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

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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.