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Frassineti L, Catrambone V, Lanatà A, Valenza G. Impaired brain-heart axis in focal epilepsy: Alterations in information flow and implications for seizure dynamics. Netw Neurosci 2024; 8:541-556. [PMID: 38952812 PMCID: PMC11168720 DOI: 10.1162/netn_a_00367] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/28/2023] [Accepted: 02/09/2024] [Indexed: 07/03/2024] Open
Abstract
This study delves into functional brain-heart interplay (BHI) dynamics during interictal periods before and after seizure events in focal epilepsy. Our analysis focuses on elucidating the causal interaction between cortical and autonomic nervous system (ANS) oscillations, employing electroencephalography and heart rate variability series. The dataset for this investigation comprises 47 seizure events from 14 independent subjects, obtained from the publicly available Siena Dataset. Our findings reveal an impaired brain-heart axis especially in the heart-to-brain functional direction. This is particularly evident in bottom-up oscillations originating from sympathovagal activity during the transition between preictal and postictal periods. These results indicate a pivotal role of the ANS in epilepsy dynamics. Notably, the brain-to-heart information flow targeting cardiac oscillations in the low-frequency band does not display significant changes. However, there are noteworthy changes in cortical oscillations, primarily originating in central regions, influencing heartbeat oscillations in the high-frequency band. Our study conceptualizes seizures as a state of hyperexcitability and a network disease affecting both cortical and peripheral neural dynamics. Our results pave the way for a deeper understanding of BHI in epilepsy, which holds promise for the development of advanced diagnostic and therapeutic approaches also based on bodily neural activity for individuals living with epilepsy.
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Affiliation(s)
- Lorenzo Frassineti
- Department of Information Engineering, Università degli Studi di Firenze, Firenze, Italy
| | - Vincenzo Catrambone
- Department of Information Engineering and Bioengineering & Robotics Research Center E. Piaggio, University of Pisa, Pisa, Italy
| | - Antonio Lanatà
- Department of Information Engineering, Università degli Studi di Firenze, Firenze, Italy
| | - Gaetano Valenza
- Department of Information Engineering and Bioengineering & Robotics Research Center E. Piaggio, University of Pisa, Pisa, Italy
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Catrambone V, Candia‐Rivera D, Valenza G. Intracortical brain-heart interplay: An EEG model source study of sympathovagal changes. Hum Brain Mapp 2024; 45:e26677. [PMID: 38656080 PMCID: PMC11041380 DOI: 10.1002/hbm.26677] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/19/2023] [Revised: 02/18/2024] [Accepted: 03/23/2024] [Indexed: 04/26/2024] Open
Abstract
The interplay between cerebral and cardiovascular activity, known as the functional brain-heart interplay (BHI), and its temporal dynamics, have been linked to a plethora of physiological and pathological processes. Various computational models of the brain-heart axis have been proposed to estimate BHI non-invasively by taking advantage of the time resolution offered by electroencephalograph (EEG) signals. However, investigations into the specific intracortical sources responsible for this interplay have been limited, which significantly hampers existing BHI studies. This study proposes an analytical modeling framework for estimating the BHI at the source-brain level. This analysis relies on the low-resolution electromagnetic tomography sources localization from scalp electrophysiological recordings. BHI is then quantified as the functional correlation between the intracortical sources and cardiovascular dynamics. Using this approach, we aimed to evaluate the reliability of BHI estimates derived from source-localized EEG signals as compared with prior findings from neuroimaging methods. The proposed approach is validated using an experimental dataset gathered from 32 healthy individuals who underwent standard sympathovagal elicitation using a cold pressor test. Additional resting state data from 34 healthy individuals has been analysed to assess robustness and reproducibility of the methodology. Experimental results not only confirmed previous findings on activation of brain structures affecting cardiac dynamics (e.g., insula, amygdala, hippocampus, and anterior and mid-cingulate cortices) but also provided insights into the anatomical bases of brain-heart axis. In particular, we show that the bidirectional activity of electrophysiological pathways of functional brain-heart communication increases during cold pressure with respect to resting state, mainly targeting neural oscillations in theδ $$ \delta $$ ,β $$ \beta $$ , andγ $$ \gamma $$ bands. The proposed approach offers new perspectives for the investigation of functional BHI that could also shed light on various pathophysiological conditions.
