Department of Mathematics, East Carolina University, 124 Austin Building, East Fifth Street, Greenville, NC, 27858-4353, USA.
Department of Physics and Computer Science, Wilfrid Laurier University, Science Building, Room N2078, 75 University Ave. W., Waterloo, ON, Canada, N2L 3C5.
Adv Exp Med Biol. 2017;988:1-29. doi: 10.1007/978-3-319-56246-9_1.
Mathematical representations of brain networks in neuroscience through the use of graph theory may be very useful for the understanding of neurological diseases and disorders and such an explanatory power is currently under intense investigation. Graph metrics are expected to vary across subjects and are likely to reflect behavioural and cognitive performances. The challenge is to set up a framework that can explain how behaviour, cognition, memory, and other brain properties can emerge through the combined interactions of neurons, ensembles of neurons, and larger-scale brain regions that make information transfer possible. "Hidden" graph theoretic properties in the construction of brain networks may limit or enhance brain functionality and may be representative of aspects of human psychology. As theorems emerge from simple mathematical properties of graphs, similarly, cognition and behaviour may emerge from the molecular, cellular and brain region substrate interactions. In this review report, we identify some studies in the current literature that have used graph theoretical metrics to extract neurobiological conclusions, we briefly discuss the link with the human connectome project as an effort to integrate human data that may aid the study of emergent patterns and we suggest a way to start categorizing diseases according to their brain network pathologies as these are measured by graph theory.
通过使用图论对神经科学中的大脑网络进行数学表示，可能非常有助于理解神经疾病和障碍，目前正在深入研究这种解释能力。图度量预计会因个体而异，并且可能反映行为和认知表现。挑战在于建立一个框架，可以解释行为、认知、记忆和其他大脑特性如何通过神经元、神经元集合和更大规模的大脑区域的组合相互作用而出现，这些区域使信息传递成为可能。在大脑网络的构建中，“隐藏”的图论性质可能会限制或增强大脑功能，并且可能代表人类心理学的某些方面。随着从图的简单数学性质中得出定理，认知和行为也可能从分子、细胞和大脑区域基质相互作用中出现。在本综述报告中，我们确定了当前文献中一些使用图论度量来提取神经生物学结论的研究，我们简要讨论了与人类连接组计划的联系，该计划是为了整合可能有助于研究涌现模式的人类数据，我们建议根据大脑网络病理学对疾病进行分类的方法，因为这些是通过图论来衡量的。
