Lise Meitner Research Group Cognition and Plasticity, Max Planck Institute for Human Cognitive and Brain Sciences, 04103 Leipzig, Germany.
Cereb Cortex. 2022 Nov 9;32(22):5050-5071. doi: 10.1093/cercor/bhab531.
Human cognition is organized in distributed networks in the brain. Although distinct specialized networks have been identified for different cognitive functions, previous work also emphasizes the overlap of key cognitive domains in higher level association areas. The majority of previous studies focused on network overlap and dissociation during resting states whereas task-related network interactions across cognitive domains remain largely unexplored. A better understanding of network overlap and dissociation during different cognitive tasks may elucidate flexible (re-)distribution of resources during human cognition. The present study addresses this issue by providing a broad characterization of large-scale network dynamics in three key cognitive domains. Combining prototypical tasks of the larger domains of attention, language, and social cognition with whole-brain multivariate activity and connectivity approaches, we provide a spatiotemporal characterization of multiple large-scale, overlapping networks that differentially interact across cognitive domains. We show that network activity and interactions increase with increased cognitive complexity across domains. Interaction patterns reveal a common core structure across domains as well as dissociable domain-specific network activity. The observed patterns of activation and deactivation of overlapping and strongly coupled networks provide insight beyond region-specific activity within a particular cognitive domain toward a network perspective approach across diverse key cognitive functions.
人类认知是大脑中分布式网络的组织。尽管已经确定了不同认知功能的不同专门网络,但以前的工作也强调了在更高层次的联合区域中关键认知领域的重叠。大多数先前的研究都集中在静息状态下的网络重叠和分离上,而跨认知领域的任务相关网络相互作用在很大程度上仍未得到探索。更好地理解不同认知任务期间的网络重叠和分离可以阐明人类认知过程中资源的灵活(重新)分配。本研究通过对三个关键认知领域的大规模网络动态进行广泛表征来解决这个问题。我们将注意力、语言和社会认知等较大领域的典型任务与全脑多变量活动和连接方法相结合,提供了跨认知领域的多个大规模、重叠网络的时空特征。我们表明,随着认知复杂度的增加,网络活动和相互作用也会增加。相互作用模式揭示了跨领域的共同核心结构以及可分离的领域特定网络活动。观察到的重叠和强耦合网络的激活和去激活模式提供了超越特定认知领域内特定区域活动的见解,朝着跨不同关键认知功能的网络视角方法发展。