Gang Li1, Depeng Han2, Chao Wang2, Wenxing Hu3, Vince D Calhoun4, Yu-Ping Wang5. 1. School of Electronic and Control Engineering, Chang'an University, Xi'an 710064, Shaanxi, China; Key Laboratory of Road Construction Technology and Equipment of MOE, Chang'an University, China. Electronic address: 15229296166@chd.edu.cn. 2. School of Electronic and Control Engineering, Chang'an University, Xi'an 710064, Shaanxi, China. 3. Biomedical Engineering Department, Tulane University, New Orleans, LA 70118, USA. Electronic address: whu@tulane.edu. 4. Mind Research Network and Department of ECE, University of New Mexico, Albuquerque, NM 87106, USA. Electronic address: vcalhoun@unm.edu. 5. Biomedical Engineering Department, Tulane University, New Orleans, LA 70118, USA. Electronic address: wyp@tulane.edu.
Abstract
BACKGROUND AND OBJECTIVE: Imaging genetics has been widely used to help diagnose and treat mental illness, e.g., schizophrenia, by combining magnetic resonance imaging of the brain and genomic information for comprehensive and systematic analysis. As a result, utilizing the correlation between magnetic resonance imaging of the brain and genomic information is becoming an important challenge. METHODS: In this paper, the joint analysis of single nucleotide polymorphisms and functional magnetic resonance imaging is conducted for comprehensive study of schizophrenia. We developed a deep canonically correlated sparse autoencoder to classify schizophrenia patients from healthy controls, which can address the limitation of many existing methods such as canonical correlation analysis, deep canonical correlation analysis and sparse autoencoder. RESULTS: The proposed deep canonically correlated sparse autoencoder can not only use complex nonlinear transformation and dimension reduction, but also achieve more accurate classifications. Our experiments showed the proposed method achieved an accuracy of 95.65% for SNP data sets and an accuracy of 80.53% for fMRI data sets. CONCLUSIONS: Experiments demonstrated higher accuracy of using the proposed method over other conventional models when classifying schizophrenia patients and healthy controls.
BACKGROUND AND OBJECTIVE: Imaging genetics has been widely used to help diagnose and treat mental illness, e.g., schizophrenia, by combining magnetic resonance imaging of the brain and genomic information for comprehensive and systematic analysis. As a result, utilizing the correlation between magnetic resonance imaging of the brain and genomic information is becoming an important challenge. METHODS: In this paper, the joint analysis of single nucleotide polymorphisms and functional magnetic resonance imaging is conducted for comprehensive study of schizophrenia. We developed a deep canonically correlated sparse autoencoder to classify schizophreniapatients from healthy controls, which can address the limitation of many existing methods such as canonical correlation analysis, deep canonical correlation analysis and sparse autoencoder. RESULTS: The proposed deep canonically correlated sparse autoencoder can not only use complex nonlinear transformation and dimension reduction, but also achieve more accurate classifications. Our experiments showed the proposed method achieved an accuracy of 95.65% for SNP data sets and an accuracy of 80.53% for fMRI data sets. CONCLUSIONS: Experiments demonstrated higher accuracy of using the proposed method over other conventional models when classifying schizophreniapatients and healthy controls.
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