Sir Peter Mansfield Imaging Centre, School of Physics and Astronomy, University of Nottingham, Nottingham, United Kingdom.
Oxford Centre for Human Brain Activity, Department of Psychiatry, University of Oxford, United Kingdom.
Neuroimage. 2019 Feb 1;186:211-220. doi: 10.1016/j.neuroimage.2018.10.079. Epub 2018 Nov 3.
Functional networks obtained from magnetoencephalography (MEG) from different frequency bands show distinct spatial patterns. It remains to be elucidated how distinct spatial patterns in MEG networks emerge given a single underlying structural network. Recent work has suggested that the eigenmodes of the structural network might serve as a basis set for functional network patterns in the case of functional MRI. Here, we take this notion further in the context of frequency band specific MEG networks. We show that a selected set of eigenmodes of the structural network can predict different frequency band specific networks in the resting state, ranging from delta (1-4 Hz) to the high gamma band (40-70 Hz). These predictions outperform predictions based from surrogate data, suggesting a genuine relationship between eigenmodes of the structural network and frequency specific MEG networks. We then show that the relevant set of eigenmodes can be excited in a network of neural mass models using linear stability analysis only by including delays. Excitation of an eigenmode in this context refers to a dynamic instability of a network steady state to a spatial pattern with a corresponding coherent temporal oscillation. Simulations verify the results from linear stability analysis and suggest that theta, alpha and beta band networks emerge very near to the bifurcation. The delta and gamma bands in the resting state emerges further away from the bifurcation. These results show for the first time how delayed interactions can excite the relevant set of eigenmodes that give rise to frequency specific functional connectivity patterns.
从不同频带的脑磁图（MEG）获得的功能网络显示出独特的空间模式。目前尚不清楚在单个基础结构网络的情况下，MEG 网络的独特空间模式是如何出现的。最近的工作表明，结构网络的本征模式可能成为功能磁共振成像情况下功能网络模式的基础集。在这里，我们在特定频带的 MEG 网络背景下进一步探讨了这一概念。我们表明，结构网络的一组选定本征模式可以预测静息状态下不同频带特定网络，从 delta（1-4 Hz）到高 gamma 带（40-70 Hz）。这些预测优于基于替代数据的预测，表明结构网络的本征模式与特定频带 MEG 网络之间存在真正的关系。然后，我们表明，可以通过仅包括延迟，使用线性稳定性分析在神经网络质量模型网络中激发相关的本征模式集。在这种情况下，激发本征模式是指网络稳态到具有相应相干时间振荡的空间模式的动态不稳定性。模拟验证了线性稳定性分析的结果，并表明θ、α和β频带网络非常接近分岔出现。静息状态下的δ和γ频带离分岔更远。这些结果首次表明，延迟相互作用如何激发产生特定频率功能连接模式的相关本征模式集。
