Department of Clinical Psychology and Psychobiology, Faculty of Psychology, University of Barcelona, Passeig de la Vall d'Hebron, 171, 08035, Barcelona, Spain.
Institut de Neurociències, University of Barcelona, Campus Mundet, Edifici de PonentPasseig de la Vall d'Hebron, 171, 08035, Barcelona, Spain.
Neuroinformatics. 2023 Jan;21(1):71-88. doi: 10.1007/s12021-022-09610-6. Epub 2022 Nov 14.
There is a growing interest in the neuroscience community on the advantages of multilayer functional brain networks. Researchers usually treated different frequencies separately at distinct functional brain networks. However, there is strong evidence that these networks share complementary information while their interdependencies could reveal novel findings. For this purpose, neuroscientists adopt multilayer networks, which can be described mathematically as an extension of trivial single-layer networks. Multilayer networks have become popular in neuroscience due to their advantage to integrate different sources of information. Here, Ι will focus on the multi-frequency multilayer functional connectivity analysis on resting-state fMRI (rs-fMRI) recordings. However, constructing a multilayer network depends on selecting multiple pre-processing steps that can affect the final network topology. Here, I analyzed the rs-fMRI dataset from a single human performing scanning over a period of 18 months (84 scans in total), and the rs-fMRI dataset containing 25 subjects with 3 repeat scans. I focused on assessing the reproducibility of multi-frequency multilayer topologies exploring the effect of two filtering methods for extracting frequencies from BOLD activity, three connectivity estimators, with or without a topological filtering scheme, and two spatial scales. Finally, I untangled specific combinations of researchers' choices that yield consistently brain networks with repeatable topologies, giving me the chance to recommend best practices over consistent topologies.
神经科学界越来越关注多层功能脑网络的优势。研究人员通常在不同的功能脑网络中分别处理不同的频率。然而，有强有力的证据表明，这些网络共享互补信息，而它们的相互依存关系可能揭示新的发现。为此，神经科学家采用了多层网络，从数学上可以描述为简单单层网络的扩展。由于其整合不同信息源的优势，多层网络在神经科学中变得流行起来。在这里，我将重点介绍静息态 fMRI（rs-fMRI）记录的多频多层功能连接分析。然而，构建多层网络取决于选择多个预处理步骤，这些步骤可能会影响最终的网络拓扑结构。在这里，我分析了一个人在 18 个月（总共 84 次扫描）期间进行扫描的 rs-fMRI 数据集，以及包含 25 名受试者、3 次重复扫描的 rs-fMRI 数据集。我专注于评估多频多层拓扑结构的可重复性，探索从 BOLD 活动中提取频率的两种过滤方法、三种连接性估计器（有无拓扑过滤方案）和两种空间尺度的影响。最后，我梳理了研究人员选择的特定组合，这些组合产生了具有可重复拓扑结构的一致脑网络，使我有机会推荐一致拓扑结构的最佳实践。
