Multi-subject hierarchical inverse covariance modelling improves estimation of functional brain networks

A Bayesian model for sparse, hierarchical, inver-covariance estimation is presented, and applied to multi-subject functional connectivity estimation in the human brain. It enables simultaneous inference of the strength of connectivity between brain regions at both subject and population level, and i...

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Detalles Bibliográficos
Autores principales: Colclough, Giles L., Woolrich, Mark W., Harrison, Samuel J., Rojas López, Pedro A., Valdes-Sosa, Pedro A., Smith, Stephen M.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Academic Press 2018
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6565932/
https://www.ncbi.nlm.nih.gov/pubmed/29746906
http://dx.doi.org/10.1016/j.neuroimage.2018.04.077

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6565932/
https://www.ncbi.nlm.nih.gov/pubmed/29746906
http://dx.doi.org/10.1016/j.neuroimage.2018.04.077

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