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Sargent KS, Martinez EL, Reed AC, Guha A, Bartholomew ME, Diehl CK, Chang CS, Salama S, Popov T, Thayer JF, Miller GA, Yee CM. Oscillatory Coupling Between Neural and Cardiac Rhythms. Psychol Sci 2024:9567976241235932. [PMID: 38568870 DOI: 10.1177/09567976241235932] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/05/2024] Open
Abstract
Oscillations serve a critical role in organizing biological systems. In the brain, oscillatory coupling is a fundamental mechanism of communication. The possibility that neural oscillations interact directly with slower physiological rhythms (e.g., heart rate, respiration) is largely unexplored and may have important implications for psychological functioning. Oscillations in heart rate, an aspect of heart rate variability (HRV), show remarkably robust associations with psychological health. Mather and Thayer proposed coupling between high-frequency HRV (HF-HRV) and neural oscillations as a mechanism that partially accounts for such relationships. We tested this hypothesis by measuring phase-amplitude coupling between HF-HRV and neural oscillations in 37 healthy adults at rest. Robust coupling was detected in all frequency bands. Granger causality analyses indicated stronger heart-to-brain than brain-to-heart effects in all frequency bands except gamma. These findings suggest that cardiac rhythms play a causal role in modulating neural oscillations, which may have important implications for mental health.
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Affiliation(s)
- Kaia S Sargent
- Department of Psychology, University of California, Los Angeles
| | | | | | - Anika Guha
- Department of Psychology, University of California, Los Angeles
| | | | | | | | - Sarah Salama
- Department of Psychology, University of California, Los Angeles
| | - Tzvetan Popov
- Department of Psychology, University of Konstanz
- Department of Psychology, University of Zurich
| | - Julian F Thayer
- Department of Psychological Science, University of California, Irvine
| | - Gregory A Miller
- Department of Psychology, University of California, Los Angeles
- Department of Psychiatry and Biobehavioral Sciences, University of California, Los Angeles
| | - Cindy M Yee
- Department of Psychology, University of California, Los Angeles
- Department of Psychiatry and Biobehavioral Sciences, University of California, Los Angeles
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2
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Antonacci Y, Barà C, Zaccaro A, Ferri F, Pernice R, Faes L. Time-varying information measures: an adaptive estimation of information storage with application to brain-heart interactions. FRONTIERS IN NETWORK PHYSIOLOGY 2023; 3:1242505. [PMID: 37920446 PMCID: PMC10619917 DOI: 10.3389/fnetp.2023.1242505] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 06/19/2023] [Accepted: 10/02/2023] [Indexed: 11/04/2023]
Abstract
Network Physiology is a rapidly growing field of study that aims to understand how physiological systems interact to maintain health. Within the information theory framework the information storage (IS) allows to measure the regularity and predictability of a dynamic process under stationarity assumption. However, this assumption does not allow to track over time the transient pathways occurring in the dynamical activity of a physiological system. To address this limitation, we propose a time-varying approach based on the recursive least squares algorithm (RLS) for estimating IS at each time instant, in non-stationary conditions. We tested this approach in simulated time-varying dynamics and in the analysis of electroencephalographic (EEG) signals recorded from healthy volunteers and timed with the heartbeat to investigate brain-heart interactions. In simulations, we show that the proposed approach allows to track both abrupt and slow changes in the information stored in a physiological system. These changes are reflected in its evolution and variability over time. The analysis of brain-heart interactions reveals marked differences across the cardiac cycle phases of the variability of the time-varying IS. On the other hand, the average IS values exhibit a weak modulation over parieto-occiptal areas of the scalp. Our study highlights the importance of developing more advanced methods for measuring IS that account for non-stationarity in physiological systems. The proposed time-varying approach based on RLS represents a useful tool for identifying spatio-temporal dynamics within the neurocardiac system and can contribute to the understanding of brain-heart interactions.
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Affiliation(s)
- Yuri Antonacci
- Department of Engineering, University of Palermo, Palermo, Italy
| | - Chiara Barà
- Department of Engineering, University of Palermo, Palermo, Italy
| | - Andrea Zaccaro
- Department of Neuroscience, Imaging and Clinical Sciences, “G. d’Annunzio” University of Chieti-Pescara, Chieti, Italy
| | - Francesca Ferri
- Department of Neuroscience, Imaging and Clinical Sciences, “G. d’Annunzio” University of Chieti-Pescara, Chieti, Italy
- Institute for Advanced Biomedical Technologies (ITAB), “G. d’Annunzio” University of Chieti-Pescara, Chieti, Italy
| | - Riccardo Pernice
- Department of Engineering, University of Palermo, Palermo, Italy
| | - Luca Faes
- Department of Engineering, University of Palermo, Palermo, Italy
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3
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Felici M, Reddon AR, Maglieri V, Lanatà A, Baragli P. Heart and brain: Change in cardiac entropy is related to lateralised visual inspection in horses. PLoS One 2023; 18:e0289753. [PMID: 37552685 PMCID: PMC10409287 DOI: 10.1371/journal.pone.0289753] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/04/2023] [Accepted: 07/24/2023] [Indexed: 08/10/2023] Open
Abstract
Cerebral lateralisation is the tendency for an individual to preferentially use one side of their brain and is apparent in the biased use of paired sensory organs. Horses vary in eye use when viewing a novel stimulus which may be due to different physiological reactions. To understand the interplay between physiology and lateralisation, we presented a novel object (an inflated balloon) to 20 horses while electrocardiogram traces were collected. We measured the amount of time each horse looked at the balloon with each eye. We calculated 'sample entropy' as a measure of non-linear heart rate variability both prior to and during the stimulus presentation. A smaller drop in sample entropy values between the habituation phase and the sample presentation indicates the maintenance of a more complex signal associated with a relaxed physiological state. Horses that spent longer viewing the balloon with their left eye had a greater reduction in sample entropy, while time spend looking with the right eye was unrelated to the change in sample entropy. Therefore, the horses that exhibited a greater reduction in sample entropy tended to use their right hemisphere more, which may take precedence in emotional reactions. These results may help to explain the variation in lateralisation observed among horses.
