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Identifying heart-brain interactions during internally and externally operative attention using conditional entropy
Abstract
Heart and brain interactions mediate human cognition. This investigation identifies heart-brain interactions during internally operative attention (AI) and externally operative attention (AE). AI attention involves short term memory, whereas AE attention deals with automatic and transient response to objects in the external world. A modified Posner’s spatial orienting task used to differentiate AI and AE attention. Heart and brain rhythms recorded in fourteen healthy participants. Functional coupling from heart-to-brain (Cheart→brain) and brain-to-heart (Cbrain→heart) time series derived using an information domain approach based on conditional entropy. The experimental results showed that low-frequency power of heart rate variability (HRV-LF) and sympathovagal balance (LF/HF ratio) during AE significantly increased compared with that for AI. Furthermore, the information flow from heart-to-brain increased and decreased form brain-to-heart during AE as compared to AI. Also, opposite trend in relationship noted between coupling index (Ci→j) and HRV-LF during AI and AE attention. The conditional entropy technique enabled simultaneous analysis of heart-brain rhythms to identify heart-brain interactions during AI and AE attention.
... Recently proposed methods for brain-heart interaction analysis exploit signal processing techniques to uncover these interactions, including inferences on causality and directionality between cortical and cardiac oscillations [7] . State-of-the-art methods of brain-heart interaction include the analysis of heartbeat-contingent responses [8] , convergent cross-mapping [9] , coupling through symbolic representations [10] , time-delay stability [11] , granger causality [12] , transfer entropy [13] , among others. Synthetic Data Generation (SDG) modeling is a framework that aims to gather the bidirectional interactions of EEG and sympathetic-vagal activity [ 4 , 5 ]. ...
... Therefore, by resolving the system of Eqs. (13) and (14) , and are computed as follows: ...
... ., , , , , or ) , during the previous time window ( − 1 ) . Eqs. (12) and (13) present the final computation of the brain-to-sympathetic and brain-to-vagal interplay coefficients C F →CSI and C F →− CVI , respectively. ...
Recent studies suggest that the interaction between the brain and heart plays a key role in cognitive processes, and measuring these interactions is crucial for understanding the interaction between the central and autonomic nervous systems. However, studying this bidirectional interplay presents methodological challenges, and there is still much room for exploration. This paper presents a new computational method called the Poincaré Sympathetic-Vagal Synthetic Data Generation Model (PSV-SDG) for estimating brain-heart interactions. The PSV-SDG combines EEG and cardiac sympathetic-vagal dynamics to provide time-varying and bidirectional estimators of mutual interplay. The method is grounded in the Poincaré plot, a heart rate variability method to estimate sympathetic-vagal activity that can account for potential non-linearities. This algorithm offers a new approach and computational tool for functional assessment of the interplay between EEG and cardiac sympathetic-vagal activity. The method is implemented in MATLAB under an open-source license.• A new brain-heart interaction modeling approach is proposed.• The modeling is based on coupled synthetic data generators of EEG and heart rate series.• Sympathetic and vagal activities are gathered from Poincaré plot geometry.
... Stress also modulates heartbeat nonlinear dynamics (20,62). Changes in attention have been referred to as a source of autonomic variability (63). Furthermore, some studies have suggested that high-frequency fluctuations in heartbeat dynamics are associated with memory retrieval, reaction time, and action execution (59,64,65), suggesting a dynamic interaction between sympathetic and parasympathetic activities under stress elicitation. ...
... The functional brain-heart interplay under stress elicitation has been shown in heartbeat-evoked potentials correlating with stress-induced changes in cardiac output (22) and correlates of functional connectivity with heart rate variability (31). The role of cardiac inputs in the neurophysiology of stress is also supported by the experimental evidence showing an increased information flow from the heart to the brain during increased attention (63) and disrupted abilities on detecting cardiac and respiratory signals from oneself under anxiety (91,92). ...
Dynamical information exchange between central and autonomic nervous systems, as referred to functional brain-heart interplay, occurs during emotional and physical arousal. It is well documented that physical and mental stress lead to sympathetic activation. Nevertheless, the role of autonomic inputs in nervous-system-wise communication under mental stress is yet unknown. In this study, we estimated the causal and bidirectional neural modulations between EEG oscillations and peripheral sympathetic and parasympathetic activities using a recently proposed computational framework for a functional brain-heart interplay assessment, namely the sympathovagal synthetic data generation model. Mental stress was elicited in 37 healthy volunteers by increasing their cognitive demands throughout three tasks associated with increased stress levels. Stress elicitation induced an increased variability in sympathovagal markers, as well as increased variability in the directional brain-heart interplay. The observed heart-to-brain interplay was primarily from sympathetic activity targeting a wide range of EEG oscillations, whereas variability in the efferent direction seemed mainly related to EEG oscillations in the gamma band. These findings extend current knowledge on stress physiology, which mainly referred to top-down neural dynamics. Our results suggest that mental stress may not cause an increase in sympathetic activity exclusively as it initiates a dynamic fluctuation within brain-body networks including bidirectional interactions at a brain-heart level. We conclude that directional brain-heart interplay measurements may provide suitable biomarkers for a quantitative stress assessment and bodily feedback may modulate the perceived stress caused by increased cognitive demand.
... More recently, stress showed changes in heart-rate's non-linear dynamics [48], indicating more complex dynamics, beyond an increase or decrease of autonomic nervous system activities. These autonomic variations have been reported as well to be associated with fluctuations in attention [49]. High frequency activity is associated with memory retrieval, as it correlated with performance and reaction times [50,51]. ...
... It is made available under a preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in The copyright holder for this this version posted September 10, 2022. ; https://doi.org/10.1101/2022.09.09.507362 doi: bioRxiv preprint the neurophysiology of stress is also supported by the experimental evidence showing an increased information flow from heart-to-brain during increased attention [49] and disrupted abilities on detecting cardiac and respiratory signals from oneself under anxiety [65,66]. ...
Previous endeavors have revealed how the dynamical information exchange between central and autonomous nervous systems, as measured from brain–heart interplay, can explain emotional arousal and physical stress. In this study, we test our recently proposed computational framework for a functional brain–heart interplay assessment; the Sympatho-Vagal Synthetic Data Generation Model. The model estimates the causal and bidirectional neural modulations between EEG oscillations and sympathetic/parasympathetic activity. Here, mental stress is elicited by increasing the cognitive demand and quantified on 37 human volunteers. The increase on mental stress induced an increased variability on heart–to–brain functional interplay, primarily from sympathetic activity on EEG oscillations in the delta and beta bands. Existing theoretical and experimental evidence has shown that stress involves top-down neural dynamics in the brain. Therefore, our results show that mental stress involves dynamic and bidirectional neural interactions at a brain–body level as well, where bodily feedback shapes the perceived stress caused by an increased cognitive demand. We conclude that brain–heart interplay estimators are suitable biomarkers for stress measurements.
Highlights
We tested a model to assess brain–heart interplay under mental stress elicitation.
The model revealed that stress levels are reflected in heart–to–brain variability.
Interplay from sympathetic to delta and beta waves are the best markers for stress.
... The early-stage detection of cardiac arrhythmia is of prime importance [1]. But during the acquisition of ECG data, different types of noise gets involved, which hide its important characteristics that mislead its analysis and introduces the non-linearity [38,39]. Analysis of this nonlinear signal requires automated analysis as provided by computer-aided diagnosis (CAD). ...
