Towards a better understanding of the impact of heart rate on the BOLD signal: a new method for physiological noise correction and its applications

Functional magnetic resonance imaging (fMRI) based on blood oxygenation level-dependent (BOLD) contrast allows non-invasive examination of brain activity and is widely used in the neuroimaging field. The BOLD contrast mechanism reflects hemodynamic changes resulting from a complex interplay of blood flow, blood volume, and oxygen consumption. Heart rate (HR) variations are the most intriguing and less understood physiological processes affecting the BOLD signal, as they are the result of a wide variety of interacting factors. The use of the response function that best models HR-induced signal changes, called cardiac response function (CRF), is an effective method to reduce HR noise in fMRI. However, current models of physiological noise correction based on CRF, i.e. canonical and individual, either do not take into account variations in HR between subjects, and are thus inadequate for cohorts with varying HR, or require time-consuming quality control of individual physiological recordings and derived CRFs. By analyzing a large cohort of healthy individuals, the results presented in this thesis show that different HRs influence the BOLD signal and their corresponding spectra differently. A further finding is that HR plays an essential role in determining the shape of the CRF. Slower HRs produce a smoothed CRF with a single well-defined maximum, while faster HRs cause a second maximum. Taking advantage of this dependence of the CRF on HR, a novel method is proposed to model HR-induced fluctuations in the BOLD signal more accurately than current approaches of physiological noise correction. This method, called HR-based CRF, consists of two CRFs: one for HRs below 68 bpm and one for HRs above this value. HR-based CRFs can be directly applied to the fMRI data without the time-consuming task of deriving a CRF for each subject while accounting for inter-subject variability in HR response

Confounding effects of heart rate, breathing rate, and frontal fNIRS on interoception

Recent studies have established that cardiac and respiratory phases can modulate perception and related neural dynamics. While heart rate and respiratory sinus arrhythmia possibly affect interoception biomarkers, such as heartbeat-evoked potentials, the relative changes in heart rate and cardiorespiratory dynamics in interoceptive processes have not yet been investigated. In this study, we investigated the variation in heart and breathing rates, as well as higher functional dynamics including cardiorespiratory correlation and frontal hemodynamics measured with fNIRS, during a heartbeat counting task. To further investigate the functional physiology linked to changes in vagal activity caused by specific breathing rates, we performed the heartbeat counting task together with a controlled breathing rate task. The results demonstrate that focusing on heartbeats decreases breathing and heart rates in comparison, which may be part of the physiological mechanisms related to “listening” to the heart, the focus of attention, and self-awareness. Focusing on heartbeats was also observed to increase frontal connectivity, supporting the role of frontal structures in the neural monitoring of visceral inputs. However, cardiorespiratory correlation is affected by both heartbeats counting and controlled breathing tasks. Based on these results, we concluded that variations in heart and breathing rates are confounding factors in the assessment of interoceptive abilities and relative fluctuations in breathing and heart rates should be considered to be a mode of covariate measurement of interoceptive processes

Multivariate assessment of linear and non-linear causal coupling pathways within the central-autonomic-network in patients suffering from schizophrenia

