Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China.
Shanghai Center for Mathematical Sciences, Fudan University, Shanghai, China.
PLoS Comput Biol. 2023 Sep 5;19(9):e1011446. doi: 10.1371/journal.pcbi.1011446. eCollection 2023 Sep.
Understanding the underlying dynamical mechanisms of the brain and controlling it is a crucial issue in brain science. The energy landscape and transition path approach provides a possible route to address these challenges. Here, taking working memory as an example, we quantified its landscape based on a large-scale macaque model. The working memory function is governed by the change of landscape and brain-wide state switching in response to the task demands. The kinetic transition path reveals that information flow follows the direction of hierarchical structure. Importantly, we propose a landscape control approach to manipulate brain state transition by modulating external stimulation or inter-areal connectivity, demonstrating the crucial roles of associative areas, especially prefrontal and parietal cortical areas in working memory performance. Our findings provide new insights into the dynamical mechanism of cognitive function, and the landscape control approach helps to develop therapeutic strategies for brain disorders.
理解大脑的基本动力学机制并对其进行控制是脑科学中的一个关键问题。能量景观和跃迁路径方法为解决这些挑战提供了一条可能的途径。在这里,我们以工作记忆为例,基于大规模猕猴模型对其景观进行了量化。工作记忆功能受景观变化和大脑整体状态切换的控制,以响应任务需求。动力学跃迁路径揭示了信息流遵循层次结构的方向。重要的是,我们提出了一种景观控制方法,通过调节外部刺激或区域间连接来操纵大脑状态的跃迁,这表明了关联区域(特别是前额叶和顶叶皮层区域)在工作记忆表现中的关键作用。我们的发现为认知功能的动力学机制提供了新的见解,而景观控制方法有助于为大脑疾病开发治疗策略。