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Affiliation(s)
- Martina Felici
- Department of Veterinary Sciences, University of Pisa, Pisa, Italy
- Department of Agricultural and Food Sciences, University of Bologna, Bologna, Italy
| | - Adam R. Reddon
- School of Biological and Environmental Sciences, Liverpool John Moores University, Liverpool, United Kingdom
| | - Veronica Maglieri
- Department of Biology, Unit of Ethology, University of Pisa, Pisa, Italy
| | - Antonio Lanatà
- Department of Information Engineering, University of Florence, Florence, Italy
| | - Paolo Baragli
- Department of Veterinary Sciences, University of Pisa, Pisa, Italy
- Bioengineering and Robotic Research Centre “E. Piaggio”, University of Pisa, Pisa, Italy
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4
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Catrambone V, Valenza G. Complex Brain-Heart Mapping in Mental and Physical Stress. IEEE JOURNAL OF TRANSLATIONAL ENGINEERING IN HEALTH AND MEDICINE 2023; 11:495-504. [PMID: 37817820 PMCID: PMC10561752 DOI: 10.1109/jtehm.2023.3280974] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/15/2022] [Revised: 04/29/2023] [Accepted: 05/25/2023] [Indexed: 10/12/2023]
Abstract
OBJECTIVE The central and autonomic nervous systems are deemed complex dynamic systems, wherein each system as a whole shows features that the individual system sub-components do not. They also continuously interact to maintain body homeostasis and appropriate react to endogenous and exogenous stimuli. Such interactions are comprehensively referred to functional brain-heart interplay (BHI). Nevertheless, it remains uncertain whether this interaction also exhibits complex characteristics, that is, whether the dynamics of the entire nervous system inherently demonstrate complex behavior, or if such complexity is solely a trait of the central and autonomic systems. Here, we performed complexity mapping of the BHI dynamics under mental and physical stress conditions. METHODS AND PROCEDURES Electroencephalographic and heart rate variability series were obtained from 56 healthy individuals performing mental arithmetic or cold-pressure tasks, and physiological series were properly combined to derive directional BHI series, whose complexity was quantified through fuzzy entropy. RESULTS The experimental results showed that BHI complexity is mainly modulated in the efferent functional direction from the brain to the heart, and mainly targets vagal oscillations during mental stress and sympathovagal oscillations during physical stress. CONCLUSION We conclude that the complexity of BHI mapping may provide insightful information on the dynamics of both central and autonomic activity, as well as on their continuous interaction. CLINICAL IMPACT This research enhances our comprehension of the reciprocal interactions between central and autonomic systems, potentially paving the way for more accurate diagnoses and targeted treatments of cardiovascular, neurological, and psychiatric disorders.
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Affiliation(s)
- Vincenzo Catrambone
- Neurocardiovascular Intelligence Laboratory, Bioengineering and Robotics Research Center E. Piaggio, and Department of Information EngineeringSchool of EngineeringUniversity of Pisa56126PisaItaly
| | - Gaetano Valenza
- Neurocardiovascular Intelligence Laboratory, Bioengineering and Robotics Research Center E. Piaggio, and Department of Information EngineeringSchool of EngineeringUniversity of Pisa56126PisaItaly
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Xu T, Wang J, Zhang G, Zhang L, Zhou Y. Confused or not: decoding brain activity and recognizing confusion in reasoning learning using EEG. J Neural Eng 2023; 20. [PMID: 36854180 DOI: 10.1088/1741-2552/acbfe0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/11/2022] [Accepted: 02/28/2023] [Indexed: 03/02/2023]
Abstract
Objective.Confusion is the primary epistemic emotion in the learning process, influencing students' engagement and whether they become frustrated or bored. However, research on confusion in learning is still in its early stages, and there is a need to better understand how to recognize it and what electroencephalography (EEG) signals indicate its occurrence. The present work investigates confusion during reasoning learning using EEG, and aims to fill this gap with a multidisciplinary approach combining educational psychology, neuroscience and computer science.Approach.First, we design an experiment to actively and accurately induce confusion in reasoning. Second, we propose a subjective and objective joint labeling technique to address the label noise issue. Third, to confirm that the confused state can be distinguished from the non-confused state, we compare and analyze the mean band power of confused and unconfused states across five typical bands. Finally, we present an EEG database for confusion analysis, together with benchmark results from conventional (Naive Bayes, Support Vector Machine, Random Forest, and Artificial Neural Network) and end-to-end (Long Short Term Memory, Residual Network, and EEGNet) machine learning methods.Main results.Findings revealed: 1. Significant differences in the power of delta, theta, alpha, beta and lower gamma between confused and non-confused conditions; 2. A higher attentional and cognitive load when participants were confused; and 3. The Random Forest algorithm with time-domain features achieved a high accuracy/F1 score (88.06%/0.88 for the subject-dependent approach and 84.43%/0.84 for the subject-independent approach) in the binary classification of the confused and non-confused states.Significance.The study advances our understanding of confusion and provides practical insights for recognizing and analyzing it in the learning process. It extends existing theories on the differences between confused and non-confused states during learning and contributes to the cognitive-affective model. The research enables researchers, educators, and practitioners to monitor confusion, develop adaptive systems, and test recognition approaches.
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Affiliation(s)
- Tao Xu
- Northwestern Polytechnical University, School of Software, Xi'an, People's Republic of China
| | - Jiabao Wang
- Northwestern Polytechnical University, School of Software, Xi'an, People's Republic of China
| | - Gaotian Zhang
- Northwestern Polytechnical University, School of Software, Xi'an, People's Republic of China
| | - Ling Zhang
- Faculty of Education, Shaanxi Normal University, Xi'an, People's Republic of China
| | - Yun Zhou
- Faculty of Education, Shaanxi Normal University, Xi'an, People's Republic of China
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Liu Y, Niu H, Zhang T, Cai L, Liu D, Zhao E, Zhu L, Qiao P, Zheng W, Ren P, Wang Z. Altered spontaneous brain activity during dobutamine challenge in healthy young adults: A resting-state functional magnetic resonance imaging study. Front Neurosci 2023; 16:1033569. [PMID: 36685245 PMCID: PMC9853379 DOI: 10.3389/fnins.2022.1033569] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2022] [Accepted: 12/14/2022] [Indexed: 01/09/2023] Open
Abstract
Introduction There is a growing interest in exploring brain-heart interactions. However, few studies have investigated the brain-heart interactions in healthy populations, especially in healthy young adults. The aim of this study was to explore the association between cardiovascular and spontaneous brain activities during dobutamine infusion in healthy young adults. Methods Forty-eight right-handed healthy participants (43 males and 5 females, range: 22-34 years) underwent vital signs monitoring, cognitive function assessment and brain MRI scans. Cardiovascular function was evaluated using blood pressure and heart rate, while two resting-state functional magnetic resonance imaging (rs-fMRI) methods-regional homogeneity (ReHo) and amplitude of low-frequency fluctuation (ALFF)-were used together to reflect the local neural activity of the brain. Logistic regression was used to model the association between brain and heart. Results Results showed that blood pressure and heart rate significantly increased after dobutamine infusion, and the performance in brain functional activity was the decrease in ReHo in the left gyrus rectus and in ALFF in the left frontal superior orbital. The results of logistic regression showed that the difference of diastolic blood pressure (DBP) had significant positive relationship with the degree of change of ReHo, while the difference of systolic blood pressure (SBP) had significant negative impact on the degree of change in ALFF. Discussion These findings suggest that the brain-heart interactions exist in healthy young adults under acute cardiovascular alterations, and more attention should be paid to blood pressure changes in young adults and assessment of frontal lobe function to provide them with more effective health protection management.
