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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Veröffentlicht in:	IEEE transactions on neural systems and rehabilitation engineering Jg. 32; S. 1
Hauptverfasser:	Alfakih, Amani, Xia, Zhengwang, Ali, Bahzar, Mamoon, Saqib, Lu, Jianfeng
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	United States IEEE 01.01.2024 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:	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
Online-Zugang:	Volltext
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