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Affiliation(s)
- Vincenzo Catrambone
- Neurocardiovascular Intelligence Laboratory & Department of Information Engineering & Bioengineering and Robotics Research Center, E. Piaggio, School of EngineeringUniversity of PisaPisaItaly
| | - Diego Candia‐Rivera
- Sorbonne Université, Paris Brain Institute (ICM), INRIA, CNRS, INSERM, AP‐HP, Hôpital Pitié‐SalpêtriŕeParisFrance
| | - Gaetano Valenza
- Neurocardiovascular Intelligence Laboratory & Department of Information Engineering & Bioengineering and Robotics Research Center, E. Piaggio, School of EngineeringUniversity of PisaPisaItaly
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Catrambone V, Zallocco L, Ramoretti E, Mazzoni MR, Sebastiani L, Valenza G. Integrative neuro-cardiovascular dynamics in response to test anxiety: A brain-heart axis study. Physiol Behav 2024; 276:114460. [PMID: 38215864 DOI: 10.1016/j.physbeh.2024.114460] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/19/2023] [Revised: 12/08/2023] [Accepted: 01/08/2024] [Indexed: 01/14/2024]
Abstract
Test anxiety (TA), a recognized form of social anxiety, is the most prominent cause of anxiety among students and, if left unmanaged, can escalate to psychiatric disorders. TA profoundly impacts both central and autonomic nervous systems, presenting as a dual manifestation of cognitive and autonomic components. While limited studies have explored the physiological underpinnings of TA, none have directly investigated the intricate interplay between the CNS and ANS in this context. In this study, we introduce a non-invasive, integrated neuro-cardiovascular approach to comprehensively characterize the physiological responses of 27 healthy subjects subjected to test anxiety induced via a simulated exam scenario. Our experimental findings highlight that an isolated analysis of electroencephalographic and heart rate variability data fails to capture the intricate information provided by a brain-heart axis assessment, which incorporates an analysis of the dynamic interaction between the brain and heart. With respect to resting state, the simulated examination induced a decrease in the neural control onto heartbeat dynamics at all frequencies, while the studying condition induced a decrease in the ascending heart-to-brain interplay at EEG oscillations up to 12Hz. This underscores the significance of adopting a multisystem perspective in understanding the complex and especially functional directional mechanisms underlying test anxiety.
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Affiliation(s)
- Vincenzo Catrambone
- Neurocardiovascular Intelligence Laboratory, Department of Information Engineering & Bioengineering and Robotics Research Center E. Piaggio, School of Engineering, University of Pisa, Pisa, Italy.
| | - Lorenzo Zallocco
- Department of Translational Research and New Technologies in Medicine and Surgery, University of Pisa, Pisa, Italy
| | - Eleonora Ramoretti
- Department of Translational Research and New Technologies in Medicine and Surgery, University of Pisa, Pisa, Italy
| | - Maria Rosa Mazzoni
- Department of Translational Research and New Technologies in Medicine and Surgery, University of Pisa, Pisa, Italy
| | - Laura Sebastiani
- Department of Translational Research and New Technologies in Medicine and Surgery, University of Pisa, Pisa, Italy; Institute of Information Science and Technologies A. Faedo, ISTI-CNR, Pisa, Italy
| | - Gaetano Valenza
- Neurocardiovascular Intelligence Laboratory, Department of Information Engineering & Bioengineering and Robotics Research Center E. Piaggio, School of Engineering, University of Pisa, Pisa, Italy
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Catrambone V, Valenza G. Microstates of the cortical brain-heart axis. Hum Brain Mapp 2023; 44:5846-5857. [PMID: 37688575 PMCID: PMC10619395 DOI: 10.1002/hbm.26480] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/02/2022] [Revised: 08/04/2023] [Accepted: 08/24/2023] [Indexed: 09/11/2023] Open
Abstract
Electroencephalographic (EEG) microstates are brain states with quasi-stable scalp topography. Whether such states extend to the body level, that is, the peripheral autonomic nerves, remains unknown. We hypothesized that microstates extend at the brain-heart axis level as a functional state of the central autonomic network. Thus, we combined the EEG and heartbeat dynamics series to estimate the directional information transfer originating in the cortex targeting the sympathovagal and parasympathetic activity oscillations and vice versa for the afferent functional direction. Data were from two groups of participants: 36 healthy volunteers who were subjected to cognitive workload induced by mental arithmetic, and 26 participants who underwent physical stress induced by a cold pressure test. All participants were healthy at the time of the study. Based on statistical testing and goodness-of-fit evaluations, we demonstrated the existence of microstates of the functional brain-heart axis, with emphasis on the cerebral cortex, since the microstates are derived from EEG. Such nervous-system microstates are spatio-temporal quasi-stable states that exclusively refer to the efferent brain-to-heart direction. We demonstrated brain-heart microstates that could be associated with specific experimental conditions as well as brain-heart microstates that are non-specific to tasks.
