Energy landscape analysis of neuroimaging data

Philosophical Transactions. Series A, Mathematical, Physical, and Engineering Sciences
Takahiro EzakiNaoki Masuda

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'.

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Citations

Dec 1, 2017·Journal of the Royal Society, Interface·Richard F Betzel, Danielle S Bassett
Mar 11, 2018·Human Brain Mapping·Takahiro EzakiNaoki Masuda
Aug 17, 2018·Physical Review. E·Naoki MasudaYukie Sano
May 17, 2017·Philosophical Transactions. Series A, Mathematical, Physical, and Engineering Sciences·José M Amigó, Michael Small
Nov 28, 2019·Nature Communications·Philip PearceJörn Dunkel
Apr 27, 2021·Physics of Fluids·Nicola SerraConsolato M Sergi
Jun 2, 2021·ELife·Nobuhiro YamagataHiromu Tanimoto
Jul 8, 2021·Cerebral Cortex·Adrián Ponce-AlvarezGustavo Deco
Oct 16, 2021·Nature Communications·Brandon R MunnJames M Shine
Dec 14, 2021·Frontiers in Psychiatry·Paul Rossener RegoniaJunichiro Yoshimoto

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Software Mentioned

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