Alderson Thomas H, Bokde Arun L W, Kelso J A Scott, Maguire Liam, Coyle Damien
Intelligent Systems Research Centre, Ulster University, Antrim, United Kingdom.
Beckman Institute for Advanced Science and Technology, University of Illinois at Urbana-Champaign, Urbana, Illinois, United States.
Hum Brain Mapp. 2020 Aug 15;41(12):3212-3234. doi: 10.1002/hbm.25009. Epub 2020 Apr 17.
Despite resting state networks being associated with a variety of cognitive abilities, it remains unclear how these local areas act in concert to express particular cognitive operations. Theoretical and empirical accounts indicate that large-scale resting state networks reconcile dual tendencies towards integration and segregation by operating in a metastable regime of their coordination dynamics. Metastability may confer important behavioural qualities by binding distributed local areas into large-scale neurocognitive networks. We tested this hypothesis by analysing fMRI data in a large cohort of healthy individuals (N = 566) and comparing the metastability of the brain's large-scale resting network architecture at rest and during the performance of several tasks. Metastability was estimated using a well-defined collective variable capturing the level of 'phase-locking' between large-scale networks over time. Task-based reasoning was principally characterised by high metastability in cognitive control networks and low metastability in sensory processing areas. Although metastability between resting state networks increased during task performance, cognitive ability was more closely linked to spontaneous activity. High metastability in the intrinsic connectivity of cognitive control networks was linked to novel problem solving or fluid intelligence, but was less important in tasks relying on previous experience or crystallised intelligence. Crucially, subjects with resting architectures similar or 'pre-configured' to a task-general arrangement demonstrated superior cognitive performance. Taken together, our findings support a key linkage between the spontaneous metastability of large-scale networks in the cerebral cortex and cognition.
尽管静息态网络与多种认知能力相关,但这些局部区域如何协同作用以表达特定的认知操作仍不清楚。理论和实证研究表明,大规模静息态网络通过在其协调动力学的亚稳态下运行来协调整合和分离的双重趋势。亚稳态可能通过将分布的局部区域绑定到大规模神经认知网络中赋予重要的行为特质。我们通过分析一大群健康个体(N = 566)的功能磁共振成像(fMRI)数据,并比较大脑大规模静息网络结构在静息状态和执行多项任务期间的亚稳态,来检验这一假设。使用一个定义明确的集体变量来估计亚稳态,该变量捕获大规模网络随时间的“锁相”水平。基于任务的推理主要表现为认知控制网络中的高亚稳态和感觉处理区域中的低亚稳态。尽管在任务执行期间静息态网络之间的亚稳态增加,但认知能力与自发活动的联系更为紧密。认知控制网络内在连接性的高亚稳态与新颖问题解决或流体智力相关,但在依赖先前经验或晶体智力的任务中不太重要。至关重要的是,具有与任务通用安排相似或“预配置”的静息结构的受试者表现出卓越的认知能力。综上所述,我们的研究结果支持大脑皮层中大规模网络的自发亚稳态与认知之间的关键联系。