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

描述（由申请人提供）：神经活动的复杂时空模式在一个复杂的区域和区域间通路的结构网络中展开，被认为是所有人类行为和认知的基础。因此，了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。提议的项目旨在a)描述用非侵入性神经成像技术测量的人类大脑结构和动态功能网络的特征；B)创建一个计算模型目录，这些模型基于结构网络的解剖结构和神经元种群的简单生物物理局部模型，并且可以生成现实的大规模神经动力学；c)应用模型比较和推理的系统标准，以深入了解哪些模型组件和参数对于生成与经验数据密切匹配的生物学上合理的脑动力学模式至关重要。识别这些模型将为生物网络结构和机制提供潜在的见解，这些结构和机制支持功能性大脑连接的固定特征，以及它们的动态重构。一个额外的目标

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.