CRCNS Research Proposal: Modeling Human Brain Development as a Dynamic Multi-Scale Network Optimization Process

CRCNS 研究提案：将人脑发育建模为动态多尺度网络优化过程

• 批准号: 2207733
• 负责人: Catherine Stamoulis
• 金额: $ 51.86万
• 依托单位: Children's Hospital Corporation
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-09-01 至 2026-08-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2207733&HistoricalAwards=false
• 关键词: CRCNS; Research; Proposal; Modeling; Human

Over a period of almost two decades (from birth to young adulthood), the human brain undergoes profound changes, driven by genetic, environmental and experiential factors. These changes are part of a maturation process that leads to optimally organized neural circuits that support complex behaviors and cognitive processes, and facilitate learning across the lifespan. Fundamental questions remain about how developing brain circuits become optimally organized. Specifically, the underlying biophysical mechanisms -- the interval drivers of this process are incompletely understood at the macroscale of the human brain. This is in part due to the complexity of some developmental periods, such as adolescence, during which a constellation of endogenous and exogenous factors contribute to an avalanche of partially unique physiological changes that are difficult to track. Using neuroimaging data collected over years of development from almost 12,000 adolescents, advanced computational tools and engineering principles, the overarching goal of this project is to understand how internal mechanisms in the brain control its functional circuits to optimally support cognitive function. Research activities aim to quantify these mechanisms and their inherent changes, as the brain becomes increasingly optimally connected with age, and to map these changes onto fundamental aspects of cognitive processing.This research aims to transform mechanistic understanding of the optimization of human brain circuits during the uniquely complex developmental period of adolescence. For this purpose, it will integrate a historically large, longitudinal neuroimaging dataset with novel tools from network science and computer science, and principles of control theory. The primary hypothesis is that the brain’s topological optimization is partly driven by an internal control process, which has a quantifiable, age-varying impact on network topology and dynamics. Thus, neural maturation leads to parsimonious network topologies that maximize efficiency of information processing but also optimal network controllability, both of which are reflected on the efficiency and flexibility of cognitive processing. Findings from this project may have a transformative impact on the understanding of mechanistic principles underlying the emergence of the adult brain circuitry, and the impact of adolescence on its development. They may also provide critical insights towards the development of targeted therapies for improving cognitive outcomes in the diseased or atypically developing brain. Given cross-disciplinary and highly computational activities, this project also involves significant tool development for use by the neuroscience research community.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.

在近二十年的时间里(从出生到成年)，人类的大脑在遗传、环境和经验因素的驱动下经历了深刻的变化。这些变化是成熟过程的一部分，该过程导致组织最佳的神经回路，支持复杂的行为和认知过程，并促进终身学习。基本的问题仍然存在，即大脑回路的发育如何变得最优。具体地说，基本的生物物理机制--这一过程的间隔驱动因素--在人脑的宏观尺度上还没有完全被理解。这在一定程度上是由于某些发育期的复杂性，如青春期，在此期间，一系列内源性和外源性因素导致了难以追踪的部分独特的生理变化的雪崩。利用多年来从近12,000名青少年那里收集的神经成像数据，先进的计算工具和工程原理，该项目的首要目标是了解大脑的内部机制如何控制其功能回路，以最佳地支持认知功能。研究活动旨在量化这些机制及其内在变化，随着大脑与年龄的联系变得越来越优化，并将这些变化映射到认知过程的基本方面。这项研究旨在改变对青春期独特复杂发育期大脑回路优化的机械性理解。为此，它将整合历史上庞大的纵向神经成像数据集，以及来自网络科学和计算机科学的新工具，以及控制理论原理。主要的假设是，大脑的拓扑优化部分是由内部控制过程驱动的，内部控制过程对网络拓扑和动力学具有可量化的、随年龄变化的影响。因此，神经成熟导致简约的网络拓扑结构，既最大化了信息处理的效率，又优化了网络的可控性，这两者都体现在认知加工的效率和灵活性上。这个项目的发现可能会对理解成人大脑回路出现背后的机械原理以及青春期对其发育的影响产生革命性的影响。它们还可能为开发有针对性的疗法以改善患病或非典型发育大脑的认知结果提供关键的见解。考虑到跨学科和高度计算性的活动，该项目还涉及供神经科学研究社区使用的重要工具开发。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

项目成果

Generative Models For Large-Scale Simulations Of Connectome Development

连接体发育大规模模拟的生成模型

• DOI: 10.1109/icasspw59220.2023.10193544
• 发表时间: 2023
• 期刊: and Signal Processing
• 作者: Brooks, Skylar J;Stamoulis, Catherine
• 通讯作者: Stamoulis, Catherine

Internal control of brain networks via sparse feedback

通过稀疏反馈对大脑网络进行内部控制

• DOI: 10.1002/aic.18061
• 发表时间: 2023
• 期刊: AIChE Journal
• 影响因子: 3.7
• 作者: Mitrai, Ilias;Jones, Victoria O.;Dewantoro, Harman;Stamoulis, Catherine;Daoutidis, Prodromos
• 通讯作者: Daoutidis, Prodromos

Community detection in the human connectome: Method types, differences and their impact on inference

• DOI: 10.1002/hbm.26669
• 发表时间: 2024-04-01
• 期刊: HUMAN BRAIN MAPPING
• 影响因子: 4.8
• 作者: Brooks，Skylar J.;Jones，Victoria O.;Stamoulis，Catherine
• 通讯作者: Stamoulis，Catherine

Modulatory effects of fMRI acquisition time of day, week and year on adolescent functional connectomes across spatial scales: Implications for inference

• DOI: 10.1016/j.neuroimage.2023.120459
• 发表时间: 2023-11-18
• 期刊: NEUROIMAGE
• 影响因子: 5.7
• 作者: Hu，Linfeng;Katz，Eliot S.;Stamoulis，Catherine
• 通讯作者: Stamoulis，Catherine