relies on behavioral criteria while this approach is not objective enough and could cause delayed diagnosis. Since functional magnetic resonance imaging (fMRI) can measure brain activity, it provides data for the study of brain dysfunction disorders and has been widely used in ASD identification. However, satisfactory accuracy for ASD identification has not been achieved. To improve the performance of ASD identification, we propose an ASD identification method based on multi-atlas deep feature representation and ensemble learning. We first calculate multiple functional connectivity based on different brain atlases from fMRI data of each subject. Then, to get the more discriminative features for ASD identification, we propose a multi-atlas deep feature representation method based on stacked denoising autoencoder (SDA). Finally, we propose multilayer perceptron (MLP) and an ensemble learning method to perform the final ASD identification task. Our proposed method is evaluated on 949 subjects (including 419 ASDs and 530 typical control (TCs)) from the Autism Brain Imaging Data Exchange (ABIDE) and achieves accuracy of 74.52% (sensitivity of 80.69%, specificity of 66.71%, AUC of 0.8026) for ASD identification. Compared with some previously published methods, our proposed method obtains the better performance for ASD identification. The results suggest that our proposed method is efficient to improve the performance of ASD identification, and is promising for ASD clinical diagnosis.