脑心轴/心脑轴/脑心耦合 脑电图研究

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.

The Brain-Heart axis regulates cardiovascular and cerebral function through bidirectional neural communication, integrating autonomic reflexes such as respiratory sinus arrhythmia and postural blood pressure regulation. Despite a growing body of literature, the quantitative dynamics of Brain-Heart interplay (BHI) underlying respiratory and baroreflex-mediated changes to postural challenges remain incompletely understood. This study aims to elucidate this relationship by investigating postural and controlled respiratory changes. Seventeen healthy volunteers (average age: 22 ± 1.83 years) participated in the study, performing a 5-minute resting state in both supine and upright positions, with breathing patterns categorized as either spontaneous or slow-paced (6 cycles/min). Throughout the experiment, we recorded high-density electroencephalography (EEG), arterial blood pressure, and electrocardiogram signals to derive RR interval series and calculate baroreflex sensitivity (BRS). Results indicate that postural changes reduced the power of EEG delta (δ) oscillations and cardiac vagal activity while enhancing EEG beta (β) and gamma (γ) oscillations and increasing diastolic blood pressure. Slow-paced breathing amplified EEG beta (β) and gamma (γ) oscillations but reduced sympathovagal activity. Finally, the upright position enhanced bidirectional BHI information transfer while weakening the functional coupling between BRS and BHI. In contrast to slow-paced breathing, spontaneous breathing is characterized by negative heart-brain correlations in both the high- and low-frequency bands across all EEG spectral bands. These findings underscore the contributions of respiratory sinus arrhythmia and baroreflex-mediated cardiovascular regulation to BHI, demonstrating that both postural and respiratory changes influence BHI directionality and autonomic cardiovascular regulation.

Brain microstates are defined as states with quasi-stable scalp activity topography and have been widely studied in literature. Whether those states are brain-specific or extend to the body level is unknown yet. We investigate the extension of cortical microstates to the peripheral autonomic nerve, specifically at the brain-heart axis level as a functional state of the central autonomic network. To achieve this, we combined Electroencephalographic (EEG) and heart rate variability (HRV) series from 36 healthy volunteers undergoing a cognitive workload elicitation after a resting state. Our results showed the existence of microstates at the functional brain-heart axis with spatio-temporal and quasi-stable states that exclusively pertained to the efferent direction from the brain to the heart. Some of the identified microstates are specific for neural or cardiovascular frequency bands, while others topographies are recurrent over the EEG and HRV spectra. Furthermore, some of the identified brain-heart microstates were associated with specific experimental conditions, while others were nonspecific to tasks. Our findings support the hypothesis that EEG microstates extend to the brain-heart axis level and may be exploited in future neuroscience and clinical research.

Dreams may be recalled after awakening from sleep following a defined electroencephalographic pattern that involves local decreases in low-frequency activity in the posterior cortical regions. While a dreaming experience implies bodily changes at many organ-, system-, and timescale-levels, the entity and causal role of such peripheral changes in a conscious dream experience are unknown. We performed a comprehensive, causal, multivariate analysis of physiological signals acquired during REM sleep at night, including high-density EEG and peripheral dynamics including electrocardiography and blood pressure. In this preliminary study, we investigated multiple recalls and non-recalls of dream experiences using data from nine healthy volunteers. The aim was not only to investigate the changes in central and autonomic dynamics associated with dream recalls and non-recalls, but also to characterize the central-peripheral dynamical and (causal) directional interactions, and the temporal relations of the related arousals upon awakening. We uncovered a brain-body network that drives a conscious dreaming experience that acts with specific interaction and time delays. Such a network is sustained by the blood pressure dynamics and the increasing functional information transfer from the neural heartbeat regulation to the brain. We conclude that bodily changes play a crucial and causative role in a conscious dream experience during REM sleep.

Successful weight loss maintainers are more vulnerable to stress induced eating. The aim of our study was to determine what effect an attention-demanding cognitive performance task had on brain-heart reactivity to visual food cues in women who maintained clinically relevant weight loss vs. women who had never weight cycled. A clinical weight loss group (CWL, n = 17) and a BMI-matched control group (CTL, n = 23) completed modified Stroop tasks that either included high calorie food pictures (Food Stroop) or excluded food cues (Office Stroop). ECG, breathing rate, and EEG were recorded. CWL participants: The Eating Restraint scores (Three Factor Eating Questionnaire) of the CWL participants correlated negatively with their heart rates recorded during the Food Stroop task (r = 0.62, p < 0.01). There was no such relationship in CTL participants. The P200 latencies in CWL participants evoked by the Stroop color-word cues at the C3 electrode were positively correlated to the log high frequency power in their cardiac spectrograms during the Food Stroop (r = 0.63, p < 0.02). There were no such relationships in the Office Stroop task nor in CTL participants. Combined Groups: Participants' heart rates were significantly lower (p < 0.05) and their RMSSD values and the log Total Power in their cardiac spectrograms were significantly greater during the Food Stroop vs. Office Stroop (p < 0.01, Bonferroni corrected). In conclusion Eating Restraint scores in CWL participants correlated with their Stroop heart rates, while the P200 latencies evoked by the Stroop cues correlated with the log high frequency power in their cardiac spectrograms (marker of cardiac vagal activation) during the Food Stroop task. This provides evidence that even 12 months after successful weight loss maintenance the cardiac ANS reactivity to food cues while completing a cognitive performance test was still different to that in individuals of normal weight who never weight cycled. Across all participants the cardiac ANS reactivity evoked by performing the Stroop task was lowered by food cues suggesting that the dampening effect of food cues on cardiac ANS reactivity may be one of the drivers of ‘stress induced' eating.

The interplay between cerebral and cardiovascular activity, known as the functional brain‐heart interplay (BHI), and its temporal dynamics, have been linked to a plethora of physiological and pathological processes. Various computational models of the brain‐heart axis have been proposed to estimate BHI non‐invasively by taking advantage of the time resolution offered by electroencephalograph (EEG) signals. However, investigations into the specific intracortical sources responsible for this interplay have been limited, which significantly hampers existing BHI studies. This study proposes an analytical modeling framework for estimating the BHI at the source‐brain level. This analysis relies on the low‐resolution electromagnetic tomography sources localization from scalp electrophysiological recordings. BHI is then quantified as the functional correlation between the intracortical sources and cardiovascular dynamics. Using this approach, we aimed to evaluate the reliability of BHI estimates derived from source‐localized EEG signals as compared with prior findings from neuroimaging methods. The proposed approach is validated using an experimental dataset gathered from 32 healthy individuals who underwent standard sympathovagal elicitation using a cold pressor test. Additional resting state data from 34 healthy individuals has been analysed to assess robustness and reproducibility of the methodology. Experimental results not only confirmed previous findings on activation of brain structures affecting cardiac dynamics (e.g., insula, amygdala, hippocampus, and anterior and mid‐cingulate cortices) but also provided insights into the anatomical bases of brain‐heart axis. In particular, we show that the bidirectional activity of electrophysiological pathways of functional brain‐heart communication increases during cold pressure with respect to resting state, mainly targeting neural oscillations in the δ$$ \delta $$ , β$$ \beta $$ , and γ$$ \gamma $$ bands. The proposed approach offers new perspectives for the investigation of functional BHI that could also shed light on various pathophysiological conditions.

This study explored the impact of one session of low-pressure leg blood flow restriction (BFR) during treadmill walking on dual-task performance in older adults using the neurovisceral integration model framework. Twenty-seven older adults participated in 20-min treadmill sessions, either with BFR (100 mmHg cuff pressure on both thighs) or without it (NBFR). Dual-task performance, measured through light-pod tapping while standing on foam, and heart rate variability during treadmill walking were compared. Following BFR treadmill walking, the reaction time (p = 0.002) and sway area (p = 0.012) of the posture dual-task were significantly reduced. Participants exhibited a lower mean heart rate (p < 0.001) and higher heart rate variability (p = 0.038) during BFR treadmill walking. Notably, BFR also led to band-specific reductions in regional brain activities (theta, alpha, and beta bands, p < 0.05). The topology of the EEG network in the theta and alpha bands became more star-like in the post-test after BFR treadmill walking (p < 0.005). BFR treadmill walking improves dual-task performance in older adults via vagally-mediated network integration with superior neural economy. This approach has the potential to prevent age-related falls by promoting cognitive reserves.

Early adverse life events (EALs) increase susceptibility to depression and impair cognitive performance, but the physiological mechanisms are still unclear. The target of this article is to clarify the impact of adverse childhood experiences on emotional and cognitive performance from the perspective of the heart–brain axis. We used the Repeatable Battery for the Assessment of Neuropsychological Status (RBANS) to test cognitive function and the Childhood Trauma Questionnaire (CTQ) to assess adverse childhood experiences. Heart rate variability (HRV) and electroencephalograms (EEG) were acquired at rest. We observed that subjects with depression had experienced more traumatic events during their childhood. Furthermore, they exhibited lower heart rate variability and higher power in the delta, theta, and alpha frequency bands. Moreover, heart rate variability partially mediated the association between childhood trauma exposure and depressive symptoms. Our findings suggested that adverse life events in childhood could influence the development of depression in adulthood, which might be linked to cardiac autonomic dysfunction and altered brain function.

Sleep and the brain-gut axis both involve the coordinated regulation of the central nervous system (CNS), autonomic nervous system (ANS), and enteric nervous system. Although the role of the brain-gut axis in sleep regulation is increasingly recognized, the dynamic information flow and its directionality among the brain, heart, and gut systems during sleep remain unclear as statistical associations. In this study, based on overnight sleep data from 24 healthy subjects, we simultaneously collected occipital electroencephalogram (EEG), electrocardiogram, and bowel sound signals, constructed a multimodal physiological signal analysis framework, and employed a multivariate transfer entropy method based on non-uniform embedding and k -nearest neighbor estimation to systematically quantify directed statistical associations at the level of slow power envelope co-modulations among the brain, heart, and gut systems within the framework of sleep staging. The results reveal a consistent pattern of directed statistical associations: information transfer from heart to brain, brain to gut, and heart to gut consistently exceeds that in the reverse directions across all sleep stages. Sleep-stage dynamics further dissociate within-system from inter-system information flow: internal information exchange within the brain and gut intensifies with sleep depth and weakens during REM, whereas inter-system pathways show divergent stage-dependent patterns: heart-to-brain flow increases from light to deep sleep, while heart-to-gut and brain-to-gut flow are suppressed during deep sleep. At the frequency-band level, the brain, heart, and gut each exhibit dynamically reorganized source–receiver roles across sleep stages, with specific frequency bands contributing differentially to each inter-system pathway depending on sleep stage. This study identifies exploratory statistical patterns of directional information flow among the brain, heart, and gut systems during sleep, providing hypothesis-generating observations for future studies on sleep and multi-organ interactions. These directed associations do not establish anatomical pathways, causal regulation, or clinical biomarkers, and should be regarded as exploratory.

Neuromodulation of cortical brain regions associated with the gut‐brain axis may have the potential to modulate gastric function. Previous studies have shown phase‐amplitude coupling between the electroencephalogram (EEG) alpha band frequency of the insula (Ins) and gastric slow wave (GSW) activity. This study investigated the first evidence of alpha band EEG‐neurofeedback (EEG‐NF) training to explore its effects on GSW activity and heart rate variability (HRV).

Background Gut health and brain activity are closely linked, and the gut−brain axis plays a crucial role in regulating mental health. Interventions using beneficial microbes, such as probiotics and postbiotics, are gaining attention as promising approaches to support mental health. This study aimed to evaluate the effects of postbiotic heat-treated Lactobacillus gasseri CP2305 (CP2305) on mental stress and alpha oscillatory activity in the brain. Methods This double-blind, randomized, crossover, placebo-controlled study included 28 healthy participants aged 20–44 years. The participants underwent two trial sessions and consumed a single dose of tablets containing heat-treated CP2305 (postbiotic; 1 × 1010 cells) or matching placebo tablets. In each session, electroencephalography (EEG) at eight positions was recorded from before to 60 min after ingestion to measure the alpha power. Heart rate variability was also recorded to calculate the root mean square of successive differences (RMSSD), an indicator of parasympathetic activity. The effects on mental state were evaluated using the visual analog scale (VAS) and Profile of Mood States, 2nd edition (POMS2). An in vitro study was conducted using the enterochromaffin cell line RIN14B to measure serotonin (5-HT) secretion levels following the heat-treated CP2305 administration. Results The alpha power showed a significantly greater increase from baseline following consumption of heat-treated CP2305 compared with the placebo. The change in the RMSSD from baseline was significantly greater following consumption of heat-treated CP2305 than following the placebo. Both the mental stress VAS and POMS2 tension–anxiety scores showed significantly greater improvements following consumption of heat-treated CP2305 compared with the placebo. In the in vitro 5-HT assay, extracellular 5-HT levels were significantly higher under the heat-treated CP2305 condition than under the control condition. Conclusion This is the first clinical study to examine the effects of postbiotics on alpha oscillatory activity in the brain. Heat-treated CP2305 consumption was associated with modulation of brain activity, enhancement of parasympathetic activity, and alleviation of stress, indicating its potential as a functional food ingredient for mental health support. These beneficial effects may be partly associated with alterations in gut-derived 5-HT signaling induced by heat-treated CP2305. However, further studies are required to elucidate the underlying mechanisms. Clinical trial registration https://center6.umin.ac.jp/cgi-open-bin/ctr_e/ctr_view.cgi?recptno=R000064175, Identifier UMIN000056164.

Autonomic dysfunction in epilepsy is well-described. Heart rate variability (HRV) is a useful method to evaluate autonomic cardiac tone. Cardiac dysfunction may be involved in sudden unexpected death in epilepsy (SUDEP). HRV is a promising biomarker to enlighten the heart-brain axis role in SUDEP, but the required duration for a proper HRV recording in clinical routine remains unknown. This study aimed to verify the reliability of ultra-short HRV indices to evaluate cardiac autonomic tone in patients with epilepsy (PWE). Thirty-nine patients with mesial temporal lobe epilepsy (MTLE) had electrocardiogram recordings during the first day of video-EEG. Pearson's correlations were performed to evaluate the association between ultra-short HRV indices (five 1-min and five 30-s epochs) with standard time recording (5-min) and ANOVA compared the differences between mean HRV indices across epochs. Time domain (TD) indices showed higher mean r values when compared to frequency domain (FD) indices in 1-min (TD: r 0.80-0.99, FD: r 0.61-0.95) and 30-s epochs (TD: r 0.69-0.99, only high frequency: mean r values of 0.96). ANOVA evidenced that standard deviation of RR intervals and very low frequency means had at least 3 epochs significantly different for 1-min and 30-s epochs. Root mean square of the successive differences of RR intervals (rMSSD) presented higher Pearson's coefficient values and lower percentage of variation at 1-min or 30-s epochs in comparison to other HRV indices. In conclusion, rMSSD is the most reliable ultra-short HRV index for cardiac autonomic tone assessment in MTLE. The prognostic value of ultra-short HRV for cardiovascular risk evaluation in epilepsy remains to be determined in future studies.

The coordinated interplay between brain and body is essential for survival, with the heart-brain axis playing a fundamental role in perceptual, cognitive and motor processes. Central to this interaction, the heart-evoked potential (HEP) represents a neural correlate of the heart activity. Further, the HEP is thought to represent a neuronal prediction for each heartbeat, raising questions about its role in arrhythmia. Yet, previous studies have primarily focused on regular heart rhythms, with only a few investigations delving into arrhythmias and, notably, none into extrasystoles, a form of benign arrhythmias that disrupt the regular heart rhythm. Extrasystoles represent a unique possibility for investigating brain responses to unexpected pronounced alterations in heart rhythm. We took advantage of the largest dataset (103 subjects with 3000 extrasystoles), including both EEG and ECG recordings, and analysed the neural response to both types of extrasystoles (supraventricular and ventricular) using multiverse approaches and control analysis in time and source space. We found that the HEP was significantly reduced for ventricular extrasystoles with underlying sources in the left insular. For the postextrasystolic beat of both types of extrasystoles, we found a significantly earlier and increased HEP originating from sources in the left frontal orbital cortex and the anterior cingulate gyri. The reduced HEP response to the ventricular extrasystole might result from inactive interoceptive cardiac pathways. In contrast, increased HEP of the postextrasystolic beat along with the anatomical neural HEP generators may reflect an interoceptive prediction error arising from a naturally occurring discrepancy between the predicted and actual heart rhythm, with a main source in the anterior cingulate gyri, a primary location for prediction error processing.

Lacticaseibacillus paracasei strain Shirota (LcS) modulates psychological homeostasis via the gut–brain axis. To explore the possible efficacy of LcS for improving daytime performance, we conducted a double-blind, randomized, crossover, placebo-controlled study of 12 healthy office workers with sleep complaints. The participants received fermented milk containing viable LcS (daily intake of 1 × 1011 colony-forming units) and non-fermented placebo milk, each for a 4-week period. In the last week of each period, the participants underwent assessments of their subjective mood and measurements of physiological state indicators via an electroencephalogram (EEG) and heart rate variability in the morning and afternoon. The attention score in the afternoon as assessed by the visual analog scale was higher in the LcS intake period than in the placebo intake period (p = 0.041). Theta power on EEG measured at rest or during an auditory oddball task in the afternoon was significantly lower in the LcS period than in the placebo period (p = 0.025 and 0.009, respectively). The change rate of theta power was associated with the change in attention score. Treatment-associated changes were also observed in heart rate and the sympathetic nerve activity index. These results indicate that LcS has possible efficacy for improving daytime performance, supported by observations of the related physiological state indicators.

