Brain and brain-heart Granger causality during wakefulness and sleep

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.

Oscillatory Coupling Between Neural and Cardiac Rhythms

“…Several previous studies have employed Granger causality or similar methods to examine directional interactions between EEG and heart rate, raw EKG, or HRV power time series. Contrary to results of the present study, the majority of these studies have reported stronger brain-to-heart than heart-to-brain effects (Abdalbari et al, 2022; Greco et al, 2019; Lin et al, 2016; Pardo-Rodriguez et al, 2021). However, one study showed that HF-HRV power changes preceded changes in the delta band during sleep (Jurysta et al, 2003), and another showed stronger heart-to-brain interactions during an emotion elicitation paradigm (Candia-Rivera et al, 2022).…”

Causal Analysis of Physiological Sleep Data Using Granger Causality and Score-Based Structure Learning

“…Granger causality has been used to learn structure from polysomnographic data in previous studies. Orjuela-Cañón et al [8] used Granger causality to study the changes brought about by a session of continuous positive air pressure (CPAP) therapy; Faes et al [10], the impact of sleep stages; Günther et al [11], the impact of sleep stages and apnoea; and Pizzi et al [12] and Abdalbari et al [13], the difference between wakefulness and sleep. These studies all involve substantially fewer subjects than this one.…”

“…Twenty seconds is quite a small window length; this was chosen to balance the need for as much data as possible with the requirement for stationarity. Previous studies have used windows of similar length when dealing with physiological signals [12,13].…”

Bibliometric Analysis of Granger Causality Studies

“…In neuroscience, Granger causality has been applied to functional magnetic resonance imaging (FMRI) [ 11 , 12 , 13 ], magnetoencephalography (MEG) [ 14 , 15 , 16 ], and local field potentials (LFP) [ 17 , 18 , 19 ]. Abdalbari et al [ 20 ] used Granger causality for sleep data analysis to capture the physiological mechanisms during wakefulness and sleep. Hartmann et al [ 21 ] studied the brain–heart interaction during sleep by using Granger causality to examine the electroencephalography (EEG) frequency bands with cortical and cardiovascular activities.…”