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      Impaired brain-heart axis in focal epilepsy: Alterations in information flow and implications for seizure dynamics

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          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.

          Author Summary

          This study focuses on brain-heart interplay (BHI) during pre- and postictal periods surrounding seizures. Employing multichannel EEG and heart rate variability data from subjects with focal epilepsy, our analysis reveals a disrupted brain-heart axis dynamic, particularly in the heart-to-brain direction. Notably, sympathovagal activity alterations during preictal to postictal transitions underscore the autonomic nervous system’s pivotal role in epilepsy dynamics. While brain-to-heart information flow targeting low-frequency band cardiac oscillations remains stable, significant changes occur in cortical oscillations, predominantly in central regions, influencing high-frequeny-band heartbeat oscillations, that is, vagal activity. Viewing seizures as states of hyperexcitability and confirming focal epilepsy as a network disease affecting both central and peripheral neural dynamics, our study enhances understanding of BHI in epilepsy. These findings offer potential for advanced diagnostic and therapeutic approaches grounded in bodily neural activity for individuals with epilepsy.

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          Most cited references66

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          An Overview of Heart Rate Variability Metrics and Norms

          Healthy biological systems exhibit complex patterns of variability that can be described by mathematical chaos. Heart rate variability (HRV) consists of changes in the time intervals between consecutive heartbeats called interbeat intervals (IBIs). A healthy heart is not a metronome. The oscillations of a healthy heart are complex and constantly changing, which allow the cardiovascular system to rapidly adjust to sudden physical and psychological challenges to homeostasis. This article briefly reviews current perspectives on the mechanisms that generate 24 h, short-term (~5 min), and ultra-short-term (<5 min) HRV, the importance of HRV, and its implications for health and performance. The authors provide an overview of widely-used HRV time-domain, frequency-domain, and non-linear metrics. Time-domain indices quantify the amount of HRV observed during monitoring periods that may range from ~2 min to 24 h. Frequency-domain values calculate the absolute or relative amount of signal energy within component bands. Non-linear measurements quantify the unpredictability and complexity of a series of IBIs. The authors survey published normative values for clinical, healthy, and optimal performance populations. They stress the importance of measurement context, including recording period length, subject age, and sex, on baseline HRV values. They caution that 24 h, short-term, and ultra-short-term normative values are not interchangeable. They encourage professionals to supplement published norms with findings from their own specialized populations. Finally, the authors provide an overview of HRV assessment strategies for clinical and optimal performance interventions.
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            ILAE official report: a practical clinical definition of epilepsy.

            Epilepsy was defined conceptually in 2005 as a disorder of the brain characterized by an enduring predisposition to generate epileptic seizures. This definition is usually practically applied as having two unprovoked seizures >24 h apart. The International League Against Epilepsy (ILAE) accepted recommendations of a task force altering the practical definition for special circumstances that do not meet the two unprovoked seizures criteria. The task force proposed that epilepsy be considered to be a disease of the brain defined by any of the following conditions: (1) At least two unprovoked (or reflex) seizures occurring >24 h apart; (2) one unprovoked (or reflex) seizure and a probability of further seizures similar to the general recurrence risk (at least 60%) after two unprovoked seizures, occurring over the next 10 years; (3) diagnosis of an epilepsy syndrome. Epilepsy is considered to be resolved for individuals who either had an age-dependent epilepsy syndrome but are now past the applicable age or who have remained seizure-free for the last 10 years and off antiseizure medicines for at least the last 5 years. "Resolved" is not necessarily identical to the conventional view of "remission or "cure." Different practical definitions may be formed and used for various specific purposes. This revised definition of epilepsy brings the term in concordance with common use. A PowerPoint slide summarizing this article is available for download in the Supporting Information section here. Wiley Periodicals, Inc. © 2014 International League Against Epilepsy.
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              Heart rate variability: a review.

              Heart rate variability (HRV) is a reliable reflection of the many physiological factors modulating the normal rhythm of the heart. In fact, they provide a powerful means of observing the interplay between the sympathetic and parasympathetic nervous systems. It shows that the structure generating the signal is not only simply linear, but also involves nonlinear contributions. Heart rate (HR) is a nonstationary signal; its variation may contain indicators of current disease, or warnings about impending cardiac diseases. The indicators may be present at all times or may occur at random-during certain intervals of the day. It is strenuous and time consuming to study and pinpoint abnormalities in voluminous data collected over several hours. Hence, HR variation analysis (instantaneous HR against time axis) has become a popular noninvasive tool for assessing the activities of the autonomic nervous system. Computer based analytical tools for in-depth study of data over daylong intervals can be very useful in diagnostics. Therefore, the HRV signal parameters, extracted and analyzed using computers, are highly useful in diagnostics. In this paper, we have discussed the various applications of HRV and different linear, frequency domain, wavelet domain, nonlinear techniques used for the analysis of the HRV.
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                Author and article information

                Contributors
                Role: Role: Role: Role: Role: Role: Role: Role:
                Role: Role: Role: Role: Role: Role: Role:
                Role: Role: Role: Role: Role: Role: Role:
                Role: Role: Role: Role: Role:
                Journal
                Netw Neurosci
                Netw Neurosci
                netn
                Network Neuroscience
                MIT Press (255 Main Street, 9th Floor, Cambridge, Massachusetts 02142, USA journals-info@mit.edu )
                2472-1751
                2024
                01 July 2024
                : 8
                : 2
                : 541-556
                Affiliations
                [1]Department of Information Engineering, Università degli Studi di Firenze, Firenze, Italy
                [2]Department of Information Engineering and Bioengineering & Robotics Research Center E. Piaggio, University of Pisa, Pisa, Italy
                Author notes

                Competing Interests: The authors have declared that no competing interests exist.

                * Corresponding Author: lorenzo.frassineti@ 123456unifi.it

                Handling Editor: Alex Fornito

                Article
                netn_a_00367
                10.1162/netn_a_00367
                11168720
                a4d18b16-dfb1-49bb-992b-9e388dae78a9
                © 2024 Massachusetts Institute of Technology

                This is an open-access article distributed under the terms of the Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. For a full description of the license, please visit https://creativecommons.org/licenses/by/4.0/.

                History
                : 28 September 2023
                : 09 February 2024
                Page count
                Pages: 16
                Funding
                Funded by: European Commission, DOI 10.13039/501100000780;
                Award ID: Horizon 2020 project EXPERIENCE No. 101017727
                Award Recipient :
                Funded by: Italian Ministry of Education and Research;
                Award ID: FoReLab project
                Award Recipient :
                Funded by: PNRR project THE;
                Award ID: ECS00000017 - CUP I53C22000780001
                Award Recipient :
                Categories
                Research Article
                Custom metadata
                Frassineti, L., Catrambone, V., Lanatà, A., & Valenza, G. (2024). Impaired brain-heart axis in focal epilepsy: Alterations in information flow and implications for seizure dynamics. Network Neuroscience, 8(2), 541–556. https://doi.org/10.1162/netn_a_00367

                brain-heart axis,eeg,heart rate variability,central autonomic network,epilepsy,sdg model

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