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Metastable neural dynamics underlies cognitive performance across multiple behavioural paradigms.
Human Brain Mapping ( IF 3.5 ) Pub Date : 2020-04-17 , DOI: 10.1002/hbm.25009 Thomas H Alderson 1, 2 , Arun L W Bokde 3 , J A Scott Kelso 1, 4 , Liam Maguire 1 , Damien Coyle 1
Human Brain Mapping ( IF 3.5 ) Pub Date : 2020-04-17 , DOI: 10.1002/hbm.25009 Thomas H Alderson 1, 2 , Arun L W Bokde 3 , J A Scott Kelso 1, 4 , Liam Maguire 1 , Damien Coyle 1
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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.
中文翻译：
亚稳态神经动力学是多种行为范式认知表现的基础。
尽管静息状态网络与多种认知能力相关，但仍不清楚这些局部区域如何协调一致地表达特定的认知操作。理论和经验说明表明，大规模静止状态网络通过在其协调动力学的亚稳态状态下运行来调和整合和分离的双重倾向。亚稳定性可以通过将分布式局部区域绑定到大规模神经认知网络中来赋予重要的行为品质。我们通过分析一大群健康个体 ( N = 566) 的功能磁共振成像数据并比较大脑大规模静息网络架构在休息时和执行多项任务期间的亚稳定性来测试这一假设。使用定义明确的集体变量来估计亚稳定性，该变量捕获大规模网络之间随时间的“锁相”水平。基于任务的推理的主要特点是认知控制网络的高亚稳定性和感觉处理区域的低亚稳定性。尽管静息状态网络之间的亚稳定性在任务执行过程中增加，但认知能力与自发活动的联系更为密切。认知控制网络内在连通性的高亚稳定性与新颖的问题解决或流体智力有关，但在依赖于先前经验或结晶智力的任务中不太重要。至关重要的是，具有与任务总体安排类似或“预先配置”的静息架构的受试者表现出卓越的认知表现。综上所述，我们的研究结果支持大脑皮层大规模网络的自发亚稳定性与认知之间的关键联系。
更新日期：2020-04-17
中文翻译：
亚稳态神经动力学是多种行为范式认知表现的基础。
尽管静息状态网络与多种认知能力相关，但仍不清楚这些局部区域如何协调一致地表达特定的认知操作。理论和经验说明表明，大规模静止状态网络通过在其协调动力学的亚稳态状态下运行来调和整合和分离的双重倾向。亚稳定性可以通过将分布式局部区域绑定到大规模神经认知网络中来赋予重要的行为品质。我们通过分析一大群健康个体 ( N = 566) 的功能磁共振成像数据并比较大脑大规模静息网络架构在休息时和执行多项任务期间的亚稳定性来测试这一假设。使用定义明确的集体变量来估计亚稳定性，该变量捕获大规模网络之间随时间的“锁相”水平。基于任务的推理的主要特点是认知控制网络的高亚稳定性和感觉处理区域的低亚稳定性。尽管静息状态网络之间的亚稳定性在任务执行过程中增加，但认知能力与自发活动的联系更为密切。认知控制网络内在连通性的高亚稳定性与新颖的问题解决或流体智力有关，但在依赖于先前经验或结晶智力的任务中不太重要。至关重要的是，具有与任务总体安排类似或“预先配置”的静息架构的受试者表现出卓越的认知表现。综上所述，我们的研究结果支持大脑皮层大规模网络的自发亚稳定性与认知之间的关键联系。
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