Microstates of the cortical brain-heart axis

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

Nervous–system–wise Functional Estimation of Directed Brain–Heart Interplay through Microstate Occurrences

Background: The quantification of functional brain–heart interplay (BHI) through analysis of the dynamics of the central and autonomic nervous systems provides effective biomarkers for cognitive, emotional, and autonomic state changes. Several computational models have been proposed to estimate BHI, focusing on a single sensor, brain region, or frequency activity. However, no models currently provide a directional estimation of such interplay at the organ level. Objective: This study proposes an analysis framework to estimate BHI that quantifies the directional information flow between whole–brain and heartbeat dynamics. Methods: System–wise directed functional estimation is performed through an ad-hoc symbolic transfer entropy implementation, which leverages on EEG-derived microstate series and on partition of heart rate variability series. The proposed framework is validated on two different experimental datasets: the first investigates the cognitive workload performed through mental arithmetic and the second focuses on an autonomic maneuver using a cold pressor test (CPT). Results: The experimental results highlight a significant bidirectional increase in BHI during cognitive workload with respect to the preceding resting phase and a higher descending interplay during a CPT compared to the preceding rest and following recovery phases. These changes are not detected by the intrinsic self entropy of isolated cortical and heartbeat dynamics. Conclusion: This study corroborates the literature on the BHI phenomenon under these experimental conditions and the new perspective provides novel insights from an organ–level viewpoint. Significance: A system–wise perspective of the BHI phenomenon may provide new insights into physiological and pathological processes that may not be completely understood at a lower level/scale of analysis

Methodological Considerations on EEG Electrical Reference: A Functional Brain-Heart Interplay Study

The growing interest in the study of functional brain-heart interplay (BHI) has motivated the development of novel methodological frameworks for its quantification. While a combination of electroencephalography (EEG) and heartbeat-derived series has been widely used, the role of EEG preprocessing on a BHI quantification is yet unknown. To this extent, here we investigate on four different EEG electrical referencing techniques associated with BHI quantifications over 4-minute resting-state in 15 healthy subjects. BHI methods include the synthetic data generation model, heartbeat-evoked potentials, heartbeat-evoked oscillations, and maximal information coefficient (MIC). EEG signals were offline referenced under the Cz channel, common average, mastoids average, and Laplacian method, and statistical comparisons were performed to assess similarities between references and between BHI techniques. Results show a topographical agreement between BHI estimation methods depending on the specific EEG reference. Major differences between BHI methods occur with the Laplacian reference, while major differences between EEG references are with the MIC analysis. We conclude that the choice of EEG electrical reference may significantly affect a functional BHI quantification

The Role of EEG Electrical Reference in the Assessment of Functional Brain-Heart Interplay: A Preliminary Study

Recent studies have proposed computational models for a functional brain-heart interplay (BHI) assessment based on electroencephalography (EEG). Nevertheless, the role of the EEG electrical reference on such BHI estimates has not been investigated yet. Here we present a pilot study assessing BHI in 4 minutes resting-state in 10 healthy subjects through methods including heartbeat-evoked potentials (HEP) and oscillations, Maximal Information Coefficient, and our recently proposed model based on Synthetic Data Generation (SDG). EEG signals were re-referenced to the Cz channel, common average, mastoids, and Laplacian. Results for EEG power in the alpha band indicate that the most significant differences between BHI methods are with the Laplacian reference while a higher agreement exists between HEP and SDG approaches

Heart rate variability in marketing research: A systematic review and methodological perspectives

Heart rate variability is a promising physiological measurement that accesses psychophysiological variations in response to a marketing stimulus. While its application spans diverse fields, there is a limited understanding of the usability and interpretation of heart rate variability in marketing research. Therefore, this hybrid literature review provides an overview of the emerging use of heart rate variability in marketing research, along with essential methodological considerations. In this context, we blend marketing mix framework with stimulus-organism-response theory, segregating the use of heart rate variability in various marketing research contexts. We follow the preferred reporting items for systematic reviews and meta-analyses (PRISMA) framework to reflect on 33 records obtained from six databases. Our findings suggest that 42% of studies used heart rate variability to investigate promotion-related topics. Overall, heart rate variability is mostly used in combination with Galvanic skin response (48%). Further, 39% of studies used non-portable systems for data collection. Last, using the theory characteristics methodology (TCM) framework, we identified six research avenues: (1) affective, cognitive, and sensorial constructs; (2) personality, thinking style, and demographics; (3) product experience; (4) advertising and branding; (5) correlation with immersive technologies; and (6) triangulation with other neurophysiological tools

Complexity Analysis on Functional-Near Infrared Spectroscopy Time Series: A Preliminary Study on Mental Arithmetic

