Journal article
Mixtures of large-scale dynamic functional brain network modes
- Abstract:
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Accurate temporal modelling of functional brain networks is essential in the quest for understanding how such networks facilitate cognition. Researchers are beginning to adopt time-varying analyses for electrophysiological data that capture highly dynamic processes on the order of milliseconds. Typically, these approaches, such as clustering of functional connectivity profiles and Hidden Markov Modelling (HMM), assume mutual exclusivity of networks over time. Whilst a powerful constraint, thi...
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- Publication status:
- Published
- Peer review status:
- Peer reviewed
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- Files:
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(Preview, Version of record, pdf, 7.0MB, Terms of use)
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- Publisher copy:
- 10.1016/j.neuroimage.2022.119595
Authors
Funding
+ Engineering and Physical Sciences Research Council
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Grant:
EP/L016044/1
EP/L016052/1
Bibliographic Details
- Publisher:
- Elsevier
- Journal:
- NeuroImage More from this journal
- Volume:
- 263
- Article number:
- 119595
- Publication date:
- 2022-08-27
- Acceptance date:
- 2022-08-26
- DOI:
- EISSN:
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1095-9572
- ISSN:
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1053-8119
- Pmid:
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36041643
Item Description
- Language:
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English
- Keywords:
- Pubs id:
-
1276434
- Local pid:
-
pubs:1276434
- Deposit date:
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2023-12-17
Terms of use
- Copyright holder:
- Gohil et al.
- Copyright date:
- 2022
- Rights statement:
- © 2022 The Authors. Published by Elsevier Inc. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/)
- Licence:
- CC Attribution (CC BY)
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