Static and dynamic functional connectome reveals reconfiguration profiles of whole-brain network across cognitive states.

IF 3.6 3区 医学 Q2 NEUROSCIENCES
Network Neuroscience Pub Date : 2023-10-01 eCollection Date: 2023-01-01 DOI:10.1162/netn_a_00314
Heming Zhang, Chun Meng, Xin Di, Xiao Wu, Bharat Biswal
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Abstract

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.

Abstract Image

Abstract Image

Abstract Image

静态和动态功能连接组揭示了整个大脑网络在认知状态下的重构特征。
功能连接(FC)的评估揭示了大量关于大脑网络宏观时空组织的知识。最近的研究发现,任务与休息网络的重新配置对认知功能至关重要。然而,考虑到聚合和时间分辨的FC特征,不同认知状态下的脑网络重构仍不清楚。当前的研究利用静态FC(sFC,即长时间尺度聚合FC)和基于滑动窗口的动态FC(dFC,即短时间尺度时变FC)方法来研究不同认知负载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网络都显示出与任务性能相关的微妙但显著的重新配置。在更高的认知负荷下,脑网络重构在基于sFC的聚合网络中表现出更强的功能整合,但在基于dFC的时变网络中,模块化重组的变化更快、更大,这表明困难的任务需要更集成、更灵活的网络重配置。此外,基于sFC的网络重构主要与感觉运动和低阶认知过程相关,而基于dFC的网络重组主要与高阶认知过程有关。我们的研究结果表明,sFC/dFC网络的重构图谱提供了有关认知功能的特定信息,有可能用于研究大脑功能和疾病。
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来源期刊
Network Neuroscience
Network Neuroscience NEUROSCIENCES-
CiteScore
6.40
自引率
6.40%
发文量
68
审稿时长
16 weeks
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