Data from: A functional cartography of cognitive systems

One of the most remarkable features of the human brain is its ability to adapt rapidly and efficiently to external task demands. Novel and non-routine tasks, for example, are implemented faster than structural connections can be formed. The neural underpinnings of these dynamics are far from understood. Here we develop and apply novel methods in network science to quantify how patterns of functional connectivity between brain regions reconfigure as human subjects perform 64 different tasks. By applying dynamic community detection algorithms, we identify groups of brain regions that form putative functional communities, and we uncover changes in these groups across the 64-task battery. We summarize these reconfiguration patterns by quantifying the probability that two brain regions engage in the same network community (or putative functional module) across tasks. These tools enable us to demonstrate that classically defined cognitive systems—including visual, sensorimotor, auditory, default mode, fronto-parietal, cingulo-opercular and salience systems—engage dynamically in cohesive network communities across tasks. We define the network role that a cognitive system plays in these dynamics along the following two dimensions: (i) stability vs. flexibility and (ii) connected vs. isolated. The role of each system is therefore summarized by how stably that system is recruited over the 64 tasks, and how consistently that system interacts with other systems. Using this cartography, classically defined cognitive systems can be categorized as ephemeral integrators, stable loners, and anything in between. Our results provide a new conceptual framework for understanding the dynamic integration and recruitment of cognitive systems in enabling behavioral adaptability across both task and rest conditions. This work has important implications for understanding cognitive network reconfiguration during different task sets and its relationship to cognitive effort, individual variation in cognitive performance, and fatigue.

人类大脑最显著的特征之一，便是能够快速且高效地适配外部任务需求。例如，新颖且非常规的任务，其执行速度快于结构连接形成的速率。支撑这些动态特性的神经基础仍远未被阐明。在此，我们开发并应用网络科学领域的全新方法，量化人类受试者完成64项不同任务时，脑区间功能连接模式如何发生重配置。通过应用动态社区检测（dynamic community detection）算法，我们识别出构成推定功能社区的脑区群组，并揭示了该64项任务组中这些群组的变化情况。我们通过量化两个脑区在各项任务中同属一个网络社区（或推定功能模块）的概率，来总结这些重配置模式。借助这些工具，我们得以证明：经典定义的认知系统——包括视觉、感觉运动、听觉、默认模式、额顶叶（fronto-parietal）、扣带回-岛叶（cingulo-opercular）以及突显（salience）系统——在各项任务中均能动态参与到具有凝聚力的网络社区之中。我们从以下两个维度定义认知系统在这些动态过程中扮演的网络角色：(i) 稳定性与灵活性，(ii) 连接性与孤立性。因此，每个系统的角色可通过其在64项任务中被募集的稳定性，以及其与其他系统交互的一致性来概括。借助这一认知系统网络角色图谱，经典定义的认知系统可被归类为短暂整合者、稳定孤立者，以及介于两者之间的各类角色。我们的研究结果为理解认知系统在任务与静息状态下实现行为适应性的动态整合与募集机制，提供了全新的概念框架。本研究对于理解不同任务范式下认知网络的重配置，及其与认知努力、认知表现的个体差异以及疲劳之间的关联，具有重要意义。