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Journal article

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

Abstract:

A Bayesian model for sparse, hierarchical inverse 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 descrip...

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Publication status:
Published
Peer review status:
Peer reviewed

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Publisher copy:
10.1016/j.neuroimage.2018.04.077

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Institution:
University of Oxford
Division:
Medical Sciences Division
Department:
Psychiatry
Role:
Author
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Institution:
University of Oxford
Division:
MSD
Department:
Psychiatry
Role:
Author
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Institution:
University of Oxford
Division:
Medical Sciences Division
Department:
Psychiatry
Role:
Author
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Funding agency for:
Colclough, G
Grant:
EP/G036861/1
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Funding agency for:
Woolrich, M
Grant:
092753
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Funding agency for:
Woolrich, M
Grant:
092753
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Funding agency for:
Harrison, S
Grant:
EP/F500394/1
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Funding agency for:
Harrison, S
Grant:
EP/F500394/1
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Publisher:
Elsevier Publisher's website
Journal:
NeuroImage Journal website
Volume:
178
Pages:
370-384
Publication date:
2018-05-07
Acceptance date:
2018-04-30
DOI:
ISSN:
1053-8119 and 1095-9572
Source identifiers:
845768
Keywords:
Pubs id:
pubs:845768
UUID:
uuid:56951ce8-aa3f-4d27-bb89-81f2972b4c9b
Local pid:
pubs:845768
Deposit date:
2018-05-03

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