The Cortical Temporal Axis: MEG-Based Cross-Frequency Gradients with Biological Anchors

Abstract

Neural oscillations have long been used to characterize the temporal dynamics of individual brain regions, yet a parsimonious, system-level representation that integrates multi-rhythmic activity has been lacking. Using source-localized resting-state magnetoencephalography (MEG) data, we computed regional power spectra and assessed spectral similarity, then applied diffusion map embedding to construct cross-frequency neurophysiological gradients that place whole-brain oscillations within a unified, low-dimensional coordinate system. We found the first three gradients accounted for over 40% of the variance, remained stable across individuals, and aligned with established functional, structural, and geometric cortical axes. Computational modeling showed that these gradients reflect local excitation-inhibition balance, while multimodal analyses revealed strong associations with neurotransmitter receptor distributions, cytoarchitecture, and cell-type-specific gene expression. Lifespan analyses further demonstrated systematic gradient reorganization, with distinct cognitive mappings onto functions such as language, memory, and multisensory integration. Clinically, Parkinsons disease patients displayed disrupted gradients, particularly in regions linked to language and social cognition. Finally, these gradient patterns exhibited high test-retest reliability. These findings establish MEG-derived neurophysiological gradients as robust, low-dimensional representations of cortical organization, offering a biologically grounded framework for studying brain aging and disease.