ArticleFrontiers in neuroscience2022
Brain and brain-heart Granger causality during wakefulness and sleep.
Article in Frontiers in neuroscience, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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10 citing papers in PubMed.
- Interpersonal heart rate synchrony and prosocial behaviors in musical contexts.Frontiers in psychology · 2026Review
- When your heart isn't in it anymore: cardiac correlates of task disengagement.Communications biology · 2025Article
- Long term monitoring shows phase lagged behaviorally driven locomotor autonomic coupling.Scientific reports · 2025Article
- Harnessing electroencephalography connectomes for cognitive and clinical neuroscience.Nature biomedical engineering · 2025Review
- Dynamic brain-heart-gut coupling during sleep: a continuous physiological signal analysis.Frontiers in neuroscience · 2025Article
- Oscillatory Coupling Between Neural and Cardiac Rhythms.Psychological science · 2024Article
- Detection of respiratory frequency rhythm in human alpha phase shifts: topographic distributions in wake and drowsy states.Frontiers in physiology · 2024Article
- The contribution of granger causality analysis to our understanding of cardiovascular homeostasis: from cardiovascular and respiratory interactions to central autonomic network control.Frontiers in network physiology · 2024Review
- Causal Analysis of Physiological Sleep Data Using Granger Causality and Score-Based Structure Learning.Sensors (Basel, Switzerland) · 2023Article
- Bibliometric Analysis of Granger Causality Studies.Entropy (Basel, Switzerland) · 2023Article
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
In this exploratory study we apply Granger Causality (GC) to investigate the brain-brain and brain-heart interactions during wakefulness and sleep. Our analysis includes electroencephalogram (EEG) and electrocardiogram (ECG) data during all-night polysomnographic recordings from volunteers with apnea, available from the Massachusetts General Hospital's Computational Clinical Neurophysiology Laboratory and the Clinical Data Animation Laboratory. The data is manually annotated by clinical staff at the MGH in 30 second contiguous intervals (wakefulness and sleep stages 1, 2, 3, and rapid eye movement (REM). We applied GC to 4-s non-overlapping segments of available EEG and ECG across all-night recordings of 50 randomly chosen patients. To identify differences in GC between the different sleep stages, the GC for each sleep stage was subtracted from the GC during wakefulness. Positive (negative) differences indicated that GC was greater (lower) during wakefulness compared to the specific sleep stage. The application of GC to study brain-brain and brain-heart bidirectional connections during wakefulness and sleep confirmed the importance of fronto-posterior connectivity during these two states, but has also revealed differences in ipsilateral and contralateral mechanisms of these connections. It has also confirmed the existence of bidirectional brain-heart connections that are more prominent in the direction from brain to heart. Our exploratory study has shown that GC can be successfully applied to sleep data analysis and captures the varying physiological mechanisms that are related to wakefulness and different sleep stages.
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