TY - JOUR
T1 - A pairwise maximum entropy model accurately describes resting-state human brain networks
AU - Watanabe, Takamitsu
AU - Hirose, Satoshi
AU - Wada, Hiroyuki
AU - Imai, Yoshio
AU - Machida, Toru
AU - Shirouzu, Ichiro
AU - Konishi, Seiki
AU - Miyashita, Yasushi
AU - Masuda, Naoki
N1 - Funding Information:
We thank Hideaki Shimazaki and Taro Toyoizumi for valuable discussions. We also thank Ms Suzuki for technical assistance of the MRI acquisition. This work was supported by Grants-in-Aid for Scientific Research (23681033, and Innovative Areas ‘Systems Molecular Ethology’ (No. 20115009)) from MEXT Japan to N.M., a grant from the Japan Society for the Promotion of Science Research Foundation for Young Scientists (222882) to T.W., a Grant-in-Aid for Specially Promoted Research (19002010) to Y.M., a Grant-in-Aid for Scientific Research B (22300134) to S.K. and a research grant from Takeda Science Foundation to Y.M.
PY - 2013
Y1 - 2013
N2 - 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.
AB - 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.
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U2 - 10.1038/ncomms2388
DO - 10.1038/ncomms2388
M3 - Article
C2 - 23340410
AN - SCOPUS:84879155057
VL - 4
JO - Nature Communications
JF - Nature Communications
SN - 2041-1723
M1 - 1370
ER -