... In this paper, 12 real-time recordings (RT DB) were also used to establish the performance of the proposed methodology in a practical scenario. The use of two databases in this paper is in line with other studies in the existing literature that made use of variety of databases for validating their work [39,[72][73][74][75]. ...
The cardiovascular system is a combination of the heart, blood and blood vessels. Cardiovascular diseases (CVD) are a key factor behind casualties worldwide among both women and men. About 9.4 million deaths occur due to high Blood Pressure (BP) only, out of which 51% deaths are due to strokes and 45% deaths are due to coronary heart diseases. The Electrocardiogram (ECG) represents the heart health condition of the subject, (patient) since it is acquired through electrical conduction, which appears in terms of P-QRS-T waves. But analysis of these waves is very tedious due to the existence of different noises/artifacts. Computer Aided Diagnosis (CAD) system is required in practical medical scenario for better and automated ECG signal analysis and to compensate for human errors. In general, implementation of a CAD system for ECG signal analysis requires; preprocessing, feature extraction and classification. In the existing literature, some authors have used time domain techniques which yield good performance for cleaned ECG signals i.e., without noise/artifact. Some authors have used frequency domain techniques later, but they suffer from the problem of spectral leakage making them unsuitable for real time/pathological datasets. The existing techniques from both these domains are not able to effectively analyze nonlinear behavior of ECG signals. These limitations have motivated this work where Continuous Wavelet Transform (CWT), Spectrogram and Autoregressive (AR) technique are used collectively for interpreting nonlinear and non-stationary features of the ECG signals. In this paper, both Massachusetts Institute of Technology-Beth Israel Hospital Arrhythmia database (MB Ar DB) and Real-time database (RT DB) have been used. Performance of the proposed method is compared with that of the previous studies on the basis of sensitivity (SE) and detection rate (D.R). The proposed technique yields SE of 99.90%, D.R of 99.81% & SE of 99.77%, D.R of 99.87% for MB Ar DB and RT DB, respectively. Therefore, the proposed technique showcases the possibility of an encouraging diagnostic tool for further improving the present situation of health informatics in cardiology labs/hospitals.
... A normalized version of the TE, estimated via non-uniform embedding [116] between the time series of HRV and EEG complexity, was employed as well by Yu and colleagues [118], who revealed the existence of unidirectional effects of the cardiac period length on the irregularity of the brain waves in the resting and mental stress states. A similar approach was employed to distinguish between physiological changes induced by internally-driven attention, linked with short-term memory assessment, and externallydriven attention, associated with automatic and transient responses to external stimuli; the findings revealed that heartto-brain information flow increased, while the brain-to-heart flow decreased, during externally-driven attention compared to internally-driven attention [119]. ...
The exploration of brain-heart interactions within various paradigms, including affective computing, human-computer interfaces, and sensorimotor evaluation, stands as a significant milestone in biomarker development and neuroscientific research. A range of techniques, spanning from molecular to behavioral approaches, has been proposed to measure these interactions. Different frameworks use signal processing techniques, from the estimation of brain responses to individual heartbeats to higher-order dynamics linking cardiac inputs to changes in brain organization. This review provides an overview to the most notable signal processing strategies currently used for measuring and modeling brain-heart interactions. It discusses their usability and highlights the main challenges that need to be addressed for future methodological developments. Current methodologies have deepened our understanding of the impact of neural disruptions on brain-heart interactions, solidifying it as a biomarker for evaluation of the physiological state of the nervous system and holding immense potential for disease stratification. The vast outlook of these methods becomes apparent specially in neurological and psychiatric disorders. As we tackle new methodological challenges, gaining a more profound understanding of how these interactions operate, we anticipate further insights into the role of peripheral neurons and the environmental input from the rest of the body in shaping brain functioning.
... State-of-the-art methods of brain-heart interaction include the analysis of heartbeat-contingent responses [8], convergent cross-mapping [9], coupling through symbolic representations [10], timedelay stability [11], granger causality [12], transfer entropy [13], among others. Synthetic Data Generation (SDG) modeling is a framework that aims to gather the bidirectional interactions of EEG and sympathetic-vagal activity [4,5]. ...
Recent studies on brain-heart interaction suggest a functional involvement of such interaction in cognitive processes. Proposals on brain-heart interaction analysis aim at understanding the information exchange between two dynamic systems. Because of methodological challenges, studying the functional value of the bidirectional interplay between central and autonomous nervous systems still has space for further exploration. Here, I introduce a new computational method to estimate brain-heart interactions, the Poincaré Sympathetic-Vagal Synthetic Data Generation Model (PSV-SDG). The PSV-SDG combines EEG and cardiac sympathetic-vagal dynamics to gather time-varying and bidirectional estimators of mutual interplay. The method is grounded on the Poincaré plot, an acknowledged heart rate variability method to estimate sympathetic-vagal activity, accounting for potential non-linearities. The proposed algorithm represents a new method and computational tool for the functional assessment of the interplay between EEG and cardiac sympathetic-vagal activity. The proposed method is implemented in MATLAB under an open-source license.
... For the next step, the sample size should be increased in order to acquire more robust data for statistical analysis. Even though, the sample size is small, it is reasonably enough to do the analysis for the physiological measurements [19,[44][45][46][47]. Also, in the future, more consideration regarding timing could be investigated. ...
An important part of nursing care is the physiotherapist’s physical exercise recovery training (for instance, walking), which is aimed at restoring athletic ability, known as rehabilitation (rehab). In rehab, the big problem is that it is difficult to maintain motivation. Therapies using robots have been proposed, such as animalistic robots that have positive psychological, physiological, and social effects on the patient. These also have an important effect in reducing the on-site human workload. However, the problem with these robots is that they do not actually understand what emotions the user is currently feeling. Some studies have been successful in estimating a person’s emotions. As for non-cognitive approaches, there is an emotional estimation of non-verbal information. In this study, we focus on the characteristics of real-time sensing of emotion through heart rates – unconsciously evaluating what a person experiences – and applying it to select the appropriate turn of phrase by a voice-casting robot. We developed a robot to achieve this purpose. As a result, we were able to confirm the effectiveness of a real-time emotion-sensitive voice-casting robot that performs supportive actions significantly different from non-voice casting robots.
We apply modified diffusion entropy analysis (MDEA) to assess multifractal dimensions of ON time series (ONTS) and complexity synchronization (CS) analysis to infer information transfer among ONs that are part of a network of organ networks (NoONs). The purpose of this paper is to advance the validation, standardization, and repeatability of MDEA and CS analysis of heterogeneous neurophysiological time series data. Results from processing these datasets show that the complexity of brain, heart, and lung ONTS significantly co-vary over time during cognitive task performance but that certain principles, guidelines, and strategies for the application of MDEA analysis need consideration.
