Representation learning of resting state fMRI with variational autoencoder

Resting state functional magnetic resonance imaging (rsfMRI) data exhibits complex but structured patterns. However, the underlying origins are unclear and entangled in rsfMRI data. Here we establish a variational auto-encoder, as a generative model trainable with unsupervised learning, to disentang...

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Detalles Bibliográficos
Autores principales: Kim, Jung-Hoon, Zhang, Yizhen, Han, Kuan, Wen, Zheyu, Choi, Minkyu, Liu, Zhongming
Formato: Online Artículo Texto
Lenguaje:English
Publicado: 2021
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8485214/
https://www.ncbi.nlm.nih.gov/pubmed/34303794
http://dx.doi.org/10.1016/j.neuroimage.2021.118423

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8485214/
https://www.ncbi.nlm.nih.gov/pubmed/34303794
http://dx.doi.org/10.1016/j.neuroimage.2021.118423

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