Resting state Rolandic mu rhythms are related to activity of sympathetic component of autonomic nervous system in healthy humans

The relationship between alpha power and heart rate variability commonly seen in various mental states

The extensive exploration of the correlation between electroencephalogram (EEG) and heart rate variability (HRV) has yielded inconsistent outcomes, largely attributable to variations in the tasks employed in the studies. The direct relationship between EEG and HRV is further complicated by alpha power, which is susceptible to influences such as mental fatigue and sleepiness. This research endeavors to examine the brain-heart interplay typically observed during periods of music listening and rest. In an effort to mitigate the indirect effects of mental states on alpha power, subjective fatigue and sleepiness were measured during rest, while emotional valence and arousal were evaluated during music listening. Partial correlation analyses unveiled positive associations between occipital alpha2 power (10-12 Hz) and nHF, an indicator of parasympathetic activity, under both music and rest conditions. These findings underscore brain-heart interactions that persist even after the effects of other variables have been accounted for.

Effects of Early Adverse Life Events on Depression and Cognitive Performance from the Perspective of the Heart-Brain Axis

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.

Magnetoencephalogram-based brain-computer interface for hand-gesture decoding using deep learning

Advancements in deep learning algorithms over the past decade have led to extensive developments in brain-computer interfaces (BCI). A promising imaging modality for BCI is magnetoencephalography (MEG), which is a non-invasive functional imaging technique. The present study developed a MEG sensor-based BCI neural network to decode Rock-Paper-scissors gestures (MEG-RPSnet). Unique preprocessing pipelines in tandem with convolutional neural network deep-learning models accurately classified gestures. On a single-trial basis, we found an average of 85.56% classification accuracy in 12 subjects. Our MEG-RPSnet model outperformed two state-of-the-art neural network architectures for electroencephalogram-based BCI as well as a traditional machine learning method, and demonstrated equivalent and/or better performance than machine learning methods that have employed invasive, electrocorticography-based BCI using the same task. In addition, MEG-RPSnet classification performance using an intra-subject approach outperformed a model that used a cross-subject approach. Remarkably, we also found that when using only central-parietal-occipital regional sensors or occipitotemporal regional sensors, the deep learning model achieved classification performances that were similar to the whole-brain sensor model. The MEG-RSPnet model also distinguished neuronal features of individual hand gestures with very good accuracy. Altogether, these results show that noninvasive MEG-based BCI applications hold promise for future BCI developments in hand-gesture decoding.

The phase of sensorimotor mu and beta oscillations has the opposite effect on corticospinal excitability

BACKGROUND

Neural oscillations in the primary motor cortex (M1) shape corticospinal excitability. Power and phase of ongoing mu (8-13 Hz) and beta (14-30 Hz) activity may mediate motor cortical output. However, the functional dynamics of both mu and beta phase and power relationships and their interaction, are largely unknown.

OBJECTIVE

Here, we employ recently developed real-time targeting of the mu and beta rhythm, to apply phase-specific brain stimulation and probe motor corticospinal excitability non-invasively. For this, we used instantaneous read-out and analysis of ongoing oscillations, targeting four different phases (0°, 90°, 180°, and 270°) of mu and beta rhythms with suprathreshold single-pulse transcranial magnetic stimulation (TMS) to M1. Ensuing motor evoked potentials (MEPs) in the right first dorsal interossei muscle were recorded. Twenty healthy adults took part in this double-blind randomized crossover study.

RESULTS

Mixed model regression analyses showed significant phase-dependent modulation of corticospinal output by both mu and beta rhythm. Strikingly, these modulations exhibit a double dissociation. MEPs are larger at the mu trough and rising phase and smaller at the peak and falling phase. For the beta rhythm we found the opposite behavior. Also, mu power, but not beta power, was positively correlated with corticospinal output. Power and phase effects did not interact for either rhythm, suggesting independence between these aspects of oscillations.

CONCLUSION

Our results provide insights into real-time motor cortical oscillation dynamics, which offers the opportunity to improve the effectiveness of TMS by specifically targeting different frequency bands.