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Affiliation(s)
- Yawen Liu
- School of Biological Science and Medical Engineering, Beihang University, Beijing, China
| | - Haijun Niu
- School of Biological Science and Medical Engineering, Beihang University, Beijing, China
| | - Tingting Zhang
- School of Biological Science and Medical Engineering, Beihang University, Beijing, China,Department of Radiology, Beijing Friendship Hospital, Capital Medical University, Beijing, China
| | - Linkun Cai
- School of Biological Science and Medical Engineering, Beihang University, Beijing, China
| | - Dong Liu
- Department of Ultrasound, Beijing Friendship Hospital, Capital Medical University, Beijing, China
| | - Erwei Zhao
- National Space Science Center, Chinese Academy of Sciences (CAS), Beijing, China
| | - Liang Zhu
- National Research Center for Rehabilitation Technical Aids, Beijing, China
| | - PengGang Qiao
- Department of Radiology, Beijing Friendship Hospital, Capital Medical University, Beijing, China
| | - Wei Zheng
- National Space Science Center, Chinese Academy of Sciences (CAS), Beijing, China
| | - Pengling Ren
- Department of Radiology, Beijing Friendship Hospital, Capital Medical University, Beijing, China,Pengling Ren,
| | - Zhenchang Wang
- School of Biological Science and Medical Engineering, Beihang University, Beijing, China,Department of Radiology, Beijing Friendship Hospital, Capital Medical University, Beijing, China,*Correspondence: Zhenchang Wang,
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7
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Understanding the Mechanisms of Sudden Cardiac Death in Bipolar Disorder: Functional Asymmetry in Brain-Heart Interactions as a Potential Culprit. Med Hypotheses 2022. [DOI: 10.1016/j.mehy.2022.110986] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
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8
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Zhou J, Zhao T, Xie Y, Xiao F, Sun L. Emotion Recognition Based on Brain Connectivity Reservoir and Valence Lateralization for Cyber-Physical-Social Systems. Pattern Recognit Lett 2022. [DOI: 10.1016/j.patrec.2022.08.009] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/15/2022]
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9
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Candia-Rivera D. Brain-heart interactions in the neurobiology of consciousness. CURRENT RESEARCH IN NEUROBIOLOGY 2022; 3:100050. [PMID: 36685762 PMCID: PMC9846460 DOI: 10.1016/j.crneur.2022.100050] [Citation(s) in RCA: 19] [Impact Index Per Article: 9.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/13/2022] [Revised: 07/23/2022] [Accepted: 07/27/2022] [Indexed: 01/25/2023] Open
Abstract
Recent experimental evidence on patients with disorders of consciousness revealed that observing brain-heart interactions helps to detect residual consciousness, even in patients with absence of behavioral signs of consciousness. Those findings support hypotheses suggesting that visceral activity is involved in the neurobiology of consciousness, and sum to the existing evidence in healthy participants in which the neural responses to heartbeats reveal perceptual and self-consciousness. More evidence obtained through mathematical modeling of physiological dynamics revealed that emotion processing is prompted by an initial modulation from ascending vagal inputs to the brain, followed by sustained bidirectional brain-heart interactions. Those findings support long-lasting hypotheses on the causal role of bodily activity in emotions, feelings, and potentially consciousness. In this paper, the theoretical landscape on the potential role of heartbeats in cognition and consciousness is reviewed, as well as the experimental evidence supporting these hypotheses. I advocate for methodological developments on the estimation of brain-heart interactions to uncover the role of cardiac inputs in the origin, levels, and contents of consciousness. The ongoing evidence depicts interactions further than the cortical responses evoked by each heartbeat, suggesting the potential presence of non-linear, complex, and bidirectional communication between brain and heartbeat dynamics. Further developments on methodologies to analyze brain-heart interactions may contribute to a better understanding of the physiological dynamics involved in homeostatic-allostatic control, cognitive functions, and consciousness.
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10
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Pernice R, Faes L, Feucht M, Benninger F, Mangione S, Schiecke K. Pairwise and higher-order measures of brain-heart interactions in children with temporal lobe epilepsy. J Neural Eng 2022; 19. [PMID: 35803218 DOI: 10.1088/1741-2552/ac7fba] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/28/2022] [Accepted: 07/08/2022] [Indexed: 11/11/2022]
Abstract
OBJECTIVE While it is well-known that epilepsy has a clear impact on the activity of both the central nervous system (CNS) and the autonomic nervous system (ANS), its role on the complex interplay between CNS and ANS has not been fully elucidated yet. In this work, pairwise and higher-order predictability measures based on the concepts of Granger causality (GC) and Partial Information Decomposition (PID) were applied on time series of electroencephalographic (EEG) brain wave amplitude and heart rate variability (HRV) in order to investigate directed brain-heart interactions associated with the occurrence of focal epilepsy. APPROACH HRV and the envelopes of δ and α EEG activity recorded from ipsilateral (ipsi-EEG) and contralateral (contra-EEG) scalp regions were analyzed in 18 children suffering from temporal lobe epilepsy monitored during pre-ictal, ictal and post-ictal periods. After linear parametric model identification, we compared pairwise GC measures computed between HRV and a single EEG component with PID measures quantifying the unique, redundant and synergistic information transferred from ipsi-EEG and contra-EEG to HRV. MAIN RESULTS The analysis of GC revealed a dominance of the information transfer from EEG to HRV and negligible transfer from HRV to EEG, suggesting that CNS activities drive the ANS modulation of the heart rhythm, but did not evidence clear differences between δ and α rhythms, ipsi-EEG and contra-EEG, or pre- and post-ictal periods. On the contrary, PID revealed that epileptic seizures induce a reorganization of the interactions from brain to heart, as the unique predictability of HRV originated from the ipsi-EEG for the δ waves and from the contra-EEG for the α waves in the pre-ictal phase, while these patterns were reversed after the seizure. SIGNIFICANCE These results highlight the importance of considering higher-order interactions elicited by PID for the study of the neuro-autonomic effects of focal epilepsy, and may have neurophysiological and clinical implications.
