功能脑网络可控性建模与分析

In recent years, both network neuroscience and cognitive science have developed vigorously. The network approach provides an analytical perspective of understanding the brain structures and functions, and uncovers the intrinsic correlation between them. However, this family of methods pays more attention on the discovery and pattern recognition in the phenomenon,thus lack a mechanistic explanation of why and how this correlation happens. In this proposal, focusing on the core scientific problem of modeling the human cognitive control, we proceed from the functional MRI signals and study the following subproblems: 1) building the control theoretical dynamics model based on the blood oxygen level neural signals; 2 ) developing the framework of control theory analysis of human brain functional network; 3) investigating the relationship between functional control measures and the descriptive cognitive measurement. Through research and summary, we devote ourselves to explaining the network dynamical foundation of the process of human cognitive control, and make use of the relevant analysis framework to help understand the complicated cognitive processes of human beings. We hope that through this project, we will make groundbreaking achievements in scientific research to further promote the exploration of the working principles of the brain and contribute to the development of brain-based artificial intelligence in the future.

近年来，网络神经科学与认知科学都得到了蓬勃发展，网络方法提供了理解脑结构与功能的分析视角，揭示了很多外在功能和内在结构的关联性。然而这些研究更多地关注于现象的发现与模式的识别，对网络功能及与结构与功能之间的关联性缺乏一个机制上的解释。在本项目中，针对人类认知控制原理建模这一核心科学问题，我们从功能核磁信号出发，主要研究如下一些子问题：1）基于血氧水平神经信号的控制理论动力学模型构建；2）人脑功能网络的控制理论分析框架构建；3）功能控制度量与认知表征度量关系探究。通过研究与总结，我们致力于解释认知控制过程发生的结构与功能网络动力学基础，比利用相关分析框架来帮助理解人类复杂的认知过程。我们希望通过本项目做出具有开创性的科研成果，进一步推进人类对于脑工作原理的探究，为将来发展类脑人工智能做出贡献。

脑网络控制分析是网络神经科学的一个蓬勃发展的新方向，在理解大脑结构和功能的关系、神经发育规律、精神疾病等领域都有着广阔的应用前景。然而，目前的工作主要集中于结构脑网络的可控性分析上，对于功能脑网络可控性直接进行建模并利用控制理论对其分析的工作尚未详细展开。因此，本研究立足于功能脑网络的控制理论模型建模，进而分析脑状态转移的路径及其可控性相关性质，并进一步构建个体水平上的功能可控性度量，探究功能可控性与结构可控性之间的关系，揭示了脑网络可控性与人类认知能力之间的关联；并基于脑网络特征启发，构建了脑启发的算法验证了脑结构与功能的优化性和其所呈现特征的必要性。

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