Inhibitory Control and Brain–Heart Interaction: An HRV-EEG Study

Background: Motor inhibition is a complex cognitive function regulated by specific brain regions and influenced by the activity of the Central Autonomic Network. We investigate the two-way Brain–Heart interaction during a Go/NoGo task. Spectral EEG ϑ, α powerbands, and HRV parameters (Complexity Index (CI), Low Frequency (LF) and High Frequency (HF) powers) were recorded. Methods: Fourteen healthy volunteers were enrolled. We used a modified version of the classical Go/NoGo task, based on Rule Shift Cards, characterized by a baseline and two different tasks of different complexity. The participants were divided into subjects with Good (GP) and Poor (PP) performances. Results: In the baseline, CI was negatively correlated with α/ϑ. In task 1, the CI was negatively correlated with the errors and α/ϑ, while the errors were positively correlated with α/ϑ. In task 2, CI was negatively correlated with the Reaction Time and positively with α, and the errors were negatively correlated with the Reaction Time and positively correlated with α/ϑ. The GP group showed, at baseline, a negative correlation between CI and α/ϑ. Conclusions: We provide a new combined Brain–Heart model underlying inhibitory control abilities. The results are consistent with the complementary role of α and ϑ oscillations in cognitive control.

An Educational and Exercise Mobile Phone–Based Intervention to Elicit Electrophysiological Changes and to Improve Psychological Functioning in Adults With Nonspecific Chronic Low Back Pain (BackFit App): Nonrandomized Clinical Trial

Background

Concomitant psychological and cognitive impairments modulate nociceptive processing and contribute to chronic low back pain (CLBP) maintenance, poorly correlated with radiological findings. Clinical practice guidelines recommend self-management and multidisciplinary educational and exercise-based interventions. However, these recommendations are based on self-reported measurements, which lack evidence of related electrophysiological changes. Furthermore, current mobile health (mHealth) tools for self-management are of low quality and scarce evidence. Thus, it is necessary to increase knowledge on mHealth and electrophysiological changes elicited by current evidence-based interventions.

Objective

The aim of this study is to investigate changes elicited by a self-managed educational and exercise-based 4-week mHealth intervention (BackFit app) in electroencephalographic and electrocardiographic activity, pressure pain thresholds (PPTs), pain, disability, and psychological and cognitive functioning in CLBP versus the same intervention in a face-to-face modality.

Methods

A 2-arm parallel nonrandomized clinical trial was conducted at the University of the Balearic Islands (Palma, Spain). A total of 50 patients with nonspecific CLBP were assigned to a self-managed group (23/50, 46%; mean age 45.00, SD 9.13 years; 10/23, 43% men) or a face-to-face group (27/50, 54%; mean age 48.63, SD 7.54 years; 7/27, 26% men). The primary outcomes were electroencephalographic activity (at rest and during a modified version of the Eriksen flanker task) and heart rate variability (at rest), PPTs, and pressure pain intensity ratings. The secondary outcomes were pain, disability, psychological functioning (mood, anxiety, kinesiophobia, pain catastrophizing, and fear-avoidance beliefs), and cognitive performance (percentage of hits and reaction times).

Results

After the intervention, frequency analysis of electroencephalographic resting-state data showed increased beta-2 (16-23 Hz; 0.0020 vs 0.0024; P=.02) and beta-3 (23-30 Hz; 0.0013 vs 0.0018; P=.03) activity. In addition, source analyses revealed higher power density of beta (16-30 Hz) at the anterior cingulate cortex and alpha (8-12 Hz) at the postcentral gyrus and lower power density of delta (2-4 Hz) at the cuneus and precuneus. Both groups also improved depression (7.74 vs 5.15; P=.01), kinesiophobia (22.91 vs 20.87; P=.002), activity avoidance (14.49 vs 12.86; P<.001), helplessness (6.38 vs 4.74; P=.02), fear-avoidance beliefs (35 vs 29.11; P=.03), and avoidance of physical activity (12.07 vs 9.28; P=.01) scores, but there was an increase in the disability score (6.08 vs 7.5; P=.01). No significant differences between the groups or sessions were found in heart rate variability resting-state data, electroencephalographic data from the Eriksen flanker task, PPTs, subjective ratings, or cognitive performance.

