Cognitive task information is transferred between brain regions via resting-state network topology

• Published in: Nature communications Vol. 8; no. 1; pp. 1027 - 14
• Main Authors: Ito, Takuya, Kulkarni, Kaustubh R., Schultz, Douglas H., Mill, Ravi D., Chen, Richard H., Solomyak, Levi I., Cole, Michael W.
• Format: Journal Article
• Language: English
• Published: London Nature Publishing Group UK 18.10.2017; Nature Publishing Group; Nature Portfolio
• Subjects: 631/378/116/1925; 631/378/116/2394; 631/378/116/2395; 631/378/2649/2150; Brain; Cognition; Cognitive ability; Computational neuroscience; Humanities and Social Sciences; Information transfer; Mapping; multidisciplinary; Network topologies; Neural networks; Science; Science (multidisciplinary); Topology
• ISSN: 2041-1723; 2041-1723
• 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. Resting-state functional connections have been associated with cognitive abilities but it is unclear how these connections contribute to cognition. Here Ito et al present a new approach, information transfer mapping, showing that task-relevant information can be predicted by estimated activity flow through resting-state networks.