Recent experimental evidence on patients with disorders of consciousness revealed that observing brain-heart interactions helps to detect residual consciousness, even in patients with absence of behavioral signs of consciousness. Those findings support hypotheses suggesting that visceral activity is involved in the neurobiology of consciousness, and sum to the existing evidence in healthy participants in which the neural responses to heartbeats reveal perceptual and self-consciousness. More evidence obtained through mathematical modeling of physiological dynamics revealed that emotion processing is prompted by an initial modulation from ascending vagal inputs to the brain, followed by sustained bidirectional brain-heart interactions. Those findings support long-lasting hypotheses on the causal role of bodily activity in emotions, feelings, and potentially consciousness. In this paper, the theoretical landscape on the potential role of heartbeats in cognition and consciousness is reviewed, as well as the experimental evidence supporting these hypotheses. I advocate for methodological developments on the estimation of brain-heart interactions to uncover the role of cardiac inputs in the origin, levels, and contents of consciousness. The ongoing evidence depicts interactions further than the cortical responses evoked by each heartbeat, suggesting the potential presence of non-linear, complex, and bidirectional communication between brain and heartbeat dynamics. Further developments on methodologies to analyze brain-heart interactions may contribute to a better understanding of the physiological dynamics involved in homeostatic-allostatic control, cognitive functions, and consciousness.
Our environment is constantly overloaded with information, although we cannot consciously process all the stimulation reaching our senses. Current theoretical models are focused on the cognitive and neural processes underlying conscious perception. However, cognitive processes do not occur in an isolated brain, but in a complex interaction between the environment, the brain, and the organism. The brain-body interaction has largely been neglected in the study of conscious perception. The aim of the present study was to explore if heart rate (HR) and skin conductance (SC) are modulated by the interaction between phasic alertness and conscious perception. We presented near-threshold visual stimuli that could be preceded by an alerting tone on 50% of the trials. Behaviorally, phasic alerting improved perceptual sensitivity for detecting a near-threshold stimulus (along with changes in response criterion). Following the alerting tone, a cardiac deceleration-acceleration pattern was observed, which was more pronounced when the near-threshold stimulus was consciously perceived in comparison with unconsciously perceived stimuli. SC results further showed some degree of subliminal processing of unseen stimuli. These results reveal that cardiac activity could be a marker of attention and consciousness interactions, emphasizing the need for taking into account brain-body interactions for current theoretical models of consciousness.
This quantitative study identifies the coupling changes occurring among cardiac (RR), vascular (SBP) and respiratory (RESP) signals during deep breathing. The deep breathing measures the dysfunction of the parasympathetic autonomic nervous system. The traditional methods based on cross-correlation and coherence analysis lack to measure nonlinear structures and unpredictability of physiological subsystems. Therefore, information domain coupling method based on conditional entropy is proposed to detect the coupling changes. Thirty healthy volunteers were examined for 5(Formula presented.)min at normal breathing and 5(Formula presented.)min during deep breathing (6(Formula presented.)cycles/min). The reduction in respiration rate detects a significant increase in information flow from RESP to RR, RESP to SBP and SBP to RR. The increased interaction from RESP to RR and RESP to SBP at reduced respiration rate indicates the enhancement of respiratory sinus arrhythmia that results in the activation of the parasympathetic nervous system. Also, the balanced cardiovascular interaction observed on normal breathing from RR to SBP disappears, but interaction occurring in baroreflex direction (SBP to RR) increases that helps in the reduction of blood pressure during deep breathing. This detected direction of information flow helps in identifying the coupling changes occurring during parasympathetic nerve activity.
In both the East and West, traditional teachings say that the mind and heart are somehow closely correlated, especially during spiritual practice. One difficulty in proving this objectively is that the natures of brain and heart activities are quite different. In this paper, we propose a methodology that uses wavelet entropy to measure the chaotic levels of both electroencephalogram (EEG) and electrocardiogram (ECG) data and show how this may be used to explore the potential coordination between the mind and heart under different experimental conditions. Furthermore, Statistical Parametric Mapping (SPM) was used to identify the brain regions in which the EEG wavelet entropy was the most affected by the experimental conditions. As an illustration, the EEG and ECG were recorded under two different conditions (normal rest and mindful breathing) at the beginning of an 8-week standard Mindfulness-based Stress Reduction (MBSR) training course (pretest) and after the course (posttest). Using the proposed method, the results consistently showed that the wavelet entropy of the brain EEG decreased during the MBSR mindful breathing state as compared to that during the closed-eye resting state. Similarly, a lower wavelet entropy of heartrate was found during MBSR mindful breathing. However, no difference in wavelet entropy during MBSR mindful breathing was found between the pretest and posttest. No correlation was observed between the entropy of brain waves and the entropy of heartrate during normal rest in all participants, whereas a significant correlation was observed during MBSR mindful breathing. Additionally, the most well-correlated brain regions were located in the central areas of the brain. This study provides a methodology for the establishment of evidence that mindfulness practice (i.e., mindful breathing) may increase the coordination between mind and heart activities. © 2017 Creative Commons Attribution-NonCommercial-NoDerivs 3.0 Unported License
Background
Mental arithmetic has been verified inducing cerebral and cardiovascular responses. However, the mechanism and sequential responses are still ambiguous. This study aims to reveal the mechanism of cardiovascular and autonomic responses and the related scalp positions that regulate the autonomic nerves system (ANS) during MA task. Methods34 healthy male subjects aged between 19 and 27 years old (mean age 23.6 ± 2.3 years) were recruited in. Electrocardiogram, impedance cardiography, beat-to-beat blood pressure and electroencephalography were measured simultaneously and continuously during the experiments. And the analysis of time–frequency, approximate entropy and Pearson correlation coefficient were adopted. For statistical comparison, paired t test is utilized in the study. ResultsThe results showed that mental arithmetic task increased heart rate (from 72.35 ± 1.88 to 80.38 ± 2.34), blood pressure (systolic blood pressure: from 112.09 ± 3.23 to 126.79 ± 3.44; diastolic blood pressure: from 74.15 ± 1.93 to 81.20 ± 1.97), and cardiac output (from 8.71 ± 0.30 to 9.68 ± 0.35), and the mental arithmetic induced physiological responses could be divided into two stages, the first stage (10–110 s) and late stage (150–250 s). The high frequency power component (HF) of HRV decreased during MA, but the normalized low frequency power component (nLF) and LF/HF ratio of HRV increased only at the late stage. Moreover, during first stage, the correlations between approximate entropy of electroencephalography at Fp2, Fz, F4, F7 and the corresponding time–frequency results of HF were significant. During the late stage, the correlations between approximate entropy of electroencephalography at Fp2, Fz, C3, C4 and the corresponding nLF was significant. Conclusions
Our results demonstrated that (1) mental stress induces time-dependent ANS activity and cardiovascular response. (2) Parasympathetic activity is lower during mental arithmetic task, but sympathetic nerve is activated only during late stage of mental arithmetic task. (3) Brain influences the cardiac activity through prefrontal and temporal cortex with the activation of ANS during mental arithmetic.
Emotion perception, occurring in brain areas such as the prefrontal cortex and amygdala, involves autonomic responses affecting cardiovascular dynamics. However, how such brain-heart dynamics is further modulated by emotional valence (pleasantness/unpleasantness), also considering different arousing levels (the intensity of the emotional stimuli), is still unknown. To this extent, we combined electroencephalographic (EEG) dynamics and instantaneous heart rate estimates to study emotional processing in healthy subjects. Twenty-two healthy volunteers were elicited through affective pictures gathered from the International Affective Picture System. The experimental protocol foresaw 110 pictures, each of which lasted 10 s, associated to 25 different combinations of arousal and valence levels, including neutral elicitations. EEG data were processed using short-time Fourier transforms to obtain timevarying maps of cortical activation, whereas the associated instantaneous cardiovascular dynamics was estimated in the time and frequency domains through inhomogeneous point-process models. Brain-heart linear and nonlinear coupling was estimated through the maximal information coefficient (MIC). Considering EEG oscillations in the θ band (4-8 Hz), MIC highlighted significant arousal-dependent changes between positive and negative stimuli, especially occurring at intermediate arousing levels through the prefrontal cortex interplay. Moreover, high arousing elicitations seem to mitigate changes in brain-heart dynamics in response to pleasant/unpleasant visual elicitation. © 2016 The Author(s) Published by the Royal Society. All rights reserved.