Conclusions

Both intervention modalities increased mainly beta activity at rest and improved psychological functioning. Given the limitations of our study, conclusions must be drawn carefully and further research will be needed. Nevertheless, to the best of our knowledge, this is the first study reporting electroencephalographic changes in patients with CLBP after an mHealth intervention.

Trial Registration

ClinicalTrials.gov NCT04576611; https://clinicaltrials.gov/ct2/show/NCT04576611

Acute Effects of Various Movement Noise in Differential Learning of Rope Skipping on Brain and Heart Recovery Analyzed by Means of Multiscale Fuzzy Measure Entropy

In search of more detailed explanations for body-mind interactions in physical activity, neural and physiological effects, especially regarding more strenuous sports activities, increasingly attract interest. Little is known about the underlying manifold (neuro-)physiological impacts induced by different motor learning approaches. The various influences on brain or cardiac function are usually studied separately and modeled linearly. Limitations of these models have recently led to a rapidly growing application of nonlinear models. This study aimed to investigate the acute effects of various sequences of rope skipping on irregularity of the electrocardiography (ECG) and electroencephalography (EEG) signals as well as their interaction and whether these depend on different levels of active movement noise, within the framework of differential learning theory. Thirty-two males were randomly and equally distributed to one of four rope skipping conditions with similar cardiovascular but varying coordinative demand. ECG and EEG were measured simultaneously at rest before and immediately after rope skipping for 25 mins. Signal irregularity of ECG and EEG was calculated via the multiscale fuzzy measure entropy (MSFME). Statistically significant ECG and EEG brain area specific changes in MSFME were found with different pace of occurrence depending on the level of active movement noise of the particular rope skipping condition. Interaction analysis of ECG and EEG MSFME specifically revealed an involvement of the frontal, central, and parietal lobe in the interplay with the heart. In addition, the number of interaction effects indicated an inverted U-shaped trend presenting the interaction level of ECG and EEG MSFME dependent on the level of active movement noise. In summary, conducting rope skipping with varying degrees of movement variation appears to affect the irregularity of cardiac and brain signals and their interaction during the recovery phase differently. These findings provide enough incentives to foster further constructive nonlinear research in exercise-recovery relationship and to reconsider the philosophy of classical endurance training.

Toward brain-heart computer interfaces: a study on the classification of upper limb movements using multisystem directional estimates

OBJECTIVE

Brain-computer interfaces (BCI) exploit computational features from brain signals to perform a given task. Despite recent neurophysiology and clinical findings indicating the crucial role of functional interplay between brain and cardiovascular dynamics in locomotion, heartbeat information remains to be included in common BCI systems. In this study, we exploit the multidimensional features of directional and functional interplay between electroencephalographic and heartbeat spectra to classify upper limb movements into three classes.

APPROACH

We gathered data from 26 healthy volunteers that performed 90 movements; the data were processed using a recently proposed framework for brain-heart interplay (BHI) assessment based on synthetic physiological data generation. Extracted BHI features were employed to classify, through sequential forward selection scheme and k-nearest neighbors algorithm, among resting state and three classes of movements according to the kind of interaction with objects.

MAIN RESULTS

The results demonstrated that the proposed brain-heart computer interface (BHCI) system could distinguish between rest and movement classes automatically with an average 90% of accuracy.

SIGNIFICANCE

Further, this study provides neurophysiology insights indicating the crucial role of functional interplay originating at the cortical level onto the heart in the upper limb neural control. The inclusion of functional BHI insights might substantially improve the neuroscientific knowledge about motor control, and this may lead to advanced BHCI systems performances.

Heart rate variability reduction is related to a high amount of visceral adiposity in healthy young women

Several heart rate variability (HRV) studies show abnormalities in autonomic nervous control in obese and overweight subjects. However, some of the results appear to be controversial. Here we investigate the HRV profile in seventy adult normotensive women and its association with general and visceral adiposity. Specifically, we recorded the electrocardiographic (ECG) activity in subjects during a supine resting state for five minutes in a quiet room late in the morning. Total fat mass (TFM) and visceral adipose tissue (VAT) were instead estimated using dual-energy X-ray absorptiometry (DXA). Finally, we used simple a linear regression analysis of frequency and time-domain parameters to study the relationship between HRV and adiposity. Our data showed an overall reduction of the HRV related to an increase of TFM although this regression appeared significant only for high frequencies (HF). When the linear regression was applied between HRV variables and VAT, the slope of the line increases, thus unveiling a statistically significant relation (i.e. the more VAT, the lower HRV). Finally, a control analysis showed that age does not alter the relation between HRV and VAT when used as a confounding factor in multiple regression. To conclude, these findings point to abnormal activity of the autonomic nervous system (ANS) in subjects with an excess of VAT and represent a starting point to determine a non-invasive index of cardiac wellness for clinical and nutritional application.

