CAREER: Control-Aware System Identification of Heterogenous Multiscale Brain Network Dynamics

职业：异构多尺度脑网络动力学的控制感知系统识别

• 批准号: 2239654
• 负责人: Erfan Nozari
• 金额: $ 54.82万
• 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: 2023-04-01 至 2028-03-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2239654&HistoricalAwards=false
• 关键词: CAREER; Control; Aware; System; Identification

This Faculty Early Career Development (CAREER) project aims to develop mathematical models of the human brain, with an eye on laying the foundation for their implementation in clinical treatments. Treatments of neurological and psychiatric disorders impose over $1 trillion annually on the US population alone. A large body of literature on mathematical modeling in neuroscience had little impact on clinical treatments because most models either rely on simplifying assumptions or use machine learning methods that obscure the link to the underlying biology. This research will develop a new category of mathematical models for the brain that are at once biologically meaningful and interpretable, without relying on simplifying assumptions. These models will be rooted in engineering approaches that combine data-driven and nonlinear dynamical systems methods. The research is tightly integrated with a diverse and solid body of educational activities targeted towards high school, undergraduate, and graduate students at UCR, the local Inland Empire community, and across the globe.This project will develop data-driven models of the human brain that rigorously incorporate three critical but often ignored aspects of biological neural networks: (i) spanning across multiple scales, (ii) heterogeneity, and (iii) response to neuromodulation. The first thrust will be achieved through data-driven modeling of feedforward and feedback interactions between neural dynamics at different spatial scales (neurons, neural populations, and brain regions), that move beyond simple macroscopic readouts of microscopic dynamics and enhance our understanding of how macroscopic dynamics emerge from, and feed back into the smaller scales. The second thrust will develop structurally heterogeneous brain models that incorporate data of brain heterogeneities in nonlinearity, dimensionality, and neural code across cortical and subcortical regions. The third thrust will generate a data-driven, sample-efficient framework for modeling the effects of deep brain stimulation at the level of the whole-brain network, thus moving beyond local predictions based on first principle modeling of electromagnetic diffusion. The expected outcomes are potentially transformative models and modeling techniques that provide the neuroscience community with solid and clinically translatable tools for the design of neuromodulation, while also significantly increasing our understanding of the multiscale, heterogeneous, and input-driven dynamics of the human brain.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.

这个教师早期职业发展（CAREER）项目旨在开发人类大脑的数学模型，着眼于为其在临床治疗中的实施奠定基础。神经和精神疾病的治疗每年仅对美国人口的影响就超过1万亿美元。大量关于神经科学数学建模的文献对临床治疗几乎没有影响，因为大多数模型要么依赖于简化的假设，要么使用机器学习方法，掩盖了与基础生物学的联系。这项研究将为大脑开发一种新的数学模型，这种模型既有生物学意义又可解释，而不依赖于简化的假设。这些模型将植根于结合联合收割机数据驱动和非线性动力系统方法的工程方法。 该研究与UCR的高中生、本科生和研究生以及地球仪各地的教育活动紧密结合。该项目将开发人类大脑的数据驱动模型，严格结合生物神经网络的三个关键但经常被忽视的方面：（i）跨越多个尺度，（ii）异质性，和（iii）对神经调节的反应。第一个推力将通过不同空间尺度（神经元，神经群体和大脑区域）的神经动力学之间的前馈和反馈相互作用的数据驱动建模来实现，这超越了微观动力学的简单宏观读数，并增强了我们对宏观动力学如何产生的理解，并反馈到更小的尺度。第二个重点是开发结构异质的大脑模型，这些模型将大脑非线性、维度和皮层和皮层下区域神经代码的异质性数据结合起来。第三个推力将产生一个数据驱动的、样本高效的框架，用于在全脑网络水平上对深部脑刺激的影响进行建模，从而超越基于电磁扩散第一原理建模的局部预测。预期的结果是潜在的变革性模型和建模技术，为神经科学界提供了用于神经调节设计的坚实的和临床可翻译的工具，同时也显着增加了我们对多尺度，异质性，和输入-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进行评估，被认为值得支持和更广泛的影响审查标准。

项目成果

Optimal decoding of neural dynamics occurs at mesoscale spatial and temporal resolutions

• DOI: 10.3389/fncel.2024.1287123
• 发表时间: 2024-02-14
• 期刊: FRONTIERS IN CELLULAR NEUROSCIENCE
• 影响因子: 5.3
• 作者: Samiei，Toktam;Zou，Zhuowen;Nozari，Erfan
• 通讯作者: Nozari，Erfan