Center for Cognitive Neuroscience and Department of Psychology and Neuroscience, Duke University, Durham, North Carolina 27708, and Department of Psychology, Rutgers University, Newark, New Jersey 07102.
J Neurosci. 2014 Jan 15;34(3):932-40. doi: 10.1523/JNEUROSCI.4227-13.2014.
Efforts to understand the functional architecture of the brain have consistently identified multiple overlapping large-scale neural networks that are observable across multiple states. Despite the ubiquity of these networks, it remains unclear how regions within these large-scale neural networks interact to orchestrate behavior. Here, we collected functional magnetic resonance imaging data from 188 human subjects who engaged in three cognitive tasks and a resting-state scan. Using multiple tasks and a large sample allowed us to use split-sample validations to test for replication of results. We parceled the task-rest pairs into functional networks using a probabilistic spatial independent components analysis. We examined changes in connectivity between task and rest states using dual-regression analysis, which quantifies voxelwise connectivity estimates for each network of interest while controlling for the influence of signals arising from other networks and artifacts. Our analyses revealed systematic state-dependent functional connectivity in one brain region: the precuneus. Specifically, task performance led to increased connectivity (compared to rest) between the precuneus and the left frontoparietal network (lFPN), whereas rest increased connectivity between the precuneus and the default-mode network (DMN). The absolute magnitude of this effect was greater for DMN, suggesting a heightened specialization for resting-state cognition. All results replicated within the two independent samples. Our results indicate that the precuneus plays a core role not only in DMN, but also more broadly through its engagement under a variety of processing states.
努力理解大脑的功能结构一直确定了多个重叠的大规模神经网络,这些网络在多个状态下都可以观察到。尽管这些网络无处不在,但仍不清楚这些大规模神经网络中的区域如何相互作用来协调行为。在这里,我们从 188 名参与了三项认知任务和一项静息状态扫描的人类受试者中收集了功能磁共振成像数据。使用多种任务和大量样本,我们可以使用分样本验证来测试结果的复制。我们使用概率空间独立成分分析将任务 - 休息对分为功能网络。我们使用双回归分析检查任务和休息状态之间的连接变化,该分析量化了每个感兴趣网络的体素连接估计值,同时控制了来自其他网络和伪影的信号的影响。我们的分析揭示了大脑一个区域中存在系统的状态依赖功能连接:楔前叶。具体来说,与休息相比,任务表现导致楔前叶和左侧额顶网络(lFPN)之间的连接增加,而休息则增加了楔前叶和默认模式网络(DMN)之间的连接。DMN 的这种效应的绝对值更大,表明静息状态认知的专业化程度更高。所有结果在两个独立样本中均得到复制。我们的结果表明,楔前叶不仅在 DMN 中起着核心作用,而且通过在各种处理状态下的参与,更广泛地起着核心作用。