Cargando…
Graph auto-encoding brain networks with applications to analyzing large-scale brain imaging datasets
There has been a huge interest in studying human brain connectomes inferred from different imaging modalities and exploring their relationships with human traits, such as cognition. Brain connectomes are usually represented as networks, with nodes corresponding to different regions of interest (ROIs...
Autores principales: | Liu, Meimei, Zhang, Zhengwu, Dunson, David B. |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
2021
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9659310/ https://www.ncbi.nlm.nih.gov/pubmed/34823023 http://dx.doi.org/10.1016/j.neuroimage.2021.118750 |
Ejemplares similares
-
Application of graph auto-encoders based on regularization in recommendation algorithms
por: Xie, Chengxin, et al.
Publicado: (2023) -
Bridging the Gap of AutoGraph Between Academia and Industry: Analyzing AutoGraph Challenge at KDD Cup 2020
por: Xu, Zhen, et al.
Publicado: (2022) -
GAT: A Graph-Theoretical Analysis Toolbox for Analyzing Between-Group Differences in Large-Scale Structural and Functional Brain Networks
por: Hosseini, S. M. Hadi, et al.
Publicado: (2012) -
Tree representations of brain structural connectivity via persistent homology
por: Li, Didong, et al.
Publicado: (2023) -
A Graph Feature Auto-Encoder for the prediction of unobserved node features on biological networks
por: Hasibi, Ramin, et al.
Publicado: (2021)