UCL logo

UCL Discovery

UCL home » Library Services » Electronic resources » UCL Discovery

Energy landscape analysis of neuroimaging data

Ezaki, T; Watanabe, T; Ohzeki, M; Masuda, N; (2017) Energy landscape analysis of neuroimaging data. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences , 375 (2096) , Article 20160287. 10.1098/rsta.2016.0287. Green open access

[img]
Preview
Text
Energy landscape analysis of neuroimaging data.pdf - ["content_typename_Published version" not defined]

Download (683kB) | Preview

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’.

Type: Article
Title: Energy landscape analysis of neuroimaging data
Open access status: An open access version is available from UCL Discovery
DOI: 10.1098/rsta.2016.0287
Publisher version: http://dx.doi.org/10.1098/rsta.2016.0287
Language: English
Additional information: Copyright © 2017 The Authors. Published by the Royal Society under the terms of the Creative Commons Attribution License http://creativecommons.org/licenses/by/4.0/, which permits unrestricted use, provided the original author and source are credited.
Keywords: functional magnetic resonance imaging, statistical physics, Ising model, Boltzmann machine
UCL classification: UCL > Provost and Vice Provost Offices
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Brain Sciences
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Brain Sciences > Div of Psychology and Lang Sciences
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Brain Sciences > Div of Psychology and Lang Sciences > Institute of Cognitive Neuroscience
URI: http://discovery.ucl.ac.uk/id/eprint/1557865
Downloads since deposit
53Downloads
Download activity - last month
Download activity - last 12 months
Downloads by country - last 12 months

Archive Staff Only

View Item View Item