NCS-FO: Collaborative Research: Analysis, prediction, and control of synchronized neural activity

了解健康和疾病状态下人类大脑的解剖结构及其功能之间的关系，不仅可以设计出新颖、有针对性、无创、高效的神经系统疾病治疗方法，还可以为创新刺激方案的应用提供信息，以提高认知表现和执行能力。利用最先进的传感和成像技术获得的数据，该项目通过创新地研究人类大脑，将其作为一个由神经元集合和白质纤维组成的动态网络系统，并受类似于社会和技术网络物理网络的原则支配，来实现这些目标。该项目开发并验证了新的严谨理论和工具，以解决网络神经科学中的一个突出问题。也就是说，利用大脑解剖结构来表征、预测和控制同步神经活动的模式，并用现实的大脑数据验证这些方法。该项目不仅将有助于网络、控制和神经科学的理论，而且还将通过利用不同层次的抽象（来自扩散成像数据的大脑表征、皮质电图时间序列、数学模型）和不同的学科方法，促进它们的整合。除了研究大脑同步活动的新方法并为下一代诊断提供信息外，该项目还开展了影响深远的教学和推广活动，包括(i)在研究生和本科生层面上的一些大学层面的倡议，（ii）将吸引来自费城和河滨当地社区的年轻人的推广活动，（iii）传播活动，将传统上分离的社区聚集在一起，促进多学科倡议，以解决神经科学中一些最紧迫的问题。这个项目的核心假设是，大脑的互联结构决定了它的表现，并控制着它在健康和患病状态之间的转变。基于这一假设，本项目解决了从稀疏和粗糙的时间测量和干预中描述、预测和控制人脑同步神经活动模式的未解决问题。此外，为了支持假设和验证神经同步理论，该项目利用了三个独特而广泛的多模态神经成像数据集，结合高分辨率皮质电成像和扩散成像，将允许评估同步模式和潜在结构网络架构之间的关系。具体来说，这个项目是围绕两个主要任务组织的。任务1将控制神经活动模式的问题抽象为控制相互连接的非线性振荡器之间的同步程度的问题，其中振荡器代表大脑区域，它们的相互连接反映了通过扩散磁共振成像重建的人脑解剖结构。提出了改变同步模式的想法，可能是对振荡器的互连结构和权重进行微小修改的结果，并且可以通过最小和局部的结构干预来恢复理想的模式。任务2使用经验数据来获得推论，以补充任务1中正式理论和建模工作中获得的推论。因为这里的重点是分析、预测和控制集群的同步，经验的努力仍然局限于研究功能神经成像数据与明确的电图特征的同步。具体来说，该项目使用皮质电图数据，它比功能性磁共振成像具有明显更高的时间分辨率，并且不会受到脑电图和脑磁图中更常见的体积传导问题的困扰。该项目融合并扩展了控制和网络理论、动力系统、数据分析和网络神经科学等工具。虽然该项目侧重于神经活动中的同步问题，但这些方法在工程中具有广泛的适用性，例如设计优化网络和稀疏控制器，网络神经科学和网络科学。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

Understanding the relations between the anatomical structure of the human brain and its functions in healthy and diseased states can not only lead to the design of novel, targeted, non-invasive, and highly-effective treatments for neurological disorders, but also inform the application of innovative stimulation schemes to enhance cognitive performance and executive capabilities. Leveraging data obtained with state-of-the-art sensing and imaging technologies, this project pursues these objectives by innovatively studying the human brain as a dynamic network system comprising neuronal ensembles and white-matter fibers, and as governed by principles similar to social and technological cyber-physical networks. This project develops and validates new rigorous theories and tools to address an outstanding problem in network neuroscience. Namely, to leverage the brain anatomical structure to characterize, predict, and control patterns of synchronized neural activity, and to validate the methods with realistic brain data. This project will not only contribute to the theories of networks, controls, and neuroscience, but also to their integration, by leveraging different levels of abstraction (brain representations from diffusion imaging data, electrocorticography time series, mathematical models) and distinct disciplinary approaches. In addition to new methods to study synchronized activity in the brain and inform the next generation of diagnostics, this project pursues far-reaching teaching and outreach activities, including (i) a number of university-level initiatives at the graduate and undergraduate levels, (ii) outreach activities that will engage young people from the local communities in Philadelphia and Riverside, and (iii) dissemination activities that will bring together traditionally separated communities and promote multi-disciplinary initiatives to tackle some of the most pressing problems in neuroscience.The central hypothesis of this project is that the interconnected structure of the brain determines its performance and controls its transitions between healthy and diseased states. Building on this hypothesis, this project addresses the unsolved problems of characterizing, predicting, and controlling patterns of synchronized neural activity in the human brain from sparse and coarse temporal measurements and interventions. Additionally, to support the hypothesis and validate the theories of neural synchronization, the project leverages three unique and extensive multimodal neuroimaging datasets combining high-resolution electrocorticography and diffusion imaging that will allow to assess the relations between synchronization patterns and underlying structural network architecture. Specifically, this project is organized around two main tasks. Task 1, abstracts the problem of controlling patterns of neural activity as the problem of controlling the degree of synchronization among interconnected nonlinear oscillators, where oscillators represent brain regions and their interconnections reflect the anatomy of the human brain as reconstructed by diffusion magnetic resonance imaging. The idea is put forth that altered synchronization patterns are the results of, possibly small, modifications to the oscillators' interconnection structure and weights, and that desirable patterns can be restored by minimal and localized structural interventions. Task 2 uses empirical data to obtain inferences complementing those acquired in the formal theoretical and modeling work in Task 1. Because the focus here is the analysis, prediction, and control of cluster synchronization, the empirical efforts remain constrained to the study of functional neuroimaging data with clear electrographic signatures of synchronization. Specifically, the project uses electrocorticography data, which boasts markedly greater temporal resolution than functional magnetic resonance imaging and does not suffer from the issues of volume conduction that are more common in electroencephalography and magnetoencephalography. The project blends and extends tools from control and network theories, dynamical systems, data analysis, and network neuroscience. While this project focuses on synchronization problems in neural activity, the methods have broad applicability in engineering, for instance to design optimized networks and sparse controllers, network neuroscience, and network science.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.