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Affiliation(s)
- Riccardo Pernice
- Department of Engineering, University of Palermo, Viale delle Scienze, Bldg. 9, Palermo, 90128, ITALY
| | - Luca Faes
- Department of Engineering, University of Palermo, Viale delle Scienze, Bldg. 9, Palermo, 90128, ITALY
| | - Martha Feucht
- Epilepsy Monitoring Unit, Department of Child and Adolenscent Neuropsychiatry, University Hospital Vienna, Währinger Gürtel 18-20, Vienna, 1090, AUSTRIA
| | - Franz Benninger
- Department of Child and Adolescent Medicine, University Hospital Vienna, Währinger Gürtel 18-20, Vienna, 1090, AUSTRIA
| | - Stefano Mangione
- Department of Engineering, University of Palermo, Viale delle Scienze, Bldg. 9, Palermo, Sicilia, 90128, ITALY
| | - Karin Schiecke
- Institute of Medical Statistics, Computer Sciences and Documentation, Jena University Hospital, Friedrich Schiller University Jena, Bachstraße 18, Jena, 07743, GERMANY
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11
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Cardiac sympathetic-vagal activity initiates a functional brain-body response to emotional arousal. Proc Natl Acad Sci U S A 2022; 119:e2119599119. [PMID: 35588453 PMCID: PMC9173754 DOI: 10.1073/pnas.2119599119] [Citation(s) in RCA: 37] [Impact Index Per Article: 18.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/27/2023] Open
Abstract
We investigate the temporal dynamics of brain and cardiac activities in healthy subjects who underwent an emotional elicitation through videos. We demonstrate that, within the first few seconds, emotional stimuli modulate heartbeat activity, which in turn stimulates an emotion intensity (arousal)–specific cortical response. The emotional processing is then sustained by a bidirectional brain–heart interplay, where the perceived arousal level modulates the amplitude of ascending heart-to-brain neural information flow. These findings may constitute fundamental knowledge linking neurophysiology and psychiatric disorders, including the link between depressive symptoms and cardiovascular disorders. A century-long debate on bodily states and emotions persists. While the involvement of bodily activity in emotion physiology is widely recognized, the specificity and causal role of such activity related to brain dynamics has not yet been demonstrated. We hypothesize that the peripheral neural control on cardiovascular activity prompts and sustains brain dynamics during an emotional experience, so these afferent inputs are processed by the brain by triggering a concurrent efferent information transfer to the body. To this end, we investigated the functional brain–heart interplay under emotion elicitation in publicly available data from 62 healthy subjects using a computational model based on synthetic data generation of electroencephalography and electrocardiography signals. Our findings show that sympathovagal activity plays a leading and causal role in initiating the emotional response, in which ascending modulations from vagal activity precede neural dynamics and correlate to the reported level of arousal. The subsequent dynamic interplay observed between the central and autonomic nervous systems sustains the processing of emotional arousal. These findings should be particularly revealing for the psychophysiology and neuroscience of emotions.
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12
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González C, Garcia-Hernando G, Jensen EW, Vallverdú-Ferrer M. Assessing rheoencephalography dynamics through analysis of the interactions among brain and cardiac networks during general anesthesia. FRONTIERS IN NETWORK PHYSIOLOGY 2022; 2:912733. [PMID: 36926077 PMCID: PMC10013012 DOI: 10.3389/fnetp.2022.912733] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/04/2022] [Accepted: 07/08/2022] [Indexed: 11/13/2022]
Abstract
Cerebral blood flow (CBF) reflects the rate of delivery of arterial blood to the brain. Since no nutrients, oxygen or water can be stored in the cranial cavity due to space and pressure restrictions, a continuous perfusion of the brain is critical for survival. Anesthetic procedures are known to affect cerebral hemodynamics, but CBF is only monitored in critical patients due, among others, to the lack of a continuous and affordable bedside monitor for this purpose. A potential solution through bioelectrical impedance technology, also known as rheoencephalography (REG), is proposed, that could fill the existing gap for a low-cost and effective CBF monitoring tool. The underlying hypothesis is that REG signals carry information on CBF that might be recovered by means of the application of advanced signal processing techniques, allowing to track CBF alterations during anesthetic procedures. The analysis of REG signals was based on geometric features extracted from the time domain in the first place, since this is the standard processing strategy for this type of physiological data. Geometric features were tested to distinguish between different anesthetic depths, and they proved to be capable of tracking cerebral hemodynamic changes during anesthesia. Furthermore, an approach based on Poincaré plot features was proposed, where the reconstructed attractors form REG signals showed significant differences between different anesthetic states. This was a key finding, providing an alternative to standard processing of REG signals and supporting the hypothesis that REG signals do carry CBF information. Furthermore, the analysis of cerebral hemodynamics during anesthetic procedures was performed by means of studying causal relationships between global hemodynamics, cerebral hemodynamics and electroencephalogram (EEG) based-parameters. Interactions were detected during anesthetic drug infusion and patient positioning (Trendelenburg positioning and passive leg raise), providing evidence of the causal coupling between hemodynamics and brain activity. The provided alternative of REG signal processing confirmed the hypothesis that REG signals carry information on CBF. The simplicity of the technology, together with its low cost and easily interpretable outcomes, should provide a new opportunity for REG to reach standard clinical practice. Moreover, causal relationships among the hemodynamic physiological signals and brain activity were assessed, suggesting that the inclusion of REG information in depth of anesthesia monitors could be of valuable use to prevent unwanted CBF alterations during anesthetic procedures.
