Causal interactions between the cerebral cortex and the autonomic nervous system

Yu, Xiaolin; Zhang, Chong; Zhang, Jianbao

doi:10.1007/s11427-014-4627-0

Cited by 8 publications

(11 citation statements)

References 39 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Time series analysis allows the exploration of the dynamical interactions between physiological systems. Recent advanced signal processing tools, such as multivariate method based on Granger causality, investigate the neural and cardiac time series and demonstrate a functional coupling between the brain and the heart ( Yu et al, 2014 ; Porta and Faes, 2016 ). In particular, reciprocal influence between cardiac component and the EEG power has been reported ( Faes et al, 2014 ).…”

Section: Discussionmentioning

confidence: 99%

Resting EEG Microstates and Autonomic Heart Rate Variability Do Not Return to Baseline One Hour After a Submaximal Exercise

2018

View full text Add to dashboard Cite

Recent findings suggest that an acute physical exercise modulates the temporal features of the EEG resting microstates, especially the microstate map C duration and relative time coverage. Microstate map C has been associated with the salience resting state network, which is mainly structured around the insula and cingulate, two brain nodes that mediate cardiovascular arousal and interoceptive awareness. Heart rate variability (HRV) is dependent on the autonomic balance; specifically, an increase in the sympathetic (or decrease in the parasympathetic) tone will decrease variability while a decrease in the sympathetic (or increase in the parasympathetic) tone will increase variability. Relying on the functional interaction between the autonomic cardiovascular activity and the salience network, this study aims to investigate the effect of exercise on the resting microstate and the possible interplay with this autonomic cardiovascular recovery after a single bout of endurance exercise. Thirty-eight young adults performed a 25-min constant-load cycling exercise at an intensity that was subjectively perceived as “hard.” The microstate temporal features and conventional time and frequency domain HRV parameters were obtained at rest for 5 min before exercise and at 5, 15, 30, 45, and 60 min after exercise. Compared to the baseline, all HRV parameters were changed 5 min after exercise cessation. The mean durations of microstate B and C, and the frequency of occurrence of microstate D were also changed immediately after exercise. A long-lasting effect was found for almost all HRV parameters and for the duration of microstate C during the hour following exercise, indicating an uncompleted recovery of the autonomic cardiovascular system and the resting microstate. The implication of an exercise-induced afferent neural traffic is discussed as a potential modulator of both the autonomic regulation of heart rate and the resting EEG microstate.

show abstract

Section: Discussionmentioning

confidence: 99%

Resting EEG Microstates and Autonomic Heart Rate Variability Do Not Return to Baseline One Hour After a Submaximal Exercise

2018

View full text Add to dashboard Cite

show abstract

Section: Perspectives and Limitationsmentioning

confidence: 99%

“…The time scale of the oscillatory activities for which we assessed regularity and connectivity is longer than the scale investigated in other recent studies performing a joint dynamic analysis of EEG and HRV time series [12,47]. While in [12,47] the time series samples were obtained with a one second resolution, our sampling period was of one minute. Moreover, the dynamic interactions among time series built in the same way as that used in this study were documented to occur with a maximal strength between brain and heart oscillations having a period greater than 15 min [3], while on the contrary several transitions in the strength of physiological interactions were observed in [12] within epochs of a few minutes.…”

Section: Perspectives and Limitationsmentioning

confidence: 99%

Information dynamics of brain–heart physiological networks during sleep

Faes

Nollo

Jurysta³

et al. 2014

New J. Phys.

View full text Add to dashboard Cite

This study proposes an integrated approach, framed in the emerging fields of network physiology and information dynamics, for the quantitative analysis of brain-heart interaction networks during sleep. With this approach, the time series of cardiac vagal autonomic activity and brain wave activities measured respectively as the normalized high frequency component of heart rate variability and the EEG power in the δ, θ, α, σ, and β bands, are considered as realizations of the stochastic processes describing the dynamics of the heart system and of different brain sub-systems. Entropy-based measures are exploited to quantify the predictive information carried by each (sub)system, and to dissect this information into a part actively stored in the system and a part transferred to it from the other connected systems. The application of this approach to polysomnographic recordings of ten healthy subjects led us to identify a structured network of sleep brain-brain and brain-heart interactions, with the node described by the β EEG power acting as a hub which conveys the largest amount of information flowing between the heart and brain nodes. This network was found to be sustained mostly by the transitions across different sleep stages, as the information transfer was weaker during specific stages than during the whole night, and vanished progressively when moving from light sleep to deep sleep and to REM sleep.

