Department of Psychology, Northwestern University, Evanston, IL 60208, United States.
Interdepartmental Neuroscience Program, Northwestern University, Chicago, IL 60611, United States.
Neuroimage. 2021 Apr 1;229:117743. doi: 10.1016/j.neuroimage.2021.117743. Epub 2021 Jan 14.
Recent work has demonstrated that individual-specific variations in functional networks (termed "network variants") can be identified in individuals using resting state functional magnetic resonance imaging (fMRI). These network variants exhibit reliability over time, suggesting that they may be trait-like markers of individual differences in brain organization. However, while networks variants are reliable at rest, is is still untested whether they are stable between task and rest states. Here, we use precision data from the Midnight Scan Club (MSC) to demonstrate that (1) task data can be used to identify network variants reliably, (2) these network variants show substantial spatial overlap with those observed in rest, although state-specific effects are present, (3) network variants assign to similar canonical functional networks in task and rest states, and (4) single tasks or a combination of multiple tasks produce similar network variants to rest. Together, these findings further reinforce the trait-like nature of network variants and demonstrate the utility of using task data to define network variants.
最近的研究表明，使用静息态功能磁共振成像（fMRI）可以在个体中识别出功能网络的个体特异性变异（称为“网络变体”）。这些网络变体随着时间的推移具有可靠性，表明它们可能是大脑组织个体差异的特质样标记。然而，尽管网络变体在静息状态下是可靠的，但它们在任务和静息状态之间是否稳定仍未得到验证。在这里，我们使用 Midnight Scan Club (MSC) 的精确数据来证明：(1) 可以使用任务数据可靠地识别网络变体；(2) 这些网络变体与在静息状态下观察到的网络变体具有显著的空间重叠，尽管存在特定状态的影响；(3) 网络变体在任务和静息状态下分配到相似的经典功能网络；(4) 单个任务或多个任务的组合产生与静息状态相似的网络变体。总之，这些发现进一步强化了网络变体的特质样性质，并证明了使用任务数据来定义网络变体的有效性。
