Breedt Lucas C, Santos Fernando A N, Hillebrand Arjan, Reneman Liesbeth, van Rootselaar Anne-Fleur, Schoonheim Menno M, Stam Cornelis J, Ticheler Anouk, Tijms Betty M, Veltman Dick J, Vriend Chris, Wagenmakers Margot J, van Wingen Guido A, Geurts Jeroen J G, Schrantee Anouk, Douw Linda
Department of Anatomy and Neurosciences, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam Neuroscience, The Netherlands.
Institute of Advanced Studies, University of Amsterdam, The Netherlands.
Netw Neurosci. 2023 Jan 1;7(1):299-321. doi: 10.1162/netn_a_00284. eCollection 2023.
Executive functioning (EF) is a higher order cognitive process that is thought to depend on a network organization facilitating integration across subnetworks, in the context of which the central role of the fronto-parietal network (FPN) has been described across imaging and neurophysiological modalities. However, the potentially complementary unimodal information on the relevance of the FPN for EF has not yet been integrated. We employ a multilayer framework to allow for integration of different modalities into one 'network of networks.' We used diffusion MRI, resting-state functional MRI, MEG, and neuropsychological data obtained from 33 healthy adults to construct modality-specific single-layer networks as well as a single multilayer network per participant. We computed single-layer and multilayer eigenvector centrality of the FPN as a measure of integration in this network and examined their associations with EF. We found that higher multilayer FPN centrality, but not single-layer FPN centrality, was related to better EF. We did not find a statistically significant change in explained variance in EF when using the multilayer approach as compared to the single-layer measures. Overall, our results show the importance of FPN integration for EF and underline the promise of the multilayer framework toward better understanding cognitive functioning.
执行功能（EF）是一种高阶认知过程，被认为依赖于一个促进跨子网络整合的网络组织，在这种背景下，额顶叶网络（FPN）在成像和神经生理模式中的核心作用已被描述。然而，关于FPN与EF相关性的潜在互补单模态信息尚未整合。我们采用一个多层框架，将不同模态整合到一个“网络的网络”中。我们使用扩散磁共振成像、静息态功能磁共振成像、脑磁图和从33名健康成年人那里获得的神经心理学数据，为每个参与者构建特定模态的单层网络以及单个多层网络。我们计算FPN的单层和多层特征向量中心性，以此作为该网络中整合的一种度量，并检查它们与EF的关联。我们发现，较高的多层FPN中心性而非单层FPN中心性与更好的EF相关。与单层测量相比，使用多层方法时，我们未发现EF解释方差有统计学上的显著变化。总体而言，我们的结果表明FPN整合对EF的重要性，并强调多层框架在更好理解认知功能方面的前景。
