Subgraphs of functional brain networks identify dynamical constraints of cognitive control

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.

大脑解剖结构与生理机能支撑人类在复杂的感知与行为空间中完成适配的能力。为在瞬息万变的心理状态图景中完成切换与适配，大脑会调用认知控制——一系列动态过程，通过激活与停用不同脑区群组，调节注意力、切换任务并抑制优势反应（prepotent responses）。当前理论认为，大脑活动的相关与反相关模式，或许代表了脑区间支撑适应性行为的合作与竞争交互作用。本研究采用定量方法，识别功能交互的不同拓扑基序（topological motifs），并探究其表达模式与认知控制过程及行为表现的关联。具体而言，本研究在28名健康受试者完成两项认知控制任务时采集其功能磁共振成像（fMRI）血氧水平依赖（BOLD）信号：一项为斯特鲁普干扰任务（Stroop interference task），另一项为采用纳文图形（Navon figures）的局部-全局感知切换任务，两项任务均设置低、高两种认知控制负荷条件。基于上述数据，本研究构建动态脑功能网络，并采用一种基于部件的网络分解技术——非负矩阵分解（non-negative matrix factorization）——来识别推定的认知控制子图，这些子图的时间表达模式能够捕捉合作与竞争控制过程不同阶段所涉及的分布式网络结构。研究结果表明，这些子图的时间表达模式随认知负荷变化而波动，并与个体间的任务表现差异相关。本研究结果为理解认知系统的合作与竞争角色如何协同变化，从而勾勒出认知负荷相关的脑状态间的转变轨迹提供了新的视角。