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.

This study explores the brain–heart axis and its directional control dynamics across various sleep stages using electroencephalography and electrocardiogram data from publicly available whole‐night recordings of 50 healthy individuals. Utilizing a validated functional brain–heart interplay (BHI) mathematical model, we identified a decrease in central control over peripheral neural activity regulating heartbeat dynamics during non‐REM sleep. In contrast, an increase in sympathovagal activity influencing cortical function was observed during deep sleep, particularly in Non‐REM3, compared to light sleep and REM phases. These results indicate a dynamic shift in the functional balance of the brain–heart axis and related BHI throughout sleep stages, characterized by predominant central control during wakefulness and enhanced bodily neuro‐cardiac‐autonomic regulation during deep sleep.

No abstract available

Abstract The heart-brain axis involves complex interactions between the cardiovascular and nervous systems via a network of cortical and subcortical structures working with the autonomic nervous system and intracardiac nervous system. Heart-brain interactions may be divided into 2 broad categories: cardiac effects of neurological disease and neurological effects of cardiac disease. The pathogenesis of neurogenic cardiac effects is thought to involve a neurogenic cascade where sudden shifts in autonomic balance lead to an exaggerated catecholamine release. This can occur in acute neurological conditions such as ischemic stroke, intracranial hemorrhage, and epilepsy. Cardiovascular complications include the stroke-heart syndrome, neurogenic pulmonary edema and cardiomyopathy, Takotsubo syndrome, arrhythmias, and even sudden cardiac death. Certain areas of the brain, such as the insular cortex, play key roles in cardiac autonomic regulation, and disorders affecting these areas have greater effects on the heart. On the other hand, cardiac conditions can also adversely impact the neurological system. Atrial fibrillation and left ventricular thrombus can cause cardioembolic strokes, whereas heart failure and severe aortic stenosis have been linked to the development of cognitive impairment. This review aims to provide a broad overview of key topics in neurocardiology as well as delve into the evidence and pathophysiology behind these conditions.

The heart-brain axis forms an important physiological network, which is increasingly gaining recognition due to its involvement in cardiac function under steady-state conditions and pathological modifications of the heart in cardiovascular disease. Neurological disorders are known to affect cardiac function by propagating structural alterations in the heart. On the other hand, cardiovascular events have detrimental effects on the central nervous system affecting several brain regions, such as the hippocampus, which is important for cognition. Several anatomical regions of the brain, such as cortical and subcortical forebrain structures, regulate cardiovascular functions via the autonomic nervous system. The sympathetic and parasympathetic nervous systems, which are parts of the autonomic nervous system, play a crucial role in cardiovascular health. Cardiovascular disease, such as myocardial infarction (MI), activates the sympathetic nervous system, leading to exaggerated cardiac remodeling and subsequent arrhythmias. MI also alters afferent sensory neurons affecting nociceptive neurotransmission. This review focuses on the significance of the heart-brain axis and summarizes recent studies in this arena.

Takotsubo syndrome (TS) is characterized by transient left ventricular dysfunction, often triggered by psychological or physiological stress. Increasing evidence highlights the critical role of the brain-heart axis in TS, with small blood vessels acting as central mediators. Recent data indicate a significant association between TS and cerebrovascular events, particularly ischemic stroke. Studies show that the incidence of stroke in TS patients is 1–2% per patient-year, which may occur during the acute phase or later due to ongoing autonomic and microvascular dysfunction. This underscores the shared mechanisms of microvascular impairment between the heart and brain in TS. Imaging techniques are essential for detecting microvascular abnormalities in both the heart and brain, providing valuable insights into the interconnected nature of microvascular dysfunction across the heart-brain axis. The aim of this review is to investigate the potential shared pathophysiological mechanisms that link TS and stroke, with a specific emphasis on microvascular dysfunction as a common factor. By examining the role of the heart-brain axis in both conditions and emphasizing the crucial role of advanced imaging techniques, this review seeks to clarify how microvascular abnormalities can simultaneously affect the cardiovascular and cerebrovascular systems. Additionally, it provides insights into the clinical implications of these findings, highlighting the importance of viewing TS and stroke as interconnected conditions within a shared pathological framework. Understanding these mechanisms may not only improve early detection but also pave the way for novel therapeutic strategies targeting both cardiac and cerebral microvascular health, potentially enhancing outcomes for TS patients.

The heart–brain axis (HBA) is a dynamic system of reciprocal communication between the cardiovascular and central nervous system, incorporating neural, immunologic, molecular and hormonal pathways. The central autonomic network is described as a key regulator of cardiovascular activity and autonomic dysfunction as an important mechanism underlying various neurologic and cardiac disorders. Heart rate variability (HRV) is identified as the key biomarker of the axis reflecting autonomic nervous system balance. Increased understanding of its molecular mechanisms has led to the proposal of new therapeutic strategies focused on modulating heart–brain communication including β-blockers, vagus nerve stimulation, neurotrophin modulation, and nanoparticle-based approaches. The integration of wearables and artificial intelligence (AI) has allowed for real-time monitoring and innovative diagnostic and prognostic applications. The present narrative review summarizes current knowledge on the mechanisms comprising the heart–brain axis, their implication in neurologic and cardiac disorders, and their potential for developing novel therapies. It also highlights how advancements in wearable technology and AI systems are being integrated into clinical practice and transforming the landscape.

No abstract available

Cardiovascular disease (CVD) and Alzheimer's disease (AD) are leading causes of death and disability worldwide, and recent research has increasingly illuminated a complex, bidirectional relationship between the two. This review synthesizes epidemiological, mechanistic, imaging, and genetic evidence linking CVD and AD through the heart-brain axis—a network of interrelated physiological and demographic pathways. We detail how cerebral hypoperfusion, inflammation, blood-brain barrier dysfunction, imbalance of the autonomic nervous system, and systemic amyloidosis contribute to shared neurodegenerative and cardiovascular outcomes. Multi-organ imaging studies, including MRI and PET, reveal that dysfunction of the cardiovascular system correlates with brain atrophy, white matter lesions, glymphatic impairment, and accumulation of AD-related proteinopathies. Genetic analyses further support overlapping risk architectures, particularly involving APOE and loci associated with lipid metabolism, vascular integrity, and inflammation. Age and sex are critical modifiers, with midlife CVD exerting the strongest influence on later cognitive decline, and sex-specific physiological responses shaping disease susceptibility. Finally, we explore how modifiable lifestyle factors, pharmacologic interventions, and precision medicine approaches targeting inflammatory and vascular pathways can jointly reduce the burden of both CVD and AD. Multidisciplinary collaboration to understand the interconnected biology of the heart and brain is essential for advancing integrated prevention and treatment strategies in aging populations.

With an aging population, the incidence of both ischemic heart disease and strokes have become the most prevalent diseases globally. These diseases have similar risk factors, such as hypertension, diabetes, and smoking. However, there is also evidence of a relationship between the heart and the brain, referred to as the heart–brain axis. In this relationship, dysfunction of either organs can lead to injury to the other. There are several proposed physiologies to explain this relationship. These theories usually involve vascular, neuromodulatory, and inflammatory processes; however, few articles have explored and compared these different mechanisms of interaction between the heart and brain. A better understanding of the heart–brain axis can inform physicians of current and future treatment and preventive care options in heart and brain pathologies. The relationship between the brain and heart depends on inflammation, vascular anatomy and function, and neuromodulation. The pathways connecting these organs often become injured or dysfunctional when a major pathology, such as a myocardial infarction or stroke, occurs. This leads to long-term impacts on the patient’s overall health and risk for future disease. This study summarizes the current research involved in the heart–brain axis, relates these interactions to different diseases, and proposes future research in the field of neurocardiology. Conditions of the brain and heart are some of the most prevalent diseases. Through understanding the connection between these two organs, we can help inform patients and physicians of novel therapeutics for these pathologies.

Postoperative delirium (POD) following cardiac surgery is a severe complication. There is evidence of a link between neuroinflammation and neurodegeneration in POD. We investigated the preoperative proinflammatory interleukin-6 (IL-6) and neuronal damage marker phosphorylated tau protein 181 (p-tau181) to POD while considering preoperative heart-brain axis related factors. The prospective FINd DElirium RIsk factors (FINDERI) is an observational study in patients undergoing cardiac surgery. Biomarkers IL-6 and p-tau181 were measured in blood samples. For statistics, we utilized multiple logistic regression analyses and advanced machine learning techniques. In 491 patients, 106 (21.6%) developed POD. The age of patients with POD was significantly higher than that of patients without POD (p < 0.001). Preoperative IL-6 and p-tau181 levels independently predicted POD [IL-6: area under the curve (AUC) = 0.605, p < 0.005; p-tau181: AUC = 0.641, p < 0.0001)]. A multiple logistic regression analysis of preoperative log-transformed biomarkers levels (p-tau181, IL-6), female sex and cognitive performance increased the AUC (0.710, p < 0.0001) in predicting POD. We created a decision tree prediction model including preoperative p-tau181, IL-6, and the severity of mitral valve disease (training data: AUC = 0.672, p < 0.0001; validation data: AUC = 0.642, p < 0.05). The LASSO regression showed an increased AUC in the training (0.751, p < 0.0001) and validation dataset (0.652, p < 0.05). Our results demonstrate that the combined assessment of preoperatively measured p-tau181 and IL-6, preoperative mitral valve disease, cognitive performance and female sex, significantly predicts POD. These findings provide evidence that neuroinflammation and neuronal cell damage are associated with POD.

Chronic kidney disease (CKD)-mediated oxidative stress, uremic toxicity, inflammation, and cardiovascular (CV) damage connect the kidney-heart-brain axis that contributes to cognitive impairment (CI) and progression to major neurological disorders. Although the sex disparity has a profound impact on CKD epidemiology, its role in the progression of CI needs to be elucidated in detail. The present study aims to unravel the sex-specific crosstalk of the kidney-heart-brain axis in CKD. CKD and control subjects, equally represented by both sexes, were included in a cross-sectional study that involved a community-dwelling rural population. CV and CKD parameters, mini-mental state examination (MMSE), markers of fibrosis and neurodegeneration were assessed. We elucidated the sex-specific associations among these factors through linear regression and structural equation modeling (SEM), or Path, analyses. CKD patients have higher blood pressure vs. controls, and men with CKD exhibited a decline in cardiac function vs. sex- and age-matched controls. Both men and women with CKD had lower MMSE scores vs. controls, although cognitive performance in CKD-women was significantly better than that of CKD-men. Path analysis revealed a direct association of the plasma phosphorylated Tau protein (pTau) and the ratio of amyloid β-42 to amyloid β-40 with MMSE scores in women only. Pro-brain natriuretic peptide and pTau were associated with short-term memory, a part of the MMSE assessment, also in women only. Our findings will broaden the current understanding and clinical consequences of the pathophysiological interactions between kidney and CV damage with brain function in a sex-dependent manner that could prompt innovative pharmacological interventions.

BACKGROUND Imminent threat does not only capture attention but also triggers defensive psychophysiological responses associated with fight-flight-freeze behavior. However, within-subject associations between central markers of attentional stimulus processing and peripheral components of early defensive activation (e.g., cardiac acceleration) are not well understood. These associations may be especially important in understanding dynamics of anxiety disorders that are characterized by cardiovascular symptoms such as panic disorder. METHODS We analyzed data from N = 90 participants with panic disorder (PD), social anxiety disorder (SAD), as well as healthy controls (CG) who completed a threat conditioning paradigm with face stimuli as conditioned stimuli (CS) and an aversive white noise burst as unconditioned stimulus (US). In addition to univariate analyses of ERPs and heartbeat, single-trial EEG evoked by the noise burst at centromedial sites and heart period were used to calculate intraindividual coupling of unconditioned cortico-cardiac responses. RESULTS The noise burst evoked strong cardiac acceleration and an event-related potential with a pronounced N100 component. Importantly, the magnitude of single-trial cardiac acceleration was predicted within individuals by the preceding single-trial N100 magnitude. This cortico-cardiac coupling, but not the N100 component or cardiac acceleration per se, was enhanced in individuals with panic disorder compared to social anxiety disorder, with intermediate levels of coupling observed in healthy controls. CONCLUSION The present results suggest a central role of early attentional stimulus processing in subsequent cardiac defensive responses. Furthermore, they indicate that cortico-cardiac defensive responses may be altered in individuals with panic disorder specifically, rather than in anxiety disorders in general.

In the face of unpredictable threat, rapid processing of external events and behavioral mobilization through early psychophysiological responses are crucial for survival. While unpredictable threat generally enhances early processing, it would seem adaptive to particularly increase sensitivity for unexpected events as they may signal danger. To examine this possibility, n = 77 participants performed an auditory oddball paradigm and received unpredictable shocks in threat but not in safe contexts while a stream of frequent (standard) and infrequent (deviant) tones was presented. We assessed event‐related potentials (ERP), heart period (HP), and time‐lagged within‐subject correlations of single‐trial EEG and HP (cardio‐EEG covariance tracing, CECT) time‐locked to the tones. N1 and P2 ERP amplitudes were generally enhanced under threat. The P3 amplitude was enhanced to deviants versus standards and this effect was reduced in the threat condition. Regarding HP, both threat versus safe and unexpected versus expected tones led to stronger cardiac acceleration, suggesting separate effects of threat and stimulus expectancy on HP. Finally, CECTs revealed two correlation clusters, indicating that single‐trial EEG magnitudes in the N1/P2 and P3 time‐windows predicted subsequent cardiac acceleration. The current results show that an unpredictable threat context enhances N1 and P2 amplitudes and cardiac acceleration to benign auditory stimuli. They further suggest separable cortical correlates of different effects on cardiac activity: an early N1/P2 correlate associated with threat‐effects on HP and a later P3 correlate associated with expectedness‐effects. Finally, the results indicate that unpredictable threat attenuates rather than enhances the processing of unexpected benign events during the P3 latency.

Multimodal sleep analysis typically requires separate processing pipelines for electroencephalography (EEG) and electrocardiography (ECG), which complicates joint investigation of sleep microstructure and autonomic regulation. Cyclic alternating pattern (CAP) scoring and heart rate variability (HRV) analysis are routinely performed in different software environments, limiting temporal alignment, reproducibility, and scalability. We present an open-source MATLAB framework that integrates rule-based CAP A1 detection and HRV feature extraction within a single, time-synchronized workflow. The pipeline performs standardized EEG and ECG preprocessing, rule-based CAP A1 detection during slow-wave sleep (SWS) implementing the Terzano scoring criteria, R-peak detection via the Pan–Tompkins algorithm, and computation of time-domain, frequency-domain, and nonlinear HRV features by calling the open-source PhysioZoo/mhrv toolbox directly. CAP annotations are aligned with 5-minute ECG epochs through the shared polysomnography (PSG) time base. The complete code is released as an open-source repository to support reuse and extension. Because HRV features are computed by calling the PhysioZoo/mhrv toolbox directly, the pipeline introduces no reimplemented HRV routine and inherits that toolbox’s validated feature definitions. CAP A1 detection is provided as a rule-based implementation of published criteria and has not yet been benchmarked against expert manual scoring. A worked example on the OSAD+ subgroup (n = 18) demonstrates pipeline output across CAP and non-CAP epochs, and a healthy-sleep A1-event HRV reference is reported from the public CAP Sleep Database (15 controls, 785 A1 epochs) as a descriptive cross-database reference. The framework provides a reproducible, rule-based, and modular tool for integrated EEG–ECG analysis of sleep microstructure and autonomic dynamics. By enabling synchronized CAP–HRV feature extraction in a single environment, it lowers the engineering barrier to brain–heart coupling research during sleep and provides a foundation for future extensions, including validated CAP detection, a matched-control study, a Python port, and cloud-based deployment.

Abstract Sleep arousals are transient events known to elicit concurrent cortical and autonomic responses, yet the directional causal structure of brain-heart coupling during these events remains unexplored. In this study, we investigated nonlinear directed brain-heart interactions during sleep arousals using Convergent Cross Mapping (CCM).We extracted eventlocked 120 s segments from simultaneous single-channel EEG and ECG full-night recordings of 53 participants. We computed EEG amplitude envelopes for sleep-specific frequency bands and heart rate variability series. Bidirectional CCM using 30-s windows with a 1-s sliding step provided time-varying coupling estimates across all arousals. We validated coupling significance using phase-randomized surrogate data, and tested band-selective coupling changes using linear mixedeffects models. Results indicated that brain-to-heart coupling dominated in all bands and pre- and post-arousal. Heart-tobrain coupling increased significantly in all EEG bands prearousal and recovered in post-arousal, indicating autonomiccortical signaling as a hallmark of sleep arousal dynamics.

