Deep Variational Autoencoder for Mapping Functional Brain Networks

In the neuroimaging and brain mapping communities, researchers have proposed a variety of computational methods to map functional brain networks (FBNs). Recently, it has been proven that deep learning (DL) can be applied on functional magnetic resonance image (fMRI) data with superb representation p...

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Veröffentlicht in:	IEEE transactions on cognitive and developmental systems Jg. 13; H. 4; S. 841 - 852
Hauptverfasser:	Qiang, Ning, Dong, Qinglin, Ge, Fangfei, Liang, Hongtao, Ge, Bao, Zhang, Shu, Sun, Yifei, Gao, Jie, Liu, Tianming
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Piscataway IEEE 01.12.2021 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:	Attention deficit hyperactivity disorder Attention deficit hyperactivity disorder (ADHD) Biological system modeling Brain Brain modeling Classification Data models Datasets Deep learning deep learning (DL) Feature extraction Functional magnetic resonance imaging generative learning Machine learning Magnetic resonance imaging Mapping Medical imaging Representations resting-state functional magnetic resonance image (rfMRI) Unsupervised learning variational autoencoder (VAE)
ISSN:	2379-8920, 2379-8939
Online-Zugang:	Volltext
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