Back

Coordinated Brain-Heart Dynamics During Human NREM Sleep

Mikutta, C. A.; Planta, M.; Koenig, T.; Feher, K.; Henckaerts, P.; Angelillo, M.; Hertenstein, E.; Schneider, C. L.; De Lucia, M.; Riemann, D.; Feige, B.; Vyazovskiy, V. V.; Nissen, C.

2026-10-06 neuroscience
10.64898/2026.09.29.755384 bioRxiv
Show abstract

Communication between the brain and cardiovascular system is increasingly recognized as a fundamental determinant of health, yet the mechanisms coordinating neural and cardiac rhythms remain poorly understood. Sleep provides a unique physiological state for investigating neurovisceral integration because cortical slow oscillations (SOs), thalamocortical sleep spindles, and autonomic activity undergo profound reorganization during non-rapid eye movement (NREM) sleep. We analyzed overnight high-density electroencephalography and electrocardiography recordings from 30 healthy adults and quantified interactions among SOs, sleep spindles, cardiac R-peaks, heart rate variability (HRV), spectral slope, and Lempel-Ziv complexity. To assess integrated brain-heart coordination, we developed a Joint Phase-Amplitude Coupling (JPAC) metric that quantifies the corelation between SO phase-dependent modulation of spindle amplitude and cardiac timing. SO-spindle coupling showed the expected fronto-central distribution but was not significantly related to autonomic or cortical state markers. In contrast, SO-R-peak coupling and JPAC systematically tracked physiological state. Stronger JPAC was associated with higher high-frequency HRV, steeper spectral slopes, and lower neural complexity, indicating enhanced coupling during synchronized, parasympathetically dominated NREM sleep. These findings demonstrate coordinated organization of cortical, thalamocortical, and cardiac rhythms within a common oscillatory framework and identify NREM sleep as a state and potential treatment target of large-scale neurovisceral integration.

Matching journals

The top 7 journals account for 50% of the predicted probability mass.

50% of probability mass above

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.