Cardiac arrest (CA) often results in severe neurological injury, with both the central (CNS) and autonomic nervous systems (ANS) playing critical roles in recovery. Brain–heart coupling (BHC) and heartbeat-evoked potentials (HEP) reflect CNS and ANS interactions, yet their temporal evolution after return of spontaneous circulation (ROSC) and prognostic relevance remain unclear. This study aims to examine frequency-specific variations in BHC and HEP within the first 96 h after ROSC in cardiac arrest patients and evaluate their prognostic value for neurological recovery. We analyzed physiological data from 277 CA patients, focusing on BHC and HEP metrics. Unlike previous studies, hourly analyses were performed for each patient in order to investigate the temporal evolution of BHC and HEP following cardiac arrest. EEG and ECG data were preprocessed, and the Poincaré Sympathetic-Vagal Synthetic Data Generation (PSV-SDG) model was used to compute the strength. Refined Composite Multiscale Entropy (RCMSE) was applied to assess BHC complexity. Additionally, HEP was further examined for spatiotemporal features. Logistic regression (LR) and support vector machine (SVM) models were applied to predict 3-month outcome using BHC and HEP features at multiple post-CA time points to assess their prognostic value over time. We found that patients with good outcome demonstrated significantly higher BHC strength and complexity, particularly in the delta and theta bands, with notable increases between 38–47 h post-ROSC. HEP analysis showed enhanced positive peaks and widespread cortical activation in this group, suggesting more robust cortical-autonomic integration. In contrast, the poor outcome group exhibited weaker, more localized BHC and unstable HEP responses. Machine learning models combining BHC and HEP features achieved strong prognostic performance, with the highest AUC of 0.98 observed at 70 h post-ROSC, identifying it as the most informative time point for outcome prediction. This study demonstrates that BHC and HEP metrics offer important insights into post-CA recovery. Notably, measures obtained at 70 h post-ROSC provided the greatest prognostic value, highlighting their potential to inform early clinical decision-making in critical care.

Functional brain networks reconfigure to support adaptive behaviour, balancing integration and segregation. These dynamics are influenced by neuromodulatory arousal, reflected in heart rate (HR) fluctuations. We leverage intracranial EEG (iEEG), functional MRI (fMRI), and HR recordings during emotional movie viewing to probe neurocardiac contributions to large-scale network dynamics. Network dynamics using high-frequency iEEG activity (60– 140 Hz), an established marker of neuronal firing, reveal that greater network integration tracks elevated HR, while segregation aligns with lower HR. However, we observe an inverted relationship between HR and network dynamics in a separate fMRI dataset utilising the same movie, and replicate this in another independent fMRI dataset. Biophysical modelling of BOLD from iEEG reproduces the iEEG-HR findings, suggesting neurovascular transformation may obscure neural-cardiac links in fMRI. These results link large-scale network dynamics to physiological arousal through HR driven adrenergic-cholinergic neuromodulation and highlight the complex interplay between neural activity, autonomic regulation, and haemodynamics.

Background and Objectives: Approximately 50% of post-cardiac arrest survivors remain comatose after 72h, a substantial proportion of which will have a poor neurological outcome, predominantly due to irreversible hypoxic-ischemic brain injury. Recent findings in healthy subjects and patients suggested that autonomic nervous system activity measured by brain-heart interactions could be reliable markers of consciousness and cognitive processing. Thus, we hypothesized that brain-heart interactions are associated with the severity of hypoxic-ischemic brain injury and the prognosis of these patients. Methods: In post-cardiac arrest patients still comatose 48h after sedation weaning, brain-heart interaction markers were computed on 5 minutes of continuous EEG/ECG recording using a synthetic data generation model, gathering bidirectional interactions between EEG frequency bands (delta, theta and alpha) and heart-rate variability frequency bands (low and high frequency). The strength and complexity of the interactions were quantified using medians and refined composite multiscale entropy. Primary outcome was the severity of brain injury, assessed by: (i) standardized qualitative EEG classification, (ii) somatosensory evoked potentials (N20), and (iii) neuron-specific enolase levels. Secondary outcome was the 3-month neurological status, assessed by the Cerebral Performance Category score [good (1-2) vs. poor outcome (3-4-5)]. Results: Between January 2007 and July 2021, 181 patients [116 males (64%), median age 61 years, age range 49-72 years] were admitted to ICU for a resuscitated cardiac arrest (76% out-of-hospital, 69% non-shockable rhythm). Poor neurological outcome was observed in 134 patients (74%). Qualitative EEG patterns suggesting high severity were associated with a decreased sympatho-vagal balance. Severity of EEG changes were proportional to higher absolute values of brain-to-heart coupling strength (p<2x10-3 for all brain-to-heart frequencies) and lower values of complexity (all p-values<0.05 except for alpha-to-low frequency). Brain-to-heart coupling strength was significantly higher in patients with bilateral absent N20 and correlated with neuron-specific enolase levels at day 3. This aberrant brain-to-heart coupling (increased strength, decreased complexity) was also associated with 3-month poor neurological outcome. Discussion : Our results suggest that autonomic dysfunctions may well represent hypoxic-ischemic brain injury post-cardiac arrest pathophysiology. These results open avenues for integrative monitoring of autonomic functioning in critical care patients with potential prognostic applications.

External and internal performance feedback triggers not only neural but also cardiac modulations, suggesting communication between brain and heart during feedback processing. Using Cardio-Electroencephalographic Covariance Tracing (CECT), it has accordingly been shown that feedback-evoked centromedial single-trial EEG at the P300 latency intraindividually predicts subsequent changes in heart period - the so called N300H phenomenon. While previous findings suggest that the N300H depends on serotonin, its relationship to central dopamine and noradrenaline is currently unknown. Here, we tested (1) the psychometric properties of this CECT-based component and (2) its putative catecholaminergic mechanisms. N = 54 healthy male participants received either a α2-adrenoceptor antagonist (yohimbine, 10 mg; n = 18), D2-dopamine-receptor antagonist (sulpiride, 200 mg; n = 18), or a placebo (n = 18). Afterwards, they performed a gambling task with feedback after each trial, while EEG and ECG were recorded. Feedback successfully evoked a significant N300H both across all 54 participants and within each substance group. Importantly, we show that N300H can be reliably measured in a priori defined time windows with as few as 240 feedback trials and is relatively unaffected when removing extreme single-trial values. However, we could not find any significant substance effects on N300H magnitude as well as on univariate feedback-related measures (FRN, P300, heart period). Altogether, the N300H component proves as a robust and reliable marker of cortico-cardiac coupling evoked by feedback. Furthermore, these findings suggest a subordinate role of catecholamines (i.e., noradrenaline and dopamine) and sympathetic pathways in feedback-evoked brain-heart communication as measured with N300H.

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Non-contact Ballistocardiography (BCG) and Electroencephalography (EEG) are widely used, yet while correlations between EEG and Electrocardiography (ECG), and BCG and ECG, are established, the feature-level correlation between BCG and EEG remains underexplored. To address this research gap, We collected 5 -minute supine EEG, ECG, and EMFi sensor signals from 10 subjects. BCG signals were extracted using Empirical Mode Decomposition (EMD) and EEG signals via Independent Component Analysis (ICA). Heart Rate Variability (HRV) features were derived from the BCG, and time-frequency features were extracted from the EEG for correlation analysis. The study revealed significant correlation patterns. Notably, BCG-derived HRV features (SDNN, RMSSD) showed strong positive correlations with EEG Hjorth Complexity and Kurtosis, alongside a strong negative correlation with EEG Hjorth Mobility ($\vert r\vert >0.9, p<0.001$). These findings demonstrate novel featurelevel correlations between BCG and EEG. The observed patterns are consistent with the subjects' physiological states and align with established principles of cardio-encephalic coherence.

Background Investigating brain-heart-gut coupling during sleep is crucial for understanding the coordinated regulatory mechanisms of multiple systems during sleep. Non-invasive continuous physiological signal acquisition techniques have been widely applied in brain-heart dynamic assessment. However, current research on gut function primarily focuses on gut microbiota, with a lack of systematic investigation into the macroscopic dynamic changes of gut function. This study, therefore, based on multiple non-invasive physiological signals, aims to explore the dynamic changes and underlying mechanisms of brain-heart-gut coupling during sleep. Methods The study enrolled 24 healthy subjects, and collected electroencephalogram (EEG), electrocardiogram (ECG), and bowel sounds (BS) signals during sleep. Through signal processing and spectral analysis, power spectral values of each physiological signal in different frequency bands were extracted. The maximal information coefficient (MIC) method was employed to dynamically monitor and quantitatively analyze the coupling strength of brain-heart-gut during sleep. Results The study revealed that the strength of brain-heart-gut coupling significantly varied with sleep stages, showing a gradual weakening trend as sleep deepened. In terms of heart-gut coupling (HGC), the coupling strength between the very low frequency (VLF) band of heart rate variability (HRV) and all BS-derived power sequences was significantly lower than other HRV frequency bands. Regarding brain-heart coupling (BHC), the EEG-beta band showed distinct sleep-stage-dependent coupling characteristics with HRV frequency bands, while the EEG-delta band exhibited higher coupling strength with HRV bands during non-rapid eye movement (NREM) sleep. Additionally, the coupling strength of HGC was significantly higher than that of BGC. Conclusion This study successfully achieved quantitative assessment of brain-heart-gut coupling during sleep based on continuous physiological signals, revealing specific patterns of coupling strength changes across different sleep stages. This research provides new methodological support for the diagnosis of sleep disorders and functional bowel diseases, holding significant theoretical value and clinical application prospects.

No abstract available

External and internal performance feedback triggers neural and visceral modulations such as reactions in the medial prefrontal cortex and insulae or changes of heart period (HP). The functional coupling of neural and cardiac responses following feedback (cortico-cardiac connectivity) is not well understood. While linear time-lagged within-subjects correlations of single-trial EEG and HP (cardio-electroencephalographic covariance tracing, CECT) indicate a robust negative coupling of EEG magnitude 300 ms after presentation of an external feedback stimulus with subsequent alterations of heart period (the so-called N300H phenomenon), the neurotransmitter systems underlying feedback-evoked cortico-cardiac connectivity are largely unknown. Because it has been shown that acute tryptophan depletion (ATD), attenuating brain serotonin (5-HT), decreases cardiac but not neural correlates of feedback processing, we hypothesized that 5-HT may be involved in feedback-evoked cortico-cardiac connectivity. In a placebo-controlled double-blind cross-over design, 12 healthy male participants received a tryptophan-free amino-acid drink at one session (TRP−) and a balanced amino-acid control-drink (TRP+) on another and twice performed a time-estimation task with feedback presented after each trial. N300H magnitude and plasma tryptophan levels were assessed. Results indicated a robust N300H after TRP+, which was significantly attenuated following TRP−. Moreover, plasma tryptophan levels during TRP+ were correlated with N300H amplitude such that individuals with lower tryptophan levels showed reduced N300H. Together, these findings indicate that 5-HT is important for feedback-induced covariation of cortical and cardiac activity. Because individual differences in anxiety have previously been linked to 5-HT, cortico-cardiac coupling and feedback processing, the present findings may be particularly relevant for futures studies on the relationship between 5-HT and anxiety.

Evidence linking cardiovascular health to cognitive function suggests that interactions within the central autonomic network (CAN) may contribute to the development and progression of post-stroke cognitive impairment (PSCI). Heart-brain coupling (HBC) can serve as a readout of coordinated CAN function and offers a novel perspective for understanding PSCI. In this cross-sectional case–control study, we enrolled 16 patients with PSCI and 19 age-matched healthy controls and acquired simultaneous electroencephalography (EEG) and electrocardiography (ECG) recordings during resting state and a cognitive load condition. At both the sensor and source levels, we used the maximal information coefficient to quantify nonlinear coupling between cortical rhythms and heart rate variability (HRV) indices derived from Poincaré plots, and examined associations between these coupling metrics and the severity of cognitive impairment. Compared with healthy controls, HBC in PSCI is characterized by altered task-related modulation patterns, with a potential reduction in the flexibility of coordination between cortical activity and autonomic regulation under cognitive load. Some coupling features partially reflect the severity of cognitive impairment. Spatially, coupling patterns tend to involve widely distributed cortical regions rather than being confined to focal alterations. This study characterizes the nonlinear coupling between cortical rhythms and HRV under both resting and cognitive load conditions in PSCI, which may support a role of CAN dysfunction in the pathophysiology of PSCI and proposes testable potential regulatory targets for PSCI risk stratification and rehabilitation monitoring.

Network physiology has increasingly revealed the dynamic interactions between physiological systems, with heart–brain coupling receiving particular attention. In this study, we propose the amplitude-aware symbolic transfer entropy (AASTE) algorithm to enhance the analysis of heart–brain coupling dynamics, specifically focusing on EEG and ECG interactions across different sleep stages: wakefulness (W), light sleep (N1), intermediate sleep (N2), and deep sleep (N3). Our results show that the transfer entropy from ECG to EEG is consistently higher than from EEG to ECG, suggesting a stronger regulatory influence of heart signals on brain activity, particularly during wakefulness. Furthermore, sleep depth significantly impacts heart–brain information coupling, with interactions gradually weakening as sleep deepens, while the central brain regions (C3, C4) play a crucial role in reflecting these changes. This study explores heart–brain interactions across sleep stages and demonstrates AASTE’s potential for quantifying directional information flow in physiological systems, providing a comprehensive tool for investigating the dynamic transmission of physiological information.

Introduction Acute pain elicits distinct autonomic responses. Electroencephalography (EEG) and heart rate variability (HRV) provide insights into autonomic and cortical activity to pain, but they often fail to capture the integrated dynamics of brain–heart connectivity. This exploratory study used raw electrocardiogram (ECG) and EEG signals to investigate brain–heart coherence during resting and tonic experimental pain, aiming to detect direct electrical coupling patterns, and explored the influence of individual autonomic response in healthy participants. Methods EEG, raw ECG data and HRV were collected from 33 healthy participants under two conditions: rest and a cold pressor test where subjects immersed their hand into ice water (tonic pain). HRV parameters were extracted to quantify autonomic dynamics in response to pain. Magnitude-squared coherence (MSC) quantified brain–heart connectivity across frequency bands (delta, theta, alpha, beta, gamma), and participants were stratified into subgroups based on changes in periodic repolarization dynamics (PRD), a marker of sympathetic modulation. Results Brain–heart coherence remained stable across conditions, reflecting robust coupling, particularly in delta bands, for both conditions. On group-level HRV analysis revealed increased sympathetic response to pain, evidenced by decreased normal-to-normal interval (p < 0.001) and faster heart rates (p < 0.001). In an exploratory analysis, elevated MSC values (all p<0.05) were seen in theta (Fp1, Cz), alpha-2 (T3, P4), and gamma (Fp1, Pz, T3, P4, O2) bands in the group where PRD decreased (n=16) compared to the group where it increased (n=17). Discussion These findings highlight the stability of brain–heart coherence during resting and tonic pain in healthy individuals. However, individual autonomic profiles influenced coherence, with enhanced synchronization in the PRD-decreased group and reduced synchronization in the PRD-increased group. These preliminary findings, limited by the exploratory nature and sparse setup, require validation in studies with denser electrode arrays. Coherence analysis provides nuanced insights into brain–heart dynamics, advancing the understanding beyond single-system measures.

In recent years, there has been a surge in interest regarding the intricate physiological interplay between the brain and the heart, particularly during emotional processing. This has led to the development of various signal processing techniques aimed at investigating Brain-Heart Interactions (BHI), reflecting a growing appreciation for their bidirectional communication and influence on each other. Our study contributes to this burgeoning field by adopting a network physiology approach, employing time-delay stability as a quantifiable metric to discern and measure the coupling strength between the brain and the heart, specifically during visual emotional elicitation. We extract and transform features from EEG and ECG signals into a 1 Hz format, facilitating the calculation of BHI coupling strength through stability analysis on their maximal cross-correlation. Notably, our investigation sheds light on the critical role played by low-frequency components in EEG, particularly in the <inline-formula> <tex-math notation="LaTeX">$\delta $ </tex-math></inline-formula>, <inline-formula> <tex-math notation="LaTeX">$\theta $ </tex-math></inline-formula>, and <inline-formula> <tex-math notation="LaTeX">$\alpha $ </tex-math></inline-formula> bands, as essential mediators of information transmission during the complex processing of emotion-related stimuli by the brain. Furthermore, our analysis highlights the pivotal involvement of frontal pole regions, emphasizing the significance of <inline-formula> <tex-math notation="LaTeX">$\delta $ </tex-math></inline-formula>-<inline-formula> <tex-math notation="LaTeX">$\theta $ </tex-math></inline-formula> coupling in mediating emotional responses. Additionally, we observe significant arousal-dependent changes in the <inline-formula> <tex-math notation="LaTeX">$\theta $ </tex-math></inline-formula> frequency band across different emotional states, particularly evident in the prefrontal cortex. By offering novel insights into the synchronized dynamics of cortical and heartbeat activities during emotional elicitation, our research enriches the expanding knowledge base in the field of neurophysiology and emotion research.