Functional Linear and Nonlinear Brain–Heart Interplay during Emotional Video Elicitation: A Maximum Information Coefficient Study

Brain and heart continuously interact through anatomical and biochemical connections. Although several brain regions are known to be involved in the autonomic control, the functional brain–heart interplay (BHI) during emotional processing is not fully characterized yet. To this aim, we investigate BHI during emotional elicitation in healthy subjects. The functional linear and nonlinear couplings are quantified using the maximum information coefficient calculated between time-varying electroencephalography (EEG) power spectra within the canonical bands (δ,θ,α,β and γ), and time-varying low-frequency and high-frequency powers from heartbeat dynamics. Experimental data were gathered from 30 healthy volunteers whose emotions were elicited through pleasant and unpleasant high-arousing videos. Results demonstrate that functional BHI increases during videos with respect to a resting state through EEG oscillations not including the γ band (>30 Hz). Functional linear coupling seems associated with a high-arousing positive elicitation, with preferred EEG oscillations in the θ band ([4,8) Hz) especially over the left-temporal and parietal cortices. Differential functional nonlinear coupling between emotional valence seems to mainly occur through EEG oscillations in the δ,θ,α bands and sympathovagal dynamics, as well as through δ,α,β oscillations and parasympathetic activity mainly over the right hemisphere. Functional BHI through δ and α oscillations over the prefrontal region seems primarily nonlinear. This study provides novel insights on synchronous heartbeat and cortical dynamics during emotional video elicitation, also suggesting that a nonlinear analysis is needed to fully characterize functional BHI.

Validity of Commonly Used Heart Rate Variability Markers of Autonomic Nervous System Function

BACKGROUND

Despite strong reservations regarding the validity of a number of heart rate variability (HRV) measures, these are still being used in recent studies.

AIMS

We aimed to compare the reactivity of ostensible sympathetic HRV markers (low and very low frequency [LF and VLF]) to that of electrodermal activity (EDA), an exclusively sympathetic marker, in response to cognitive and orthostatic stress, investigate the possibility of LF as a vagal-mediated marker of baroreflex modulation, and compare the ability of HRV markers of parasympathetic function (root mean square of successive differences [RMSSD] and high frequency [HF]) to quantify vagal reactivity to cognitive and orthostatic stress.

RESULTS

None of the purported sympathetic HRV markers displayed a reactivity that correlated with electrodermal reactivity. LF (ms2) reactivity correlated with the reactivity of both RMSSD and HF during baroreflex modulation. RMSSD and HF indexed the reactivity of the parasympathetic nervous system under conditions of normal breathing; however, RMSSD performed better as a marker of vagal activity when the task required breathing changes.

CONCLUSIONS

Neither LF (in ms2 or normalized units [nu]) nor VLF represent cardiac sympathetic modulation of the heart. LF (ms2) may reflect vagally mediated baroreflex cardiac effects. HRV linear analysis therefore appears to be restricted to the determination of vagal influences on heart rate. With regard to HRV parasympathetic markers, this study supports the suggestion that HRV frequency domain analyses, such as HF, should not be used as an index of vagal activity in study tasks where verbal responses are required, as these responses may induce respiratory changes great enough to distort HF power.

