Understanding the relations between the anatomical structure of the human brain and its functions in healthy and diseased states can not only lead to the design of novel, targeted, non-invasive, and highly-effective treatments for neurological disorders, but also inform the application of innovative stimulation schemes to enhance cognitive performance and executive capabilities. Leveraging data obtained with state-of-the-art sensing and imaging technologies, this project pursues these objectives by innovatively studying the human brain as a dynamic network system comprising neuronal ensembles and white-matter fibers, and as governed by principles similar to social and technological cyber-physical networks. This project develops and validates new rigorous theories and tools to address an outstanding problem in network neuroscience. Namely, to leverage the brain anatomical structure to characterize, predict, and control patterns of synchronized neural activity, and to validate the methods with realistic brain data. This project will not only contribute to the theories of networks, controls, and neuroscience, but also to their integration, by leveraging different levels of abstraction (brain representations from diffusion imaging data, electrocorticography time series, mathematical models) and distinct disciplinary approaches. In addition to new methods to study synchronized activity in the brain and inform the next generation of diagnostics, this project pursues far-reaching teaching and outreach activities, including (i) a number of university-level initiatives at the graduate and undergraduate levels, (ii) outreach activities that will engage young people from the local communities in Philadelphia and Riverside, and (iii) dissemination activities that will bring together traditionally separated communities and promote multi-disciplinary initiatives to tackle some of the most pressing problems in neuroscience.
The central hypothesis of this project is that the interconnected structure of the brain determines its performance and controls its transitions between healthy and diseased states. Building on this hypothesis, this project addresses the unsolved problems of characterizing, predicting, and controlling patterns of synchronized neural activity in the human brain from sparse and coarse temporal measurements and interventions. Additionally, to support the hypothesis and validate the theories of neural synchronization, the project leverages three unique and extensive multimodal neuroimaging datasets combining high-resolution electrocorticography and diffusion imaging that will allow to assess the relations between synchronization patterns and underlying structural network architecture. Specifically, this project is organized around two main tasks. Task 1, abstracts the problem of controlling patterns of neural activity as the problem of controlling the degree of synchronization among interconnected nonlinear oscillators, where oscillators represent brain regions and their interconnections reflect the anatomy of the human brain as reconstructed by diffusion magnetic resonance imaging. The idea is put forth that altered synchronization patterns are the results of, possibly small, modifications to the oscillators' interconnection structure and weights, and that desirable patterns can be restored by minimal and localized structural interventions. Task 2 uses empirical data to obtain inferences complementing those acquired in the formal theoretical and modeling work in Task 1. Because the focus here is the analysis, prediction, and control of cluster synchronization, the empirical efforts remain constrained to the study of functional neuroimaging data with clear electrographic signatures of synchronization. Specifically, the project uses electrocorticography data, which boasts markedly greater temporal resolution than functional magnetic resonance imaging and does not suffer from the issues of volume conduction that are more common in electroencephalography and magnetoencephalography. The project blends and extends tools from control and network theories, dynamical systems, data analysis, and network neuroscience. While this project focuses on synchronization problems in neural activity, the methods have broad applicability in engineering, for instance to design optimized networks and sparse controllers, network neuroscience, and network science.
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.
