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Large-Scale Brain Networks Underlying Successful and Unsuccessful Encoding, Maintenance, and Retrieval of Everyday Scenes in Visuospatial Working Memory

机译:视觉空间工作记忆中每天场景成功和失败的编码,维护和检索的大规模脑网络

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摘要

Recent research on working memory (WM) identified the contribution of several large-scale brain networks operating during WM tasks, such as the frontoparietal attention network (AN), the default mode network (DMN), and the salience network (SN). To date, however, the dynamical interplay among these networks is largely unexplored during successful or unsuccessful WM performance, especially with complex and ecological stimuli. Here we systematically characterized the selective contribution of these networks during a visuospatial WM task requiring the encoding, maintenance and retrieval of real-life scenes. While undergoing fMRI scans, participants were presented with everyday life visual scenes for 4 s (encoding phase). After a delay of 8 s (maintenance phase), participants were presented with a target-object extracted from the previous scene. Participants had to judge whether the target-object was presented at the same or in a different location compared to the original scene (retrieval phase) and then provide a confidence judgment. Using the independent component analysis (ICA), we found that subsequent remembering was associated with the activity of the AN at encoding, the attention and SN at maintenance, plus the visual network at retrieval. Conversely, subsequent forgetting was associated with the activity of the DMN at maintenance, and the SN at retrieval. Overall, these findings reveal a dynamical interplay between large-scale brain networks during visuospatial WM performance related to complex, real-life stimuli.
机译:对工作记忆(WM)的最新研究确定了在WM任务期间运行的几个大型大脑网络的贡献,例如额顶额叶注意力网络(AN),默认模式网络(DMN)和显着网络(SN)。然而,迄今为止,在成功或失败的WM表演期间,尤其是在复杂和生态刺激的情况下,这些网络之间的动态相互作用尚待开发。在这里,我们系统地描述了在视觉空间WM任务中需要对真实场景进行编码,维护和检索的这些网络的选择性贡献。在进行fMRI扫描时,向参与者展示了4 s的日常生活视觉场景(编码阶段)。经过8 s的延迟(维护阶段)后,向参与者展示了从先前场景中提取的目标对象。参与者必须判断目标对象是与原始场景相比处于相同还是不同的位置(检索阶段),然后提供置信度判断。使用独立成分分析(ICA),我们发现随后的记忆与编码时AN的活动,维护时的注意力和SN以及检索时的可视网络有关。相反,随后的遗忘与维护时DMN的活动以及获取时的SN相关。总体而言,这些发现揭示了在视觉空间WM表现期间,与复杂的现实生活刺激相关的大规模大脑网络之间的动态相互作用。

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