Collaborative Research: Analysis and Control of Nonlinear Oscillatory Networks for the Design of Novel Cortical Stimulation Strategies

合作研究：用于设计新型皮质刺激策略的非线性振荡网络的分析和控制

• 批准号: 2308639
• 负责人: Fabio Pasqualetti
• 金额: $ 30万
• 依托单位: University of California-Riverside
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-10-01 至 2026-09-30
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2308639&HistoricalAwards=false
• 关键词: Collaborative; Research; Analysis; Control; Nonlinear

It is estimated that 1.2 percent of Americans have active epilepsy, and the annual cost for treating these cases is estimated at $12.5 billion. While brain stimulation is routinely used in clinical practice, stimulation signals and parameters are often applied and tuned empirically, and can be substantially improved by a quantitative model of the brain network dynamics. Indeed, human brain function function arises from complex dynamics between intricate and time-varying interconnection of dynamically rich neural components, and multiple neurological disorders are linked to disruptions of these network mechanisms. This project will develop new theories and tools to predict the onset and spreading of epileptic seizures and to inform the use of novel electrical brain stimulation strategies to treat neurological disorders. By constructing mathematical models that incorporate epileptic data and dynamical analysis, this work aims at uncovering the phenomena that underlie epileptic events and linking them to features of the anatomy of the brain. The intended outcome will be a novel theoretical basis to analyze and optimize practical noninvasive stimulation of brain networks. This project will also pursue educational initiatives at the graduate and undergraduate levels that will contribute to the growth of a large and diverse STEM workforce, outreach activities to engage the local community, and dissemination activities to promote multi-disciplinary approaches to problems in neuroscience.The project will develop novel methods to analyze the spreading of oscillations in complex networks and derive control mechanisms to regulate the spatiotemporal evolution of network dynamics, such as neurological oscillations. In particular, the research will aim to (i) characterize a novel set of dynamical biomarkers that explain qualitative changes in neurological recordings during epileptic events -- these biomarkers provide a quantitative link between the structure and parameters of nonlinear, networked, neural mass models and the features of epileptic recordings; (ii) reveal the structural properties that allow healthy, localized neural oscillations to cascade into brain-wide pathological seizures, and (iii) develop spatiotemporal control strategies to regulate the spreading of oscillatory dynamics over networks, which will provide a solid theoretical basis to analyze and optimize practical noninvasive brain stimulation. In addition to contributing to the fields of network control and dynamical systems, this project will also contribute to the integration of these disciplines with computational neuroscience and promote the translation of control-theoretic tools towards the design of novel, targeted, non-invasive, and highly effective treatments for neurological disorders.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.

据估计，1.2%的美国人患有活动性癫痫，每年治疗这些病例的费用估计为125亿美元。虽然脑刺激在临床实践中被常规地使用，但是刺激信号和参数通常根据经验来应用和调整，并且可以通过脑网络动力学的定量模型来显著地改进。事实上，人类大脑的功能源于动态丰富的神经成分之间错综复杂且随时间变化的相互连接之间的复杂动态，并且多种神经系统疾病与这些网络机制的中断有关。该项目将开发新的理论和工具来预测癫痫发作的发作和传播，并为使用新型脑电刺激策略治疗神经系统疾病提供信息。通过构建结合癫痫数据和动力学分析的数学模型，这项工作旨在揭示癫痫事件背后的现象，并将其与大脑解剖学特征联系起来。预期的结果将是一个新的理论基础，分析和优化实际的非侵入性刺激的大脑网络。该项目还将在研究生和本科生层面开展教育活动，这将有助于培养一支庞大而多样化的STEM劳动力队伍，开展外联活动，吸引当地社区，和传播活动，以促进多方面的该项目将开发新的方法来分析复杂网络中振荡的传播，并推导出控制机制来调节神经科学中的问题。网络动态的时空演化，如神经振荡。特别是，该研究的目标是（i）描述一组新的动态生物标志物，解释癫痫事件期间神经记录的定性变化-这些生物标志物提供了非线性，网络化，神经质量模型的结构和参数与癫痫记录特征之间的定量联系;（ii）揭示允许健康的局部神经振荡级联成全脑病理性癫痫发作的结构特性，以及（iii）开发时空控制策略来调节振荡动力学在网络上的传播，这将为分析和优化实际的无创脑刺激提供坚实的理论基础。除了对网络控制和动力系统领域做出贡献外，该项目还将有助于这些学科与计算神经科学的整合，并促进控制理论工具向新颖，有针对性，非侵入性，该奖项反映了NSF的法定使命，并被认为值得通过使用基金会的学术价值和更广泛的影响审查标准。