The ongoing exploration of brain-heart interactions in the literature, driven by its significant health implications, remains incomplete despite increasing recent efforts. This study delves into the regularity of electroencephalographic (EEG) oscillations and their synchronization with cardiac vagal longterm variability using Information Storage (IS) and Mutual Information Rate (MIR) measures estimated by linear autoregressive analysis on sleep-time series data. Results reveal significant regularity in slow EEG dynamics, alongside a stronger dynamical coupling with cardiac parasympathetic activity, specifically for high-frequency neural oscillations.

Obstructive Sleep Apnea (OSA) is a prevalent sleep disorder marked by repeated interruptions in breathing, leading to disrupted brain and cardiovascular regulation. While EEG and ECG signals are routinely used in polysomnography, their interaction specifically brain and heart coupling remains underutilized in automated diagnostic systems. This study introduces a novel, interpretable Neurocardiac Coupling Index (NCI) that quantifies bidirectional interactions between EEG and HRV signals across matched frequency bands. Using data from the DREAMS Apnea Database, EEG and ECG signals were processed to extract spectral features and compute directional coupling components: Brain-to-Heart Coupling (BHC) and Heart-to-Brain Coupling (HBC). The final NCI was derived as a normalized, weighted combination of these features. Statistical analysis revealed significantly elevated NCI values in the OSA group compared to controls (p = 0.006), with stronger BHC evident in θ-LF and α-HF pairs. These results suggest enhanced cortical modulation of autonomic activity in OSA, supporting the potential of NCI as a signal-level, non-invasive marker for sleep apnea screening.

This study aimed to explore the influence of sound stimulation on heart rate and the potential coupling between cardiac and cerebral activities. Thirty-one participants underwent exposure to periods of silence and two distinct continuous, non-repetitive pure tone stimuli: low pitch (110 Hz) and high pitch (880 Hz). Electroencephalography (EEG) data from electrodes F3, F4, F7, F8, Fp1, Fp2, T3, T4, T5, and T6 were recorded, along with R-R interval data for heart rate. Heart-brain connectivity was assessed using wavelet coherence between heart rate variability (HRV) and EEG envelopes (EEGE). Heart rates were significantly lower during high and low-pitch sound periods than in silence ( p  < 0.002). HRV-EEGE coherence was significantly lower during high-pitch intervals than silence and low-pitch sound intervals ( p  < 0.048), specifically between the EEG Beta band and the low-frequency HRV range. These results imply a differential involvement of the frontal and temporal brain regions in response to varying auditory stimuli. Our findings highlight the essential nature of discerning the complex interrelations between sound frequencies and their implications for heart-brain connectivity. Such insights could have ramifications for conditions like seizures and sleep disturbances. A deeper exploration is warranted to decipher specific sound stimuli’s potential advantages or drawbacks in diverse clinical scenarios.

The study of functional brain-heart interplay (BHI) aims to describe the dynamical interactions between central and peripheral autonomic nervous systems. Here, we introduce the Sympathovagal Synthetic Data Generation Model, which constitutes a new computational framework for the assessment of functional BHI. The model estimates the bidirectional interplay with novel quantifiers of cardiac sympathovagal activity gathered from Laguerre expansions of RR series (from the ECG), as an alternative to the classical spectral analysis. The main features of the model are time-varying coupling coefficients linking Electroencephalography (EEG) oscillations and cardiac sympathetic or parasympathetic activity, for either ascending or descending direction of the information flow. In this proof-of-concept study, functional BHI is quantified in the direction from-heart-to-brain, on data from 16 human volunteers undergoing a cold-pressor test. Results show that thermal stress induces heart-to-brain functional interplay originating from sympathetic and parasympathetic activities and sustaining EEG oscillations mainly in the $\delta$ and $\gamma$ bands. The proposed computational framework could provide a viable tool for the functional assessment of the causal interplay between cortical and cardiac sympathovagal dynamics.

Abstract We propose a method to analyze cortico-muscular and cortico-cardiac coupling during a cycling exercise in order to study the development of muscular fatigue. Our analysis is based in finding statistically significant changes in the coherence between electromyography (EMG) and electrocardiography (ECG) when compared against electroencephalography (EEG) signals. The coherence is a measure of the degree of correlation of the spectral power in an specified bandwidth between two signals, then EMG-EEG and ECG-EEG coherences are here analyzed during the development of muscular fatigue. For that purpose, we propose an experimental protocol in which a personalized fatigue threshold is found for the volunteers, then different levels of physical exertion are considered. Our results showed significant changes in the maximum cortico-muscular coherence in the β brain rhythm as the exercise progresses and fatigue develops, as well as changes in the maximum cortico-cardiac coherence, again in the β band, from the beginning to the end of the exercise protocol.

Communication between the brain and cardiovascular system is increasingly recognized as a fundamental determinant of health, yet the mechanisms coordinating neural and cardiac rhythms remain poorly understood. Sleep provides a unique physiological state for investigating neurovisceral integration because cortical slow oscillations (SOs), thalamocortical sleep spindles, and autonomic activity undergo profound reorganization during non–rapid eye movement (NREM) sleep. We analyzed overnight high-density electroencephalography and electrocardiography recordings from 30 healthy adults and quantified interactions among SOs, sleep spindles, cardiac R-peaks, heart rate variability (HRV), spectral slope, and Lempel–Ziv complexity. To assess integrated brain–heart coordination, we developed a Joint Phase–Amplitude Coupling (JPAC) metric that quantifies the corelation between SO phase–dependent modulation of spindle amplitude and cardiac timing. SO–spindle coupling showed the expected fronto-central distribution but was not significantly related to autonomic or cortical state markers. In contrast, SO–R-peak coupling and JPAC systematically tracked physiological state. Stronger JPAC was associated with higher high-frequency HRV, steeper spectral slopes, and lower neural complexity, indicating enhanced coupling during synchronized, parasympathetically dominated NREM sleep. These findings demonstrate coordinated organization of cortical, thalamocortical, and cardiac rhythms within a common oscillatory framework and identify NREM sleep as a state and potential treatment target of large-scale neurovisceral integration.

As contribution to the 2023 George B. Moody challenge, we - team “BrAInstorm” - aimed for fusing semantic features based on medical knowledge with an end-to-end residual neural network to predict the secondary neurological outcome after successful resuscitation. More precisely, we fused numerical (e.g. age) and categorical (e.g. gender) information as well as features extracted from biosignals: We extracted absolute and relative power bands, coupling, and coherence from standard electroencephalography (EEG) frequency bands. To investigate the interplay between heart and brain, we computed deceleration capacity (DC) from electrocardiograms (ECGs). In contrast to these semantic features, we adapted a residual neural network based on agnostic features which are derived from the training data. The network architecture was originally developed for classification of ECGs and was adjusted to the challenge EEG data. The best metric scores were reached using only the neural network, demonstrating the complexity of outcome prediction and effectiveness of end-to-end methods. We received a challenge score of 0.57 ± 0.15 during 5-fold cross validation on training data and 0.448 on the hidden validation data. On the hidden test data we received a final score of 0.68 (rank 8 of 36).

No abstract available

No abstract available

The insula is implicated in heart rate modulation within brain-cardiac interactions, yet direct electrophysiological evidence remains limited. This study investigates dynamic insula-cardiac coupling during ictal heart rate fluctuations to elucidate the insula's role in chronotropic regulation. Patients with drug-resistant epilepsy exhibiting both insular stereoelectroencephalography coverage and ictal heart rate changes underwent simultaneous intracranial electroencephalography and electrocardiography monitoring during seizures. Phase transfer entropy (PTE) was used to analyze directed information flow between the seizure onset zone (SOZ) and the insula. PTE dynamics between the insula and the heart, along with heart rate changes, were tracked to determine insular involvement in heart rate regulation. Thirteen patients (24 seizures) were included, with 11 patients (20 seizures) showing tachycardia and 2 patients (4 seizures) showing bradycardia. SOZ-to-insula PTE increased in 22 of 24 seizures. Insula-to-heart PTE inversely correlated with heart rate: during tachycardia (20 seizures), PTE was decreased in 17 cases; during bradycardia (4 seizures), PTE increased in all cases. These changes occurred exclusively in the 0.01-1 Hz band. Temporal analysis revealed that changes in heart rate did not coincide with changes in PTE. The insula may impose frequency-specific inhibitory control over heart rate. The insula may function as a coordinator rather than an initiator of heart rate modulation.

Introduction: Accumulating evidence suggests that enhanced external counterpulsation (EECP) influences cardiac functions, hemodynamic characteristics and cerebral blood flow. However, little is known about whether or how the EECP affects the brain-heart coupling to produce these physiological and functional changes. We aimed to determine if the brain-heart coupling is altered during or after EECP intervention by assessing the heartbeat evoked potential (HEP) in healthy adults. Methods: Based on a random sham-controlled design, simultaneous electroencephalography and electrocardiography signals as well as blood pressure and flow status data were recorded before, during and after two consecutive 30-min EECP in 40 healthy adults (female/male: 17/23; age: 23.1 ± 2.3 years). HEP amplitude, frequency domain heart rate variability, electroencephalographic power and hemodynamic measurements of 21 subjects (female/male: 10/11; age: 22.7 ± 2.1 years) receiving active EECP were calculated and compared with those of 19 sham control subjects (female/male: 7/12; age: 23.6 ± 2.5 years). Results: EECP intervention caused immediate obvious fluctuations of HEP from 100 to 400 ms after T-peak and increased HEP amplitudes in the (155–169) ms, (354–389) ms and (367–387) ms time windows after T-peak in the region of the frontal pole lobe. The modifications in HEP amplitude were not associated with changes in the analyzed significant physiological measurements and hemodynamic variables. Discussion: Our study provides evidence that the HEP is modulated by immediate EECP stimuli. We speculate that the increased HEP induced by EECP may be a marker of enhanced brain-heart coupling. HEP may serve as a candidate biomarker for the effects and responsiveness to EECP.

The dorsolateral prefrontal cortex (DLPFC) is a principal target for transcranial magnetic stimulation (TMS) in treating major depressive disorder, with therapeutic effects thought to be mediated by its connectivity with the subgenual anterior cingulate cortex. As both regions are involved in autonomic regulation, short-term heart rate changes following DLPFC stimulation may serve as physiological markers to identify stimulation targets. We employed neuro-cardiac guided TMS in a cohort of healthy participants to examine the effects of stimulation intensity and DLPFC target specificity on heart–brain coupling (HBC). We used generalized additive models to assess nonlinear effects of stimulation intensity and target location on HBC, while accounting for pain ratings and other side effects. Intra-subject repeatability across three sessions was evaluated using intraclass correlation coefficients. We observed a non-linear modulation of HBC depending on stimulation intensity and target location, with greater effects at the F3 lateral and F3 posterior targets compared to sham. By evaluating these effects across sessions within participants, we demonstrate the robustness of our results beyond the influence of pain and other side effects on HBC modulation. Exploratory analyses of the directionality show a consistent decrease in HR only at the F3 lateral target with suprathreshold stimulation. These results demonstrate that HBC is modulated in a target- and intensity-specific manner, with particularly consistent effects at F3 lateral sites within the DLPFC. The findings enhance the understanding of TMS-modulated heart-brain interactions, offering a potential framework for optimizing individualized rTMS treatment protocols for depression.

To investigate autonomic nervous system activity measured by brain–heart interactions in comatose patients after cardiac arrest in relation to the severity and prognosis of hypoxic–ischemic brain injury.

Neurological complications after cardiac surgery, ranging from temporary cognitive issues to severe strokes, are a major concern, especially for elderly patients. Many in­dividuals over 70 already have a history of neurological conditions, which increases their risk of complications and long-term effects. Serious neurological events, such as strokes, occur in up to 6% of cardiac surgery patients. Given the vulnerability of these patients, prevention is crucial. Several factors can contribute to neurological damage du­ring surgery, including embolism, inflammation, anemia, and arterial disease, however hypoperfusion is usually the main cause. Over the years, various systems have been developed to monitor brain oxygen levels and help doctors maintain proper cerebral perfusion. Neuromoni­toring during cardiac surgery utilizes multiple techniques to assess cerebral perfusion and oxygenation, such as near infrared spectroscopy, transcranial Doppler, and jugular venous bulb saturation monitoring – as well as cerebral function, monitored through both processed and raw electroencephalography. Electroencephalography in cardiac surgery is used to assess cerebral function, depth of anesthesia, and detect signs of neurological damage. It can help guide anesthetic management, ensuring adequ­ate brain protection during surgery.

In α‐synucleinopathies, the dysfunction of the autonomic nervous system which typically manifests as orthostatic hypotension (OH) often leads to severe consequences and poses therapeutic challenges. This study aims to discover the brain‐cardiac electrophysiological changes in OH patients with α‐synucleinopathies using the rapid quantitative electroencephalography (qEEG) coupled with heart rate variability (HRV) technique to identify rapid, noninvasive biomarkers for early warning and diagnosis, as well as shed new light on complementary treatment approaches such as brain stimulation targets.

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The nervous and cardiovascular systems are intricately interconnected in both healthy and diseased conditions. The field of Neurocardiology, which focuses on the complex interaction between the nervous and cardiovascular systems, has grown rapidly over the past years. In addition, growing evidence shows that alterations in Brain-Heart Interplay (BHI) may contribute to neurological and cardiovascular disorders. BHI has great potential as a valuable biomarker to detect autonomic dysfunction, and reveal the mechanisms underlying conditions including sleep-related autonomic disorders and neurodegenerative diseases. However, the physiological characterization of linear or non-linear brain-heart relationships remains unclear. This review presents a comprehensive analysis of the current methods used to characterize BHI across multiple domains, with an emphasis on Electroencephalogram (EEG), Electrocardiogram (ECG), or Photoplethysmogram (PPG). Such non-invasive modalities allow for long-term and time-varying assessment of cortical and autonomic activity. In this work, we review how BHI modulates across various physiological and pathological states and highlight key findings from recent studies. In addition, we review potential data-driven methods to examine complex BHI, including measures of synchronization, directionality, and information transfer. As a result, studying BHI using these strategies offers a new angle on the complex and dynamic interaction of cortical and autonomic processes. This provides an opportunity to understand the intricate interplay between brain and heart functions, and has the potential to advance diagnosis, monitoring, and therapeutic interventions in neurocardiac conditions.

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Objective: Since human life and society depend on brain functions, our understanding of the brain and mind can significantly impact all dimensions of human life. Therefore, the main objective of this study was to assess the coherence of brain waves (EEG) and cardiac wave rhythms (HRV) based on neuroscientific and neurocardiology principles. Methods and Materials: This research utilized an experimental design, specifically a quasi-experimental approach without a control group, and was applied in nature. The statistical population consisted of managers (90 individuals) from both public and private sectors in Gilan Province in 2022. From this population, 30 individuals were randomly selected. Their brain waves and cardiac rhythms were simultaneously recorded in both resting and decision-making states from the CZ region of the brain and triangular regions around the heart using the ProComp 2 neurofeedback tool while performing the emotional intelligence assessment test. The research instrument included the ERT cognitive test (Emotional Recognition Task) designed by Cambridge University in 1980 and administered through the Cantab software and BioGraph Infiniti version 0.6 software. For the purposes of this study, the cognitive task was provided to participants via software. The data were analysed using analysis of variance and mean comparison test via SPSS-26. Findings: The correlation test of brain waves and cardiac waves in resting and cognitive task states showed significant differences. No coherence between EEG and ECG was observed in the resting state compared to the cognitive task state. Conclusion: The results indicate that changes in brain waves and cardiac waves, and the coherence between these waves, vary among managers with similar decision-making styles. These variations depend on the environmental, mental, and emotional conditions of the individuals.

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BACKGROUND Sleep disturbance and autonomic dysfunction are often found in Parkinson's disease (PD) patients, but little is known about changes in cyclic alternating patterns (CAPs) of electroencephalographic (EEG) activities and heart rate variability (HRV) during deep sleep in PD patients. OBJECTIVES To investigate changes in EEG activities and HRV during CAPs and non-CAPs (NCAPs) of N3 sleep in PD patients. METHODS Polysomnographic (PSG) examinations were carried out on 18 PD patients and 18 healthy controls, and power spectral analysis of EEG activities and HRV during CAPs and NCAPs (the segment of sleep without CAPs for more than 60 seconds) of N3 sleep were carried out. RESULTS The percentages of N3 sleep with CAPs and CAP A1, as well as the CAP A1 index in the PD patients, were significantly smaller compared with the healthy controls. In addition, the power of α waves in NCAPs was significantly higher, while the powers of δ waves in Phase A and B of CAP A1 and A3, and NCAPs were significantly smaller. Furthermore, the durations of total δ waves and δ waves with an amplitude ≥75 μV were significantly shorter, and the low frequency (LF) power of HRV during CAPs and the LF/high frequency (HF) HRV ratio during both CAPs and NCAPs were significantly smaller. CONCLUSIONS The changes documented in EEG activities and HRV in PD patients during CAPs and NCAPs of N3 sleep compared with healthy controls suggest that N3 sleep quality and sympathetic function are compromised in PD patients.