To study the mechanisms underlying covert orienting of attention in visual space, subjects were given advance cues indicating the probable locations of targets that they had to discriminate and localize. Direct peripheral cues (brightening of one of four boxes in peripheral vision) and symbolic central cues (an arrow at the fixation point indicating a probable peripheral box) were compared. Peripheral and central cues are believed to activate different reflexive and voluntary modes of orienting (Jonides, 1981; Posner, 1980). Experiment 1 showed that the time courses of facilitation and inhibition from peripheral and central cues were characteristic and different. Experiment 2 showed that voluntary orienting in response to symbolic central cues is interrupted by reflexive orienting to random peripheral flashes. Experiment 3 showed that irrelevant peripheral flashes also compete with relevant peripheral cues. The amount of interference varied systematically with the interval between the onset of the relevant cue and of the distracting flash (cue-flash onset asynchrony) and with the cuing condition. Taken together, these effects support a model for spatial attention with distinct but interacting reflexive and voluntary orienting mechanisms.
In this study, the physiological networks underlying the joint modulation of the parasympathetic component of heart rate variability (HRV) and of the different electroencephalographic (EEG) rhythms during sleep were assessed using two popular measures of directed interaction in multivariate time series, namely Granger causality (GC) and transfer entropy (TE). Time series representative of cardiac and brain activities were obtained in 10 young healthy subjects as the normalized high frequency (HF) component of HRV and EEG power in the δ, θ, α, σ, and β bands, measured during the whole duration of sleep. The magnitude and statistical significance of GC and TE were evaluated between each pair of series, conditional on the remaining series, using respectively a linear model-based approach exploiting regression models, and a nonlinear model-free approach combining nearest-neighbor entropy estimation with a procedure for dimensionality reduction. The contribution of nonlinear dynamics to the TE was also assessed using surrogate data. GC and TE consistently detected structured networks of physiological interactions, with links directed predominantly from HRV to the EEG waves in the brain-heart network, and from the σ and β EEG waves to the δ, θ, and α waves in the brain-brain network. While these common patterns supported the suitability of a linear model-based analysis, we also found a significant contribution of nonlinear dynamics, particularly involving the information transferred out of the δ node in the two networks. This suggested the importance of nonparametric TE estimation for evidencing the fine structure of the physiological networks underlying the autonomic regulation of cardiac and brain functions during sleep.
The ability to alter one’s emotional responses is central to overall well-being and to effectively meeting the demands of life. One of the chief symptoms of events such as trauma, that overwhelm our capacities to successfully handle and adapt to them, is a shift in our internal baseline reference such that there ensues a repetitive activation of the traumatic event. This can result in high vigilance and over-sensitivity to environmental signals which are reflected in inappropriate emotional responses and autonomic nervous system dynamics. In this article we discuss the perspective that one’s ability to self-regulate the quality of feeling and emotion of one’s moment-to-moment experience is intimately tied to our physiology, and the reciprocal interactions among physiological, cognitive, and emotional systems. These interactions form the basis of information processing networks in which communication between systems occurs through the generation and transmission of rhythms and patterns of activity. Our discussion emphasizes the communication pathways between the heart and brain, as well as how these are related to cognitive and emotional function and self-regulatory capacity. We discuss the hypothesis that self-induced positive emotions increase the coherence in bodily processes, which is reflected in the pattern of the heart’s rhythm. This shift in the heart rhythm in turn plays an important role in facilitating higher cognitive functions, creating emotional stability and facilitating states of calm. Over time, this establishes a new inner-baseline reference, a type of implicit memory that organizes perception, feelings, and behavior. Without establishing a new baseline reference, people are at risk of getting “stuck” in familiar, yet unhealthy emotional and behavioral patterns and living their lives through the automatic filters of past familiar or traumatic experience.
Several authors argued that retrieval of an item from visual short term memory (internal spatial attention) and focusing attention on an externally presented item (external spatial attention) are similar. Part of the neuroimaging support for this view may be due to the employed experimental procedures. Furthermore, as internal spatial attention may have a more induced than evoked nature some effects may not have been visible in event related analyses of the electroencephalogram (EEG), which limits the possibility to demonstrate differences. In the current study, a colored frame cued which stimulus, one out of four presented in separate quadrants, required a response, which depended on the form of the cued stimulus (circle or square). Importantly, the frame occurred either before (precue), simultaneously with (simultaneous cue), or after the stimuli (postcue). The precue and simultaneous cue condition both concern external attention, while the postcue condition implies the involvement of internal spatial attention. Event-related lateralizations (ERLs), reflecting evoked effects, and lateralized power spectra (LPS), reflecting both evoked and induced effects, were determined. ERLs revealed a posterior contralateral negativity (PCN) only in the precue condition. LPS analyses on the raw EEG showed early increased contralateral theta power at posterior sites and later increased ipsilateral alpha power at occipito-temporal sites in all cue conditions. Responses were faster when the internally or externally attended location corresponded with the required response side than when not. These findings provide further support for the view that internal and external spatial attention share their underlying mechanism.
The heart begins to beat before the brain is formed. Whether conventional hierarchical central commands sent by the brain to the heart alone explain all the interplay between these two organs should be reconsidered. Here, we demonstrate correlations between the signal complexity of brain and cardiac activity. Eighty-seven geriatric outpatients with healthy hearts and varied cognitive abilities each provided a 24-hour electrocardiography (ECG) and a 19-channel eye-closed routine electroencephalography (EEG). Multiscale entropy (MSE) analysis was applied to three epochs (resting-awake state, photic stimulation of fast frequencies (fast-PS), and photic stimulation of slow frequencies (slow-PS)) of EEG in the 1-58 Hz frequency range, and three RR interval (RRI) time series (awake-state, sleep and that concomitant with the EEG) for each subject. The low-to-high frequency power (LF/HF) ratio of RRI was calculated to represent sympatho-vagal balance. With statistics after Bonferroni corrections, we found that: (a) the summed MSE value on coarse scales of the awake RRI (scales 11-20, RRI-MSE-coarse) were inversely correlated with the summed MSE value on coarse scales of the resting-awake EEG (scales 6-20, EEG-MSE-coarse) at Fp2, C4, T6 and T4; (b) the awake RRI-MSE-coarse was inversely correlated with the fast-PS EEG-MSE-coarse at O1, O2 and C4; (c) the sleep RRI-MSE-coarse was inversely correlated with the slow-PS EEG-MSE-coarse at Fp2; (d) the RRI-MSE-coarse and LF/HF ratio of the awake RRI were correlated positively to each other; (e) the EEG-MSE-coarse at F8 was proportional to the cognitive test score; (f) the results conform to the cholinergic hypothesis which states that cognitive impairment causes reduction in vagal cardiac modulation; (g) fast-PS significantly lowered the EEG-MSE-coarse globally. Whether these heart-brain correlations could be fully explained by the central autonomic network is unknown and needs further exploration.
