Energy landscape analysis of neuroimaging data

Takahiro Ezaki, Takamitsu Watanabe, Masayuki Ohzeki, Naoki Masuda

研究成果: Review article査読

21 被引用数 (Scopus)

抄録

Computational neuroscience models have been used for understanding neural dynamics in the brain and how they may be altered when physiological or other conditions change. We review and develop a data-driven approach to neuroimaging data called the energy landscape analysis. The methods are rooted in statistical physics theory in particular the Ising model, also known as the (pairwise) maximum entropy model and Boltzmann machine. The methods have been applied to fitting electrophysiological data in neuroscience for a decade, but their use in neuroimaging data is still in its infancy. We first review the methods and discuss some algorithms and technical aspects. Then, we apply the methods to functional magnetic resonance imaging data recorded from healthy individuals to inspect the relationship between the accuracy of fitting, the size of the brain system to be analysed and the data length. This article is part of the themed issue 'Mathematical methods in medicine: neuroscience, cardiology and pathology'.

本文言語English
論文番号20160287
ジャーナルPhilosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences
375
2096
DOI
出版ステータスPublished - 2017 6 28

ASJC Scopus subject areas

  • Mathematics(all)
  • Engineering(all)
  • Physics and Astronomy(all)

フィンガープリント 「Energy landscape analysis of neuroimaging data」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