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2017 ; 375
(2096
): ä Nephropedia Template TP
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Energy landscape analysis of neuroimaging data
#MMPMID28507232
Ezaki T
; Watanabe T
; Ohzeki M
; Masuda N
Philos Trans A Math Phys Eng Sci
2017[Jun]; 375
(2096
): ä PMID28507232
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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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