Sir Peter Mansfield Imaging Centre, School of Physics and Astronomy, University of Nottingham, Nottingham, UK.
Faculty of Electrical Engineering, Mathematics and Computer Science, Delft University of Technology, Delft, The Netherlands.
Hum Brain Mapp. 2022 Oct 1;43(14):4475-4491. doi: 10.1002/hbm.25967. Epub 2022 Jun 1.
How temporal modulations in functional interactions are shaped by the underlying anatomical connections remains an open question. Here, we analyse the role of structural eigenmodes, in the formation and dissolution of temporally evolving functional brain networks using resting-state magnetoencephalography and diffusion magnetic resonance imaging data at the individual subject level. Our results show that even at short timescales, phase and amplitude connectivity can partly be expressed by structural eigenmodes, but hardly by direct structural connections. Albeit a stronger relationship was found between structural eigenmodes and time-resolved amplitude connectivity. Time-resolved connectivity for both phase and amplitude was mostly characterised by a stationary process, superimposed with very brief periods that showed deviations from this stationary process. For these brief periods, dynamic network states were extracted that showed different expressions of eigenmodes. Furthermore, the eigenmode expression was related to overall cognitive performance and co-occurred with fluctuations in community structure of functional networks. These results implicate that ongoing time-resolved resting-state networks, even at short timescales, can to some extent be understood in terms of activation and deactivation of structural eigenmodes and that these eigenmodes play a role in the dynamic integration and segregation of information across the cortex, subserving cognitive functions.
功能相互作用中的时变调制如何受到基础解剖连接的影响，这仍然是一个悬而未决的问题。在这里，我们使用个体水平的静息态脑磁图和弥散磁共振成像数据，分析了结构本征模在随时间演变的功能脑网络的形成和溶解中的作用。我们的研究结果表明，即使在短时间尺度上，相位和幅度连接也可以部分由结构本征模来表示，但几乎不能由直接的结构连接来表示。尽管在结构本征模和时间分辨幅度连接之间发现了更强的关系。对于相位和幅度的时间分辨连接，主要由一个静态过程来描述，叠加了非常短暂的时期，这些时期表现出与这个静态过程的偏离。对于这些短暂的时期，提取了动态网络状态，表现出不同的本征模表达。此外，本征模的表达与整体认知表现有关，并且与功能网络的社区结构波动有关。这些结果表明，即使在短时间尺度上，持续的时间分辨静息状态网络在一定程度上可以用结构本征模的激活和失活来理解，并且这些本征模在大脑皮层的信息动态整合和分离中发挥作用，支持认知功能。
