Ito Takuya, Kulkarni Kaustubh R, Schultz Douglas H, Mill Ravi D, Chen Richard H, Solomyak Levi I, Cole Michael W
Center for Molecular and Behavioral Neuroscience, Rutgers University, Newark, NJ, 07102, USA.
Behavioral and Neural Sciences Graduate Program, Rutgers University, Newark, NJ, 07102, USA.
Nat Commun. 2017 Oct 18;8(1):1027. doi: 10.1038/s41467-017-01000-w.
Resting-state network connectivity has been associated with a variety of cognitive abilities, yet it remains unclear how these connectivity properties might contribute to the neurocognitive computations underlying these abilities. We developed a new approach-information transfer mapping-to test the hypothesis that resting-state functional network topology describes the computational mappings between brain regions that carry cognitive task information. Here, we report that the transfer of diverse, task-rule information in distributed brain regions can be predicted based on estimated activity flow through resting-state network connections. Further, we find that these task-rule information transfers are coordinated by global hub regions within cognitive control networks. Activity flow over resting-state connections thus provides a large-scale network mechanism for cognitive task information transfer and global information coordination in the human brain, demonstrating the cognitive relevance of resting-state network topology.
静息态网络连通性已与多种认知能力相关联，但这些连通性属性如何对这些能力背后的神经认知计算产生作用仍不清楚。我们开发了一种新方法——信息传递映射，以检验静息态功能网络拓扑描述携带认知任务信息的脑区之间计算映射的假设。在此，我们报告，基于通过静息态网络连接估计的活动流，可以预测分布式脑区中不同任务规则信息的传递。此外，我们发现这些任务规则信息传递由认知控制网络内的全局枢纽区域协调。因此，静息态连接上的活动流为人类大脑中的认知任务信息传递和全局信息协调提供了一种大规模网络机制，证明了静息态网络拓扑的认知相关性。
