Ericson Julia, Ruiz Ibáñez Nieves, Lundqvist Mikael, Klingberg Torkel
Department of Neuroscience, Karolinska Institutet, Stockholm, Sweden.
Department of Clinical Neuroscience, Karolinska Institutet, Stockholm, Sweden.
Nat Commun. 2025 Jun 25;16(1):5381. doi: 10.1038/s41467-025-60821-2.
Cognitive processing relies on the brain's ability to balance flexibility for encoding new information with stability for maintaining it. We examined these dynamics in three magnetoencephalography (MEG) datasets of visuospatial working memory (vsWM) tasks. Across all tasks, we identified four distinct networks in the theta and alpha bands, which were used to define functional states. Optimal transitioning rate between states was associated with better cognitive performance. Further, two of the states were linked to flexibility and stability, respectively: an encoding state dominated by a posterior theta and a maintenance state dominated by a dorsal alpha. We simulated the states in an in-silico model with biologically realistic cortical connectivity. The model, featuring spiking and oscillatory cortical layers interacting via phase-amplitude coupling, demonstrated how frequency and spatial region could modulate information flow. Our findings suggest a cognitive control mechanism, where selective transitions between large-scale networks optimize information flow, enabling both stable and flexible visual representations.
认知处理依赖于大脑平衡编码新信息的灵活性与维持信息的稳定性的能力。我们在三个视觉空间工作记忆（vsWM）任务的脑磁图（MEG）数据集中研究了这些动态变化。在所有任务中，我们在θ波和α波段识别出四个不同的网络，这些网络被用来定义功能状态。状态之间的最佳转换率与更好的认知表现相关。此外，其中两个状态分别与灵活性和稳定性相关：一个由后部θ波主导的编码状态和一个由背侧α波主导的维持状态。我们在具有生物学现实皮质连接性的计算机模拟模型中模拟了这些状态。该模型具有通过相位-振幅耦合相互作用的脉冲发放和振荡皮质层，展示了频率和空间区域如何调节信息流。我们的研究结果提出了一种认知控制机制，其中大规模网络之间的选择性转换优化了信息流，从而实现稳定且灵活的视觉表征。
