Deep causality variational autoencoder network for identifying the potential biomarkers of brain disorders

Identifying causality from observational time-series data is a key problem in dealing with complex dynamic systems. Inferring the direction of connection between brain regions (i.e., causality) has become the central topic in the domain of fMRI. The purpose of this study is to obtain causal graphs t...

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Published in:	IEEE transactions on neural systems and rehabilitation engineering Vol. 32; p. 1
Main Authors:	Alfakih, Amani, Xia, Zhengwang, Ali, Bahzar, Mamoon, Saqib, Lu, Jianfeng
Format:	Journal Article
Language:	English
Published:	United States IEEE 01.01.2024 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:	Alzheimer Disease - diagnostic imaging Alzheimer's disease Alzheimer’s Disease (AD) Autism Autism Spectrum Disorder (ASD) Autoencoder Biomarkers Brain Brain - diagnostic imaging Brain mapping Brain modeling Causal inference Causality Data exchange Data models Diseases fMRI Functional magnetic resonance imaging Humans Magnetic Resonance Imaging - methods Medical imaging Neurodegenerative diseases Neuroimaging Neuroimaging - methods Time series Time series analysis
ISSN:	1534-4320, 1558-0210, 1558-0210
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Abstract	Identifying causality from observational time-series data is a key problem in dealing with complex dynamic systems. Inferring the direction of connection between brain regions (i.e., causality) has become the central topic in the domain of fMRI. The purpose of this study is to obtain causal graphs that characterize the causal relationship between brain regions based on time series data. To address this issue, we designed a novel model named deep causal variational autoencoder (CVAE) to estimate the causal relationship between brain regions. This network contains a causal layer that can estimate the causal relationship between different brain regions directly. Compared with previous approaches, our method relaxes many constraints on the structure of underlying causal graph. Our proposed method achieves excellent performance on both the Alzheimer's Disease Neuroimaging Initiative (ADNI) and the Autism Brain Imaging Data Exchange 1 (ABIDE1) databases. Moreover, the experimental results show that deep CVAE has promising applications in the field of brain disease identification.
AbstractList	Identifying causality from observational time-series data is a key problem in dealing with complex dynamic systems. Inferring the direction of connection between brain regions (i.e., causality) has become the central topic in the domain of fMRI. The purpose of this study is to obtain causal graphs that characterize the causal relationship between brain regions based on time series data. To address this issue, we designed a novel model named deep causal variational autoencoder (CVAE) to estimate the causal relationship between brain regions. This network contains a causal layer that can estimate the causal relationship between different brain regions directly. Compared with previous approaches, our method relaxes many constraints on the structure of underlying causal graph. Our proposed method achieves excellent performance on both the Alzheimer’s Disease Neuroimaging Initiative (ADNI) and the Autism Brain Imaging Data Exchange 1 (ABIDE1) databases. Moreover, the experimental results show that deep CVAE has promising applications in the field of brain disease identification. Identifying causality from observational time-series data is a key problem in dealing with complex dynamic systems. Inferring the direction of connection between brain regions (i.e., causality) has become the central topic in the domain of fMRI. The purpose of this study is to obtain causal graphs that characterize the causal relationship between brain regions based on time series data. To address this issue, we designed a novel model named deep causal variational autoencoder (CVAE) to estimate the causal relationship between brain regions. This network contains a causal layer that can estimate the causal relationship between different brain regions directly. Compared with previous approaches, our method relaxes many constraints on the structure of underlying causal graph. Our proposed method achieves excellent performance on both the Alzheimer's Disease Neuroimaging Initiative (ADNI) and the Autism Brain Imaging Data Exchange 1 (ABIDE1) databases. Moreover, the experimental results show that deep CVAE has promising applications in the field of brain disease identification.Identifying causality from observational time-series data is a key problem in dealing with complex dynamic systems. Inferring the direction of connection between brain regions (i.e., causality) has become the central topic in the domain of fMRI. The purpose of this study is to obtain causal graphs that characterize the causal relationship between brain regions based on time series data. To address this issue, we designed a novel model named deep causal variational autoencoder (CVAE) to estimate the causal relationship between brain regions. This network contains a causal layer that can estimate the causal relationship between different brain regions directly. Compared with previous approaches, our method relaxes many constraints on the structure of underlying causal graph. Our proposed method achieves excellent performance on both the Alzheimer's Disease Neuroimaging Initiative (ADNI) and the Autism Brain Imaging Data Exchange 1 (ABIDE1) databases. Moreover, the experimental results show that deep CVAE has promising applications in the field of brain disease identification.
Author	Alfakih, Amani Lu, Jianfeng Xia, Zhengwang Mamoon, Saqib Ali, Bahzar
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SubjectTerms	Alzheimer Disease - diagnostic imaging Alzheimer's disease Alzheimer’s Disease (AD) Autism Autism Spectrum Disorder (ASD) Autoencoder Biomarkers Brain Brain - diagnostic imaging Brain mapping Brain modeling Causal inference Causality Data exchange Data models Diseases fMRI Functional magnetic resonance imaging Humans Magnetic Resonance Imaging - methods Medical imaging Neurodegenerative diseases Neuroimaging Neuroimaging - methods Time series Time series analysis
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Title	Deep causality variational autoencoder network for identifying the potential biomarkers of brain disorders
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