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A pairwise maximum entropy model accurately describes resting-state human brain networks

Watanabe, T; Hirose, S; Wada, H; Imai, Y; Machida, T; Shirouzu, I; Konishi, S; ... Masuda, N; + view all (2013) A pairwise maximum entropy model accurately describes resting-state human brain networks. Nature Communications , 4 , Article 1370. 10.1038/ncomms2388. Green open access

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Abstract

The resting-state human brain networks underlie fundamental cognitive functions and consist of complex interactions among brain regions. However, the level of complexity of the resting-state networks has not been quantified, which has prevented comprehensive descriptions of the brain activity as an integrative system. Here, we address this issue by demonstrating that a pairwise maximum entropy model, which takes into account region-specific activity rates and pairwise interactions, can be robustly and accurately fitted to resting-state human brain activities obtained by functional magnetic resonance imaging. Furthermore, to validate the approximation of the resting-state networks by the pairwise maximum entropy model, we show that the functional interactions estimated by the pairwise maximum entropy model reflect anatomical connexions more accurately than the conventional functional connectivity method. These findings indicate that a relatively simple statistical model not only captures the structure of the resting-state networks but also provides a possible method to derive physiological information about various large-scale brain networks.

Type: Article
Title: A pairwise maximum entropy model accurately describes resting-state human brain networks
Location: England
Open access status: An open access version is available from UCL Discovery
DOI: 10.1038/ncomms2388
Publisher version: http://dx.doi.org/10.1038/ncomms2388
Language: English
Additional information: Copyright © 2013 Macmillan Publishers Limited. This work is licensed under a Creative Commons AttributionNonCommercial-NoDerivs 3.0 Unported License. To view a copy of this license, visit http://creativecommons.org/licenses/by-nc-nd/3.0/.
Keywords: Brain, Entropy, Female, Humans, Magnetic Resonance Imaging, Male, Models, Neurological, Nerve Net, ROC Curve, Reproducibility of Results, Rest, Signal Processing, Computer-Assisted, Statistics as Topic, Young Adult
UCL classification: UCL
UCL > Provost and Vice Provost Offices
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Brain Sciences
URI: https://discovery.ucl.ac.uk/id/eprint/1476954
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