Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China.
National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China.
Hum Brain Mapp. 2018 Sep;39(9):3546-3557. doi: 10.1002/hbm.24193. Epub 2018 Apr 20.
Different cognitively demanding tasks recruit globally distributed but functionally specific networks. However, the configuration of core networks and their reconfiguration patterns across cognitive loads remain unclear, as does whether these patterns are indicators for the performance of cognitive tasks. In this study, we analyzed functional magnetic resonance imaging data of a large cohort of 448 subjects, acquired with the brain at resting state and executing N-back working memory (WM) tasks. We discriminated core networks by functional interaction strength and connection flexibility. Results demonstrated that the frontoparietal network (FPN) and default mode network (DMN) were core networks, but each exhibited different patterns across cognitive loads. The FPN and DMN both showed strengthened internal connections at the low demand state (0-back) compared with the resting state (control level); whereas, from the low (0-back) to high demand state (2-back), some connections to the FPN weakened and were rewired to the DMN (whose connections all remained strong). Of note, more intensive reconfiguration of both the whole brain and core networks (but no other networks) across load levels indicated relatively poor cognitive performance. Collectively these findings indicate that the FPN and DMN have distinct roles and reconfiguration patterns across cognitively demanding loads. This study advances our understanding of the core networks and their reconfiguration patterns across cognitive loads and provides a new feature to evaluate and predict cognitive capability (e.g., WM performance) based on brain networks.
不同认知要求的任务会募集全球分布但功能特定的网络。然而,核心网络的配置及其在认知负荷下的重新配置模式仍然不清楚,这些模式是否是认知任务表现的指标也不清楚。在这项研究中,我们分析了 448 名受试者的大量功能磁共振成像数据,这些数据是在大脑处于休息状态和执行 N 回工作记忆(WM)任务时获得的。我们通过功能相互作用强度和连接灵活性来区分核心网络。结果表明,额顶网络(FPN)和默认模式网络(DMN)是核心网络,但它们在认知负荷下表现出不同的模式。与休息状态(对照水平)相比,FPN 和 DMN 在低需求状态(0 回)下都显示出内部连接的增强;然而,从低(0 回)到高需求状态(2 回),一些连接到 FPN 的连接减弱,并重新连接到 DMN(其连接均保持较强)。值得注意的是,整个大脑和核心网络(而非其他网络)在负荷水平上的更强烈的重新配置表明认知表现相对较差。总的来说,这些发现表明 FPN 和 DMN 在认知要求的负荷下具有不同的作用和重新配置模式。这项研究提高了我们对核心网络及其在认知负荷下的重新配置模式的理解,并提供了一个新的特征来评估和预测基于大脑网络的认知能力(例如 WM 表现)。