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

Takamitsu Watanabe, Satoshi Hirose, Hiroyuki Wada, Yoshio Imai, Toru Machida, Ichiro Shirouzu, Seiki Konishi, Yasushi Miyashita and Naoki Masuda ()
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Takamitsu Watanabe: The University of Tokyo School of Medicine
Satoshi Hirose: The University of Tokyo School of Medicine
Hiroyuki Wada: NTT Medical Center Tokyo
Yoshio Imai: NTT Medical Center Tokyo
Toru Machida: NTT Medical Center Tokyo
Ichiro Shirouzu: NTT Medical Center Tokyo
Seiki Konishi: The University of Tokyo School of Medicine
Yasushi Miyashita: The University of Tokyo School of Medicine
Naoki Masuda: The University of Tokyo

Nature Communications, 2013, vol. 4, issue 1, 1-10

Abstract: 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.

Date: 2013
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Persistent link: https://EconPapers.repec.org/RePEc:nat:natcom:v:4:y:2013:i:1:d:10.1038_ncomms2388

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DOI: 10.1038/ncomms2388

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