Representation learning of resting state fMRI with variational autoencoder

• 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

•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...