Brain space-time: graph neural fields capture multimodal spectra and functional properties of brain dynamics

Abstract I investigate a graph neural field model implemented on high-resolution multimodal individual human connectomes. The model, based on Wilson-Cowan and wave-diffusion equations, captures the harmonic power spectrum of functional magnetic resonance imaging (fMRI) and the temporal power spectrum of magnetoencephalography (MEG) over a wide range of scales. Additionally, the model displays properties of neuronal activity thought to be relevant for healthy brain function, such as proximity to instability and long-range temporal correlations (LRTCs), without being explicitly designed or optimized to achieve them. Finally, I find that model LRTCs originate in specific temporal frequency bands, display distinct patterns of spatial localization on the cortical surface, and are nontrivially linked to structural connectivity, with particular contributions of long-range white-matter fibers. Together, these findings extend the scope of graph neural fields as an effective framework for model-based investigations of multimodal neuroimaging data.