Zhang Heming, Meng Chun, Di Xin, Wu Xiao, Biswal Bharat
The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Laboratory for Neuroinformation, Center for Information in Medicine, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China.
Department of Biomedical Engineering, New Jersey Institute of Technology, Newark, NJ, USA.
Netw Neurosci. 2023 Oct 1;7(3):1034-1050. doi: 10.1162/netn_a_00314. eCollection 2023.
Assessment of functional connectivity (FC) has revealed a great deal of knowledge about the macroscale spatiotemporal organization of the brain network. Recent studies found task-versus-rest network reconfigurations were crucial for cognitive functioning. However, brain network reconfiguration remains unclear among different cognitive states, considering both aggregate and time-resolved FC profiles. The current study utilized static FC (sFC, i.e., long timescale aggregate FC) and sliding window-based dynamic FC (dFC, i.e., short timescale time-varying FC) approaches to investigate the similarity and alterations of edge weights and network topology at different cognitive loads, particularly their relationships with specific cognitive process. Both dFC/sFC networks showed subtle but significant reconfigurations that correlated with task performance. At higher cognitive load, brain network reconfiguration displayed increased functional integration in the sFC-based aggregate network, but faster and larger variability of modular reorganization in the dFC-based time-varying network, suggesting difficult tasks require more integrated and flexible network reconfigurations. Moreover, sFC-based network reconfigurations mainly linked with the sensorimotor and low-order cognitive processes, but dFC-based network reconfigurations mainly linked with the high-order cognitive process. Our findings suggest that reconfiguration profiles of sFC/dFC networks provide specific information about cognitive functioning, which could potentially be used to study brain function and disorders.
功能连接性（FC）评估揭示了大量关于脑网络宏观时空组织的知识。最近的研究发现，任务与静息网络的重新配置对认知功能至关重要。然而，考虑到总体和时间分辨的FC概况，不同认知状态下的脑网络重新配置仍不清楚。当前的研究利用静态FC（sFC，即长时间尺度的总体FC）和基于滑动窗口的动态FC（dFC，即短时间尺度的时变FC）方法，来研究不同认知负荷下边缘权重和网络拓扑的相似性和变化，特别是它们与特定认知过程的关系。dFC/sFC网络均显示出与任务表现相关的细微但显著的重新配置。在较高认知负荷下，脑网络重新配置在基于sFC的总体网络中表现为功能整合增加，但在基于dFC的时变网络中模块化重组的变异性更快且更大，这表明困难任务需要更整合且灵活的网络重新配置。此外，基于sFC的网络重新配置主要与感觉运动和低阶认知过程相关，而基于dFC的网络重新配置主要与高阶认知过程相关。我们的研究结果表明，sFC/dFC网络的重新配置概况提供了有关认知功能的特定信息，这可能潜在地用于研究脑功能和疾病。
