An Inhomogeneous Point-process Model for the Assessment of the Brain-to-Heart Functional Interplay: a Pilot Study

We propose a novel computational framework for the estimation of functional directional brain-to-heart interplay in an instantaneous fashion. The framework is based on inhomogeneous point-process models for human heartbeat dynamics and employs inverse-Gaussian probability density functions characterizing the timing of R-peak events. The instantaneous estimation of the functional directional coupling is based on the definition of point-process transfer entropy, which is here retrieved from heart rate variability (HRV) and Electroencephalography (EEG) power spectral series gathered from 12 healthy subjects undergoing significant sympathovagal changes induced by a cold-pressor test. Results suggest that EEG oscillations dynamically influence heartbeat dynamics with specific time delays in the 30-60s and 90-120s ranges, and through a functional activity over specific cortical regions.

[1]  Karl J. Friston,et al.  Assessing the significance of focal activations using their spatial extent , 1994, Human brain mapping.

[2]  Luca Citi,et al.  Instantaneous Transfer Entropy for the Study of Cardiovascular and Cardiorespiratory Nonstationary Dynamics , 2018, IEEE Transactions on Biomedical Engineering.

[3]  Luca Citi,et al.  A Real-Time Automated Point-Process Method for the Detection and Correction of Erroneous and Ectopic Heartbeats , 2012, IEEE Transactions on Biomedical Engineering.

[4]  Daniel B Vigneron,et al.  Abnormal brain development in newborns with congenital heart disease. , 2007, The New England journal of medicine.

[5]  J. Bhattacharya,et al.  Aroused with heart: Modulation of heartbeat evoked potential by arousal induction and its oscillatory correlates , 2015, Scientific Reports.

[6]  Enzo Pasquale Scilingo,et al.  Functional Linear and Nonlinear Brain–Heart Interplay during Emotional Video Elicitation: A Maximum Information Coefficient Study , 2019, Entropy.

[7]  M. H. Kempski,et al.  Autonomic cardiovascular state after severe brain injury and brain death in children , 1993, Critical care medicine.

[8]  Jieli Chen,et al.  Brain–Heart Interaction: Cardiac Complications After Stroke , 2017, Circulation research.

[9]  April R. Levin,et al.  The Harvard Automated Processing Pipeline for Electroencephalography (HAPPE): Standardized Processing Software for Developmental and High-Artifact Data , 2018, Front. Neurosci..

[10]  V. Napadow,et al.  The Autonomic Brain: An Activation Likelihood Estimation Meta-Analysis for Central Processing of Autonomic Function , 2013, The Journal of Neuroscience.

[11]  S. Pyner.  The paraventricular nucleus and heart failure , 2014, Experimental physiology.

[12]  Stefan Braune,et al.  Symptoms of autonomic failure in Parkinson’s disease: prevalence and impact on daily life , 2005, Clinical Autonomic Research.

[13]  Lars Arendt-Nielsen,et al.  Dynamic changes and spatial correlation of EEG activities during cold pressor test in man , 2002, Brain Research Bulletin.

[14]  Jens Haueisen,et al.  Altered Causal Coupling Pathways within the Central-Autonomic-Network in Patients Suffering from Schizophrenia , 2019, Entropy.

[15]  Simiat O Elias,et al.  Effect of sympathetic autonomic stress from the cold pressor test on left ventricular function in young healthy adults , 2019, Physiological reports.

[16]  L. Faes,et al.  Information dynamics of brain–heart physiological networks during sleep , 2014, New Journal of Physics.

[17]  Andrea Duggento,et al.  Uncovering complex central autonomic networks at rest: a functional magnetic resonance imaging study on complex cardiovascular oscillations , 2020, Journal of the Royal Society Interface.

[18]  A. Dorrance,et al.  Effects of Stroke on the Autonomic Nervous System. , 2015, Comprehensive Physiology.

[19]  Andrea Duggento,et al.  The central autonomic network at rest: Uncovering functional MRI correlates of time-varying autonomic outflow , 2019, NeuroImage.

[20]  Wan-Hua Lin,et al.  Time-frequency analysis of heart rate variability during the cold pressor test using a time-varying autoregressive model , 2015, Physiological measurement.

[21]  Enzo Pasquale Scilingo,et al.  Time-Resolved Directional Brain–Heart Interplay Measurement Through Synthetic Data Generation Models , 2019, Annals of Biomedical Engineering.

[22]  Jian Cui,et al.  Baroreflex modulation of muscle sympathetic nerve activity during cold pressor test in humans. , 2002, American journal of physiology. Heart and circulatory physiology.

[23]  S Seri,et al.  Quantitative EEG modifications during the Cold Water Pressor Test: hemispheric and hand differences. , 1994, International journal of psychophysiology : official journal of the International Organization of Psychophysiology.

[24]  J. Thayer,et al.  A meta-analysis of non-invasive brain stimulation and autonomic functioning: Implications for brain-heart pathways to cardiovascular disease , 2017, Neuroscience & Biobehavioral Reviews.