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

NCS-FO：协作研究：同步神经活动的分析、预测和控制

• 批准号: 1926757
• 负责人: Danielle Bassett
• 金额: $ 50万
• 依托单位: University of Pennsylvania
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-09-01 至 2024-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1926757&HistoricalAwards=false
• 关键词: NCS; FO; Collaborative; Research; Analysis; NCS; FO; Collaborative; Research; Analysis

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.

了解人脑的解剖结构及其在健康和患病状态中的功能之间的关系不仅可以导致对神经疾病的新颖，有针对性，无创和高效治疗的设计，还可以告知创新刺激方案的应用，以增强认知性能和执行能力。通过最先进的传感和成像技术获得的数据，该项目通过创新研究人脑作为一个动态网络系统，以神经元合成和白色纤维纤维的方式来追求这些目标，并受类似于社会和技术网络物质网络类似的原理。该项目开发并验证了新的严格理论和工具，以解决网络神经科学中的杰出问题。也就是说，要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，并用逼真的大脑数据验证方法。该项目不仅将通过利用不同水平的抽象（来自扩散成像数据，电代理时间序列，数学模型）和独特的纪律方法来利用不同级别的抽象（大脑表示）来促进网络，控制和神经科学的理论。 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多学科的举措旨在解决神经科学中一些最紧迫的问题。该项目的中心假设是大脑的互连结构决定了其性能并控制健康和患病状态之间的过渡。基于这一假设，该项目解决了通过稀疏和粗糙的时间测量和干预措施来表征，预测和控制人脑同步神经活动模式的尚未解决的问题。此外，为了支持假设并验证神经同步的理论，该项目利用了三个独特而广泛的多模式神经成像数据集，结合了高分辨率电视学和扩散成像，这些数据将允许评估同步模式与底层结构网络结构之间的关系。具体而言，该项目围绕两个主要任务进行组织。任务1摘要控制神经活动模式的问题是控制互连非线性振荡器之间同步程度的问题，在该问题中，振荡器代表大脑区域及其互连反映了人脑的解剖结构，以扩散磁共振成像重建。提出的想法是，改变的同步模式是对振荡器的互连结构和权重的修改的结果，并且可以通过最小和局部结构干预来恢复所需的模式。任务2使用经验数据来获得推论，以补充任务1中形式的理论和建模工作中获得的内容。由于这里的重点是群集同步的分析，预测和控制，因此经验努力仍然限制在对具有清晰的同步电视学签名的功能神经影像学数据的研究。具体而言，该项目使用的皮质学数据比功能磁共振成像具有明显更高的时间分辨率，并且不会遭受量传导问题，这些问题在脑电图和磁脑摄影中更常见。该项目将工具从控制和网络理论，动态系统，数据分析和网络神经科学融合并扩展。尽管该项目着重于神经活动中的同步问题，但这些方法在工程方面具有广泛的适用性，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的法定任务，并认为通过使用该基金会的知识智能和更广泛的影响来评估CRITERIA的评估。