Im Bereich der Zeitreihenanalyse richtet sich das Interesse zunehmend darauf, wie Einblicke in die Interaktions- und Regulationsprozesse von pathophysiologischen- und physiologischen Zuständen erlangt werden können. Neuste Fortschritte in der nichtlinearen Dynamik, der Informationstheorie und der Netzwerktheorie liefern dabei fundiertes Wissen über Kopplungswege innerhalb (patho)physiologischer (Sub)Systeme. Kopplungsanalysen zielen darauf ab, ein besseres Verständnis dafür zu erlangen, wie die verschiedenen integrierten regulatorischen (Sub)Systeme mit ihren komplexen Strukturen und Regulationsmechanismen das globale Verhalten und die unterschiedlichen physiologischen Funktionen auf der Ebene des Organismus beschreiben. Insbesondere die Erfassung und Quantifizierung der Kopplungsstärke und -richtung sind wesentliche Aspekte für ein detaillierteres Verständnis physiologischer Regulationsprozesse. Ziel dieser Arbeit war die Charakterisierung kurzfristiger unmittelbarer zentral-autonomer Kopplungspfade (top-to-bottom und bottom to top) durch die Kopplungsanalysen der Herzfrequenz, des systolischen Blutdrucks, der Atmung und zentraler Aktivität (EEG) bei schizophrenen Patienten und Gesunden. Dafür wurden in dieser Arbeit neue multivariate kausale und nicht-kausale, lineare und nicht-lineare Kopplungsanalyseverfahren (HRJSD, mHRJSD, NSTPDC) entwickelt, die in der Lage sind, die Kopplungsstärke und -richtung, sowie deterministische regulatorische Kopplungsmuster innerhalb des zentralen-autonomen Netzwerks zu quantifizieren und zu klassifizieren. Diese Kopplungsanalyseverfahren haben ihre eigenen Besonderheiten, die sie einzigartig machen, auch im Vergleich zu etablierten Kopplungsverfahren. Sie erweitern das Spektrum neuartiger Kopplungsansätze für die Biosignalanalyse und tragen auf ihre Weise zur Gewinnung detaillierter Informationen und damit zu einer verbesserten Diagnostik/Therapie bei. Die Hauptergebnisse dieser Arbeit zeigen signifikant schwächere nichtlineare zentral-kardiovaskuläre und zentral-kardiorespiratorische Kopplungswege und einen signifikant stärkeren linearen zentralen Informationsfluss in Richtung des Herzkreislaufsystems auf, sowie einen signifikant stärkeren linearen respiratorischen Informationsfluss in Richtung des zentralen Nervensystems in der Schizophrenie im Vergleich zu Gesunden. Die detaillierten Erkenntnisse darüber, wie die verschiedenen zentral-autonomen Netzwerke mit paranoider Schizophrenie assoziiert sind, können zu einem besseren Verständnis darüber führen, wie zentrale Aktivierung und autonome Reaktionen und/oder Aktivierung in physiologischen Netzwerken unter pathophysiologischen Bedingungen zusammenhängen.In the field of time series analysis, increasing interest focuses on insights gained how the coupling pathways of regulatory mechanisms work in healthy and ill states. Recent advances in non-linear dynamics, information theory and network theory lead to a new sophisticated body of knowledge about coupling pathways within (patho)physiological (sub)systems. Coupling analyses aim to provide a better understanding of how the different integrated physiological (sub)systems, with their complex structures and regulatory mechanisms, describe the global behaviour and distinct physiological functions at the organism level. In particular, the detection and quantification of the coupling strength and direction are important aspects for a more detailed understanding of physiological regulatory processes. This thesis aimed to characterize short-term instantaneous central-autonomic-network coupling pathways (top-to-bottom and bottom to top) by analysing the coupling of heart rate, systolic blood pressure, respiration and central activity (EEG) in schizophrenic patients and healthy participants. Therefore, new multivariate causal and non-causal linear and non-linear coupling approaches (HRJSD, mHRJSD, NSTPDC) that are able to determine the coupling strength and direction were developed. Whereby, the HRJSD and mHRJSD approaches allow the quantification and classification of deterministic regulatory coupling patterns within and between the cardiovascular- the cardiorespiratory system and the central-autonomic-network were developed. These coupling approaches have their own unique features, even as compared to well-established coupling approaches. They expand the spectrum of novel coupling approaches for biosignal analysis and thus contribute in their own way to detailed information obtained, and thereby contribute to improved diagnostics/therapy. The main findings of this thesis revealed significantly weaker non-linear central-cardiovascular and central-cardiorespiratory coupling pathways, and significantly stronger linear central information flow in the direction of the cardiac- and vascular system, and a significantly stronger linear respiratory information transfer towards the central nervous system in schizophrenia in comparison to healthy participants. This thesis provides an enhanced understanding of the interrelationship of central and autonomic regulatory mechanisms in schizophrenia. The detailed findings on how variously-pronounced, central-autonomic-network pathways are associated with paranoid schizophrenia may enable a better understanding on how central activation and autonomic responses and/or activation are connected in physiology networks under pathophysiological conditions

Multivariate correlation measures reveal structure and strength of brain–body physiological networks at rest and during mental stress

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 delta, theta, alpha, and beta electroencephalographic (EEG) wave amplitudes, and of heart rate, respiration amplitude, and pulse arrival time (PAT) variability (eta, rho, pi). 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 eta-rho and delta-theta, theta-alpha, alpha-beta), but also statistically significant interactions between the two subnetworks (mainly eta-beta and eta-delta). 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

Cardiac and Respiratory Patterns Synchronize between Persons during Choir Singing

Dyadic and collective activities requiring temporally coordinated action are likely to be associated with cardiac and respiratory patterns that synchronize within and between people. However, the extent and functional significance of cardiac and respiratory between-person couplings have not been investigated thus far. Here, we report interpersonal oscillatory couplings among eleven singers and one conductor engaged in choir singing. We find that: (a) phase synchronization both in respiration and heart rate variability increase significantly during singing relative to a rest condition; (b) phase synchronization is higher when singing in unison than when singing pieces with multiple voice parts; (c) directed coupling measures are consistent with the presence of causal effects of the conductor on the singers at high modulation frequencies; (d) the different voices of the choir are reflected in network analyses of cardiac and respiratory activity based on graph theory. Our results suggest that oscillatory coupling of cardiac and respiratory patterns provide a physiological basis for interpersonal action coordination

Not All Competitions Come to Harm! Competitive Biofeedback to Increase Respiratory Sinus Arrhythmia in Managers

