Division of Neuroscience and Experimental Psychology, University of Manchester, Manchester, UK.
Hum Brain Mapp. 2023 Jun 1;44(8):3007-3022. doi: 10.1002/hbm.26258. Epub 2023 Mar 7.
Graph theory has been used in cognitive neuroscience to understand how organisational properties of structural and functional brain networks relate to cognitive function. Graph theory may bridge the gap in integration of structural and functional connectivity by introducing common measures of network characteristics. However, the explanatory and predictive value of combined structural and functional graph theory have not been investigated in modelling of cognitive performance of healthy adults. In this work, a Principal Component Regression approach with embedded Step-Wise Regression was used to fit multiple regression models of Executive Function, Self-regulation, Language, Encoding and Sequence Processing with a collection of 20 different graph theoretic measures of structural and functional network organisation used as regressors. The predictive ability of graph theory-based models was compared to that of connectivity-based models. The present work shows that using combinations of graph theory metrics to predict cognition in healthy populations does not produce a consistent benefit relative to making predictions based on structural and functional connectivity values directly.
图论已被用于认知神经科学，以了解结构和功能脑网络的组织属性如何与认知功能相关。图论可以通过引入网络特征的常用测量方法来弥合结构连接和功能连接整合的差距。然而，在健康成年人认知表现的建模中，尚未研究组合结构和功能图论的解释和预测价值。在这项工作中，使用具有嵌入式逐步回归的主成分回归方法，使用作为回归量的结构和功能网络组织的 20 种不同图论度量的集合来拟合执行功能、自我调节、语言、编码和序列处理的多元回归模型。基于图论的模型的预测能力与基于连接的模型进行了比较。目前的工作表明，相对于直接基于结构和功能连接值进行预测，使用图论指标的组合来预测健康人群的认知并不会产生一致的好处。
