Understanding brain structure and dynamics through the lens of geometric eigenmodes

The dynamics of many physical systems are often shaped by the constraints imposed by their underlying structure, and the brain is no exception. While many studies have explored relationships between brain anatomy and function, a unified framework for understanding how brain dynamics emerge from its relatively stable neuroanatomical scaffold has been lacking. Moreover, the specific anatomical properties that fundamentally constrain neuronal dynamics remain unclear. In our recent work, we have highlighted the previously underappreciated role of brain geometry in constraining brain dynamics. Specifically, we showed that geometric eigenmodes—eigenmodes derived from the brain’s cortical and subcortical geometry—can effectively capture diverse experimental human fMRI data from spontaneous and task-evoked recordings.

In this talk, I will provide an overview of the potential of geometric eigenmodes in understanding brain structure and dynamics. Key topics will include: (1) An accessible description of the theoretical foundation of geometric eigenmodes and their connection to neural field theory, a popular class of macroscopic mathematical model of the brain; (2) A practical demonstration of how geometric eigenmodes of the cortex and subcortex can be extracted from T1-weighted MRI data using our open-access code at https://github.com/NSBLab/BrainEigenmodes; (3) A description of how geometric eigenmodes can be applied to reconstruct diverse neuroimaging data; (4) A discussion of the advantages of using geometric eigenmodes, as well as common implantation challenges that one need to; (5) A demonstration that geometric eigenmodes are tied to a generative model of wave dynamics, which can reproduce numerous canonical features of functional brain organization; (6) A discussion of the generalizability of geometric eigenmodes across individuals and species; and (7) An overview of some examples of diverse applications of geometric eigenmodes in studying brain structure, function, and organization.