Yufei Wang1, Jianxin Wang2, Fang-Xiang Wu3, Rahmatjan Hayrat4, Jin Liu5. 1. Hunan Provincial Key Lab on Bioinformatics, School of Computer Science and Engineering, Central South University, Changsha 410083, China. Electronic address: willem@csu.edu.cn. 2. Hunan Provincial Key Lab on Bioinformatics, School of Computer Science and Engineering, Central South University, Changsha 410083, China. Electronic address: jxwang@mail.csu.edu.cn. 3. Division of Biomedical Engineering and Department of Mechanical Engineering, University of Saskatchewan, Saskatoon S7N 5A9, Canada. Electronic address: faw341@mail.usask.ca. 4. Hunan Provincial Key Lab on Bioinformatics, School of Computer Science and Engineering, Central South University, Changsha 410083, China. Electronic address: rahmatjan@csu.edu.cn. 5. Hunan Provincial Key Lab on Bioinformatics, School of Computer Science and Engineering, Central South University, Changsha 410083, China. Electronic address: liujin06@csu.edu.cn.
Abstract
BACKGROUND: 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. NEW METHOD: 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. RESULTS: 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. COMPARISON WITH EXISTING METHODS: Compared with some previously published methods, our proposed method obtains the better performance for ASD identification. CONCLUSION: The results suggest that our proposed method is efficient to improve the performance of ASD identification, and is promising for ASD clinical diagnosis.
BACKGROUND: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. NEW METHOD: 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. RESULTS: 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. COMPARISON WITH EXISTING METHODS: Compared with some previously published methods, our proposed method obtains the better performance for ASD identification. CONCLUSION: The results suggest that our proposed method is efficient to improve the performance of ASD identification, and is promising for ASD clinical diagnosis.
Authors: Parisa Moridian; Navid Ghassemi; Mahboobeh Jafari; Salam Salloum-Asfar; Delaram Sadeghi; Marjane Khodatars; Afshin Shoeibi; Abbas Khosravi; Sai Ho Ling; Abdulhamit Subasi; Roohallah Alizadehsani; Juan M Gorriz; Sara A Abdulla; U Rajendra Acharya Journal: Front Mol Neurosci Date: 2022-10-04 Impact factor: 6.261