2016/11/30 by Takahiro Ezaki, Takamitsu Watanabe, Masayuki Ohzeki +1
Biochemistry, Genetics and Molecular Biology · Neuroscience · Physics and Astronomy · #Artificial neural network #Embodied and Extended Cognition #Energy (signal processing) #Entropy (arrow of time) #Experimental data #Functional Brain Connectivity Studies #Functional magnetic resonance imaging #Neuroimaging #Neuroinformatics #Principle of maximum entropy #Statistical Mechanics and Entropy #Systems neuroscience #cond-mat.stat-mech #physics.data-an #q-bio.NC
paper · pdf · doi:10.1098/rsta.2016.0287
published as Phil. Trans. R. Soc. A 375, 20160287 (2017) · 22 pages, 4 figures, 1 table
openalex created_date 2016/11/30 · openalex publication_date 2017/05/15 · arxiv created 2017/05/25 · arxiv updated 2017/05/26 · openalex updated_date 2026/08/08
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'.