The underlying changes in heart coherence that are associated with reported EEG changes in response to meditation have been explored. We measured EEG and heart rate variability (HRV) before and during autogenic meditation. Fourteen subjects participated in the study. Heart coherence scores were significantly increased during meditation compared to the baseline. We found near significant decrease in high beta absolute power, increase in alpha relative power and significant increases in lower (alpha) and higher (above beta) band coherence during 3~min epochs of heart coherent meditation compared to 3~min epochs of heart non-coherence at baseline. The coherence and relative power increase in alpha band and absolute power decrease in high beta band could reflect relaxation state during the heart coherent meditation. The coherence increase in the higher (above beta) band could reflect cortico-cortical local integration and thereby affect cognitive reorganization, simultaneously with relaxation. Further research is still needed for a confirmation of heart coherence as a simple window for the meditative state.
Covert visual spatial attention is a relatively new task used in brain computer interfaces (BCIs) and little is known about the characteristics which may affect performance in BCI tasks. We investigated whether eccentricity and task difficulty affect alpha lateralization and BCI performance.
We conducted a magnetoencephalography study with 14 participants who performed a covert orientation discrimination task at an easy or difficult stimulus contrast at either a near (3.5°) or far (7°) eccentricity. Task difficulty was manipulated block wise and subjects were aware of the difficulty level of each block.
Grand average analyses revealed a significantly larger hemispheric lateralization of posterior alpha power in the difficult condition than in the easy condition, while surprisingly no difference was found for eccentricity. The difference between task difficulty levels was significant in the interval between 1.85 s and 2.25 s after cue onset and originated from a stronger decrease in the contralateral hemisphere. No significant effect of eccentricity was found. Additionally, single-trial classification analysis revealed a higher classification rate in the difficult (65.9%) than in the easy task condition (61.1%). No effect of eccentricity was found in classification rate.
Our results indicate that manipulating the difficulty of a task gives rise to variations in alpha lateralization and that using a more difficult task improves covert visual spatial attention BCI performance. The variations in the alpha lateralization could be caused by different factors such as an increased mental effort or a higher visual attentional demand. Further research is necessary to discriminate between them. We did not discover any effect of eccentricity in contrast to results of previous research.
This study is aimed to determine significant physiological parameters of brain and heart under meditative state, both in each activities and their dynamic correlations. Electrophysiological changes in response to meditation were explored in 12 healthy volunteers who completed 8 weeks of a basic training course in autogenic meditation. Heart coherence, representing the degree of ordering in oscillation of heart rhythm intervals, increased significantly during meditation. Relative EEG alpha power and alpha lagged coherence also increased. A significant slowing of parietal peak alpha frequency was observed. Parietal peak alpha power increased with increasing heart coherence during meditation, but no such relationship was observed during baseline. Average alpha lagged coherence also increased with increasing heart coherence during meditation, but weak opposite relationship was observed at baseline. Relative alpha power increased with increasing heart coherence during both meditation and baseline periods. Heart coherence can be a cardiac marker for the meditative state and also may be a general marker for the meditative state since heart coherence is strongly correlated with EEG alpha activities. It is expected that increasing heart coherence and the accompanying EEG alpha activations, heart brain synchronicity, would help recover physiological synchrony following a period of homeostatic depletion.
The physiological mechanisms related to cardio-vascular (CV), cardio-pulmonary (CP), and vasculo-pulmonary (VP) regulation may be probed through multivariate time series analysis tools. This study applied an information domain approach for the evaluation of non-linear causality to the beat-to-beat variability series of heart period (t), systolic arterial pressure (s), and respiration (r) measured during tilt testing and paced breathing (PB) protocols. The approach quantifies the causal coupling from the series i to the series j (Cij) as the amount of information flowing from i to j. A measure of directionality is also obtained as the difference between two reciprocal causal couplings (Di,j = Cij − Cji). Significant causal coupling and directionality were detected respectively when the median of Cij over subjects was positive (Cij > 0), and when Di,j was statistically different from zero (Di,j > 0 or Di,j < 0). The method was applied on t, s, and r series measured in 15 healthy subjects (22–32 years, 8 males) in the supine (su) and upright (up) positions, and in further 15 subjects (21–29 years, 7 males) during spontaneous (sp) and paced (pa) breathing. In the control condition (su, sp), a significant causal coupling was observed for Crs, Crt, Cst, and Cts, and significant directionality was present only from r to t (Dr,t > 0). During head-up tilt (up, sp), Crs was preserved, Crt decreased to zero median, and Cst and Cts increased significantly; directionality vanished between r and t (Dr,t = 0) and raised from s to t (Ds,t > 0). During PB (su, pa), Crs increased significantly, Crt and Cts were preserved, and Cst decreased to zero median; directionality was preserved from r to t (Dr,t > 0), and raised from r to s (Dr,s > 0). These results suggest that the approach may reflect modifications of CV, CP, and VP mechanisms consequent to altered physiological conditions, such as the baroreflex engagement and the dampening of respiratory sinus arrhythmia induced by tilt, or the respiratory driving on arterial pressure induced by PB. Thus, it could be suggested as a tool for the non-invasive monitoring of CV and cardiorespiratory control systems in normal and impaired conditions.
This study introduces a new approach for the detection of nonlinear Granger causality between dynamical systems. The approach is based on embedding the multivariate (MV) time series measured from the systems X and Y by means of a sequential, non-uniform procedure, and on using the corrected conditional entropy (CCE) as unpredictability measure. The causal coupling from X to Y is quantified as the relative decrease of CCE measured after allowing the series of X to enter the embedding procedure for the description of Y. The ability of the approach to quantify nonlinear causality is assessed on MV time series measured from simulated dynamical systems with unidirectional coupling (the Rössler-Lorenz deterministic system) and bidirectional coupling (two coupled stochastic systems). The method is then applied to real magnetoencephalographic data measured during a visuo-tactile cognitive experiment, showing values of causal coupling consistent with the hypothesis of a cross-processing of different sensory modalities.
Heart rate variability (HRV) is traditionally derived from RR interval time series of electrocardiography (ECG). Photoplethysmography (PPG) also reflects the cardiac rhythm since the mechanical activity of the heart is coupled to its electrical activity. Thus, theoretically, PPG can be used for determining the interval between successive heartbeats and heart rate variability. However, the PPG wave lags behind the ECG signal by the time required for transmission of pulse wave. In this study, finger-tip PPG and standard lead II ECG were recorded for five minutes from 10 healthy subjects at rest. The results showed a high correlation (median = 0.97) between the ECG-derived RR intervals and PPG-derived peak-to-peak (PP) intervals. PP variability was accurate (0.1 ms) as compared to RR variability. The time domain, frequency domain and Poincaré plot HRV parameters computed using RR interval method and PP interval method showed no significant differences (p < 0.05). The error analysis also showed insignificant differences between the HRV indices obtained by the two methods. Bland-Altman analysis showed high degree of agreement between the two methods for all the parameters of HRV. Thus, HRV can also be reliably estimated from the PPG based PP interval method.
