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, Naoki Masuda*

*この研究の対応する著者

研究成果査読

84 被引用数 (Scopus)

抄録

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.

本文言語English
論文番号1370
ジャーナルNature communications
4
DOI
出版ステータスPublished - 2013
外部発表はい

ASJC Scopus subject areas

  • 化学 (全般)
  • 生化学、遺伝学、分子生物学(全般)
  • 物理学および天文学(全般)

フィンガープリント

「A pairwise maximum entropy model accurately describes resting-state human brain networks」の研究トピックを掘り下げます。これらがまとまってユニークなフィンガープリントを構成します。

引用スタイル