A Functional Cartography of Cognitive Systems.
A Functional Cartography of Cognitive Systems.
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DOI：
10.1371/journal.pcbi.1004533
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发表时间：
2015-12
影响因子：
4.3
通讯作者：
Bassett DS
中科院分区：
文献类型：
--
作者：
Mattar MG;Cole MW;Thompson-Schill SL;Bassett DS
人类大脑最显著的特征之一是它能够快速有效地适应外部任务需求。例如，新的和非常规的任务的执行速度比结构连接的形成速度要快。这些动力学的神经基础还远未被理解。在这里，我们开发并应用网络科学中的新方法来量化人类受试者执行64种不同任务时大脑区域之间的功能连接模式如何重新配置。通过应用动态社区检测算法，我们确定了形成假定功能社区的大脑区域组，并发现了这些区域在64个任务电池中的变化。我们通过量化两个大脑区域在不同任务中参与同一网络社区（或假定的功能模块）的概率来总结这些重构模式。这些工具使我们能够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、扣带回-盖和显着系统）动态地参与跨任务的内聚网络社区。我们定义的网络角色，认知系统在这些动态沿着以下两个维度：（i）稳定性与灵活性和（ii）连接与孤立。因此，每个系统的作用可以通过该系统在64项任务中的稳定性以及该系统与其他系统的交互一致性来概括。使用这种地图，经典定义的认知系统可以被归类为短暂的整合者，稳定的孤独者，以及介于两者之间的任何东西。我们的研究结果提供了一个新的概念框架，了解动态整合和招聘的认知系统，使行为适应性的任务和休息条件。这项工作具有重要的意义，了解认知网络重构在不同的任务集及其关系的认知努力，认知表现的个体差异，疲劳。当我们一天都在工作时，我们的大脑必须根据我们的内部目标快速适应。这些修改通常发生在一个固定的结构架构的限制，从而表现为快速变化的招聘和整合的大脑区域符合任务的要求。例如，当我们执行视觉任务时，视觉系统的区域优先被招募，运动区域在执行动作时被招募，额叶网络通过差异整合将信号偏向其他网络，默认模式网络在内省期间被激活，而不是任务执行。然而，目前还没有一个共同的框架，在这个框架下，每一个认知系统在一项任务中的作用可以以一种规范的方式相对于其他系统和大脑的其他部分来定义。在这里，我们解决这个问题，通过形式化的基于网络的理论框架中，每个认知系统的作用是基于其水平的招聘和集成定义动态跨电池的认知任务。
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. As we go about the day, our brains must quickly adapt in accordance with our internal goals. These modifications generally occur within the constraints of a fixed structural architecture, thus manifesting as rapid changes in the recruitment of and integration between brain regions conforming to task demands. For example, regions of the visual system are preferentially recruited as we perform a visual task, motor regions are recruited in the execution of actions, frontal networks bias signals to other networks by differentially integrating across them, and the default mode network is activated during introspection as opposed to task execution. However, there is as yet no common framework under which the role of each cognitive system in a task can be defined in a normative way with respect to both other systems and the rest of the brain. Here, we address this issue by formalizing a network-based theoretical framework in which the role of each cognitive system is defined based on its level of recruitment and integration defined dynamically across a battery of cognitive tasks.