Components of heart rate variability have attracted considerable attention in psychology and medicine and have become important dependent measures in psychophysiology and behavioral medicine. Quantification and interpretation of heart rate variability, however, remain complex issues and are fraught with pitfalls. The present report (a) examines the physiological origins and mechanisms of heart rate variability, (b) considers quantitative approaches to measurement, and (c) highlights important caveats in the interpretation of heart rate variability. Summary guidelines for research in this area are outlined, and suggestions and prospects for future developments are considered.
Entropy, as it relates to dynamical systems, is the rate of information production. Methods for estimation of the entropy of a system represented by a time series are not, however, well suited to analysis of the short and noisy data sets encountered in cardiovascular and other biological studies. Pincus introduced approximate entropy (ApEn), a set of measures of system complexity closely related to entropy, which is easily applied to clinical cardiovascular and other time series. ApEn statistics, however, lead to inconsistent results. We have developed a new and related complexity measure, sample entropy (SampEn), and have compared ApEn and SampEn by using them to analyze sets of random numbers with known probabilistic character. We have also evaluated cross-ApEn and cross-SampEn, which use cardiovascular data sets to measure the similarity of two distinct time series. SampEn agreed with theory much more closely than ApEn over a broad range of conditions. The improved accuracy of SampEn statistics should make them useful in the study of experimental clinical cardiovascular and other biological time series.
Although patterns of heart rate variability (HRV) hold considerable promise for clarifying issues in clinical applications, the inappropriate quantification and interpretation of these patterns may obscure critical issues or relationships and may impede rather than foster the development of clinical applications. The duration of the RR interval series is not a matter of convenience but a fine balance between two important issues: acceptable variance and stationarity of the time series on one hand, and acceptable resolution of the spectral estimate and reduced spectral leakage on the other. Further, in the standard short-term HRV analysis, it has been observed that the previous studies in HRV spectral analysis use a wide range of RR interval segment duration for spectral estimation by Welch's algorithm. The standardization of RR interval segment duration is also important for comparisons among studies and is essential for within-study experimental contrasts. In the present study, a comparative analysis for RR interval segment durations has been made to propose an optimal RR interval segment duration. Firstly a simulated signal was analyzed with Hann window and zero padding for the segment lengths of 1024, 512, 256 and 128 samples resampled at 4 Hz with 50% overlapping. Again, the above procedure was applied to RR interval series and it was concluded that segment length of 256 samples with 50% overlapping provides a smoothed spectral estimate with clearly outlined peaks in low- and high-frequency bands. This easily understandable and interpretable spectral estimate leads to a better visual and automated analysis, which is not only desirable in basic physiology studies, but also a prerequisite for a widespread utilization of frequency domain techniques in clinical studies, where simplicity and effectiveness of information are of primary importance.
The authors propose that there are 2 different mechanisms whereby spatial cues capture attention. The voluntary mechanism is the strategic allocation of perceptual resources to the location most likely to contain the target. The involuntary mechanism is a reflexive orienting response that occurs even when the spatial cue does not indicate the probable target location. Voluntary attention enhances the perceptual representation of the stimulus in the cued location relative to other locations. Hence, voluntary attention affects performance in experiments designed around both accuracy and reaction time. Involuntary attention affects a decision as to which location should be responded to. Because involuntary attention does not change the perceptual representation, it affects performance in reaction time experiments but not accuracy experiments. The authors obtained this pattern of results in 4 different versions of the spatial cuing paradigm.
The relation between reflexive and voluntary orienting of visual attention was investigated with 4 experiments: a simple detection task, a localization task, a saccade toward the target task, and a target identification task in which discrimination difficulty was manipulated. Endogenous and exogenous orienting cues were presented in each trial and their validity was manipulated orthogonally to examine whether attention mechanisms are mediated by separate systems and whether they have additive and independent effects on visual detection and discrimination. The results showed that each orienting mechanism developed its typical and independent effect in every case except for the difficult identification task. A theoretical framework for understanding the relationship between endogenous and exogenous orienting of attention is proposed, tested, and confirmed.
The autonomic nervous system (ANS) plays a role in a wide range of somatic and mental diseases. Using a model of neurovisceral integration, this article describes how autonomic imbalance and decreased parasympathetic tone in particular may be the final common pathway linking negative affective states and conditions to ill health. The central nervous system (CNS) network that regulates autonomic balance (central autonomic network, CAN) is closely related and partially overlaps with networks serving executive, social, affective, attentional, and motivated behavior (anterior executive region, AER; and Damasio's [Damasio, A.R., 1998. Emotion in the perspective of an integrated nervous system. Brain Res. Rev. 26, 83-86.] 'emotion circuit'). A common reciprocal inhibitory cortico-subcortical neural circuit serves to regulate defensive behavior, including autonomic, emotional and cognitive features. This inhibitory cortico-subcortical circuit may structurally, as well as functionally, link psychological processes with health-related physiology. When the prefrontal cortex is taken 'offline' for whatever reason, parasympathetic inhibitory action is withdrawn and a relative sympathetic dominance associated with disinhibited defensive circuits is released, which can be pathogenic when sustained for long periods. This state is indicated by low heart rate variability (HRV), which is a marker for low parasympathetic activation and prefrontal hypoactivity. Consistent with this, HRV is associated with a range of psychological and somatic pathological conditions, including immune dysfunction. Finally, we discuss supportive evidence from recent studies of the reflexive startle blink, attention and working memory, which shows that low HRV predicts hypervigilance and inefficient allocation of attentional and cognitive resources.
The ability to continuously and unobtrusively monitor levels of task engagement and mental workload in an operational environment could be useful in identifying more accurate and efficient methods for humans to interact with technology. This information could also be used to optimize the design of safer, more efficient work environments that increase motivation and productivity.
The present study explored the feasibility of monitoring electroencephalo-graphic (EEG) indices of engagement and workload acquired unobtrusively and quantified during performance of cognitive tests. EEG was acquired from 80 healthy participants with a wireless sensor headset (F3-F4,C3-C4,Cz-POz,F3-Cz,Fz-C3,Fz-POz) during tasks including: multi-level forward/backward-digit-span, grid-recall, trails, mental-addition, 20-min 3-Choice Vigilance, and image-learning and memory tests. EEG metrics for engagement and workload were calculated for each 1 -s of EEG.
Across participants, engagement but not workload decreased over the 20-min vigilance test. Engagement and workload were significantly increased during the encoding period of verbal and image-learning and memory tests when compared with the recognition/ recall period. Workload but not engagement increased linearly as level of difficulty increased in forward and backward-digit-span, grid-recall, and mental-addition tests. EEG measures correlated with both subjective and objective performance metrics.
These data in combination with previous studies suggest that EEG engagement reflects information-gathering, visual processing, and allocation of attention. EEG workload increases with increasing working memory load and during problem solving, integration of information, analytical reasoning, and may be more reflective of executive functions. Inspection of EEG on a second-by-second timescale revealed associations between workload and engagement levels when aligned with specific task events providing preliminary evidence that second-by-second classifications reflect parameters of task performance.
Previous studies have shown that EEG activity in the gamma range can be modulated by attention. Here, we compared this activity for voluntary and involuntary spatial attention in a spatial-cueing paradigm with faces as targets. The stimuli and trial timing were kept constant across attention conditions with only the predictive value of the cue changing. Gamma-band response was linked to voluntary shifts of attention, but not to the involuntary capture of attention. The presence of increased gamma responses for the voluntary allocation of attention, and its absence in cases of involuntary capture suggests that the neural mechanisms governing these two types of attention are different. Moreover, these data allow a description of the temporal dynamics contributing to the dissociation between voluntary and involuntary attention. The distribution of this correlate of voluntary attention is consistent with a top-down process involving contralateral anterior and posterior regions.