Introduction The mechanisms by which conscious breathing influences brain-body signaling remain largely unexplored. Understanding how controlled breathing modulates neural and autonomic activity can offer insights into self-regulation and adaptive physiological control. This study investigates how conscious breathing affects cortical-autonomic communication by analyzing bidirectional interactions between EEG band power time series (BPts), heart rate variability (HRV), and breathing signals. Methods Data were collected from fifteen healthy subjects during three experimental conditions: a spontaneous breathing state (Rest) and two controlled breathing tasks (CBT 1 and CBT 2). EEG recordings were analyzed to compute BPts across the δ, θ, α, β, and γ frequency bands, while HRV and breathing signals were derived from ECG data. Cross-spectrum analysis and Granger causality tests were performed between HRV and BPts. To further investigate directional interactions, Granger-causal relationships were explored between components of the BPts extracted using empirical mode decomposition and the HRV and breathing signals. Results Bidirectional Granger-causal relationships were found between neural and autonomic systems, emphasizing the dynamic interaction between the brain and body. Specific BPts components mediated neural-autonomic communication, with one component consistently aligning with the frequency of conscious breathing (~0.05 Hz) during the CBTs. Cross-spectral peaks at this frequency and its harmonics highlight the role of respiratory entrainment in optimizing neuro-autonomic synchronization. Frequency-specific mechanisms observed in both fast and slow components reflect the complex regulation of autonomic functions through cortical modulation. The most prominent causal effects were observed in the γ band, suggesting its pivotal role in dynamic autonomic regulation, potentially acting as a communication pathway between the brain and body. Discussion Our results demonstrate that conscious breathing enhances bidirectional cortical-autonomic modulation through frequency-specific dynamic neural mechanisms. These findings support a closed-loop model of physiological regulation driven by neural-respiratory entrainment and suggest that respiration can serve as a top-down mechanism for autonomic control. By clarifying how conscious breathing shapes brain-body dynamics, this work lays the foundation for research on neural self-regulation and supports the development of non-pharmacological interventions for improving mental and physiological health.

Stress from daily psychosocial challenges is a significant health concern with limited pharmacological treatment options. Psychosocial stress triggers distinct responses in the autonomic and central nervous systems, measurable via heart rate variability (HRV) and electroencephalogram (EEG). This post hoc analysis of clinical trial data explores the impact of the anti‐stress medication Neurexan (Nx4) on HRV and EEG signals, and their correlation in a resting state following acute psychosocial stress induction.

<italic>Goal:</italic> Brain-heart interactions have been linked to physiological and pathological states and are typically studied through the use of electroencephalographic (EEG) signal and heart rate variability (HRV) time series. However, there are still major challenges to overcome, particularly in establishing a robust methodology to assess these complex multi-scale interactions and to extract meaningful information. To this end, we explore the time scale-dependent nature of brain-heart interactions by exploiting information-theoretic measures. <italic>Methods:</italic> We analyze cardiac vagal activity and EEG brain wave amplitudes at two time scales—heart rhythm (<inline-formula><tex-math notation="LaTeX">$\sim$</tex-math></inline-formula>1 s) and longer (<inline-formula><tex-math notation="LaTeX">$\sim$</tex-math></inline-formula>1 min)—in two groups of healthy subjects monitored during wakefulness and sleep, respectively. Different entropy-based measures are then employed to evaluate the regularity of each system's dynamics, as well as their static and dynamic coupling. <italic>Results:</italic> Different time-scales are involved in different physiological coupling mechanisms. While overall coupling strength values are low, longer time-scales show a stronger presence of coupling in terms of statistically validated brain-heart connections compared to shorter time-scales. <italic>Conclusions:</italic> This study shows that the presence and the strength of brain-heart interactions are highly dependent on the time-scale, which in turn is affected by the underlying physiological processes.

Highlights What are the main findings? Eyeblink indices are potentially useful biomarkers for the effects of electroacupuncture and other neuromodulatory interventions. Significant autonomic correlates for some eyeblink parameters and EEG measures appear likely. What is the implication of the main finding? Traditionally viewed artefacts, such as eyeblink measures, may themselves possess diagnostic and research value. Transcutaneous electroacupuncture stimulation can modulate brain activity and autonomic function in a time- and frequency-dependent manner. Abstract This study investigates the effects of transcutaneous electroacupuncture stimulation (TEAS) on eyeblink rate, EEG, and heart rate variability (HRV), emphasising whether eyeblink data—often dismissed as artefacts—can serve as useful physiological markers. Sixty-six participants underwent four TEAS sessions with different stimulation frequencies (2.5, 10, 80, and 160 pps, with 160 pps as a low-amplitude sham). EEG, ECG, PPG, and respiration data were recorded before, during, and after stimulation. Using non-parametric statistical analyses, including Friedman’s test, Wilcoxon, Conover–Iman, and bootstrapping, the study found significant changes across eyeblink, EEG, and HRV measures. Eyeblink laterality, particularly at 2.5 and 10 pps, showed strong frequency-specific effects. EEG power asymmetry and spectral centroids were associated with HRV indices, and 2.5 pps stimulation produced the strongest parasympathetic HRV response. Blink rate correlated with increased sympathetic and decreased parasympathetic activity. Baseline HRV measures, such as lower heart rate, predicted participant dropout. Eyeblinks were analysed using BLINKER software (v. 1.1.0), and additional complexity and entropy (‘CEPS-BLINKER’) metrics were derived. These measures were more predictive of adverse reactions than EEG-derived indices. Overall, TEAS modulates multiple physiological markers in a frequency-specific manner. Eyeblink characteristics, especially laterality, may offer valuable insights into autonomic function and TEAS efficacy in neuromodulation research.

Heart rate variability biofeedback (HRVBF) is a promising anxiety-reducing intervention that increases vagally-mediated heart rate variability (vmHRV) through slow-paced breathing and feedback of heart rhythm. Several studies have reported the anxiety-reducing effects of HRVBF; however, some studies have reported such training as ineffective. Furthermore, the effects of training and underlying brain activity changes remain unclear. This study examined the anxiety-reducing effects of HRVBF training and related brain activity changes by randomly assigning participants, employing an active control group, and measuring anxiety-related attentional bias using the emotional Stroop task and electroencephalography (EEG). Fifty-five healthy students with anxiety were randomly assigned to the HRVBF or control groups, and 21 in the HRVBF group and 19 in the control group were included in the analysis. Both groups performed 10 training sessions of 20 min each within 3 weeks. They were assessed using resting vmHRV, event-related potential (ERP), time-frequency EEG, attentional bias, and the State-Trait Anxiety Inventory-JYZ (STAI-JYZ) before and after training. The results demonstrated increased resting vmHRV in the HRVBF group compared to the control group after training. However, no differences were observed in ERP, time-frequency EEG, attentional bias, and STAI-JYZ. Participants with higher pre-training resting vmHRV achieved higher heart rhythm coherence in HRVBF training and had reduced attentional bias. This study suggests that individuals with higher resting vmHRV are more likely to be proficient in HRVBF training and benefit from its anxiety-reducing effects. The findings contribute to participant selection to benefit from HRVBF training and modification of the training protocols for non-responders. Clinical trial registration Organization: University Hospital Medical Information Network Clinical Trials Registry (UMIN-CTR), Japan Registration number: UMIN000047096 Registration date: March 6, 2022

Objective: Stress is a significant risk factor for various diseases such as hypertension, heart attack, stroke, and even sudden death. Stress can also lead to psychological and behavioral disorders. Heart rate variability (HRV) can reflect changes in stress levels while other physiological factors, like blood pressure, are within acceptable ranges. Electroencephalogram (EEG) is a vital technique for studying brain activities and provides useful data regarding changes in mental status. This study incorporates EEG and a detailed HRV analysis to have a better understanding and analysis of stress. Investigating the correlation between EEG and HRV under stress conditions is valuable since they provide complementary information regarding stress. Methods: Simultaneous electrocardiogram (ECG) and EEG recordings were obtained from fifteen subjects. HRV /EEG features were analyzed and compared in rest, stress, and meditation conditions. A one-way ANOVA and correlation coefficient were used for statistical analysis to explore the correlation between HRV features and features extracted from EEG. Results: The HRV features LF (low frequency), HF (high frequency), LF/HF, and rMSSD (root mean square of the successive differences) correlated with EEG features, including alpha power band in the left hemisphere and alpha band power asymmetry. Conclusion: This study demonstrated five significant relationships between EEG and HRV features associated with stress. The ability to use stress-related EEG features in combination with correlated HRV features could help improve detecting stress and monitoring the progress of stress treatments/therapies. The outcomes of this study could enhance the efficiency of stress management technologies such as meditation studies and bio-feedback training.

The aim of the work was to determine the relationship of individual typological reactions of bioelectric activity of the brain, variability of the cardiac heart (HRV) when exposed to cold with the parameters of voluntary attention. EEG, HRV and body temperature were recorded in the heat, with short-term air cooling (-200C, 10 minutes) and after being in the cold. The evaluation of indicators of voluntary attention was performed using the Toulouse-Pieron test and included parameters such as speed, accuracy of the test and the number of errors. The results of the study revealed three variants of the formation of adaptive reactions in response to cold. The obtained results suggest that the best indicators of voluntary attention and the effectiveness of adaptive response to cold were observed with optimal interaction of the central and autonomic nervous systems. The correlation between the low speed of the test and the high accuracy and the tension of the regulatory systems of the body in response to cold with the predominance of the activity of the sympathetic nervous system and the high activity of the hypothalamic-diencephalic structures of the brain was found. The average speed of completing test tasks with a large number of errors reveals a relationship with insufficient resource mobilization in adapting to cold with a predominance of activity of the parasympathetic nervous system and an increase in the activity of subcortical structures of the brain. Thus, in the conditions of the North, individual variants of adaptive reactions in response to cold have been identified, associated with various neurophysiological mechanisms of mobilization of functional systems and indicators of the level of voluntary attention.

We induced placebo analgesia (PA), a phenomenon explicitly attenuating the self-pain feeling, to assess whether this resulted in reduced empathy pain when witnessing a confederate undergoing such pain experience. We recorded EEG and electrocardiogram during a painful Control and PA treatment in healthy adults who rated their experienced pain and empathy for pain. We derived HRV changes and, using wavelet analysis of non-phase-locked event-related EEG oscillations, EEG spectral power differences for self-pain and other-pain conditions. First-hand PA reduced self-pain and self-unpleasantness, whereas we observed only a slight decrease in other unpleasantness. We derived linear combinations of HRV and EEG band power changes significantly associated with self-pain and empathy for pain changes using PCAs. Lower Behavioral Inhibition System scores predicted self-pain reduction through the mediating effect of a relative HR-slowing and a decreased midline ϑ-band (4–8 Hz) power factor moderated by lower Fight-Flight-Freeze System trait scores. In the other-pain condition, we detected a direct positive influence of Total Empathic Ability on the other-pain decline with a mediating role of the midline β2-band (22–30 Hz) power reduction. These findings suggest that PA modulation of first-hand versus other pain relies on functionally different physiological processes involving different personality traits.

Some hypotheses relate oscillations of EEG band power with autonomic processes derived from homeostatic control modulated by structures like the Central Autonomic Network and the Autonomic Nervous System. This research project studies the causal relationships between fluctuations of an autonomic process marker like the Heart Rate Variability (HRV) and the proposed EEG band power time series (BPts). To verify the existence of directional causal relationships, using Granger Causality (GC) test between HRV and BPts. Analyses were performed using two databases, of 9 and 14 subjects respectively. Experiments consisted of spontaneous breathing and a controlled breathing task (CBT). GC was tested over Intrinsinc Mode Functions of HRV derived from Empirical Mode Decomposition and BPts computed over α, β and γ bands. Positive GC tests were observed through each experimental task, channels, IMFs, EEG band, and direction. The largest number of positive GC relationships were found from BPts to HRV when testing, higher EEG band and IMF with lower spectral content. Opposite direction achieves lower total counts, but more related with IMFs of higher spectral content. Its presence also suggests that some homeostatic condition alters the BPts course given its increment under the CBT. It is important to notice that in both cases γ band achieves larger values for almost all of the studied conditions. Suggesting that such band has an important influence over HRV, but alterations on breathing condition also produce changes on BPts evolution, suggesting that the closed loop for homeostatic control alters neural dynamics at cortical level.

BACKGROUND Infantile apneic seizures (IASs) are unexpected life-threatening events. We aimed to determine the utility of prolonged video-electroencephalography (vEEG) and heart rate variability (HRV) in IAS. METHODS The study included seven infants with apneic seizures captured by vEEG, percutaneous oxygen saturation (SpO2), and electrocardiography (ECG). Interictal, preictal, and postictal HRV of patients and N2 sleep HRV of 10 age-matched controls were determined. RESULTS We analyzed seven vEEGs (duration = 17 to 87 hours) of seven patients aged three to 13 months (mean onset age of apneic event = 6.3 months). Fifteen apneic seizures (one to five per infant) were captured. The initial apneic seizure was captured at 7.5 to 76 hours (mean = 36.6 hours) after vEEG initiation. Ictal rhythmic delta/theta/fast waves were seen over temporal (five patients), central (one), and diffuse areas (one). Ictal SpO2 decreased between 1.5% and 90% (mean = 47.9%). Ictal decreased heart rate (HR) (six seizures) and ictal increased HR (14) was detected. Both decreased and increased HR was observed (five). The preictal low-frequency (LF)/high-frequency (HF) ratio was significantly higher than the interictal LF/HF ratio (P = 0.048). Preictal (P = 0.048), and postictal (P = 0.019) root mean square of successive differences (RMSSDs) of patients were lower than the sleep RMSSD of controls. These results indicated dominant sympathetic activity. RMSSD from interictal to preictal periods tended to be higher in IAS with decreased HR than in IAS with increased HR alone (P = 0.066). The postictal RMSSD showed tendency to be higher in IAS with decreased HR than in IAS with increased HR alone (P = 0.088). The decreased HR and increased RMSSD suggested not only sympathetic activity but also escalated parasympathetic activity in IAS. CONCLUSIONS Infants with unexpected apneic events should be monitored with prolonged vEEG, SpO2, and ECG. Abnormal HRV in infants with apneic seizures might indicate additional autonomic dysregulation in IAS.

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Background: Altered pattern of respiration has been shown to affect both the cardiac as well as cortical activity, which is the basis of central–autonomic dual interaction concept. On the other hand, effect of this association between altered breathing with slow cortical activity, that is, electroencephalography (EEG) theta waves (associated with learning and relaxed alertness) on the cardiac autonomic balance is largely unclear. Objective: The study aims to understand this interaction in response to altered respiratory patterns, for example, voluntary apnea, bradypnea, and tachypnea in terms of EEG and heart rate variability (HRV) correlates in normal healthy subjects. Methods: This study was conducted on 32 adult male subjects. EEG from F3, F4, P3, P4, O1 and O2 cortical areas and Lead II electrocardiography for HRV analysis was continuously recorded during aforesaid respiratory interventions. Power spectral analysis of EEG for theta waves and HRV measures, that is, RMSSD, pNN50, HF, LF, and LF/HF was calculated as % change taking resting value as 100%. Results: Apnea caused decrease in theta power, whereas an increase in LF/HF was observed in HRV. Bradypnea on the other hand, did not elicit any significant change in power of theta waves. However, decreased RMSSD and pNN50 were observed in HRV. Tachypnea led to increase in theta power with HRV depicting significantly decreased RMSSD and pNN50. Besides, significant correlation between EEG and HRV measures was found during tachypnea, which shifted toward posterior cortical sites as compared to resting condition. Conclusion: Various altered respiratory patterns caused either depressed parasympathetic or increased sympathetic output, whereas increased theta power along with posterior shift of correlation between theta power and HRV measures observed during post tachypnea might be due to involvement of global brain areas due to respiration-coupled neuronal activity. Thus, a definite link between cortical activity and autonomic output in relation to altered respiratory patterns may be suggested.

Heart rate variability (HRV), the variation of the period between consecutive heartbeats, is an established tool for assessing physiological indicators such as stress and fatigue. In non-clinical settings, HRV is often computed from signals acquired using wearable devices that are susceptible to strong artifacts. In EEG (electroencephalography) experiments, these devices must be synchronized with the EEG and typically provide intermittent interbeat interval information based on proprietary artifact-removal algorithms. This paper describes an automated algorithm that uses the output of an EEG sensor mounted on a subject’s chest to accurately detect interbeat intervals and to calculate time-varying metrics. The algorithm is designed for raw signals and is robust to artifacts, resulting in fine-grained capture of HRV that is synchronized with the EEG. An open-source MATLAB toolbox (EEG-Beats) is available to calculate interbeat intervals and many standard HRV time and frequency indicators. EEG-Beats is designed to run in a completely automated fashion on an entire study without manual intervention. The paper applies EEG-Beats to EKG signals measured with an EEG sensor in a large longitudinal study (17 subjects, 6 tasks, 854 datasets). The toolbox is available at https://github.com/VisLab/EEG-Beats.

