Department of Psychology & York Biomedical Research Institute, University of York, York, UK; MRC Cognition & Brain Sciences Unit, University of Cambridge, Cambridge, UK.
MRC Cognition & Brain Sciences Unit, University of Cambridge, Cambridge, UK.
Cortex. 2023 Aug;165:141-159. doi: 10.1016/j.cortex.2022.12.016. Epub 2023 May 16.
Resting-state network research is extremely influential, yet the functions of many networks remain unknown. In part, this is due to typical (e.g., univariate) analyses independently testing the function of individual regions and not examining the full set of regions that form a network whilst co-activated. Connectivity is dynamic and the function of a region may change based on its current connections. Therefore, determining the function of a network requires assessment at this network-level. Yet popular theories implicating the default mode network (DMN) in episodic memory and social cognition, rest principally upon analyses performed at the level of individual brain regions. Here we use independent component analysis to formally test the role of the DMN in episodic and social processing at the network level. As well as an episodic retrieval task, two independent datasets were employed to assess DMN function across the breadth of social cognition; a person knowledge judgement and a theory of mind task. Each task dataset was separated into networks of co-activated regions. In each, the co-activated DMN, was identified through comparison to an a priori template and its relation to the task model assessed. This co-activated DMN did not show greater activity in episodic or social tasks than high-level baseline conditions. Thus, no evidence was found to support hypotheses that the co-activated DMN is involved in explicit episodic or social tasks at a network-level. The networks associated with these processes are described. Implications for prior univariate findings and the functional significance of the co-activated DMN are considered.
静息态网络研究极具影响力，但许多网络的功能仍然未知。部分原因在于，典型的（例如，单变量）分析独立地测试了单个区域的功能，而没有检查在共同激活时构成网络的完整区域集。连接是动态的，一个区域的功能可能会根据其当前的连接而改变。因此，确定网络的功能需要在网络层面进行评估。然而，将默认模式网络（DMN）牵涉到情节记忆和社会认知的流行理论，主要基于在单个脑区水平上进行的分析。在这里，我们使用独立成分分析（ICA）正式测试 DMN 在情节和社会处理中的网络层面的作用。除了情节检索任务外，我们还使用了两个独立的数据集来评估 DMN 在社会认知广泛范围内的功能；一个是人物知识判断任务，另一个是心理理论任务。每个任务数据集都被分割为共同激活的区域网络。在每个网络中，通过与先验模板的比较以及对任务模型的评估，确定了共同激活的 DMN。与高级基线条件相比，共同激活的 DMN 在情节或社会任务中并没有显示出更高的活动。因此，没有发现证据支持共同激活的 DMN 参与网络层面的明确情节或社会任务的假设。描述了与这些过程相关的网络。考虑了对先前单变量发现的影响以及共同激活的 DMN 的功能意义。
