MRC Cognition & Brain Sciences Unit, University of Cambridge, Cambridge, UK.
Neuroscience and Aphasia Research Unit (NARU), Division of Neuroscience & Experimental Psychology (Zochonis Building), University of Manchester, Manchester, UK.
Cortex. 2019 Apr;113:279-297. doi: 10.1016/j.cortex.2018.12.019. Epub 2019 Jan 14.
Resting-state networks (RSNs; groups of regions consistently co-activated without an explicit task) are hugely influential in modern brain research. Despite this popularity, the link between specific RSNs and their functions remains elusive, limiting the impact on cognitive neuroscience (where the goal is to link cognition to neural systems). Here we present a series of logical steps to formally test the relationship between a coherent RSN with a cognitive domain. This approach is applied to a challenging and significant test-case; extracting a recently-proposed semantic RSN, determining its relation with a well-known RSN, the default mode network (DMN), and assessing their roles in semantic cognition. Results showed the DMN and semantic network are two distinct coherent RSNs. Assessing the cognitive signature of these spatiotemporally coherent networks directly (and therefore accounting for overlapping networks) showed involvement of the proposed semantic network, but not the DMN, in task-based semantic cognition. Following the steps presented here, researchers could formally test specific hypotheses regarding the function of RSNs, including other possible functions of the DMN.
静息态网络（RSN；一组在没有明确任务的情况下始终一致地共同激活的区域）在现代脑科学研究中具有巨大的影响力。尽管如此，特定的 RSN 与其功能之间的联系仍然难以捉摸，限制了它们在认知神经科学中的应用（认知神经科学的目标是将认知与神经系统联系起来）。在这里，我们提出了一系列逻辑步骤，以正式测试与认知领域一致的 RSN 之间的关系。这种方法应用于一个具有挑战性和重要意义的测试案例；提取最近提出的语义 RSN，确定它与众所周知的默认模式网络（DMN）之间的关系，并评估它们在语义认知中的作用。结果表明，DMN 和语义网络是两个不同的一致 RSN。直接评估这些时空一致网络的认知特征（因此考虑到重叠网络）表明，所提出的语义网络而不是 DMN 参与了基于任务的语义认知。按照这里提出的步骤，研究人员可以正式测试关于 RSN 功能的具体假设，包括 DMN 的其他可能功能。
