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

了解人类大脑的解剖结构及其在健康和疾病状态下的功能之间的关系，不仅可以设计新颖，有针对性，非侵入性和高效的神经系统疾病治疗方法，而且还可以应用创新的刺激方案来提高认知能力和执行能力。利用最先进的传感和成像技术获得的数据，该项目通过创新性地研究人类大脑作为一个动态网络系统来实现这些目标，该系统包括神经元集合和白质纤维，并受类似于社会和技术网络物理网络的原理支配。该项目开发和验证新的严格的理论和工具，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，并利用真实的大脑数据来验证这些方法。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，皮层电图时间序列，数学模型的大脑表示）和不同的学科方法来整合它们。除了研究大脑同步活动和为下一代诊断提供信息的新方法外，该项目还开展了影响深远的教学和推广活动，包括（i）研究生和本科生一级的一些大学一级的举措，（ii）吸引费城和滨江当地社区年轻人参与的推广活动，以及（iii）传播活动，将传统上分离的社区聚集在一起，该项目的中心假设是，大脑的相互连接的结构决定了它的性能，并控制着它在健康和健康之间的转换。和病态的国家基于这一假设，该项目解决了表征，预测和控制人类大脑中同步神经活动模式的未解决问题，这些模式来自稀疏和粗糙的时间测量和干预。此外，为了支持这一假设并验证神经同步理论，该项目利用了三个独特而广泛的多模态神经成像数据集，这些数据集结合了高分辨率皮质电图和扩散成像，可以评估同步模式与底层结构网络架构之间的关系。具体而言，该项目围绕两个主要任务组织。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，并且它们的相互连接反映了通过扩散磁共振成像重建的人脑的解剖结构。提出的想法是，改变同步模式的结果，可能是小的，修改振荡器的互连结构和权重，并且所需的模式可以通过最小的和本地化的结构干预恢复。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。因为这里的重点是集群同步的分析，预测和控制，经验的努力仍然局限于功能性神经影像数据的研究与明确的同步电图签名。具体来说，该项目使用的是皮层脑电图数据，其时间分辨率明显高于功能性磁共振成像，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影响。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的工具。虽然该项目的重点是神经活动中的同步问题，但该方法在工程中具有广泛的适用性，例如设计优化的网络和稀疏控制器，网络神经科学和网络科学。该奖项反映了NSF的法定使命，并通过使用基金会的智力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.