Controlling brain dynamics: Landscape and transition path for working memory.

IF 4.3 2区生物学 PLoS Computational Biology Pub Date : 2023-09-05 eCollection Date: 2023-09-01 DOI:10.1371/journal.pcbi.1011446

Leijun Ye, Jianfeng Feng, Chunhe Li

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引用次数: 2

Abstract

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.

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控制大脑动力学：工作记忆的景观和过渡路径。

理解大脑的潜在动力机制并控制它是脑科学中的一个关键问题。能源景观和过渡路径方法为应对这些挑战提供了一条可能的途径。在这里，以工作记忆为例，我们基于大型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全脑状态转换的控制，以响应任务需求。动态转换路径揭示了信息流动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转换，证明了联想区域，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，景观控制方法有助于制定大脑疾病的治疗策略。

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来源期刊

PLoS Computational Biology 生物-生化研究方法

CiteScore

7.10

自引率

4.70%

发文量

820

期刊介绍： PLOS Computational Biology features works of exceptional significance that further our understanding of living systems at all scales—from molecules and cells, to patient populations and ecosystems—through the application of computational methods. Readers include life and computational scientists, who can take the important findings presented here to the next level of discovery. Research articles must be declared as belonging to a relevant section. More information about the sections can be found in the submission guidelines. Research articles should model aspects of biological systems, demonstrate both methodological and scientific novelty, and provide profound new biological insights. Generally, reliability and significance of biological discovery through computation should be validated and enriched by experimental studies. Inclusion of experimental validation is not required for publication, but should be referenced where possible. Inclusion of experimental validation of a modest biological discovery through computation does not render a manuscript suitable for PLOS Computational Biology. Research articles specifically designated as Methods papers should describe outstanding methods of exceptional importance that have been shown, or have the promise to provide new biological insights. The method must already be widely adopted, or have the promise of wide adoption by a broad community of users. Enhancements to existing published methods will only be considered if those enhancements bring exceptional new capabilities.