It is well known that physiological systems show complex and nonlinear behaviours. In spite of that, functional near-infrared spectroscopy (fNIRS) is usually analyzed in the time and frequency domains with the assumption that metabolic activity is generated from a linear system. To leverage the full information provided by fNIRS signals, in this study we investigate topological entropy in fNIRS series collected from 10 healthy subjects during mental mental arithmetic task. While sample entropy and fuzzy entropy were used to estimate time series irregularity, distribution entropy was used to estimate time series complexity. Our findings show that entropy estimates may provide complementary characterization of fNIRS dynamics with respect to reference time domain measurements. This finding paves the way to further investigate functional activation in fNIRS in different case studies using nonlinear and complexity system theory

Functional assessment of bidirectional cortical and peripheral neural control on heartbeat dynamics: A brain-heart study on thermal stress

The study of functional Brain-Heart Interplay (BHI) from non-invasive recordings has gained much interest in recent years. Previous endeavors aimed at understanding how the two dynamical systems exchange information, providing novel holistic biomarkers and important insights on essential cognitive aspects and neural system functioning. However, the interplay between cardiac sympathovagal and cortical oscillations still has much room for further investigation. In this study, we introduce a new computational framework for a functional BHI assessment, namely the Sympatho-Vagal Synthetic Data Generation Model, combining cortical (electroencephalography, EEG) and peripheral (cardiac sympathovagal) neural dynamics. The causal, bidirectional neural control on heartbeat dynamics was quantified on data gathered from 26 human volunteers undergoing a cold-pressor test. Results show that thermal stress induces heart-to-brain functional interplay sustained by EEG oscillations in the delta and gamma bands, primarily originating from sympathetic activity, whereas brain-to-heart interplay originates over central brain regions through sympathovagal control. The proposed methodology provides a viable computational tool for the functional assessment of the causal interplay between cortical and cardiac neural control

Toward the manipulation of time and space in extended reality: a preliminary study on multimodal Tau and Kappa illusions in the visual-tactile domain

In the last few years, Extended reality (XR) has enabled novel forms of sensory experiences and social interplay, which can be hardly experienced in real life. However, the full potential of XR has not been exploited yet, since vision remains the main interaction modality, and the time-and space-modulation of the sense of self-which could open interesting perspectives in several scenarios-is still largely unexplored. To pave the path to a multi-modal manipulation of the sense of time and space in immersive XR, in this work we discuss the preliminary outcomes of the first investigation in the visual-tactile domain of two well known perceptual illusions affecting spatial and temporal perception, i.e. Tau and Kappa effects, respectively. We compared the effects originated from unimodal stimulation (i.e., only visual or tactile) with the same effects induced by convergent bimodal stimulation (i.e., visual and tactile), delivered to the forearm. Results show that both Tau and Kappa effects are affected by the multi-modality of the stimulation, and that the perceptual bias differently affects time-or space-perception based on the modality used for stimulus delivery. Our results, although preliminary, seem to suggest that multimodal perceptual illusions could be a viable solution for time-and space-modulation of the sense of self in immersive XR and advanced social human-robot interaction

Estimation of Dynamical Noise Power in Unknown Systems

Noise can be modeled as a sequence of random variables defined on a probability space that may be added to a given dynamical system $T$, which is a map on a phase space. In the non-trivial case of dynamical noise $\lbrace \varepsilon _{n}\rbrace _{n}$, where $\varepsilon _{n}$ follows a Gaussian distribution $\mathcal {N}(0,\sigma ^{2})$ and the system output is $x_{n} = T(x_{n-1};x_{0})+\varepsilon _{n}$, without any specific knowledge or assumption about $T$, the quantitative estimation of the noise power $\sigma ^{2}$ is a challenge. Here, we introduce a formal method based on the nonlinear entropy profile to estimate the dynamical noise power $\sigma ^{2}$ without requiring knowledge of the specific $T$ function. We tested the correctness of the proposed method using time series generated from Logistic maps and Pomeau-Manneville systems under different conditions. Our results demonstrate that the proposed estimation algorithm can properly discern different noise levels without any a priori information

Altering Time Perception in Virtual Reality through Multimodal Visual-tactile Kappa Effect

The perception of time is highly subjective and intertwined with space perception. In a well-known perceptual illusion, called Kappa effect, the distance between consecutive stimuli is modified to induce time distortions in the perceived inter-stimulus interval that are proportional to the distance between the stimuli. However, to the best of our knowledge, this effect has not been characterized and exploited in virtual reality (VR) within a multisensory elicitation framework. This paper investigates the Kappa effect elicited by concurrent visual-tactile stimuli delivered to the forearm, through a multimodal VR interface. This paper compares the outcomes of an experiment in VR with the results of the same experiment performed in the “physical world”, where a multimodal interface was applied to participants' forearm to deliver controlled visual-tactile stimuli. Our results suggest that a multimodal Kappa effect can be elicited both in VR and in the physical world relying on concurrent visual-tactile stimulation. Moreover, our results confirm the existence of a relation between the ability of participants in discriminating the duration of time intervals and the magnitude of the experienced Kappa effect. These outcomes can be exploited to modulate the subjective perception of time in VR, paving the path toward more personalised human-computer interaction