Sleep Oscillations Across Cortical, Subcortical and Cerebellar Structures in Magnetoencephalography

Sleep involves widespread changes in neural activity, with distinctive oscillatory patterns emerging across frequency bands and brain regions. Characterising these dynamics is essential for understanding their functional roles in health and their disruption in sleep-related disorders. However, most work on healthy humans has used techniques with limited temporal or spatial resolution, focusing mainly on the cerebral cortex. Growing evidence suggests that subcortical and cerebellar structures contribute to sleep dynamics, yet these regions remain largely unexplored in human neuroimaging due to methodological limitations. Magnetoencephalography (MEG) offers millisecond temporal resolution with spatial precision to localise activity across cortical, subcortical and cerebellar regions. Recent evidence demonstrates that MEG can detect signals from deep brain structures, challenging assumptions about its spatial limitations, but systematic validation and whole-brain mapping of oscillatory activity during sleep remain lacking. In this study, we provide comprehensive maps of oscillatory power across the whole brain during non-rapid eye movement (NREM) sleep using source-localised MEG. We first validated signal differentiability across cortical, subcortical and cerebellar regions using spectral fingerprinting analysis. We then characterised frequency-specific and stage-specific changes in oscillatory power across six frequency bands and three NREM sleep stages. Finally, we examined sigma-band dynamics during spindle-rich stage 2 sleep to investigate spindle-related activity across brain regions. Our results reveal structured, region-specific patterns of sleep modulation that extend beyond traditional cortical-thalamic circuits, including novel evidence for cerebellar engagement in fast spindle frequencies. These findings expand models of sleep-related brain activity and demonstrate the utility of whole-brain MEG for understanding distributed sleep networks.