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Affiliation(s)
- Carmen González
- Biomedical Engineering Research Centre, CIBER of Bioengineering, Biomaterials and Nanomedicine (CIBER-BBN), Universitat Politècnica de Catalunya, Barcelona, Spain.,Research and Development Department, Quantium Medical, Mataró, Spain
| | - Gabriel Garcia-Hernando
- Biomedical Engineering Research Centre, CIBER of Bioengineering, Biomaterials and Nanomedicine (CIBER-BBN), Universitat Politècnica de Catalunya, Barcelona, Spain.,Research and Development Department, Quantium Medical, Mataró, Spain
| | - Erik W Jensen
- Research and Development Department, Quantium Medical, Mataró, Spain
| | - Montserrat Vallverdú-Ferrer
- Biomedical Engineering Research Centre, CIBER of Bioengineering, Biomaterials and Nanomedicine (CIBER-BBN), Universitat Politècnica de Catalunya, Barcelona, Spain
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13
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Catrambone V, Barbieri R, Wendt H, Abry P, Valenza G. Functional brain-heart interplay extends to the multifractal domain. PHILOSOPHICAL TRANSACTIONS. SERIES A, MATHEMATICAL, PHYSICAL, AND ENGINEERING SCIENCES 2021; 379:20200260. [PMID: 34689620 PMCID: PMC8543048 DOI: 10.1098/rsta.2020.0260] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Accepted: 02/12/2021] [Indexed: 05/09/2023]
Abstract
The study of functional brain-heart interplay has provided meaningful insights in cardiology and neuroscience. Regarding biosignal processing, this interplay involves predominantly neural and heartbeat linear dynamics expressed via time and frequency domain-related features. However, the dynamics of central and autonomous nervous systems show nonlinear and multifractal behaviours, and the extent to which this behaviour influences brain-heart interactions is currently unknown. Here, we report a novel signal processing framework aimed at quantifying nonlinear functional brain-heart interplay in the non-Gaussian and multifractal domains that combines electroencephalography (EEG) and heart rate variability series. This framework relies on a maximal information coefficient analysis between nonlinear multiscale features derived from EEG spectra and from an inhomogeneous point-process model for heartbeat dynamics. Experimental results were gathered from 24 healthy volunteers during a resting state and a cold pressor test, revealing that synchronous changes between brain and heartbeat multifractal spectra occur at higher EEG frequency bands and through nonlinear/complex cardiovascular control. We conclude that significant bodily, sympathovagal changes such as those elicited by cold-pressure stimuli affect the functional brain-heart interplay beyond second-order statistics, thus extending it to multifractal dynamics. These results provide a platform to define novel nervous-system-targeted biomarkers. This article is part of the theme issue 'Advanced computation in cardiovascular physiology: new challenges and opportunities'.
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Affiliation(s)
- Vincenzo Catrambone
- Research Center E.Piaggio, Department of Information Engineering, School of Engineering, University of Pisa, Pisa, Italy
| | - Riccardo Barbieri
- Department of Electronics, Informatics and Bioengineering, Politecnico di Milano, Milano, Italy
| | - Herwig Wendt
- IRIT–ENSEEIHT, Université de Toulouse, CNRS, Toulouse, France
| | - Patrice Abry
- University of Lyon, ENS de Lyon, University Claude Bernard, CNRS, Laboratoire de Physique, Lyon, France
| | - Gaetano Valenza
- Research Center E.Piaggio, Department of Information Engineering, School of Engineering, University of Pisa, Pisa, Italy
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Hartmann S, Ferri R, Bruni O, Baumert M. Causality of cortical and cardiovascular activity during cyclic alternating pattern in non-rapid eye movement sleep. PHILOSOPHICAL TRANSACTIONS. SERIES A, MATHEMATICAL, PHYSICAL, AND ENGINEERING SCIENCES 2021; 379:20200248. [PMID: 34689628 DOI: 10.1098/rsta.2020.0248] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Accepted: 02/01/2021] [Indexed: 06/13/2023]
Abstract
The dynamic interplay between central and autonomic nervous system activities plays a pivotal role in orchestrating sleep. Macrostructural changes such as sleep-stage transitions or phasic, brief cortical events elicit fluctuations in neural outflow to the cardiovascular system, but the causal relationships between cortical and cardiovascular activities underpinning the microstructure of sleep are largely unknown. Here, we investigate cortical-cardiovascular interactions during the cyclic alternating pattern (CAP) of non-rapid eye movement sleep in a diverse set of overnight polysomnograms. We determine the Granger causality in both 507 CAP and 507 matched non-CAP sequences to assess the causal relationships between electroencephalography (EEG) frequency bands and respiratory and cardiovascular variables (heart period, respiratory period, pulse arrival time and pulse wave amplitude) during CAP. We observe a significantly stronger influence of delta activity on vascular variables during CAP sequences where slow, low-amplitude EEG activation phases (A1) dominate than during non-CAP sequences. We also show that rapid, high-amplitude EEG activation phases (A3) provoke a more pronounced change in autonomic activity than A1 and A2 phases. Our analysis provides the first evidence on the causal interplay between cortical and cardiovascular activities during CAP. Granger causality analysis may also be useful for probing the level of decoupling in sleep disorders. This article is part of the theme issue 'Advanced computation in cardiovascular physiology: new challenges and opportunities'.
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Affiliation(s)
- Simon Hartmann
- School of Electrical and Electronic Engineering, University of Adelaide, Adelaide, Australia
| | - Raffaele Ferri
- Sleep Research Center, Department of Neurology IC, Oasi Research Institute-IRCCS, Troina, Italy
| | - Oliviero Bruni
- Department of Social and Developmental Psychology, Sapienza University, Rome, Italy
| | - Mathias Baumert
- School of Electrical and Electronic Engineering, University of Adelaide, Adelaide, Australia
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Nardelli M, Catrambone V, Grandi G, Banfi T, Bruno RM, Scilingo EP, Faraguna U, Valenza G. Activation of brain-heart axis during REM sleep: a trigger for dreaming. Am J Physiol Regul Integr Comp Physiol 2021; 321:R951-R959. [PMID: 34704848 DOI: 10.1152/ajpregu.00306.2020] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/22/2022]
Abstract
Dreams may be recalled after awakening from sleep following a defined electroencephalographic pattern that involves local decreases in low-frequency activity in the posterior cortical regions. While a dreaming experience implies bodily changes at many organ-, system-, and timescale-levels, the entity and causal role of such peripheral changes in a conscious dream experience are unknown. We performed a comprehensive, causal, multivariate analysis of physiological signals acquired during REM sleep at night, including high-density EEG and peripheral dynamics including electrocardiography and blood pressure. In this preliminary study, we investigated multiple recalls and non-recalls of dream experiences using data from nine healthy volunteers. The aim was not only to investigate the changes in central and autonomic dynamics associated with dream recalls and non-recalls, but also to characterize the central-peripheral dynamical and (causal) directional interactions, and the temporal relations of the related arousals upon awakening. We uncovered a brain-body network that drives a conscious dreaming experience that acts with specific interaction and time delays. Such a network is sustained by the blood pressure dynamics and the increasing functional information transfer from the neural heartbeat regulation to the brain. We conclude that bodily changes play a crucial and causative role in a conscious dream experience during REM sleep.
