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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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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:
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
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author Colclough, Giles L.
Woolrich, Mark W.
Harrison, Samuel J.
Rojas López, Pedro A.
Valdes-Sosa, Pedro A.
Smith, Stephen M.
author_facet Colclough, Giles L.
Woolrich, Mark W.
Harrison, Samuel J.
Rojas López, Pedro A.
Valdes-Sosa, Pedro A.
Smith, Stephen M.
author_sort Colclough, Giles L.
collection PubMed
description 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 is applicable to fMRI, MEG and EEG data. Two versions of the model can encourage sparse connectivity, either using continuous priors to suppress irrelevant connections, or using an explicit description of the network structure to estimate the connection probability between each pair of regions. A large evaluation of this model, and thirteen methods that represent the state of the art of inverse covariance modelling, is conducted using both simulated and resting-state functional imaging datasets. Our novel Bayesian approach has similar performance to the best extant alternative, Ng et al.'s Sparse Group Gaussian Graphical Model algorithm, which also is based on a hierarchical structure. Using data from the Human Connectome Project, we show that these hierarchical models are able to reduce the measurement error in MEG beta-band functional networks by 10%, producing concomitant increases in estimates of the genetic influence on functional connectivity.
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spelling pubmed-65659322019-06-18 Multi-subject hierarchical inverse covariance modelling improves estimation of functional brain networks Colclough, Giles L. Woolrich, Mark W. Harrison, Samuel J. Rojas López, Pedro A. Valdes-Sosa, Pedro A. Smith, Stephen M. Neuroimage Article 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 is applicable to fMRI, MEG and EEG data. Two versions of the model can encourage sparse connectivity, either using continuous priors to suppress irrelevant connections, or using an explicit description of the network structure to estimate the connection probability between each pair of regions. A large evaluation of this model, and thirteen methods that represent the state of the art of inverse covariance modelling, is conducted using both simulated and resting-state functional imaging datasets. Our novel Bayesian approach has similar performance to the best extant alternative, Ng et al.'s Sparse Group Gaussian Graphical Model algorithm, which also is based on a hierarchical structure. Using data from the Human Connectome Project, we show that these hierarchical models are able to reduce the measurement error in MEG beta-band functional networks by 10%, producing concomitant increases in estimates of the genetic influence on functional connectivity. Academic Press 2018-09 /pmc/articles/PMC6565932/ /pubmed/29746906 http://dx.doi.org/10.1016/j.neuroimage.2018.04.077 Text en © 2018 The Authors http://creativecommons.org/licenses/by/4.0/ This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Colclough, Giles L.
Woolrich, Mark W.
Harrison, Samuel J.
Rojas López, Pedro A.
Valdes-Sosa, Pedro A.
Smith, Stephen M.
Multi-subject hierarchical inverse covariance modelling improves estimation of functional brain networks
title Multi-subject hierarchical inverse covariance modelling improves estimation of functional brain networks
title_full Multi-subject hierarchical inverse covariance modelling improves estimation of functional brain networks
title_fullStr Multi-subject hierarchical inverse covariance modelling improves estimation of functional brain networks
title_full_unstemmed Multi-subject hierarchical inverse covariance modelling improves estimation of functional brain networks
title_short Multi-subject hierarchical inverse covariance modelling improves estimation of functional brain networks
title_sort multi-subject hierarchical inverse covariance modelling improves estimation of functional brain networks
topic Article
url 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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