Despite the positive impact on achievement, competition has been associated with elevated psychophysiological activation, potentially leading to a greater risk of cardiovascular diseases. Competitive biofeedback (BF) can be used to highlight the effects of competition on the same physiological responses that are going to be controlled through BF. However, it is still unknown whether competition could enhance the effects of respiratory sinus arrhythmia (RSA)-BF training in improving cardiac vagal control. The present study explored whether competitive RSA-BF could be more effective than non-competitive RSA-BF in increasing RSA in executive managers, who are at higher cardiovascular risk of being commonly exposed to highly competitive conditions. Thirty managers leading outstanding private or public companies were randomly assigned to either a Competition (n = 14) or a Control (n = 16) RSA-BF training lasting five weekly sessions. Managers in the Competition group underwent the RSA-BF in couples and each participant was requested to produce a better performance (i.e., higher RSA) than the paired challenger. After the training, results showed that managers in the Competition group succeeded in increasing cardiac vagal control, as supported by the specific increase in RSA (p < 0.001), the standard deviation of R-R wave intervals (SDNN; p < 0.001), and root mean square of the successive differences between adjacent heartbeats (rMSSD; p < 0.001). A significant increase in the percentage of successive normal sinus beat to beat intervals more than 50 ms (pNN50; p = 0.023; partial eta squared = 0.17), low frequency (p < 0.001; partial eta squared = 0.44), and high frequency power (p = 0.005; partial eta squared = 0.25) emerged independently from the competitive condition. Intriguingly, managers who compete showed the same reduction in resting heart rate (HR; p = 0.003, partial eta squared = 0.28), systolic blood pressure (SBP; p = 0.013, partial eta squared = 0.20), respiration rate (p < 0.001; partial eta squared = 0.46), and skin conductance level (SCL; p = 0.001, partial eta squared = 0.32) as non-competitive participants. Also, the same reduction in social anxiety (p = 0.005; partial eta squared = 0.25), state (p = 0.038, partial eta squared = 0.14) and trait anxiety (p = 0.001, partial eta squared = 0.31), and depressive symptoms (p = 0.023, partial eta squared = 0.17) emerged in the two groups. The present results showed that managers competing for increasing RSA showed a greater improvement in their parasympathetic modulation than non-competing managers. Most importantly, competition did not lead to the classic pattern of increased psychophysiological activation under competitive RSA-BF. Therefore, competition could facilitate the use of self-regulation strategies, especially in highly competitive individuals, to promote adaptive responses to psychological stress

Causal influence of brainstem response to transcutaneous vagus nerve stimulation on cardiovagal outflow

background: the autonomic response to transcutaneous auricular vagus nerve stimulation (taVNS) has been linked to the engagement of brainstem circuitry modulating autonomic outflow. However, the physiological mechanisms supporting such efferent vagal responses are not well understood, particularly in humans. hypothesis: we present a paradigm for estimating directional brain-heart interactions in response to taVNS. We propose that our approach is able to identify causal links between the activity of brainstem nuclei involved in autonomic control and cardiovagal outflow. methods: we adopt an approach based on a recent reformulation of granger causality that includes permutation-based, nonparametric statistics. The method is applied to ultrahigh field (7T) functional magnetic resonance imaging (fMRI) data collected on healthy subjects during taVNS. results: our framework identified taVNS-evoked functional brainstem responses with superior sensitivity compared to prior conventional approaches, confirming causal links between taVNS stimulation and fMRI response in the nucleus tractus solitarii (NTS). furthermore, our causal approach elucidated potential mechanisms by which information is relayed between brainstem nuclei and cardiovagal, i.e., high-frequency heart rate variability, in response to taVNS. Our findings revealed that key brainstem nuclei, known from animal models to be involved in cardiovascular control, exert a causal influence on taVNS-induced cardiovagal outflow in humans. conclusion: our causal approach allowed us to noninvasively evaluate directional interactions between fMRI BOLD signals from brainstem nuclei and cardiovagal outflow

Assessing High-Order Links in Cardiovascular and Respiratory Networks via Static and Dynamic Information Measures

Goal: The network representation is becoming increasingly popular for the description of cardiovascular interactions based on the analysis of multiple simultaneously collected variables. However, the traditional methods to assess network links based on pairwise interaction measures cannot reveal high-order effects involving more than two nodes, and are not appropriate to infer the underlying network topology. To address these limitations, here we introduce a framework which combines the assessment of high-order interactions with statistical inference for the characterization of the functional links sustaining physiological networks. Methods: The framework develops information-theoretic measures quantifying how two nodes interact in a redundant or synergistic way with the rest of the network, and employs these measures for reconstructing the functional structure of the network. The measures are implemented for both static and dynamic networks mapped respectively by random variables and random processes using plug-in and model-based entropy estimators. Results: The validation on theoretical and numerical simulated networks documents the ability of the framework to represent high-order interactions as networks and to detect statistical structures associated to cascade, common drive and common target effects. The application to cardiovascular networks mapped by the beat-to-beat variability of heart rate, respiration, arterial pressure, cardiac output and vascular resistance allowed noninvasive characterization of several mechanisms of cardiovascular control operating in resting state and during orthostatic stress. Conclusion: Our approach brings to new comprehensive assessment of physiological interactions and complements existing strategies for the classification of pathophysiological states