Entropy™ is a proprietary algorithm which uses spectral entropy analysis of electroencephalographic (EEG) signals to produce indices which are used as a measure of depth of hypnosis. We describe a report of electrocardiographic (ECG) contamination of EEG signals leading to fluctuating erroneous Entropy values. An explanation is provided for mechanism behind this observation by describing the spread of ECG signals in head and neck and its influence on EEG/Entropy by correlating the observation with the published Entropy algorithm. While the Entropy algorithm has been well conceived, there are still instances in which it can produce erroneous values. Such erroneous values and their cause may be identified by close scrutiny of the EEG waveform if Entropy values seem out of sync with that expected at given anaesthetic levels.
This study employed a paired stimulus paradigm to compare phasic changes in heart rate among children (age categories 6-8, 9-10, and 11-12) and adults (age categories 18-19 and 20-22) with attention-deficit/hyperactivity disorder (ADHD) and age-matched controls. A sample of 95 participants (19 ADHD-diagnosed children, 34 controls, 20 ADHD-diagnosed adults, and 22 controls) solved a planning task, the Tower of London, through 4 levels of difficulty. It was hypothesized that groups with ADHD would show greater heart rate acceleration and less final deceleration than would controls, and that these heart rate responses would change with age and difficulty level as well. Though heart rate differences were found among age categories and difficulty levels, none were found between participants with ADHD and controls. The lack of ADHD differences are not consistent with the behavioral evidence that planning by itself is one of the marked executive function deficits in ADHD. Because ADHD differences were not evident, the effects either were not present or were smaller than that of difficulty level and age. Possible explanations for this lack of difference and future directions are discussed.
This work assessed the influence of the autoregressive model order (ARMO) on the spectral analysis of the heart rate variability (HRV). A sample of 68 R-R series obtained from digital ECG records of young healthy adults in the supine position was used. Normalized spectral indexes for each ARMO were compared by Friedman test followed by the Dunn's procedure and statistical significance was set at P<0.05. The results showed that the AR method using orders from 9 to 25 produces normalized spectral parameters statistically similar and, hence, the algorithms commonly employed to estimate optimum order are not mandatory in this case.
The complexity of the short-term cardiovascular control prompts for the introduction of multivariate (MV) nonlinear time series analysis methods to assess directional interactions reflecting the underlying regulatory mechanisms. This study introduces a new approach for the detection of nonlinear Granger causality in MV time series, based on embedding the series by a sequential, non-uniform procedure, and on estimating the information flow from one series to another by means of the corrected conditional entropy. The approach is validated on short realizations of linear stochastic and nonlinear deterministic processes, and then evaluated on heart period, systolic arterial pressure and respiration variability series measured from healthy humans in the resting supine position and in the upright position after head-up tilt.
Recent studies applying functional magnetic resonance imaging have focused on the description of cerebral substrates of changes in cardiac function during diverse autonomic maneuvers or stressful cognitive tasks. These studies might be limited by the indistinguishable neuronal activity due to cognitive processes, which are known to influence autonomic function, and the 'baseline' activity in the central autonomic network. We therefore investigated 26 healthy volunteers in the magnetic resonance scanner to simultaneously obtain functional brain images and RR intervals (intervals between ventricular depolarizations) of the high-resolution electrocardiogram. The mean RR interval length within each functional scan was computed, which was finally convolved with the canonical hemodynamic response function to obtain a regressor for the functional time series. The resulting individual contrast image indicated a positive covariation of the blood oxygen level-dependent signal and RR interval length in the ventromedial prefrontal cortex (vmPFC). Furthermore, a reduced mean cross-approximate entropy value was shown for the interaction between the vmPFC and individual RR intervals. This suggests reduced asynchrony between the heart rate and vmPFC activity in contrast to other brain areas. Our findings confirm data obtained in animals describing the vmPFC as an important forebrain structure of the central autonomic network and an influence of the vmPFC in the cortical generation of efferent vagal activity. This finding needs to be investigated in diseases with known suppression of efferent vagal modulation.
In the present paper, we describe a model of neurovisceral integration in which a set of neural structures involved in cognitive, affective, and autonomic regulation are related to heart rate variability (HRV) and cognitive performance.
We detail the pathways involved in the neural regulation of the cardiovascular system and provide pharmacological and neuroimaging data in support of the neural structures linking the central nervous system to HRV in humans. We review a number of studies from our group showing that individual differences in HRV are related to performance on tasks associated with executive function and prefrontal cortical activity. These studies include comparisons of executive- and nonexecutive-function tasks in healthy participants, in both threatening and nonthreatening conditions. In addition, we show that manipulating resting HRV levels is associated with changes in performance on executive-function tasks. We also examine the relationship between HRV and cognitive performance in ecologically valid situations using a police shooting simulation and a naval navigation simulation. Finally, we review our studies in anxiety patients, as well as studies examining psychopathy.
These findings in total suggest an important relationship among cognitive performance, HRV, and prefrontal neural function that has important implications for both physical and mental health. Future studies are needed to determine exactly which executive functions are associated with individual differences in HRV in a wider range of situations and populations.
The cerebral cortex had massive bidirectional connections to autonomic nervous system and mental performance can induce change of autonomic activity, but which regions are related to autonomic function is not clear. The study was to analyze the scalp positions which may affect cardiac autonomic nervous activity during a mental arithmetic (MA) task. Forty-three healthy male subjects were voluntarily participated in the study. Sympathetic and parasympathetic activities were estimated with heart rate variability. Scalp potential was determined by the wavelet packet parameters and approximate entropy (ApEn) of Electroencephalogram (EEG). The results showed that heart rate and the normalized low frequency power component were significantly increased (p<0.01) and the high frequency power component was decreased (p<0.01). Meanwhile relative wavelet packet energy in alpha band of EEG at P3, P4, Pz, O1, O2 and Oz electrodes were decreased and the beta band of EEG at the same electrodes were increased significantly (p<0.01). ApEn was significantly increased in MA (p<0.01). Moreover, changes of brain activity were earlier than the changes of autonomic activity and significantly correlations existed between heart rate variability and wavelet packet energy (p<0.05). In addition, a significant positive correlation between HR change and the laterality ratio score of alpha band in P3 v P4 (p<0.05) were observed. It is noted that cerebral conscious activity enhanced with the decrease of parasympathetic activity and increase of sympathetic activity, and the right post-central areas dominated sympathetic activity during stress-inducing mental tasks.