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Affiliation(s)
- Mimma Nardelli
- Bioengineering and Robotics Research Centre E. Piaggio and Department of Information Engineering, University of Pisa, Italy
| | - Vincenzo Catrambone
- Bioengineering and Robotics Research Centre E. Piaggio and Department of Information Engineering, University of Pisa, Italy
| | - Giulia Grandi
- Department of Translational Research and of New Surgical and Medical Technologies, University of Pisa, Italy
| | - Tommaso Banfi
- Department of Translational Research and of New Surgical and Medical Technologies, University of Pisa, Italy
| | - Rosa Maria Bruno
- INSERM U970 Team 7, Paris Cardiovascular Research Centre - PARCC, University Paris Descartes, Sorbonne Paris Cité, Paris, France
| | - Enzo Pasquale Scilingo
- Bioengineering and Robotics Research Centre E. Piaggio and Department of Information Engineering, University of Pisa, Italy
| | - Ugo Faraguna
- Department of Translational Research and of New Surgical and Medical Technologies, University of Pisa, Italy.,Department of Developmental Neuroscience, IRCCS Fondazione Stella Maris, Pisa, Italy
| | - Gaetano Valenza
- Bioengineering and Robotics Research Centre E. Piaggio and Department of Information Engineering, University of Pisa, Italy
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16
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Candia-Rivera D, Catrambone V, Valenza G. The role of electroencephalography electrical reference in the assessment of functional brain-heart interplay: From methodology to user guidelines. J Neurosci Methods 2021; 360:109269. [PMID: 34171310 DOI: 10.1016/j.jneumeth.2021.109269] [Citation(s) in RCA: 28] [Impact Index Per Article: 9.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/23/2020] [Revised: 06/16/2021] [Accepted: 06/18/2021] [Indexed: 12/25/2022]
Abstract
BACKGROUND The choice of EEG reference has been widely studied. However, the choice of the most appropriate re-referencing for EEG data is still debated. Moreover, the role of EEG reference in the estimation of functional Brain-Heart Interplay (BHI), together with different multivariate modelling strategies, has not been investigated yet. METHODS This study identifies the best methodology combining a proper EEG electrical reference and signal processing methods for an effective functional BHI assessment. The effects of the EEG reference among common average, mastoids average, Laplacian reference, Cz reference, and the reference electrode standardization technique (REST) were explored throughout different BHI methods including synthetic data generation (SDG) model, heartbeat-evoked potentials, heartbeat-evoked oscillations, and maximal information coefficient. RESULTS The SDG model exhibited high robustness between EEG references, whereas the maximal information coefficient method exhibited a high sensitivity. The common average and REST references for EEG showed a good consistency in the between-method comparisons. Laplacian, and Cz references significantly bias a BHI measurement. COMPARISON WITH EXISTING METHODS The use of EEG reference based on a common average outperforms on the use of other references for consistency in estimating directed functional BHI. We do not recommend the use of EEG references based on analytical derivations as the experimental conditions may not meet the requirements of their optimal estimation, particularly in clinical settings. CONCLUSION The use of a common average for EEG electrical reference is concluded to be the most appropriate choice for a quantitative, functional BHI assessment.
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Affiliation(s)
- Diego Candia-Rivera
- Bioengineering and Robotics Research Center E. Piaggio and the Department of Information Engineering, School of Engineering, University of Pisa, Pisa, Italy.
| | - Vincenzo Catrambone
- Bioengineering and Robotics Research Center E. Piaggio and the Department of Information Engineering, School of Engineering, University of Pisa, Pisa, Italy
| | - Gaetano Valenza
- Bioengineering and Robotics Research Center E. Piaggio and the Department of Information Engineering, School of Engineering, University of Pisa, Pisa, Italy
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17
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Catrambone V, Messerotti Benvenuti S, Gentili C, Valenza G. Intensification of functional neural control on heartbeat dynamics in subclinical depression. Transl Psychiatry 2021; 11:221. [PMID: 33854037 PMCID: PMC8046790 DOI: 10.1038/s41398-021-01336-4] [Citation(s) in RCA: 18] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/15/2021] [Accepted: 03/30/2021] [Indexed: 01/06/2023] Open
Abstract
Subclinical depression (dysphoria) is a common condition that may increase the risk of major depression and leads to impaired quality of life and severe comorbid somatic diseases. Despite its prevalence, specific biological markers are unknown; consequently, the identification of dysphoria currently relies exclusively on subjective clinical scores and structured interviews. Based on recent neurocardiology studies that link brain and cardiovascular disorders, it was hypothesized that multi-system biomarkers of brain-body interplay may effectively characterize dysphoria. Thus, an ad hoc computational technique was developed to quantify the functional bidirectional brain-heart interplay. Accordingly, 32-channel electroencephalographic and heart rate variability series were obtained from 24 young dysphoric adults and 36 healthy controls. All participants were females of a similar age, and results were obtained during a 5-min resting state. The experimental results suggest that a specific feature of dysphoria is linked to an augmented functional central-autonomic control to the heart, which originates from central, frontopolar, and occipital oscillations and acts through cardiovascular sympathovagal activity. These results enable further development of a large set of novel biomarkers for mood disorders based on comprehensive brain-body measurements.
