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.

这个教师的早期职业发展（职业）项目旨在开发人脑的数学模型，并着眼于为其在临床治疗中实施的基础奠定基础。神经和精神疾病的治疗仅每年就将超过1万亿美元施加在美国人群中。关于神经科学中数学建模的大量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来掩盖与基本生物学的联系。这项研究将为大脑开发新的数学模型类别，这些模型立即在生物学上有意义和可解释，而无需简化假设。这些模型将植根于结合数据驱动和非线性动力学系统方法的工程方法。 这项研究与针对高中，本科和研究生的各种各样的教育活动紧密整合在一起神经调节。第一个推力将通过数据驱动的模型来实现，在不同的空间尺度（神经元，神经群体和大脑区域）之间进行进发和反馈相互作用的模型，它们超越了简单的显微镜动力学宏观读数，并增强了我们对宏观动力学从较小尺度中的出现方式的理解。第二个推力将开发结构上异质的大脑模型，这些模型将跨皮质和皮层下区域的非线性，维度和神经代码中的大脑异质性数据结合在一起。第三个推力将生成一个数据驱动的样品效率框架，用于对整个脑网络水平的深脑刺激的影响进行建模，从而超越基于电磁扩散的第一原理建模的局部预测。 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 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