CRCNS: Linking connectomic and large-Scale Dynamics of the Human Brain

描述（由申请人提供）：在区域和区域间通路的复杂结构网络内展开的神经活动的复杂时空模式被认为是所有人类行为和认知的基础。因此，理解结构网络如何塑造和约束功能性大脑网络是计算认知神经科学的一个关键挑战。拟议项目的目的是：（a）用非侵入性神经成像技术测量人脑结构和动态功能网络的特征;（B）建立一个计算模型目录，这些模型基于结构网络的解剖和神经元群体的简单生物物理局部模型，并能产生现实的大规模神经动力学;以及c）应用模型比较和推断的系统标准，以深入了解哪些模型组件和参数对于生成与经验数据紧密匹配的脑动力学的生物学上合理的模式是关键的。识别这些模型将提供对生物网络结构和机制的潜在见解，这些结构和机制支持功能性大脑连接的静态特征，以及它们的动态重构。的另一个目标 该项目的目的是根据从个体受试者获得的网络数据创建此类模型，从而为使用建模工具比较和表征大脑动力学关键特征的个体差异铺平道路。 在追求这些核心目标的过程中，新的知识将被创造出来。该项目旨在增加我们对塑造结构连接关系的因素和限制的理解 和局部生物物理特性的电路与新兴的大规模动态的人类大脑。该提案的核心是部署复杂的计算建模方法，以建立逼真的和神经生物学接地的人脑动力学模型。通过采用这种基于计算机的计算方法，该项目将通过在与大脑结构和功能相关的数据之间建立新的桥梁，帮助推进快速发展的大脑连接和动力学领域。它还将为一些国家和国际倡议所追求的正在进行的探索增加一个重要方面，以创建人脑结构和功能的全面和神经生物学现实的计算模型。 美国/德国的合作将有助于在一个重要研究领域的跨大西洋合作。该项目的多学科性质（结合脑成像和脑电图记录，动态脑建模，网络科学和图论）将为研究生和研究生学员提供丰富的教育环境，使他们能够在科学生涯的早期获得广泛的技能。学员将接触到实验室的做法和科学景观在欧洲和美国。另一个具有更广泛影响的领域涉及数据/工具共享。虽然计算方法在现代神经科学中的应用越来越广泛，但软件工具的配置和神经生物学逼真模拟的实现仍然需要大量的知识和培训。通过他们在以前的项目中的联合参与，PI和Co-PI在公开共享计算工具和资源，组织教育活动以扩大对复杂计算平台的访问以及致力于计算神经科学的毕业生和研究生培训方面有着良好的记录。所有来自拟议项目的计算工具，方法和结果将与更大的神经科学社区自由和公开地共享。具有更广泛影响的第三个也是更长期的领域是将该提议的计算建模方法部署到有针对性的临床应用中。个性化的大脑建模可能最终有助于监测个体患者大脑健康的动态特征。本文开发的动态网络建模方法的进一步临床应用可能包括脑损伤或病理情况下的新治疗策略。

DESCRIPTION (provided by applicant): Complex spatiotemporal patterns of neural activity unfolding within an intricate structural network of regions and inter-regional pathways are thought to underlie all of human behavior and cognition. Understanding how structural networks shape and constrain functional brain networks therefore represents a key challenge to computational cognitive neuroscience. The proposed project aims to a) characterize the repertoire of structural and dynamic functional networks of the human brain as measured with noninvasive neuroimaging techniques; b) create a catalogue of computational models that are based on the anatomy of structural networks and simple biophysical local models of neuronal populations, and that can generate realistic large-scale neural dynamics; and c) apply systematic criteria of model comparison and inference to gain insight into which model components and parameters are critical for generating biologically plausible patterns of brain dynamics that closely match empirical data. Identifying these models would offer potential insights into biological network structures and mechanisms that underpin stationary features of functional brain connectivity, as well as their dynamic reconfigurations. An additional goal of the project is to create such models based on network data acquired from individual subjects, thus paving the way for using modeling tools to compare and characterize individual differences in key features of brain dynamics. In the pursuit of these central aims, new knowledge will be created. The project aims to add to our understanding of the factors and constraints that shape the relation of structural connectivity and local biophysical properties of circuits with the emerging large-scale dynamics of the human brain. The core of the proposal is to deploy sophisticated computational modeling methods in order to build realistic and neurobiologically grounded models of human brain dynamics. In taking this empirically-based computational approach the project will help to advance the rapidly growing fields of brain connectivity and dynamics by creating new bridges between data relating to brain structure and function. It will also add an important dimension to the ongoing quest, pursued in a number of national and international initiatives, to create comprehensive and neurobiologically realistic computational models of the structure and function of the human brain. This U.S./German collaboration will contribute to trans-Atlantic cooperation in an important research area. The multi-disciplinary character of the project (combining brain imaging and EEG recording, dynamic brain modeling, network science and graph theory) will provide a rich educational environment for graduate and post-graduate trainees, allowing them to acquire broad skills at an early point in their scientific careers. Trainees will be exposed to laboratory practices and the scientific landscape in both Europe and the United States. An additional area of broader impact relates to data/tool sharing. While computational methods are becoming more widely used in modern neuroscience, the configuration of software tools, and implementation of neurobiologically realistic simulations still requires significant knowledge and training. Through their joint involvement in previous projects, the PI and Co-PIs have a proven track record of publicly sharing computational tools and resources, organizing educational activities to broaden access to sophisticated computational platforms, and dedication to graduate and post-graduate training in computational neuroscience. All computational tools, methods and results coming from the proposed project will be freely and openly shared with the larger neuroscience community. A third and longer term area of broader impact is to deploy the computational modeling approach underlying this proposal for targeted clinical applications. Personalized brain modeling may ultimately help to monitor dynamic signatures of brain health in individual patients. Further clinical applications of the dynamic network modeling approaches developed here could include novel therapeutic strategies in the case of brain injury or pathology.