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Affiliation(s)
- Vincenzo Catrambone
- Research Center E. Piaggio & Department of Information Engineering, School of Engineering, University of Pisa, 56126, Pisa, Italy.
| | | | - Claudio Gentili
- grid.5608.b0000 0004 1757 3470Department of General Psychology, University of Padua, 35131 Padua, Italy
| | - Gaetano Valenza
- grid.5395.a0000 0004 1757 3729Research Center E. Piaggio & Department of Information Engineering, School of Engineering, University of Pisa, 56126 Pisa, Italy
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18
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Catrambone V, Averta G, Bianchi M, Valenza G. Toward brain-heart computer interfaces: a study on the classification of upper limb movements using multisystem directional estimates. J Neural Eng 2021; 18. [PMID: 33601354 DOI: 10.1088/1741-2552/abe7b9] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/12/2020] [Accepted: 02/18/2021] [Indexed: 12/13/2022]
Abstract
OBJECTIVE Brain-computer interfaces (BCI) exploit computational features from brain signals to perform a given task. Despite recent neurophysiology and clinical findings indicating the crucial role of functional interplay between brain and cardiovascular dynamics in locomotion, heartbeat information remains to be included in common BCI systems. In this study, we exploit the multidimensional features of directional and functional interplay between electroencephalographic and heartbeat spectra to classify upper limb movements into three classes. APPROACH We gathered data from 26 healthy volunteers that performed 90 movements; the data were processed using a recently proposed framework for brain-heart interplay (BHI) assessment based on synthetic physiological data generation. Extracted BHI features were employed to classify, through sequential forward selection scheme and k-nearest neighbors algorithm, among resting state and three classes of movements according to the kind of interaction with objects. MAIN RESULTS The results demonstrated that the proposed brain-heart computer interface (BHCI) system could distinguish between rest and movement classes automatically with an average 90% of accuracy. SIGNIFICANCE Further, this study provides neurophysiology insights indicating the crucial role of functional interplay originating at the cortical level onto the heart in the upper limb neural control. The inclusion of functional BHI insights might substantially improve the neuroscientific knowledge about motor control, and this may lead to advanced BHCI systems performances.
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Affiliation(s)
- Vincenzo Catrambone
- Research Center E. Piaggio, Information Engineering, University of Pisa School of Engineering, Largo L. Lazzarino,1, Pisa, Italy, 56126, ITALY
| | - Giuseppe Averta
- Research Center E. Piaggio, Information Engineering, University of Pisa School of Engineering, Largo L. Lazzarino, 1, Pisa, Italy, 56126, ITALY
| | - Matteo Bianchi
- Research Center E. Piaggio, Information Engineering, University of Pisa School of Engineering, Largo L. Lazzarino, 1, Pisa, Toscana, 56126, ITALY
| | - Gaetano Valenza
- Research Center E. Piaggio, Information Engineering, University of Pisa School of Engineering, Largo L. Lazzarino, 1, Pisa, Toscana, 56126, ITALY
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Pernice R, Antonacci Y, Zanetti M, Busacca A, Marinazzo D, Faes L, Nollo G. Multivariate Correlation Measures Reveal Structure and Strength of Brain-Body Physiological Networks at Rest and During Mental Stress. Front Neurosci 2021; 14:602584. [PMID: 33613173 PMCID: PMC7890264 DOI: 10.3389/fnins.2020.602584] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/03/2020] [Accepted: 12/16/2020] [Indexed: 12/13/2022] Open
Abstract
In this work, we extend to the multivariate case the classical correlation analysis used in the field of network physiology to probe dynamic interactions between organ systems in the human body. To this end, we define different correlation-based measures of the multivariate interaction (MI) within and between the brain and body subnetworks of the human physiological network, represented, respectively, by the time series of δ, θ, α, and β electroencephalographic (EEG) wave amplitudes, and of heart rate, respiration amplitude, and pulse arrival time (PAT) variability (η, ρ, π). MI is computed: (i) considering all variables in the two subnetworks to evaluate overall brain-body interactions; (ii) focusing on a single target variable and dissecting its global interaction with all other variables into contributions arising from the same subnetwork and from the other subnetwork; and (iii) considering two variables conditioned to all the others to infer the network topology. The framework is applied to the time series measured from the EEG, electrocardiographic (ECG), respiration, and blood volume pulse (BVP) signals recorded synchronously via wearable sensors in a group of healthy subjects monitored at rest and during mental arithmetic and sustained attention tasks. We find that the human physiological network is highly connected, with predominance of the links internal of each subnetwork (mainly η-ρ and δ-θ, θ-α, α-β), but also statistically significant interactions between the two subnetworks (mainly η-β and η-δ). MI values are often spatially heterogeneous across the scalp and are modulated by the physiological state, as indicated by the decrease of cardiorespiratory interactions during sustained attention and by the increase of brain-heart interactions and of brain-brain interactions at the frontal scalp regions during mental arithmetic. These findings illustrate the complex and multi-faceted structure of interactions manifested within and between different physiological systems and subsystems across different levels of mental stress.
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Affiliation(s)
- Riccardo Pernice
- Department of Engineering, University of Palermo, Palermo, Italy
| | - Yuri Antonacci
- Department of Physics and Chemistry “Emilio Segrè,” University of Palermo, Palermo, Italy
| | - Matteo Zanetti
- Department of Industrial Engineering, University of Trento, Trento, Italy
| | | | | | - Luca Faes
- Department of Engineering, University of Palermo, Palermo, Italy
| | - Giandomenico Nollo
- Department of Industrial Engineering, University of Trento, Trento, Italy
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Catrambone V, Talebi A, Barbieri R, Valenza G. Time-resolved Brain-to-Heart Probabilistic Information Transfer Estimation Using Inhomogeneous Point-Process Models. IEEE Trans Biomed Eng 2021; 68:3366-3374. [DOI: 10.1109/tbme.2021.3071348] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/05/2022]
Affiliation(s)
- Vincenzo Catrambone
- Research Center E. Piaggio, Information Engineering, University of Pisa, 9310 Pisa, Toscana, Italy, (e-mail: )
| | - Alireza Talebi
- Research Center E. Piaggio, Information Engineering, University of Pisa, 9310 Pisa, Toscana, Italy, (e-mail: )
| | | | - Gaetano Valenza
- Research Center E. Piaggio, Information Engineering, University of Pisa, 9310 Pisa, Toscana, Italy, (e-mail: )
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21
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Mijatovic G, Pernice R, Perinelli A, Antonacci Y, Busacca A, Javorka M, Ricci L, Faes L. Measuring the Rate of Information Exchange in Point-Process Data With Application to Cardiovascular Variability. FRONTIERS IN NETWORK PHYSIOLOGY 2021; 1:765332. [PMID: 36925567 PMCID: PMC10013020 DOI: 10.3389/fnetp.2021.765332] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/26/2021] [Accepted: 11/26/2021] [Indexed: 02/01/2023]
Abstract
The amount of information exchanged per unit of time between two dynamic processes is an important concept for the analysis of complex systems. Theoretical formulations and data-efficient estimators have been recently introduced for this quantity, known as the mutual information rate (MIR), allowing its continuous-time computation for event-based data sets measured as realizations of coupled point processes. This work presents the implementation of MIR for point process applications in Network Physiology and cardiovascular variability, which typically feature short and noisy experimental time series. We assess the bias of MIR estimated for uncoupled point processes in the frame of surrogate data, and we compensate it by introducing a corrected MIR (cMIR) measure designed to return zero values when the two processes do not exchange information. The method is first tested extensively in synthetic point processes including a physiologically-based model of the heartbeat dynamics and the blood pressure propagation times, where we show the ability of cMIR to compensate the negative bias of MIR and return statistically significant values even for weakly coupled processes. The method is then assessed in real point-process data measured from healthy subjects during different physiological conditions, showing that cMIR between heartbeat and pressure propagation times increases significantly during postural stress, though not during mental stress. These results document that cMIR reflects physiological mechanisms of cardiovascular variability related to the joint neural autonomic modulation of heart rate and arterial compliance.
