在自适应控制过程中,大型网络的灵活整合和分离
Yilu Li1, Yanqing Wang2, Antao Chen3
1The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Laboratory for Neuroinformation, Center for Information in Medicine, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 611731, China.
Behavioural brain research
|June 2, 2023
概括
适应性控制动态调整认知过程. 大脑网络的集成和分离灵活地支持这一点,增加了高要求的集成和更容易解决冲突的分离.
科学领域:
- 神经科学是一个神经科学.
- 认知神经科学 认知神经科学
- 网络科学 网络科学
背景情况:
- 适应性控制涉及到对环境需求的动态认知调整.
- 通过集成和分离的网络重新配置,为认知任务的神经结构提供了洞察力.
- 大脑网络架构与自适应控制之间的确切关系仍然不完全理解.
研究的目的:
- 研究大脑网络的整合和分离如何支持适应性控制.
- 通过适应性控制需求分析图形理论指标的调制.
- 确定网络指标是否可以预测认知控制环境.
主要方法:
- 量化全脑网络集成 (全球效率,参与系数,互子网络效率) 和分离 (局部效率,模块化).
- 分析了这些图形理论指标在不同程度的认知控制需求 (冲突比例) 下如何变化.
- 使用多变量分类器使用图表指标来预测认知上下文条件.
主要成果:
- 在罕见的冲突 (高需求) 期间,网络集成在前端-双层网络 (FPN),视觉网络 (VIN) 和感官-运动网络 (SMN) 中增加.
- 网络分离在线圈-操作网络 (CON) 和默认模式网络 (DMN) 中,增强了冲突比例 (更容易解决冲突).
- 一个多变量分类器准确地预测了使用图表指标作为特征的上下文条件.
结论:
- 大规模的大脑网络通过灵活的整合和分离来支持适应性控制.
- 集成和分离之间的动态相互作用优化了基于任务需求的认知控制.
- 网络拓提供了认知控制状态的预测签名.
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