Energy landscape analysis of neuroimaging data

Takahiro Ezaki, Takamitsu Watanabe, Masayuki Ohzeki, Naoki Masuda

Research output: Contribution to journalArticle (Academic Journal)peer-review

16 Citations (Scopus)
301 Downloads (Pure)

Abstract

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 its 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 analyzed, and the data length.
Original languageEnglish
Article number20160287
Number of pages14
JournalPhilosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences
Volume375
Issue number2096
Early online date15 May 2017
DOIs
Publication statusPublished - 28 Jun 2017

Keywords

  • functional magnetic resonance imaging
  • statistical physics
  • Ising model
  • Boltzmann machine

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