In our research, we use a reaction-diffusion model to simulate brain spacetime dynamics. The model is inspired by theoretical physics, and the FitzHugh-Nagumo model is used to simulate neural activity. This framework allows us to capture the emergence of complex spatiotemporal patterns resulting from the interplay between local excitability and long-range propagation across the cortical surface. In addition, we propose that the reaction-diffusion model can serve not only as a compelling framework for modeling brain spacetime dynamics, but also as a potential foundation for a new theoretical model of brain function.
In the cover image of the September 10 issue of Biophysical Reports, we present a brain activity phase field projected onto a flattened cortical surface, derived from a Human Connectome Project resting-state fMRI dataset. The image shows three instances of the flattened brain surface, each overlaid with the phase field of brain activity at different time points. This visualization captures the spatiotemporal evolution of resting-state brain activity, illustrating how neural dynamics unfold across both space and time. The phase field representation highlights structured patterns of propagation and synchronization, offering insights into the intrinsic organization of brain spacetime during rest.
The research shown in this article indicates that reaction-diffusion is a compelling model for capturing brain spacetime dynamics. It indeed provides us with a powerful framework to understand how local interactions can give rise to large-scale spatiotemporal patterns in brain activity. This approach offers a novel perspective on brain dynamics and function and opens up exciting opportunities for further exploration and discovery.
You can find more information on our work at https://trendscenter.org/. The Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), a collaboration between Georgia State University, Georgia Institute of Technology, and Emory University, led by Vince D. Calhoun, is dedicated to developing, applying, and disseminating advanced analytic methods and neuroinformatics tools. By leveraging cutting-edge brain imaging and omics data, TReNDS aims to translate these approaches into meaningful biomarkers that can advance our understanding of brain health and disease.
— Qiang Li and Vince D. Calhoun