show abstract

“…Subjects in these studies are recruited based on the history of their neurological disorder [21]- [44] diabetes [45], [46], obesity [46], cardiovascular disease [25], [27], [30], [34], [42], [45]- [47], muscular issues [44], [48], and sleep disorder [49]. People who are taking medications [21], [22], [32], [43], [45], [46], [50]- [52], smoking [21], [39], [45], [49], [51], alcohol, tea, coffee [22], [25], [35], [37], [38], [45], [52], [53] are not considered. People who had done strenuous exercise before the experiment are avoided [22], [37], [38], [44], [52].…”

Section: B Subject Informationmentioning

confidence: 99%

“…People who are taking medications [21], [22], [32], [43], [45], [46], [50]- [52], smoking [21], [39], [45], [49], [51], alcohol, tea, coffee [22], [25], [35], [37], [38], [45], [52], [53] are not considered. People who had done strenuous exercise before the experiment are avoided [22], [37], [38], [44], [52].…”

Section: B Subject Informationmentioning

confidence: 99%

Exploring Central-Peripheral Nervous System Interaction Through Multimodal Biosignals: A Systematic Review

Banik,

Kumar,

Ganapathy

et al. 2024

IEEE Access

View full text Add to dashboard Cite

The interaction between the central nervous system (CNS) and peripheral nervous system (PNS) governs various physiological functions, influences cognitive processes and emotional states. It is necessary to unravel the mechanisms governing the interaction between the brain and the body, enhancing our understanding of physical and mental well-being. Neuro-ergonomics-based human-computer interaction can be improved by comprehending the intricate interrelation between the CNS and PNS. Various studies have been explored using diverse methodologies to study CNS-PNS interaction in specific psychophysiological states, such as emotion, stress, or cognitive tasks. However, there is a need for a thorough, extensive, and systematic review covering diverse interaction forms, applications, and assessments. In this work, an attempt has been made to perform a systematic review that examines the interaction between the CNS and PNS across diverse psychophysiological states, focusing on varied physiological signals. For this, scientific repositories, namely Scopus, PubMed, Association for Computing Machinery, and Web of Science, are accessed. In total, 61 articles have been identified within the period of January 2008 to April 2023 for systematic review. The selected research articles are analyzed based on factors, namely subject information, stimulation modality, types of interactions between the brain and other organs, feature extraction techniques, classification methods, and statistical approaches. The evaluation of the existing literature indicates a scarcity of publicly available databases for CNS-PNS interaction and limited application of machine learning and deep learning-based advanced tools. Furthermore, this review underscores the urgent need for enhancements in several key areas including the development of a more refined psycho-physiological model, improved analysis techniques, and better electrode-surface interface technology. Additionally, there is a need for more research involving daily life activities, female-oriented studies, and privacy considerations. This review contributes to standardizing protocols, improves the diagnostic relevance of various instruments, and extracts more reliable biomarkers. The novelty of this study lies in guiding researchers to point out various issues and potential solutions for future research in the field of bio-signal-based CNS-PNS interaction. INDEX TERMS Central Nervous System (CNS), Peripheral Nervous System (PNS), Electroencephalogram (EEG), Brain-heart, Brain-skin, Brain-muscle, differentiation, classification I. INTRODUCTION T HE perception of human existence consists of thoughts, dreams, and memories. These phenomena are controlled by the interactions that take place between the human brain, body, and the external environment. Such interactions primarily rely on the transmission of information through neu-ral pathways. These pathways connect the brain to various sensors and actuators distributed throughout the human body [1]. The nervous system is remarkably intricate. It en...

show abstract

Causal interactions between the cerebral cortex and the autonomic nervous system

Cited by 8 publications

References 39 publications

Resting EEG Microstates and Autonomic Heart Rate Variability Do Not Return to Baseline One Hour After a Submaximal Exercise

Resting EEG Microstates and Autonomic Heart Rate Variability Do Not Return to Baseline One Hour After a Submaximal Exercise

Information dynamics of brain–heart physiological networks during sleep

Exploring Central-Peripheral Nervous System Interaction Through Multimodal Biosignals: A Systematic Review

Contact Info

Product

Resources

About