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Affiliation(s)
- Vincenzo Catrambone
- Neurocardiovascular Intelligence Laboratory, Bioengineering and Robotics Research Center E. Piaggio, & Department of Information Engineering, School of EngineeringUniversity of PisaPisaItaly
| | - Gaetano Valenza
- Neurocardiovascular Intelligence Laboratory, Bioengineering and Robotics Research Center E. Piaggio, & Department of Information Engineering, School of EngineeringUniversity of PisaPisaItaly
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Catrambone V, Valenza G. Towards the definition of Microstates of the Cortical Brain-Heart Axis. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2023; 2023:1-4. [PMID: 38082600 DOI: 10.1109/embc40787.2023.10340338] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/18/2023]
Abstract
Brain microstates are defined as states with quasi-stable scalp activity topography and have been widely studied in literature. Whether those states are brain-specific or extend to the body level is unknown yet. We investigate the extension of cortical microstates to the peripheral autonomic nerve, specifically at the brain-heart axis level as a functional state of the central autonomic network. To achieve this, we combined Electroencephalographic (EEG) and heart rate variability (HRV) series from 36 healthy volunteers undergoing a cognitive workload elicitation after a resting state. Our results showed the existence of microstates at the functional brain-heart axis with spatio-temporal and quasi-stable states that exclusively pertained to the efferent direction from the brain to the heart. Some of the identified microstates are specific for neural or cardiovascular frequency bands, while others topographies are recurrent over the EEG and HRV spectra. Furthermore, some of the identified brain-heart microstates were associated with specific experimental conditions, while others were nonspecific to tasks. Our findings support the hypothesis that EEG microstates extend to the brain-heart axis level and may be exploited in future neuroscience and clinical research.
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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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Manzoni D, Catrambone V, Valenza G. Causal Symbolic Information Transfer for the Assessment of functional Brain-Heart Interplay through EEG Microstates Occurrences: a proof-of-concept study. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2022; 2022:255-258. [PMID: 36086149 DOI: 10.1109/embc48229.2022.9871000] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
Electroencephalography (EEG) microstates analysis provides a sequence of topographies representing the scalp-related electric field over time, and each microstate is synthetically represented by a symbol. Despite recent advances on functional brain-heart interplay (BHI) assessment, to our knowledge no methodology takes EEG microstates into account to relate the causal heartbeat dynamics. Moreover, standard BHI methods are tailored to a single EEG-channel analysis, neglecting the comprehensive information associated with a multichannel cluster or a whole-brain activity. To overcome these limitations, we devised a novel methodological frame-work for the assessment of functional BHI that exploits the symbolic representation of both EEG microstates and heart rate variability (HRV) series. Directional BHI quantification is then performed through Kullback-Leibler Divergence (KLD) and Transfer Entropy. The proposed methodology is here preliminarily tested on a dataset gathered from healthy subjects undergoing a resting state and a mental arithmetic task. Except for the KLD in the from-brain-to-heart direction, all other estimates showed significant differences between the two experimental conditions. We conclude that the proposed frame-work may promisingly provide novel insights on brain-heart phenomena through a whole-brain symbolic representation.
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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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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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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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