Pairwise and higher-order measures of brain-heart interactions in children with temporal lobe epilepsy

Pernice, Riccardo; Faes, Luca; Feucht, Martha; Benninger, Franz; Mangione, Stefano; Schiecke, Karin

doi:10.1088/1741-2552/ac7fba

Cited by 10 publications

(8 citation statements)

References 79 publications

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“…Nevertheless, functional BHI assessments may be performed using different methods at a single EEG channel level. For example, some methods focused on the direct fast brain response to a single heartbeat, namely heartbeat evoked potentials (for a review see; Park & Blanke, 2019 ), while others have attempted to estimate the information exchange in a larger time window (Catrambone & Valenza, 2021 ; Faes et al, 2011 ; Pernice et al, 2022 ). Moreover, it has been argued that the spectral approach to quantify ANS activity through LF and HF separation is not specific for sympathetic or vagal activity only; nevertheless, its use remains a public standard in the scientific community (Rajendra Acharya et al, 2006 ; Valenza et al, 2018 ).…”

Section: Discussionmentioning

confidence: 99%

“…Research on functional BHI revealed through the analysis of the EEG and heart rate variability (HRV) series revealed that healthy BHI variations occur as physiological responses to several events, such as emotion perception (Candia‐Rivera, Catrambone, Thayer, et al, 2022 ), alternating sleep stages (Silvani et al, 2016 ), cognitive load (Yu et al, 2018 ), intentional movements (Catrambone, Averta, et al, 2021 ), and autonomic maneuvers (Catrambone, Talebi, et al, 2021 ; Jerath & Barnes, 2009 ). Moreover, BHI time‐series alterations have been reportedly associated with neuropathological conditions, such as mild depression (Catrambone, Messerotti Benvenuti, et al, 2021 ), epilepsy (Pernice et al, 2022 ), and schizophrenia (Schulz et al, 2013 ). Functional BHI is a directional phenomenon, meaning that the influence of brain activity on cardiovascular function is not necessarily equivalent to the influence of heart activity on cerebral dynamics (Catrambone, Messerotti Benvenuti, et al, 2021 ; Catrambone & Valenza, 2021 ; Catrambone & Valenza, 2023 ).…”

Section: Introductionmentioning

confidence: 99%

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Microstates of the cortical brain‐heart axis

Catrambone,

Valenza

2023

Human Brain Mapping

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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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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Microstates of the cortical brain‐heart axis

Catrambone,

Valenza

2023

Human Brain Mapping

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“…Brain-heart interplay (BHI) refers to the continuous functional communication that affects the activity of the autonomous nervous system (ANS) and the central nervous system (CNS). Studies on BHI can reveal perspectives facilitating the understanding of several patho-physiological conditions [1] , [2] , [3] , [4] . The BHI represents the functional outcome of a network of chemical, electrical, and anatomical connections that link the CNS and ANS, and has been found to originate in the central autonomic network (CAN) [1] , [4] , [5] , [6] , [7] , [8] .…”

Section: Introductionmentioning

confidence: 99%

Complex Brain–Heart Mapping in Mental and Physical Stress

Catrambone

Valenza

2023

IEEE J. Transl. Eng. Health Med.

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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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“…These interactions have been used to make distinctions between healthy and diseased conditions under conditions such as congestive heart failure ( Radovanović et al, 2018 ), and pre-eclampsia ( Riedl et al, 2010 ). Recent studies successfully used GC between brain and heart signals to characterize epileptic seizures ( Pernice et al, 2022 ) and GC between activity in different brain areas to distinguish between patients with cognitive impairment associated with epilepsy and healthy controls ( Jiang et al, 2021 ).…”

Section: Introductionmentioning

confidence: 99%

Exploring the use of Granger causality for the identification of chemical exposure based on physiological data

Difrancesco

Baardewijk

Cornelissen

et al. 2023

Front. Netw. Physiol.

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Wearable sensors offer new opportunities for the early detection and identification of toxic chemicals in situations where medical evaluation is not immediately possible. We previously found that continuously recorded physiology in guinea pigs can be used for early detection of exposure to an opioid (fentanyl) or a nerve agent (VX), as well as for differentiating between the two. Here, we investigated how exposure to these different chemicals affects the interactions between ECG and respiration parameters as determined by Granger causality (GC). Features reflecting such interactions may provide additional information and improve models differentiating between chemical agents. Traditional respiration and ECG features, as well as GC features, were extracted from data of 120 guinea pigs exposed to VX (n = 61) or fentanyl (n = 59). Data were divided in a training set (n = 99) and a test set (n = 21). Minimum Redundancy Maximum Relevance (mRMR) and Support Vector Machine (SVM) algorithms were used to, respectively, perform feature selection and train a model to discriminate between the two chemicals. We found that ECG and respiration parameters are Granger-related under healthy conditions, and that exposure to fentanyl and VX affected these relationships in different ways. SVM models discriminated between chemicals with accuracy of 95% or higher on the test set. GC features did not improve the classification compared to traditional features. Respiration features (i.e., peak inspiratory and expiratory flow) were the most important to discriminate between different chemical’s exposure. Our results indicate that it may be feasible to discriminate between chemical exposure when using traditional physiological respiration features from wearable sensors. Future research will examine whether GC features can contribute to robust detection and differentiation between chemicals when considering other factors, such as generalizing results across species.

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Pairwise and higher-order measures of brain-heart interactions in children with temporal lobe epilepsy

Cited by 10 publications

References 79 publications

Microstates of the cortical brain‐heart axis

Microstates of the cortical brain‐heart axis

Complex Brain–Heart Mapping in Mental and Physical Stress

Exploring the use of Granger causality for the identification of chemical exposure based on physiological data

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