Energy landscape analysis of neuroimaging data

Takahiro Ezaki, Takamitsu Watanabe, Masayuki Ohzeki, Naoki Masuda*

*Corresponding author for this work

Research output: Contribution to journalReview articlepeer-review

30 Citations (Scopus)

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 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'.

Original languageEnglish
Article number20160287
JournalPhilosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences
Volume375
Issue number2096
DOIs
Publication statusPublished - 2017 Jun 28
Externally publishedYes

Keywords

  • Boltzmann machine
  • Functional magnetic resonance imaging
  • Ising model
  • Statistical physics

ASJC Scopus subject areas

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

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