The neural regulation of circulatory function is mainly effected through the interplay of the sympathetic and vagal outflows. This interaction can be explored by assessing cardiovascular rhythmicity with appropriate spectral methodologies. Spectral analysis of cardiovascular signal variability, and in particular of RR period (heart rate variability, HRV), is a widely used procedure to investigate autonomic cardiovascular control and/or target function impairment. The oscillatory pattern which characterizes the spectral profile of heart rate and arterial pressure short-term variability consists of two major components, at low (LF, 0.04-0.15Hz) and high (HF, synchronous with respiratory rate) frequency, respectively, related to vasomotor and respiratory activity. With this procedure the state of sympathovagal balance modulating sinus node pacemaker activity can be quantified in a variety of physiological and pathophysiological conditions. Changes in sympathovagal balance can be often detected in basal conditions, however a reduced responsiveness to an excitatory stimulus is the most common feature that characterizes numerous pathophysiological states. Moreover the attenuation of an oscillatory pattern or its impaired responsiveness to a given stimulus can also reflect an altered target function and thus can furnish interesting prognostic markers. The dynamic assessment of these autonomic changes may provide crucial diagnostic, therapeutic and prognostic information, not only in relation to cardiovascular, but also non-cardiovascular disease. As linear methodologies fail to provide significant information in conditions of extremely reduced variability (e.g. strenuous exercise, heart failure) and in presence of rapid and transients changes or coactivation of the two branches of autonomic nervous system, the development of new non-linear approaches seems to provide a new perspective in investigating neural control of cardiovascular system.
In recent years, several attempts have been made to characterize the nature of the cognitive deficits shown by patients with Parkinson's disease. It has been suggested variously that they have difficulty in switching cognitive set, in performing effortful (or controlled) as opposed to automatic tasks, or that their impairment is found in tasks which maximize the amount of 'self-directed task specific planning'. It is proposed that this latter distinction may be reformulated in terms of the degree of internal versus external attentional control which is required by the task. An experiment is described which attempted to manipulate this parameter. A version of the Stroop colour-word test was used, in which the words 'red' and 'green' were presented in the complementary coloured 'ink'. Subjects responded either to the colour of the ink in which the word was written or the colour named by the word. The relevant attribute changed at intervals during the course of the experiment. In one condition, the relevant stimulus attribute was cued before each trial. In another condition, subjects had to remember which attribute was currently relevant. Results revealed that patients with Parkinson's disease were impaired mainly on the second version of the task which required internal attentional control. The results are discussed in relation to the models of Working Memory (Baddeley, 1986), and attentional control (Norman and Shallice, 1980). Exploration of these models leads to the formulation of a theory in which the crucial determinant of cognitive impairment in Parkinson's disease is reduced resources in the Supervisory Attentional System. Provided the demands of the task are within the patient's available attentional resources the patient may not show any deficit. If, however, the attentional demands exceed available resources, as in tasks which depend upon internal cues, then deficits will be observed.
In the present paper we present the outlines of a model that integrates autonomic, attentional, and affective systems into a functional and structural network that may help to guide us in our understanding of emotion regulation and dysregulation. We will emphasize the relationship between attentional regulation and affective processes and propose a group of underlying physiological systems that serve to integrate these functions in the service of self-regulation and adaptability of the organism. We will attempt to place this network in the context of dynamical systems models which involve feedback and feedforward circuits with special attention to negative feedback mechanisms, inhibitory processes, and their role in response selection. From a systems perspective, inhibitory processes can be viewed as negative feedback circuits that allow for the interruption of ongoing behavior and the re-deployment of resources to other tasks. When these negative feedback mechanisms are compromised, positive feedback loops may develop as a result (of dis-inhibition). From this perspective, the relative sympathetic activation seen in anxiety disorders may represent dis-inhibition due to faulty inhibitory mechanisms.
Techniques to determine changing system complexity from data are evaluated. Convergence of a frequently used correlation dimension algorithm to a finite value does not necessarily imply an underlying deterministic model or chaos. Analysis of a recently developed family of formulas and statistics, approximate entropy (ApEn), suggests that ApEn can classify complex systems, given at least 1000 data values in diverse settings that include both deterministic chaotic and stochastic processes. The capability to discern changing complexity from such a relatively small amount of data holds promise for applications of ApEn in a variety of contexts.
The aim of the present study was to investigate the effect of vagal tone on performance during executive and non-executive tasks, using a working memory and a sustained attention test. Reactivity to cognitive tasks was also investigated using heart rate (HR) and heart rate variability (HRV). Fifty-three male sailors from the Royal Norwegian Navy participated in this study. Inter-beat-intervals were recorded continuously for 5 min of baseline, followed by randomized presentation of a working memory test (WMT) based on Baddeley and Hitch's research (1974) and a continuous performance test (CPT). The session ended with a 5-min recovery period. High HRV and low HRV groups were formed based on a median split of the root mean squared successive differences during baseline. The results showed that the high HRV group showed more correct responses than the low HRV group on the WMT. Furthermore, the high HRV group showed faster mean reaction time (mRT), more correct responses and less error, than the low HRV group on the CPT. Follow-up analysis revealed that this was evident only for components of the CPT where executive functions were involved. The analyses of reactivity showed a suppression of HRV and an increase in HR during presentation of cognitive tasks compared to recovery. This was evident for both groups. The present results indicated that high HRV was associated with better performance on tasks involving executive function.
Sensory processing is affected by both endogenous and exogenous mechanisms of attention, although how these mechanisms interact in the brain has remained unclear. In the present study, we recorded event-related potentials (ERPs) to investigate how multiple stages of information processing in the brain are affected when endogenous and exogenous mechanisms are concurrently engaged. We found that the earliest stage of cortical visual processing, the striate-cortex-generated C1, was immune to attentional modulation, even when endogenous and exogenous attention converged on a common location. The earliest stage of processing to be affected in this experiment was the late phase of the extrastriate-cortex-generated P1 component, which was dominated by exogenous attention. Processing at this stage was enhanced by exogenous attention, regardless of where endogenous attention had been oriented. Endogenous attention, however, dominated a later, higher-order stage of processing indexed by an enhancement of the P300 that was unaffected by exogenous attention. Critically, between these early and late stages, an interaction was found wherein endogenous and exogenous attention produced distinct, and overlapping, effects on information processing. At the same time that exogenous attention was producing an extended enhancement of the late-P1, endogenous attention was enhancing the occipital-parietal N1 component. These results provide neurophysiological support for theories suggesting that endogenous and exogenous mechanisms represent two attention systems that can affect information processing in the brain in distinct ways. Furthermore, these data provide new evidence regarding the precise stages of neural processing that are, and are not, affected when endogenous and exogenous attentions interact.
Do voluntary (endogenous) and involuntary (exogenous) attention have the same perceptual consequences? Here we used fMRI to examine activity in the fusiform face area (FFA--a region in ventral visual cortex responsive to faces) and frontal-parietal areas (dorsal regions involved in spatial attention) under voluntary and involuntary spatial cueing conditions. The trial and stimulus parameters were identical for both cueing conditions. However, the cue predicted the location of an upcoming target face in the voluntary condition but was nonpredictive in the involuntary condition. The predictable cue condition led to increased activity in the FFA compared to the nonpredictable cue condition. These results show that voluntary attention leads to more activity in areas of the brain associated with face processing than involuntary attention, and they are consistent with differential behavioral effects of attention on recognition-related processes.
We propose that voluntary and involuntary attention affect different mechanisms and have different consequences for performance measured in reaction time. Voluntary attention enhances the perceptual representation whereas involuntary attention affects the tendency to respond to stimuli in one location or another. In a spatial-cueing paradigm, we manipulated perceptual difficulty and compared voluntary and involuntary attention. For the voluntary-attention condition, the spatial cue was predictive of the target location, whereas in the involuntary-attention condition it was not. Increasing perceptual difficulty increased the attention effect with voluntary attention, but decreased it with involuntary attention. Thus voluntary and involuntary attention have different consequences when perceptual difficulty is manipulated and hence are probably caused by different mechanisms.