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Functional seizures (FS) are common, and distinguishing FS from epileptic seizures (ES) can be challenging despite video telemetry (VT), and the pathophysiology is not well-understood. The heartbeat evoked potential (HEP) reflects the central processing of cardiac signals and bodily attention. Our group previously demonstrated that HEP amplitude may differentiate FS from ES. Here, we sought to replicate these HEP findings in an independent sample and test if HEP can characterise semiological subtypes of FS and ES. Lastly, we examined whether HEP modulation was associated with real-time bodily symptom reporting in functional and vasovagal syncope. In a first study, we identified FS or ES from VT recordings of patients. We categorised FS and ES into 'motile' or 'non-motile' according to semiology with predominantly positive motor features, or with subjective sensory or negative motor features, respectively. HEP amplitude was calculated by averaging EEG segments time-locked to ECG R-waves, correcting for pre-R wave baseline, to quantify the average voltage between 0.455 and 0.595 seconds after the R wave. We compared HEP amplitude across FS and ES of equivalent semiology. For the second study, we quantified the HEP amplitude in patients with functional syncope and vasovagal syncope, from EEG recorded during head-up tilt procedure, focusing on reported symptom onset rather than the onset of clinical events. Sixty-three and sixty individuals were included in study one and two, respectively. HEP amplitude distinguished FS from ES with matched semiology: In non-motile FS, HEP become more positive at the scalp from the interictal to preictal period, whereas in motile FS, the HEP became less positive at the scalp. ES were not associated with significant changes in HEP. In individuals with functional syncope, a more positive HEP was associated with reported bodily symptoms, but not for non-bodily (psychological or emotional) symptoms. In individuals with vasovagal syncope, a less positive HEP was associated with (most) bodily symptoms. These findings indicate that FS semiology relates to patterns of bodily attention, as reflected by HEP amplitude change. Non-motile FS were associated with preictal increased HEP amplitude, suggesting greater bodily awareness, whereas motile FS showed a decrease in HEP amplitude. The increased HEP amplitude associated with bodily symptom reporting in functional syncope further supports a role for the HEP in tracking interoceptive processing and bodily attention. Together, this highlights the potential utility of the HEP as a marker for distinguishing FS from ES and for probing the interoceptive mechanisms underlying functional symptoms.

Summary Respiration and cardiac activity intricately interact through complex physiological mechanisms. The heartbeat-evoked potential (HEP) is an EEG fluctuation reflecting the cortical processing of cardiac signals. We recently found higher HEP amplitude during exhalation than inhalation during a task involving attention to cardiac sensations. This may have been due to reduced cardiac perception during inhalation and heightened perception during exhalation through attentional mechanisms. To investigate relationships between HEP, attention, and respiration, we introduced an experimental setup that included tasks related to cardiac and respiratory interoceptive and exteroceptive attention. Results revealed HEP amplitude increases during the interoceptive tasks over fronto-central electrodes. When respiratory phases were taken into account, HEP increases were primarily driven by heartbeats recorded during exhalation, specifically during the cardiac interoceptive task, while inhalation had minimal impact. These findings emphasize the role of respiration in cardiac interoceptive attention and could have implications for respiratory interventions to fine-tune cardiac interoception.

Self-disturbance is considered a core feature underlying the psychopathology of schizophrenia. Interoception has an important role in the development of a sense of self, leading to increased interest in the potential contribution of abnormal interoception to self-disturbances in schizophrenia. Several neuropsychological studies have demonstrated aberrant interoception in schizophrenia. However, cortical interoceptive processing has not yet been thoroughly investigated. Thus, we sought to examine resting-state heartbeat-evoked potential (HEP) in this population. We hypothesized that patients with schizophrenia would exhibit significant alterations in HEP compared to healthy controls (HCs). In this cross-sectional electroencephalogram (EEG) study, we compared the HEPs between age- and sex-matched groups of patients with schizophrenia and HCs. A 10-min resting-state EEG with eyes closed and an electrocardiogram (ECG) were recorded and analyzed for the time window of 450 ms to 500 ms after an ECG R peak. A positive HEP shift was observed in the frontal-central regions (F [1, 82] = 7.402, p = 0.008, partial η2 = 0.009) in patients with schizophrenia (n = 61) when compared with HCs (n = 31) after adjusting for confounders such as age, sex, and heart rate. A cluster-based correction analysis revealed that the HEP around the right frontal area (Fp2, F4, and F8) showed the most significant group differences (F [1, 82] = 10.079, p = 0.002, partial η2 = 0.021), with a peak at the F4 electrode site (F [1, 82] = 12.646, p < 0.001, partial η2 = 0.069). We observed no correlation between HEP and symptoms in patients with schizophrenia. A positive shift of HEP during the late component could reflect a trait abnormality in schizophrenia. Further research is required to determine the association between the altered cortical interoceptive processing indexed with HEP and self-disturbances in schizophrenia.

Objective. The heartbeat evoked potential (HEP) is a brain response time-locked to the heartbeat and a potential marker of interoceptive processing that may be generated in the insula and dorsal anterior cingulate cortex (dACC). Low-intensity focused ultrasound (LIFU) can selectively modulate sub-regions of the insula and dACC to better understand their contributions to the HEP. Methods. Healthy participants (n=16) received stereotaxically targeted LIFU to the anterior insula (AI), posterior insula (PI), dACC, or Sham at rest during continuous electroencephalography (EEG) and electrocardiography (ECG) recording on separate days. Primary outcome was HEP amplitudes. Relationships between LIFU pressure and HEP changes and effects of LIFU on heart rate and heart rate variability (HRV) were also explored. Results. Relative to sham, LIFU to the PI, but not AI or dACC, decreased HEP amplitudes; PI effects were partially explained by increased LIFU pressure. LIFU did not affect heart rate or HRV. Conclusions. These results demonstrate the ability to modulate HEP amplitudes via non-invasive targeting of key interoceptive brain regions. Significance. Our findings have implications for the causal role of these areas in bottom-up heart-brain communication that could guide future work investigating the HEP as a marker of interoceptive processing in healthy and clinical populations.

Purpose: Interoceptions are a combination of sensation, integration, and interpretation of internal bodily signals. Interoceptions are bidirectionally related to the human being mental and physiological health, and well-being. Sleep and different interoceptive modalities are proven to share common relations. Heartbeat Evoked Potential (HEP) is known as a robust readout to interoceptive processes. In this study, we focused on the relation between HEP modulations and sleep-related disorders. Materials and Methods: We investigated four different sleep-related disorders, including insomnia, rapid eye movement behavior disorder, periodic limb movements and nocturnal frontal lobe epilepsy, and provided HEP signals of multiple Electroencephalogram (EEG) channels over the right hemisphere to compare these disorders with the control group. Here, we investigated and compared the results of 35 subjects, including seven subjects for the control group and seven subjects for each of above-mentioned sleep disorders. Results: By comparing HEP responses of the control group with sleep-related patients’ groups, statistically significant HEP differences were detected over right hemisphere EEG channels, including FP2, F4, C4, P4, and O2 channels. These significant differences were also observed over the grand average HEP amplitude activity of channels over the right hemisphere in the sleep-related disorders as well. Conclusion: Our results between the control group and groups of patients suffering from sleep-related disorders demonstrated that during different stages of sleep, HEPs show significant differences over multiple right hemisphere EEG channels. Interestingly, by comparing different sleep disorders with each other, we observed that each of these HEP differences’ patterns over specific channels and during certain sleep stages bear considerable resemblances to each other.

Unconsciousness in severe acquired brain injury (sABI) patients occurs with different cognitive and neural profiles. Perturbational approaches, which enable the estimation of proxies for brain reorganization, have added a new avenue for investigating the non‐behavioural diagnosis of consciousness. In this prospective observational study, we conducted a comparative analysis of the topological patterns of heartbeat‐evoked potentials (HEP) between patients experiencing a prolonged disorder of consciousness (pDoC) and patients emerging from a minimally consciousness state (eMCS). A total of 219 sABI patients were enrolled, each undergoing a synchronous EEG‐ECG resting‐state recording, together with a standardized consciousness diagnosis. A number of graph metrics were computed before/after the HEP (Before/After) using the R‐peak on the ECG signal. The peak value of the global field power of the HEP was found to be significantly higher in eMCS patients with no difference in latency. Power spectrum was not able to discriminate consciousness neither Before nor After. Node assortativity and global efficiency were found to vary with different trends at unconsciousness. Lastly, the Perturbational Complexity Index of the HEP was found to be significantly higher in eMCS patients compared with pDoC. Given that cortical elaboration of peripheral inputs may serve as a non‐behavioural determinant of consciousness, we have devised a low‐cost and translatable technique capable of estimating causal proxies of brain functionality with an endogenous, non‐invasive stimulus. Thus, we present an effective means to enhance consciousness assessment by incorporating the interaction between the autonomic nervous system (ANS) and central nervous system (CNS) into the loop.

Background The heartbeat evoked potential (HEP) is a brain response time-locked to the heartbeat and a potential marker of interoceptive processing. The insula and dorsal anterior cingulate cortex (dACC) are brain regions that may be involved in generating the HEP. Low-intensity focused ultrasound (LIFU) is a non-invasive neuromodulation technique that can selectively target sub-regions of the insula and dACC to better understand their contributions to the HEP. Objective Proof-of-concept study to determine whether LIFU modulation of the anterior insula (AI), posterior insula (PI), and dACC influences the HEP. Methods In a within-subject, repeated-measures design, healthy human participants (n=16) received 10 minutes of stereotaxically targeted LIFU to the AI, PI, dACC or Sham at rest during continuous electroencephalography (EEG) and electrocardiography (ECG) recording on separate days. Primary outcome was change in HEP amplitudes. Relationships between LIFU pressure and HEP changes were examined using linear mixed modelling. Peripheral indices of visceromotor output including heart rate and heart rate variability (HRV) were explored between conditions. Results Relative to sham, LIFU to the PI, but not AI or dACC, decreased HEP amplitudes; this was partially explained by increased LIFU pressure. LIFU did not affect time or frequency dependent measures of HRV. Conclusions These results demonstrate the ability to modulate HEP amplitudes via non-invasive targeting of key interoceptive brain regions. Our findings have implications for the causal role of these areas in bottom-up heart-brain communication that could guide future work investigating the HEP as a marker of interoceptive processing in healthy and clinical populations.

Background Patients with functional seizures (FS) can experience dissociation (depersonalisation) before their seizures. Depersonalisation reflects disembodiment, which may be related to changes in interoceptive processing. The heartbeat-evoked potential (HEP) is an electroencephalogram (EEG) marker of interoceptive processing. Aim To assess whether alterations in interoceptive processing indexed by HEP occur prior to FS and compare this with epileptic seizures (ES). Methods HEP amplitudes were calculated from EEG during video-EEG monitoring in 25 patients with FS and 19 patients with ES, and were compared between interictal and preictal states. HEP amplitude difference was calculated as preictal HEP amplitude minus interictal HEP amplitude. A receiver operating characteristic (ROC) curve analysis was used to evaluate the diagnostic performance of HEP amplitude difference in discriminating FS from ES. Results The FS group demonstrated a significant reduction in HEP amplitude between interictal and preictal states at F8 (effect size rB=0.612, false discovery rate (FDR)-corrected q=0.030) and C4 (rB=0.600, FDR-corrected q=0.035). No differences in HEP amplitude were found between states in the ES group. Between diagnostic groups, HEP amplitude difference differed between the FS and ES groups at F8 (rB=0.423, FDR-corrected q=0.085) and C4 (rB=0.457, FDR-corrected q=0.085). Using HEP amplitude difference at frontal and central electrodes plus sex, we found that the ROC curve demonstrated an area under the curve of 0.893, with sensitivity=0.840 and specificity=0.842. Conclusion Our data support the notion that aberrant interoception occurs prior to FS. Changes in HEP amplitude may reflect a neurophysiological biomarker of FS and may have diagnostic utility in differentiating FS and ES.

The heartbeat-evoked potential (HEP), a cortical response to heartbeats and neural marker of interoception, is clinically relevant but heavily contaminated by scalp cardiac-artefact (CA), making reliable distinction challenging. Because HEP and cardiac potentials are anatomically distinct, they may be separable via beamforming, a source localisation method isolating brain activity while suppressing external interference. Here, the first known validation of EEG beamforming for HEP source reconstruction was attempted, quantifying waveform recovery and spatial accuracy using simulated data. Linearly constrained minimal variance (LCMV) beamforming was used (A) to test signal recovery, 128-channel EEG was simulated for three models with known HEP waveforms: (1) a single right insula (R-Ins) HEP; (2) temporally distinct HEPs in R-Ins and right anterior cingulate cortex (R-ACC); (3) overlapping HEPs in both regions. Recovery was assessed by correlating reconstructed and known waveforms. (B) To test CA suppression, CA from brain-dead isoelectric EEGs was integrated to simulations, with varying source (-10-50dB) and sensor (0-30dB) signal-to-noise ratios (SNR). (C) To analyse empirical EEG from hypertensive and anxiety individuals (n=106). Null-space projection, using subject-specific QRS waveforms and its temporal derivative, removed residual CA in source waveforms. In model-1, beamforming achieved near-perfect recovery without CA (r>0.99, error=0mm) at -30dB source SNR, remaining robust in the presence of CA (r=0.72-0.94, error=0-5.7mm) but degrading below -20dB (r<0.3, error=27mm). Models 2 and 3 showed similar recovery but introduced R-ACC-to-R-Ins leakage. In empirical data, beamforming revealed significant HEP activity in R-Ins and R-ACC (p<0.001). A significant late R-ACC HEP difference (250-500ms post-R-peak) between hypertension and controls emerged after excluding low-SNR subjects and strengthened following QRS cleaning (p=0.035, d=0.96). LCMV beamforming reliably recovers simulated HEPs while suppressing CA given sufficient SNR. Applied empirically, beamforming detected clinically meaningful group differences in interoceptive regions (R-Ins/R-ACC), offering a source-level approach to separate HEP from CA that complements sensor-level methods.

Introduction The heartbeat-evoked potential (HEP) is widely interpreted as an EEG/MEG marker of cortical processing of cardiac afferent signals. A central methodological challenge in HEP research is separating genuine cortical responses to cardiac afferent signals from heartbeat-related cardiac field artefact (CA). Because both neural HEP activity and cardiac artefact are time-locked to the same heartbeat, conventional averaging and blind-source correction methods lack an observable cardiac-only reference, making their separation difficult to validate. Methods Individuals with isoelectric-appearing cerebral activity i.e. those with significant brain injury leading to a lack of detectable brain activity at the scalp, have an absence of recordable HEP signal yet preserved cardiac field artifact in brain. We used clinically acquired EEGs with isoelectric-appearing cerebral activity and preserved cardiac activity as a negative control for non-neural heartbeat-locked scalp signal. Thirty-nine isoelectric participants were compared with 118 heart-rate-matched participants whose clinical EEGs were reported as normal. Participant-level heartbeat averages were analysed using covariate-adjusted spatiotemporal permutation tests. We characterised interindividual cardiac-artefact morphology and tested the primary group contrast across average-reference, surface-Laplacian and cardiac-artefact-directed ICA analyses, with pseudotrial correction for heartbeat-independent EEG activity. Results Cardiac-artefact morphology varied markedly across isoelectric participants, was predominantly posterior or posterolateral, and was not predicted by ECG morphology. We attempted delineation of the HEP from the CA by comparing the two groups, revealing a negative frontocentral cluster that persisted in control participants at 90–365 ms, p=0.16 and a posterior cluster at 145–310 ms, p=0.03. The topography of the first cluster aligns with the spatiotemporal characteristics of the early window of the HEP as identified in the literature. This cluster was negative in polarity at the scalp. After independent component analysis, the same comparison produced a less prominent cluster. There was no significant group difference in ECG. Conclusion These findings provide an important first empirical scalp-level dissociation of the early HEP from CA using an inversion-of-ground-truth approach. A cluster of difference consistent with typical distributions of the early HEP, suggests that this part of the HEP is a distinct, frontocentral, negative cortical response. The effects of conventional independent component analysis for removing the CA suggest a risk of also removing veridical HEP signal with standard correction methods. SIGNIFICANCE STATEMENT This study uses an inversion of ground truth approach to characterise the cardiac artefact seen in EEG in the absence of brain activity, and thereby better characterise the heartbeat-evoked potential. This is of benefit for future interoceptive research as characterisation of the artefact can future approaches to preprocessing, and knowledge of the polarity of an evoked potential allows inference about whether a change in the amplitude represents more or less event-related activity. HIGHLIGHTS Heartbeat-related artefact varies in distribution between individuals. The heartbeat evoked potential is distinct from the cardiac artefact. The early part of the heartbeat evoked potential is negative.

