Ezaki Takahiro, Watanabe Takamitsu, Ohzeki Masayuki, Masuda Naoki
National Institute of Informatics, Hitotsubashi, Chiyoda-ku, Tokyo, Japan.
Kawarabayashi Large Graph Project, ERATO, JST, c/o Global Research Center for Big Data Mathematics, NII, Chiyoda-ku, Tokyo, Japan.
Philos Trans A Math Phys Eng Sci. 2017 Jun 28;375(2096). doi: 10.1098/rsta.2016.0287.
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'.
计算神经科学模型已被用于理解大脑中的神经动力学，以及当生理或其他条件发生变化时神经动力学可能如何改变。我们回顾并开发了一种用于神经影像数据的数据驱动方法，称为能量景观分析。这些方法源于统计物理理论，特别是伊辛模型，也被称为（成对）最大熵模型和玻尔兹曼机。这些方法已在神经科学中用于拟合电生理数据长达十年，但它们在神经影像数据中的应用仍处于起步阶段。我们首先回顾这些方法，并讨论一些算法和技术方面。然后，我们将这些方法应用于从健康个体记录的功能磁共振成像数据，以考察拟合精度、待分析脑系统大小和数据长度之间的关系。本文是主题为“医学中的数学方法：神经科学、心脏病学和病理学”特刊的一部分。
