Cai Weidong, Ryali Srikanth, Pasumarthy Ramkrishna, Talasila Viswanath, Menon Vinod
Department of Psychiatry and Behavioral Sciences, Stanford University School of Medicine, Stanford, CA, USA.
Wu Tsai Neurosciences Institute, Stanford University School of Medicine, Stanford, CA, USA.
Nat Commun. 2021 Jun 29;12(1):3314. doi: 10.1038/s41467-021-23509-x.
Control processes associated with working memory play a central role in human cognition, but their underlying dynamic brain circuit mechanisms are poorly understood. Here we use system identification, network science, stability analysis, and control theory to probe functional circuit dynamics during working memory task performance. Our results show that dynamic signaling between distributed brain areas encompassing the salience (SN), fronto-parietal (FPN), and default mode networks can distinguish between working memory load and predict performance. Network analysis of directed causal influences suggests the anterior insula node of the SN and dorsolateral prefrontal cortex node of the FPN are causal outflow and inflow hubs, respectively. Network controllability decreases with working memory load and SN nodes show the highest functional controllability. Our findings reveal dissociable roles of the SN and FPN in systems control and provide novel insights into dynamic circuit mechanisms by which cognitive control circuits operate asymmetrically during cognition.
与工作记忆相关的控制过程在人类认知中起着核心作用,但其潜在的动态脑回路机制却鲜为人知。在这里,我们使用系统识别、网络科学、稳定性分析和控制理论来探究工作记忆任务执行过程中的功能回路动力学。我们的结果表明,包含突显网络(SN)、额顶网络(FPN)和默认模式网络的分布式脑区之间的动态信号能够区分工作记忆负荷并预测表现。对定向因果影响的网络分析表明,SN的前岛叶节点和FPN的背外侧前额叶皮层节点分别是因果流出和流入枢纽。网络可控性随工作记忆负荷而降低,且SN节点表现出最高的功能可控性。我们的研究结果揭示了SN和FPN在系统控制中的不同作用,并为认知控制回路在认知过程中不对称运作的动态回路机制提供了新的见解。