Objective: The comorbidity between Attention Deficit/Hyperactivity Disorder (ADHD) and Autism Spectrum Disorder (ASD) is a complex, poorly understood phenomenon. This study investigates the neural mechanisms underlying this comorbidity through the lens of brain-heart interactions, integrating distinct physiological systems to elucidate potential pathophysiological differences. Methods: Resting-state EEG and ECG were recorded from 63 children with ADHD, 44 with comorbid ADHD-ASD, and an exploratory cohort of 23 with pure ASD. Heartbeat-Evoked Potentials (HEP) and Heartbeat-Induced Networks (HINs) were computed. Mediation analysis explored whether brain connectivity features mediate the observed HEP waveform differences. Results: Compared to the comorbid group, the ADHD group exhibited attenuated negative HEP amplitudes (150–200 ms post-R-peak). The comorbid group also demonstrated distinct characteristic path lengths in theta and alpha bands (eyes-open) and higher theta global efficiency (eyes-closed). Importantly, theta global efficiency mediated the group differences in HEP amplitudes (p=0.016). Exploratory analyses with the pure ASD cohort confirmed these alterations were comorbidity-specific and not strictly driven by ASD traits. Conclusion: This study reveals distinct resting-state brain-heart interaction patterns in children with pure ADHD compared to comorbid ADHD-ASD. The enhanced brain network efficiency observed in the comorbid group suggests the presence of distinct neural network reorganizations arising from the complex interaction of dual traits, providing neurophysiological evidence that challenges the simple additive model. Significance: By characterizing these divergent neural signatures, this work contributes to unraveling the complex mechanisms of comorbidity and highlights potential biomarkers for more precise clinical diagnosis and intervention.

Introduction: Currently, major depressive disorder (MDD) treatment plans are based on trial-and-error, and remission rates remain low. A strategy to replace trial-and-error and increase remission rates could be treatment stratification. We explored the heartbeat-evoked potential (HEP) as a biomarker for treatment stratification to either antidepressant medication or rTMS treatment. Methods: Two datasets were analyzed: (1) the International Study to Predict Optimized Treatment in Depression (iSPOT-D; n = 1,008 MDD patients, randomized to escitalopram, sertraline, or venlafaxine, and n = 336 healthy controls) and (2) a multi-site, open-label rTMS study (n = 196). The primary outcome measure was remission. Cardiac field artifacts were removed from the baseline EEG using independent component analysis (ICA). The HEP-peak was detected in a bandwidth of 20 ms around 8 ms and 270 ms (N8, N270) after the R-peak of the electrocardiogram signal. Differences between remitters and non-remitters were statistically assessed by repeated-measures ANOVAs for electrodes Fp1, Cz, and Oz. Results: In the venlafaxine subgroup, remitters showed a lower HEP around the N8 peak than non-remitters on electrode site Cz (p = 0.004; d = 0.497). The rTMS group showed a non-significant difference in the opposite direction (d = −0.051). Retrospective stratification to one of the treatments based on the HEP resulted in enhanced treatment outcome prediction for venlafaxine (+22.98%) and rTMS (+10.66%). Conclusion: These data suggest that the HEP could be used as a stratification biomarker between venlafaxine and rTMS; however, future out-of-sample replication is warranted.

OBJECTIVE A deficit in interoception - the ability to perceive, interpret and integrate afferent signals about the physiological state of the body - has been shown in Anorexia Nervosa (AN), and linked to altered hunger sensations, body dysmorphia, and abnormal emotional awareness. The present high-density electroencephalography (hdEEG) study aims to assess cardiac interoception in AN and to investigate its neural correlates, using an objective neurophysiological measure. METHOD Heartbeat-evoked potentials (HEPs) were computed from 5 min of resting-state EEG and electrocardiogram (ECG) data and compared between individuals with AN (N = 22) and healthy controls (HC) (N = 19) with waveform, topographic, and source imaging analyses. RESULTS Differences in the cortical representation of heartbeats were present between AN and HC at a time window of 332-348 ms after the ECG R-peak. Source imaging analyses revealed a right-sided hypoactivation in AN of brain regions linked to interoceptive processing, such as the anterior cingulate and orbitofrontal areas. CONCLUSIONS To the best of our knowledge, this is the first study using hdEEG to localise the underlying sources of HEPs in AN. Results point to altered interoceptive processing during resting-state in AN. As our participants had a short duration of illness, this might not be the consequence of prolonged starvation. Interventions targeted at interoception could provide an additional tool to facilitate recovery.

The Heartbeat Evoked Potential (HEP) is an EEG fluctuation that reflects the cortical processing of cardiac signals. HEP amplitude increases during various tasks involving cardiac interoception. Recent research has also indicated that HEP amplitude and cardiac interoceptive accuracy are higher during exhalation compared to inhalation. This difference may be due to the suppression of heartbeat-related sensations during inhalation and the amplification of sensations during exhalation through attentional mechanisms. Despite significant advancements in HEP research, the interactions between the HEP, interoceptive attention, and respiration are still unclear. In this study, we developed a novel experimental paradigm to investigate the relationship between HEP amplitude and respiratory phases during tasks that involve attention to cardiac interoception, non-cardiac interoception (specifically, respiration), and exteroceptive stimuli. The tasks included the Heartbeat Counting Task and the Breath Counting Task as interoceptive tasks, as well as the Cardiac-Tone Counting Task and the Breath-Tone Counting Task as exteroceptive tasks. Results demonstrated significant increases in HEP amplitude during the Heartbeat Counting Task compared to the Cardiac-Tone Counting Task and the Breath Counting Task, mostly observed over fronto-central electrodes in a late time-window. Notably, the amplitude increases during the Heartbeat Counting Task were primarily driven by HEPs recorded during exhalation, while inhalation had minimal impact. These findings align with the predictive coding model of interoceptive perception, suggesting that HEP amplitude reflects a precision-weighting process of prediction errors related to cardiac sensations that is specifically influenced by attention directed toward the heart. Furthermore, our findings emphasize the crucial role of exhalation in this precision-weighting process. These results may have considerable implications for the development of respiratory interventions to fine-tune cardiac interoception.

OBJECTIVE To investigate the neurophysiological effects of minimal social co-presence on the Heartbeat Evoked Potential (HEP) and EEG dynamics, providing a normative baseline for altered interoception in clinical disorders. METHODS Thirty healthy adults performed a repetitive crafting task in "alone" and "parallel social presence" conditions. EEG and ECG were recorded, and HEPs plus time-frequency dynamics were compared using permutation testing. Changes in cardiac vagal activity and subjective stress were also assessed. RESULTS Parallel co-presence significantly altered HEP morphology (reduced frontal negativity, central-parietal positivity) and EEG power (beta, theta, alpha bands). Only under parallel condition, frontal HEP amplitude correlated with increased cardiac vagal activity (r = 0.39) and reduced subjective stress (rS = -0.55). CONCLUSION Minimal social context alters the brain's processing of cardiac signals and its autonomic relationship in healthy young adults, establishing a neurophysiological benchmark for brain-body-society interactions. As this study used a non-clinical sample, the findings are not direct evidence of HEP's clinical utility. This work provides a foundational baseline for future comparative studies needed to determine its potential as a clinical biomarker. SIGNIFICANCE This study is the first to demonstrate normative neurophysiological effects of minimal social presence on HEP, providing critical evidence and guidance for future clinical research.

Cardiac interoception exhibits tight coupling with brain activity, deeply engaging in emotional behavior. However, the neural mechanisms underlying how heart activity influences brain emotional processing remain poorly understood. This study introduced the heartbeat oscillatory potential (HOP), a novel EEG-based index time-locked to ∼0.1 Hz heartbeat oscillations, and examined the change of HOP during 0.1 Hz slow-paced breathing (SPB). Resting-state data from 108 healthy adults revealed that HOP was involved in the frontal and parietal cortices. Data collected from 37 subjects showed that SPB increased HOP in a spatial- and phase-dependent manner, with increased HOP in the right prefrontal cortex around the peak of the ∼0.1 Hz heartbeat oscillations, mediated the association between the heartbeat oscillations and enhanced emotional control. These findings underscore the pivotal role of the right prefrontal cortex in linking cardiac interoception, providing insights into the benefits of SPB on emotional control from a heart-brain interaction perspective.

Generalized anxiety disorder (GAD) is one of the most common anxiety disorders. The brain’s dysfunctional processing of interoceptive information is increasingly recognized as an important component of anxiety disorders. However, the neural mechanisms remain insufficiently understood. In the present study, patients with GAD and healthy control participants underwent an eyes-closed (EC) resting state (interoception) and eyes-open (EO) resting state (exteroception) without paying conscious attention to heartbeat. Electrocardiography (ECG) and electroencephalography (EEG) signals were recorded at the same time. The results show that in healthy controls, the heartbeat-evoked brain potential (HEP) was modulated by the conditions, with a significantly higher amplitude under EC than EO, while this was not the case in GAD patients. Further analysis revealed that the dysfunction of HEP modulation in GAD patients may be attributed to excessive interoceptive processing under EO, with a marginally higher HEP in GAD than in the healthy controls. Finally, the right prefrontal HEP amplitude during EC condition was significantly correlated with the severity of the patients’ anxiety symptoms. Our results suggest that altered cortical processing of interoceptive signals may play an important role in the pathophysiology of generalized anxiety disorder.

Nightmares are characterized by the experience of strong negative emotions occurring mainly during REM sleep. Some people suffer from nightmare disorder, which is defined by the repeated occurrence of nightmares and by significant distress in wakefulness. Yet, whether frequent nightmares relate to a general increase in emotional reactivity or arousal during sleep remains unclear. To address this question, we recorded heartbeat-evoked potentials (HEPs) during wakefulness, NREM and REM sleep in patients with nightmare disorder and healthy participants. The HEP represents a cortical (EEG) response to the heartbeat and indexes brain-body interactions, such as interoceptive processing and intrinsic levels of arousal. HEP amplitude is typically increased during states of high emotional arousal and motivation, and is decreased in depression. Here we compared the amplitude of HEPs between nightmare patients and healthy controls separately during AWAKE, NREM, REM periods, and found higher HEP amplitude in nightmare patients compared to healthy controls over a cluster of frontal regions only during REM sleep. This effect was not paralleled by any group difference in cardiac control measures (e.g. heart rate variability, interbeat interval). These findings corroborate the notion that nightmares are essentially a REM pathology and suggest that increased emotional arousal during REM sleep, as measured by HEP, is a physiological condition responsible for frequent nightmares. This result also supports that HEP may be used as a biomarker of increased emotional and sensory processing during REM sleep in these patients.

Obstructive Sleep Apnea (OSA) is a systemic disorder with far-reaching neuro-cardiac consequences that extend beyond sleep. While autonomic and cortical dysregulation in OSA have been studied, the temporal interactions between cortical interoceptive processing and autonomic variability during wakefulness remain poorly understood. This study investigates the severity-dependent alterations in cortical response to cardiac activity and the directionality of HEP–HRV temporal interactions in 366 adults stratified by OSA severity. We analyzed Heartbeat-Evoked Potentials (HEPs) derived from wakeful electroencephalography (EEG) as a marker of cortical interoception and examined their coupling dynamics with heart rate variability (HRV) features using Granger Causality (GC). Our results reveal a significant, monotonic increase in HEP amplitude over central (C3/C4, 320–480 ms) and frontal (F3/F4, 540–680 ms) regions with escalating OSA severity (p < 0.001), indicating heightened cortical sensitivity to cardiac afferent signals. Concurrently, HRV analysis shows progressive autonomic dysfunction, marked by reduced RMSSD, SDNN, SD1, and SD2 in severe OSA (p < 0.01). Crucially, GC analysis uncovers a severity-dependent reorganization of temporal predictive influence: while control individuals exhibit relatively balanced bidirectional interactions, severe OSA displays a predominant strength-based HRV \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\rightarrow$$\end{document} HEP influence, particularly in frontal regions and parasympathetic-related metrics. These GC-derived findings reflect altered temporal dynamics between cortical interoceptive responses and autonomic variability. Overall, the results demonstrate that OSA induces a persistent, severity-dependent disruption of cortical–autonomic integration during wakefulness, positioning HEP amplitude and GC-based temporal coupling measures as promising non-invasive biomarkers for assessing the systemic neurological impact of OSA.

Panic disorder (PD) is associated with altered interoceptive experience, but it remains unclear how self-reported interoception relates to neurophysiological indices of bodily-signal processing. This study examined whether associations between self-reported interoception and resting-state electroencephalography (EEG) markers differ between participants with PD and healthy controls (HC). This analysis included 19 PD participants and 21 HCs from a previously published resting-state EEG dataset. Self-reported interoception was assessed using the Body Perception Questionnaire-Very Short Form (BPQ-VSF) and the Multidimensional Assessment of Interoceptive Awareness (MAIA). Resting-state EEG signals were recorded during eyes-closed and eyes-open conditions. Heartbeat-evoked potentials (HEP) and relative band power (RBP) were extracted to index cardiac-related cortical processing and frequency-specific resting-state activity. Associations between self-report measures and EEG indices were examined using Pearson correlations with 95% confidence intervals, Bayes factors, and false-discovery-rate (FDR) correction across scalp regions within predefined analysis families. Age-adjusted sensitivity analyses and group × self-report interaction models were used to assess robustness and formally test group differences in association patterns. Compared with HCs, PD participants reported higher body-focused attention and lower scores on several adaptive interoceptive dimensions, including Not-Distracting, Not-Worrying, and Trusting. In the 235—301 ms HEP window, age-adjusted interaction models showed that BPQ-VSF HEP coupling differed between groups, especially under eyes-open conditions. Within-group analyses showed an exploratory opposite-direction BPQ-VSF HEP pattern, with positive associations in HC and negative associations in PD. The clearest FDR-corrected HEP association within panic disorder involved Not-Worrying under eyes closed. RBP analyses showed PD-concentrated \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\theta$$\end{document} (theta)- and \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\beta$$\end{document} (beta)-band association profiles. Trusting was positively associated with \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\theta$$\end{document} power in PD, and BPQ-VSF showed positive \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\beta$$\end{document}-band associations under eyes closed that remained robust after age adjustment. Not-Distracting showed negative \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\theta$$\end{document}-band associations in the primary correlation analyses, but these were attenuated after controlling for age. These findings provide preliminary FDR-controlled and age-informed evidence that PD involves altered, condition- and frequency-dependent mappings between subjective bodily experience and resting-state EEG markers. The clearest formal group-difference evidence was observed for BPQ-VSF HEP coupling, whereas PD-specific associations were most robust for Not-Worrying-HEP, Trusting-\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\theta$$\end{document} power, and BPQ-VSF \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\beta$$\end{document} power. Given the modest sample size and exploratory correlational design, replication in larger samples is needed.

Heartbeat-evoked response (HER) is considered a neural correlate of interoceptive information processing. While HERs are modulated during interoception tasks, distinguishing the unique contributions of conscious cardiac awareness (the subjective recognition of a heartbeat) from interoceptive attention (the state of focusing on one's heartbeat) remains challenging. To dissociate these processes, we developed a novel task where participants (N=29) reported the timing of perceived heartbeats using a clock hand, followed by objective feedback. We analyzed electroencephalography data using HERs, phase-based connectivity, and microstate analysis. Our results revealed distinct neural mechanisms underlying conscious cardiac awareness and interoceptive attention. Specifically, conscious cardiac awareness was associated with earlier HER modulation localized to the dorsomedial prefrontal cortex, increased beta synchronization between key nodes of the interoceptive network, and a higher prevalence of microstates involving temporal/opercular regions. Conversely, interoceptive attention modulated HERs at later latencies, localized to the somatosensory cortex and insula, and preferentially engaged microstates associated with the visual and frontoparietal network. These findings confirm the multifaceted nature of HERs. We argue that HER modulations reflect not only the optimization of precision via attention but also the emergence of cardiac awareness through the integration of bottom-up afferent inputs and top-down predictions.

Abstract Each heartbeat creates an electro-magnetic field that propagates throughout the body and is superimposed on measured brain activity in electroencephalography (EEG) and magnetoencephalography (MEG) recordings. Being several orders of magnitude larger than a typical evoked response in the brain, the Cardiac Field Artefact (CFA) is especially problematic in studies where cortical activity time locked to the cardiac cycle is of interest, such as in interoception research. In this work, we develop a model-based CFA correction method, which exploits the fact that the magnetic field propagates relatively evenly through the mostly diamagnetic tissue of the human body, and that in studies using wearable optically pumped magnetometers (OP-MEG), participants are free to rotate their head. Free head rotation decouples the heart’s magnetic field from that of the brain, allowing for the creation of a cardiac dipole moment (CDM) estimate, that is, an estimate of the electrical current flow in the heart, which is minimally confounded by concurrent neuronal activity in the brain. We show in simulations that, given information about the relative position and orientation of head and chest, this CDM estimate can be used to predict and correct the sensor-level CFA by dynamically accounting for the spatial relationship between heart, brain, and sensors. We further provide an empirical proof-of-principle demonstration of the pipeline in a single participant using recordings with intermittent head movement, tracked by optical motion capture, as well as an established auditory response paradigm with well-characterized response pattern.

