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功能脑网络的子图识别认知控制的动力学约束。

Subgraphs of functional brain networks identify dynamical constraints of cognitive control.

机构信息

Department of Bioengineering, University of Pennsylvania, Philadelphia, Pennsylvania, United States of America.

Department of Psychology, Drexel University, Philadelphia, Pennsylvania, United States of America.

出版信息

PLoS Comput Biol. 2018 Jul 6;14(7):e1006234. doi: 10.1371/journal.pcbi.1006234. eCollection 2018 Jul.

Abstract

Brain anatomy and physiology support the human ability to navigate a complex space of perceptions and actions. To maneuver across an ever-changing landscape of mental states, the brain invokes cognitive control-a set of dynamic processes that engage and disengage different groups of brain regions to modulate attention, switch between tasks, and inhibit prepotent responses. Current theory posits that correlated and anticorrelated brain activity may signify cooperative and competitive interactions between brain areas that subserve adaptive behavior. In this study, we use a quantitative approach to identify distinct topological motifs of functional interactions and examine how their expression relates to cognitive control processes and behavior. In particular, we acquire fMRI BOLD signal in twenty-eight healthy subjects as they perform two cognitive control tasks-a Stroop interference task and a local-global perception switching task using Navon figures-each with low and high cognitive control demand conditions. Based on these data, we construct dynamic functional brain networks and use a parts-based, network decomposition technique called non-negative matrix factorization to identify putative cognitive control subgraphs whose temporal expression captures distributed network structures involved in different phases of cooperative and competitive control processes. Our results demonstrate that temporal expression of the subgraphs fluctuate alongside changes in cognitive demand and are associated with individual differences in task performance. These findings offer insight into how coordinated changes in the cooperative and competitive roles of cognitive systems map trajectories between cognitively demanding brain states.

摘要

大脑解剖学和生理学支持人类在复杂的感知和行为空间中导航的能力。为了在不断变化的心理状态景观中操纵,大脑调用认知控制——一组动态过程,这些过程使不同的大脑区域参与和脱离,以调节注意力、在任务之间切换,并抑制优势反应。目前的理论假设,相关和反相关的大脑活动可能表示服务于适应性行为的大脑区域之间的合作和竞争相互作用。在这项研究中,我们使用定量方法来识别功能相互作用的不同拓扑模式,并研究它们的表达如何与认知控制过程和行为相关。具体来说,我们在二十八位健康受试者执行两项认知控制任务时获取 fMRI BOLD 信号——一项 Stroop 干扰任务和一项使用 Navon 图形的局部-全局感知转换任务——每个任务都有低和高认知控制需求条件。基于这些数据,我们构建动态功能大脑网络,并使用基于部分的网络分解技术,即非负矩阵分解,来识别潜在的认知控制子图,其时间表达捕获了参与合作和竞争控制过程不同阶段的分布式网络结构。我们的结果表明,子图的时间表达随认知需求的变化而波动,并与任务表现的个体差异相关。这些发现提供了关于认知系统合作和竞争角色的协调变化如何映射认知需求大脑状态之间轨迹的见解。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0ead/6056061/64ad4dc015fd/pcbi.1006234.g001.jpg

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