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Affiliation(s)
- Gorana Mijatovic
- Faculty of Technical Science, University of Novi Sad, Novi Sad, Serbia
| | - Riccardo Pernice
- Department of Engineering, University of Palermo, Palermo, Italy
| | - Alessio Perinelli
- CIMeC, Center for Mind/Brain Sciences, University of Trento, Rovereto, Italy
| | - Yuri Antonacci
- Department of Physics and Chemistry "Emilio Segrè," University of Palermo, Palermo, Italy
| | | | - Michal Javorka
- Department of Physiology and Biomedical Center Martin, Jessenius Faculty of Medicine, Comenius University, Martin, Slovakia
| | - Leonardo Ricci
- Department of Physics, University of Trento, Trento, Italy
| | - Luca Faes
- Department of Engineering, University of Palermo, Palermo, Italy
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22
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Antonacci Y, Astolfi L, Nollo G, Faes L. Information Transfer in Linear Multivariate Processes Assessed through Penalized Regression Techniques: Validation and Application to Physiological Networks. ENTROPY (BASEL, SWITZERLAND) 2020; 22:E732. [PMID: 33286504 PMCID: PMC7517272 DOI: 10.3390/e22070732] [Citation(s) in RCA: 18] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/16/2020] [Revised: 06/16/2020] [Accepted: 06/26/2020] [Indexed: 01/28/2023]
Abstract
The framework of information dynamics allows the dissection of the information processed in a network of multiple interacting dynamical systems into meaningful elements of computation that quantify the information generated in a target system, stored in it, transferred to it from one or more source systems, and modified in a synergistic or redundant way. The concepts of information transfer and modification have been recently formulated in the context of linear parametric modeling of vector stochastic processes, linking them to the notion of Granger causality and providing efficient tools for their computation based on the state-space (SS) representation of vector autoregressive (VAR) models. Despite their high computational reliability these tools still suffer from estimation problems which emerge, in the case of low ratio between data points available and the number of time series, when VAR identification is performed via the standard ordinary least squares (OLS). In this work we propose to replace the OLS with penalized regression performed through the Least Absolute Shrinkage and Selection Operator (LASSO), prior to computation of the measures of information transfer and information modification. First, simulating networks of several coupled Gaussian systems with complex interactions, we show that the LASSO regression allows, also in conditions of data paucity, to accurately reconstruct both the underlying network topology and the expected patterns of information transfer. Then we apply the proposed VAR-SS-LASSO approach to a challenging application context, i.e., the study of the physiological network of brain and peripheral interactions probed in humans under different conditions of rest and mental stress. Our results, which document the possibility to extract physiologically plausible patterns of interaction between the cardiovascular, respiratory and brain wave amplitudes, open the way to the use of our new analysis tools to explore the emerging field of Network Physiology in several practical applications.
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Affiliation(s)
- Yuri Antonacci
- Department of Computer, Control and Management Engineering, Sapienza University of Rome, 00185 Rome, Italy;
- Istituto di Ricovero e Cura a Carattere Scientifico (IRCCS) Fondazione Santa Lucia, 00179 Rome, Italy
| | - Laura Astolfi
- Department of Computer, Control and Management Engineering, Sapienza University of Rome, 00185 Rome, Italy;
- Istituto di Ricovero e Cura a Carattere Scientifico (IRCCS) Fondazione Santa Lucia, 00179 Rome, Italy
| | - Giandomenico Nollo
- Department of Industrial Engineering, University of Trento, 38123 Trento, Italy;
| | - Luca Faes
- Department of Engineering, University of Palermo, 90128 Palermo, Italy;
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Functional Linear and Nonlinear Brain–Heart Interplay during Emotional Video Elicitation: A Maximum Information Coefficient Study. ENTROPY 2019. [PMCID: PMC7515428 DOI: 10.3390/e21090892] [Citation(s) in RCA: 16] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
Brain and heart continuously interact through anatomical and biochemical connections. Although several brain regions are known to be involved in the autonomic control, the functional brain–heart interplay (BHI) during emotional processing is not fully characterized yet. To this aim, we investigate BHI during emotional elicitation in healthy subjects. The functional linear and nonlinear couplings are quantified using the maximum information coefficient calculated between time-varying electroencephalography (EEG) power spectra within the canonical bands (δ,θ,α,β and γ), and time-varying low-frequency and high-frequency powers from heartbeat dynamics. Experimental data were gathered from 30 healthy volunteers whose emotions were elicited through pleasant and unpleasant high-arousing videos. Results demonstrate that functional BHI increases during videos with respect to a resting state through EEG oscillations not including the γ band (>30 Hz). Functional linear coupling seems associated with a high-arousing positive elicitation, with preferred EEG oscillations in the θ band ([4,8) Hz) especially over the left-temporal and parietal cortices. Differential functional nonlinear coupling between emotional valence seems to mainly occur through EEG oscillations in the δ,θ,α bands and sympathovagal dynamics, as well as through δ,α,β oscillations and parasympathetic activity mainly over the right hemisphere. Functional BHI through δ and α oscillations over the prefrontal region seems primarily nonlinear. This study provides novel insights on synchronous heartbeat and cortical dynamics during emotional video elicitation, also suggesting that a nonlinear analysis is needed to fully characterize functional BHI.
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