Representation learning of resting state fMRI with variational autoencoder

•Variational auto-encoder disentangles generative factors of resting state fMRI activity.•Variational auto-encoder trained with unsupervised learning extracts useful latent representations discriminative across individuals.•Temporal gradients of the latent representations report various types of tra...

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Bibliographic Details
Published in:	NeuroImage (Orlando, Fla.) Vol. 241; p. 118423
Main Authors:	Kim, Jung-Hoon, Zhang, Yizhen, Han, Kuan, Wen, Zheyu, Choi, Minkyu, Liu, Zhongming
Format:	Journal Article
Language:	English
Published:	United States Elsevier Inc 01.11.2021 Elsevier Limited Elsevier
Subjects:	Brain - diagnostic imaging Brain - physiology Connectome - methods Databases, Factual Deep generative model Deep learning Functional magnetic resonance imaging Humans Individuality Latent gradients Learning Magnetic Resonance Imaging - methods Neural networks Random variables Rest - physiology Unsupervised learning Unsupervised Machine Learning Variational autoencoder Variational autoencoder Deep generative model Latent gradients Unsupervised learning
ISSN:	1053-8119, 1095-9572, 1095-9572
Online Access:	Get full text
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