The brain continuously integrates information from the external environment (exteroception) and the internal bodily milieu (interoception). However, how the balance between these two processing streams shifts across vigilance states with differing levels of environmental responsiveness remains poorly understood. Here, we examined neural responses to external auditory and internal cardiac signals across wakefulness and rapid eye movement (REM) sleep microstates-tonic and phasic REM-which are characterized by progressively reduced responsiveness to external stimulation. High-density electroencephalography (EEG) was recorded in healthy participants (n = 25). Auditory evoked potentials (AEPs) and heartbeat evoked potentials (HEPs) served as indices of exteroception and interoception, respectively, and were compared across vigilance states. AEPs progressively decreased from wakefulness to tonic REM and were most attenuated during phasic REM. In contrast, HEPs were preserved across REM microstates and were enhanced relative to wakefulness, indicating sustained-and even amplified-processing of cardiac signals during REM sleep. To quantify the relative weighting of external and internal signals, we introduce an audio-cardio index, defined as the ratio of auditory to cardiac neural responses. This index decreased systematically across vigilance states, revealing a graded shift from auditory-oriented processing during wakefulness to cardiac-oriented processing during phasic REM, with tonic REM occupying an intermediate position. Together, these findings demonstrate that while responsiveness to auditory stimuli diminishes during phasic REM, the brain continues to prioritize physiologically relevant cardiac signals. The exteroceptive-interoceptive balance may thus provide a novel, mechanistically grounded marker of altered consciousness, which is particularly informative in contexts where behavioral responsiveness cannot be assessed.

Some of the most important integrative control centers for the autonomic nervous system are located in the brainstem and the hypothalamus. However, growing recent neuroimaging evidence support that a set of cortical regions, named the central autonomic network (CAN), is involved in autonomic control and seems to play a major role in continuous autonomic cardiac adjustments to high-level emotional, cognitive or sensorimotor cortical activities. Intracranial explorations during stereo-electroencephalography (SEEG) offer a unique opportunity to address the question of the brain regions involved in heart-brain interaction, by studying: (i) direct cardiac effects produced by the electrical stimulation of specific brain areas; (ii) epileptic seizures inducing cardiac modifications; (iii) cortical regions involved in cardiac interoception and source of cardiac evoked potentials. In this review, we detail the available data assessing cardiac central autonomic regulation using SEEG, address the strengths and also the limitations of this technique in this context, and discuss perspectives. The main cortical regions that emerge from SEEG studies as being involved in cardiac autonomic control are the insula and regions belonging to the limbic system: the amygdala, the hippocampus, and the anterior and mid-cingulate. Although many questions remain, SEEG studies have already demonstrated afferent and efferent interactions between the CAN and the heart. Future studies in SEEG should integrate these afferent and efferent dimensions as well as their interaction with other cortical networks to better understand the functional heart-brain interaction.

Heartbeat-evoked responses (HER), as measured by electroencephalography (EEG) or magnetoencephalography (MEG), represent neural activity time-locked to heartbeats and are widely used as a marker of cardiac interoception in the study of brain-body interactions. However, HER studies report largely variable findings, at least partially due to methodological variability. To achieve consensus on HER processing and improve the reproducibility of findings, the field urgently requires a structured summary of the methods employed so far. To this end, we conducted a systematic review of 132 HER studies using non-invasive M/EEG recordings in humans. Our results reveal substantial heterogeneity across most steps of HER analysis, ranging from data acquisition and preprocessing to HER estimation and statistical approaches. The large diversity in the processing choices is accompanied by considerable proportions of unreported methodological information across reviewed studies, reaching up to 80% for key processing steps. In addition, less than 33% of studies had enough statistical power to reliably detect meta-level HER effects, while their reported spatiotemporal locations varied substantially. We provide a comprehensive reporting and quality control checklist to aid in the development of more standardized procedures, highlighting critical steps for robust HER investigations. Additionally, we share the full extracted dataset, including an interactive version, to support other researchers in answering additional specific questions they may have. We hope that these resources will improve the robustness, reproducibility, and transparency of research in the growing HER field.

No abstract available

Cardioception is the ability of the central nervous system to process signals from the heart. Methods for determining cardioception are still under discussion. In the present study, we considered metrics for cardiac interoceptive accuracy (CA) assessments in three behavioral cardioception tasks − (1) the heartbeat tapping (HTT), (2) the heartbeat discrimination (HDT), and (3) the heartbeat counting (HCT) - and heartbeat evoked potentials (HEP) recorded by an electroencephalography during resting state and the tasks. The study included forty-eight healthy volunteers (25 females, 36 ± 7 age). CA in the HTT assessed using various metrics, except for the metric based on the circular variation between heartbeat and pressing time, positively correlated both with each other and with the metric in the HCT. The HDT showed no correlation with the other tasks. However, none of the metrics showed a clear advantage over the others in their association with the neurophysiological marker of interoception, the mean HEP amplitudes, during task performance or at rest. During all three tasks, the HEP amplitudes (1) did not differ between individuals with high and low CA metrics, (2) was not different from the HEP amplitudes during the resting state, (3) was lower during the HDT compared to the HCT. Thus, our results contribute to the debate on the interaction between behavioral cardioception tasks and the HEP.

The heartbeat-evoked potential (HEP) is a growingly used electrophysiological method to study cardiac interoception; however, cardiovascular influences on these responses are not fully understood. In the present study, we studied the effect of changes in blood pressure through positional modifications and slow-paced breathing on HEP. 18 volunteers (22 ± 1.79 years old) underwent 5-min tasks in upright and supine positions, with spontaneous and slow-paced breathing at 6 cycles/min. We continuously recorded blood pressure, electrocardiography and high-density (128 electrodes) electroencephalography (EEG). We observed an increase in early (around 200 ms) and late (around 400 ms) HEP components in the supine position (p<0.001) and more pronounced with slow-paced breathing (p<0.001). HEP exhibited a frontocentral topography and source modeling indicated mainly insular and cingulate cortex of the early component, which extended to frontal regions during the late component (p<0.05). Diastolic (DBP) and pulse (PBP) blood pressure increased in an upright position (p<0.05), whereas baroreflex sensitivity (BRS) increased in the supine position (p<0.001). Changes in early and late HEP were mainly correlated to changes in PBP (r = 0.55; r = 0.49, respectively p<0.001), DBP (r = -0.35; r = -0.35, p<0.010) and BRS (r = 0.61; r = 0.47, p<0.001). The present study demonstrated modulations of HEP according to cardiovascular activities, suggesting a heightened integration of baroreflex afferents by positional modulations and to a lesser extent in respiratory modulations. These changes should be considered in assessments of interoception in clinical populations.

The investigation of the neural evoked response to the heartbeat quantified using electroencephalography (i.e., heartbeat evoked potentials or HEPs), has gained recent attention in neuroscience, notably as a measure of interoception, the sensory system responding to internal bodily states. One main challenge in measuring HEPs is their susceptibility to cardiac artifacts contamination, including the cardiac field artifact and the pulse artifact (PA), the latter possibly caused by the mechanical pressure of pulsating vessels. Here, we aimed at assessing the impact of PAs on HEPs and auditory evoked potentials (AEPs, a proxy of the neural responses to exteroceptive sensory stimuli). To this aim, we compared two pre-processing pipelines using independent component analysis in healthy volunteers (N=30). The first, standard, pipeline excluded ocular, muscle, sweating-related activity and cardiac-related activity stemming from the cardiac field artifact. The second, pairwise phase consistency (PPC) pipeline, in addition to the removal of the aforementioned components, used the quantitative metric of PPC between independent components and the ECG to remove the cardiac-related PA. We tested how these two pre-processing approaches influenced HEPs and AEPs recorded under diverse neurophysiological conditions (wakefulness, N2, N3, and REM sleep). Comparing the HEPs from the standard and the PPC approaches (cluster-based permutation statistics, p<0.05, two-tailed) revealed a significant effect of PAs, particularly in wakefulness, followed by REM and N2 sleep, with Cohen’s d effect sizes of 1.92, 0.95 and 0.88, respectively. By contrast, PA correction had a negligible effect (p>0.05, two-tailed) on the HEPs in N3 sleep and on the AEPs in all vigilance states. Our results emphasize the need to account for the PA as a significant confounding factor when comparing HEPs across groups with varying vascular or cardiac conditions.

BACKGROUND The heartbeat-evoked potential (HEP) is a brain response to each heartbeat, which is thought to reflect cardiac signaling to central autonomic areas and suggested to be a marker of internal body awareness (eg, interoception). OBJECTIVES Because cardiac communication with central autonomic circuits has been shown to be impaired in patients with atrial fibrillation (AF), we hypothesized that HEPs are attenuated in these patients. METHODS By simultaneous electroencephalography and electrocardiography recordings, HEP was investigated in 56 individuals with persistent AF and 56 control subjects matched for age, sex, and body mass index. RESULTS HEP in control subjects was characterized by right frontotemporal negativity peaking around 300 to 550 ms after the R-peak, consistent with previous studies. In comparison with control subjects, HEP amplitudes were attenuated, and HEP amplitude differences remained significant when matching the samples for heart frequency, stroke volume (assessed by echocardiography), systolic blood pressure, and the amplitude of the T-wave. Effect sizes for the group differences were medium to large (Cohen's d between 0.6 and 0.9). EEG source analysis on HEP amplitude differences pointed to a neural representation within the right insular cortex, an area known as a hub for central autonomic control. CONCLUSIONS The heartbeat-evoked potential is reduced in AF, particularly in the right insula. We speculate that the attenuated HEP in AF may be a marker of impaired heart-brain interactions. Attenuated interoception might furthermore underlie the frequent occurrence of silent AF.

Internal bodily signals, notably the heartbeat, influence our perception of the external world – but the nature of this influence remains unclear. Different frameworks, originating in opposing views of the function of interoception, have developed largely in parallel. One line of evidence (Internal/External Competition) indicates that interoceptive and exteroceptive inputs compete for neural resources. Another line (Self-related Facilitation) shows a link between interoceptive and self-related processing, which might include computing the self-relevance of exteroceptive inputs. We contrasted these accounts within a single experimental task for which they yielded distinct predictions. We measured heartbeat-evoked potentials (HEPs, a measure of cardiac interoception) with EEG, and manipulated the self-relevance of an audio-tactile stimulus by placing the audio source either inside or outside the peripersonal space immediately around the body. On one hand, pre-stimulus HEP amplitudes over somatosensory cortex were linked to slower reaction times, and affected audio-tactile stimulus-evoked responses in the same area, indicating competition for shared neural resources. On the other hand, pre-stimulus HEPs over integrative sensorimotor and default-mode network regions facilitated stimulus self-relevance encoding, both in reaction times and audio-tactile evoked responses. Importantly, Competition and Facilitation effects were spatially and statistically independent from each other. We therefore reconcile the two views by showing the co-existence of two independent mechanisms: one that allocates neural resources to either internal bodily signals or the external world, and another by which interoception and exteroception are combined to determine the self-relevance of external signals. Our results highlight the multi-dimensionality of HEPs, and of internal states more generally. Significance Statement Do internal bodily signals distract us from the external world (Internal/External Competition account)? Or do internal signals contribute to conscious perception, by situating the perceived external world relative to the organism (Self-related Facilitation account)? So far, both accounts – reflecting fundamentally different views of brain-body interactions – received experimental support, but have never been compared directly. We measured neural responses to heartbeats, and tested how they influenced perception in an audio-tactile reaction time task where self-relevance was manipulated. We found evidence for both accounts, reflecting independent mechanisms in distinct brain regions. Our results reconcile two hitherto independent and seemingly contradictory research programmes on the relationship between interoception and exteroception. They further highlight the multi-dimensionality of cardiac-brain interactions, and hence of internal state.

Our thoughts often drift away from the tasks at hand. Various factors influence this phenomenon, including changes in the external environment, individual cognitive characteristics, and fluctuations in bodily reactions. This study investigated the relationship between fluctuations in cardiac activity and thought state transitions, focusing on individual differences such as cardiac interoception. First, the heartbeat counting task was conducted as an index of interoceptive accuracy. Participants then completed an auditory attention task with simultaneous electrocardiogram monitoring, during which they periodically reported on their thoughts. We estimated trial-by-trial thought states in a data-driven manner and examined the interplay among the current thought state, cardiac activity, and individual cardiac interoceptive accuracy. The results revealed that for individuals with high cardiac interoceptive accuracy, the persistence of self-related thought states was associated with concurrent heart rate acceleration. Furthermore, participants with higher depressive tendencies and interoceptive accuracy exhibited an increased likelihood of transitioning to self-related states when experiencing decreased heart rate during task-focused states. These findings suggest that interoception plays a dynamic role in shaping consciousness; the accurate detection of physiological arousal co-occurring with a specific thought might amplify that thought's salience, thereby influencing its persistence. This study provides new insights into the cognitive and physiological mechanisms underlying the dynamics of spontaneous thought.

Interoception, the perception and processing of signals from within the body, plays a central role in various aspects of human cognition, affect, and behavior. This study investigates the effects of theta burst stimulation (TBS) on cardiac interoception, focusing on modulation of activity of the interoceptive brain network (IBN). Twenty-two participants underwent three stimulation protocols: an inhibitory (continuous TBS, cTBS), a facilitatory (intermittent TBS, iTBS), and an ineffective control (intermediate TBS, imTBS) protocol, targeting the right supramarginal gyrus (rSMG). Stimulation of the rSMG allows, thanks to its strong functional connectivity with the right anterior insula, an indirect modulation of the activity of the IBN. We observed an increase in heartbeat-evoked potentials following facilitatory stimulation (iTBS), suggesting increased activity within the interoceptive network. In contrast, we found no effects of inhibitory (cTBS) or ineffective control (imTBS) protocols on HEPs. Furthermore, cardiac interoceptive accuracy (IAc) in a heartbeat perception task was not significantly affected by the different stimulation protocols. In conclusion, the current rSMG facilitation protocol based on iTBS is the first to enhance activity in the IBN. The result that only HEPs, but not cardiac IAc was affected, suggests that rSMG stimulation selectively enhances cardiac interoceptive attention.

Research on the interaction between interoception and external sensory processing has expanded rapidly, particularly in how stimulus processing is influenced by concurrent cardiac phases (systole and diastole). However, definitions of these phases vary widely. Some studies follow biomedical conventions, defining systole as the period from the R-peak to the end of the T-wave in the electrocardiogram (ECG), with diastole encompassing the remainder of the cycle. Others adopt an estimated-latency approach, defining systole based on the estimated arrival of afferent cardiac signals in the brain. This approach assumes that baroreceptor activity plays a key role in cognition, though conclusive evidence for this is lacking. This variability leads to inconsistencies, where systole in one study may correspond partly or entirely to diastole in another. To address this, we review studies examining how cardiac phases modulate stimulus perception. Second, we highlight methodological inconsistencies in defining cardiac phases, particularly when using estimated latencies. Third, we discuss additional sources of variability, such as differences in recording equipment and measures of interoceptive ability. Rather than focusing on specific findings, we highlight broader challenges in replicability and interpretation. Additionally, we introduce the HEARTS framework, a set of guidelines for standardising methodologies in cardiac interoception research. This consists of six recommendations: Harmonizing physiological terms across fields, Establishing clear nomenclature, Avoiding selective reporting, Refining mechanistic understanding, Tailoring methods for precision, and Standardizing study comparability. Implementing these recommendations can improve methodological consistency, cross-study reliability, and interdisciplinary collaboration, ensuring a more accurate understanding of how interoceptive signals shape perception and cognition.

Our thoughts often drift away from the tasks at hand. Various factors influence this phenomenon, including changes in the external environment, individual cognitive characteristics, and fluctuations in bodily responses. This study investigated the relationship between autonomic nervous fluctuations and thought state transitions, focusing on individual differences such as cardiac interoception. First, the heartbeat counting task was conducted, and the difference between the reported and actual number of heartbeats was used as an index of interoceptive accuracy. The participants then completed an auditory attention task while their cardiac activities were monitored. During the task, thought probes were randomly presented, and participants selected their thought content from eight categories and rated aspects such as task concentration and arousal. We estimated trial-by-trial thought states in a data-driven manner and examined how the current thought state, autonomic nervous activity, and individual cardiac interoceptive accuracy influenced the thought state in the next trial. The results demonstrated a strong association between higher cardiac interoceptive accuracy and the maintenance of similar states in subsequent trials when accelerated heart rates occurred during self-related thought states. Furthermore, the participants with higher depressive tendencies and interoceptive accuracy exhibited an increased likelihood of transitioning to self-related states when experiencing decreased heart rate during task-concentrated states. These results suggest that accurately detecting heart rate changes associated with specific thought states facilitates updates in first-person conscious experience, thereby biasing the transition patterns of subsequent thought states. This study provides new insights into the cognitive and physiological mechanisms underlying the dynamics of spontaneous thought.

The interactions between the brain and heart during sleep are responsible for regulating autonomic function. While brain-heart coupling has been studied in healthy populations, the relationships between neural and cardiac activity across sleep stages in the presence of sleep disorders are not clear. This study examines the influence of brain-driven cardiac activity across sleep stages for individuals with sleep disorders. Overnight recordings of C3 and C4 electroencephalogram (EEG) channels and electrocardiogram (ECG) signals from 146 individuals were preprocessed and analyzed in the frequency domain through a linear mixed-effect model. Our results show that parasympathetic activity is sensitive to changes in delta and beta powers during later stages of non-rapid eye movement (NREM) sleep, as both band powers exhibited strong negative effects on high-frequency heart rate variability (HF-HRV) power. These findings show that neural activity can drive vagal tone across sleep stages, suggesting that treatments on key EEG bands during NREM and REM stages may help restore regular cardiac behaviour.