Modulation of brain alpha rhythm and heart rate variability by attention-related mechanisms

According to recent evidence, oscillations in the alpha-band (8–14 Hz) play an active role in attention via allocation of cortical resources: decrease in alpha activity enhances neural processes in task-relevant regions, while increase in alpha activity reduces processing in task-irrelevant regions. Here, we analyzed changes in alpha-band power of 13-channel electroencephalogram (EEG) acquired from 30 subjects while performing four tasks that differently engaged visual, computational and motor attentional components. The complete (visual + computational + motor) task required to read and solve an arithmetical operation and provide a motor response; three simplified tasks involved a subset of these components (visual + computational task, visual task, motor task). Task-related changes in alpha power were quantified by aggregating electrodes into two main regions (fronto-central and parieto-occipital), to test regional specificity of alpha modulation depending on the involved attentional aspects. Independent Component Analysis (ICA) was applied to discover the main independent processes accounting for alpha power over the two scalp regions. Furthermore, we performed analysis of Heart Rate Variability (HRV) from one electrocardiogram signal acquired simultaneously with EEG, to test autonomic reaction to attentional loads. Results showed that alpha power modulation over the two scalp regions not only reflected the number of involved attentional components (the larger their number the larger the alpha power suppression) but was also fine-tuned by the nature of the recruited mechanisms (visual, computational, motor) relative to the functional specification of the regions. ICA revealed topologically dissimilar and differently attention-regulated processes of alpha power over the two regions. HRV indexes were less sensitive to different attentional aspects compared to alpha power, with vagal activity index presenting larger changes. This study contributes to improve our understanding of the electroencephalographic and autonomic correlates of attention and may have practical implications in neurofeedback, brain-computer interfaces, neuroergonomics as well as in clinical practice and neuroscience research exploring attention-deficit disorders.

Acute Effects of Instructed and Self-Created Variable Rope Skipping on EEG Brain Activity and Heart Rate Variability

The influence of physical activity on brain and heart activity dependent on type and intensity of exercise is meanwhile widely accepted. Mainly cyclic exercises with longer duration formed the basis for showing the influence on either central nervous system or on heart metabolism. Effects of the variability of movement sequences on brain and heart have been studied only sparsely so far. This study investigated effects of three different motor learning approaches combined with a single bout of rope skipping exercises on the spontaneous electroencephalographic (EEG) brain activity, heart rate variability (HRV) and the rate of perceived exertion (RPE). Participants performed repetitive learning (RL) and two extremely variable rope skipping schedules according to the differential learning approach. Thereby one bout was characterized by instructed variable learning (DLi) and the other by self-created variable learning (DLc) in randomized order each on three consecutive days. The results show higher RPE after DLi and DLc than after RL. HRV analysis demonstrates significant changes in pre-post exercise comparison in all training approaches. No statistically significant differences between training schedules were identified. Slightly greater changes in HRV parameters were observed in both DL approaches indicating a higher activation of the sympathetic nervous system. EEG data reveals higher parietal alpha1 and temporal alpha2 power in RL compared to both DL schedules immediately post exercise. During the recovery of up to 30 min, RL shows higher temporal and occipital theta, temporal, parietal and occipital alpha, temporal and occipital beta and frontal beta3 power. In conclusion, already a single bout of 3 min of rope skipping can lead to brain states that are associated with being advantageous for cognitive learning. Combined with additional, cognitively demanding tasks in form of the DL approach, it seems to lead to an overload of the mental capacity, at least on the short term. Further research should fathom the reciprocal influence of cardiac and central-nervous strain in greater detail.

An Autonomic Network: Synchrony Between Slow Rhythms of Pulse and Brain Resting State Is Associated with Personality and Emotions

The sympathetic system's role in modulating vasculature and its influence on emotions and personality led us to test the hypothesis that interactions between brain resting-state networks (RSNs) and pulse amplitude (indexing sympathetic activity) would be associated with emotions and personality. In 203 participants, we characterized RSN spatiotemporal characteristics, and phase-amplitude associations of RSN fluctuations with pulse and respiratory recordings. We found that RSNs are spatially reproducible within participants and were temporally associated with low frequencies (LFs < 0.1 Hz) in physiological signals. LF fluctuations in pulse amplitude were not related to cardiac electrical activity and preceded LF fluctuations in RSNs, while LF respiratory amplitude fluctuations followed LF fluctuations in RSNs. LF phase dispersion (PD) (lack of synchrony) between RSNs and pulse (PDpulse) (not respiratory) correlated with the common variability in measures of personality and emotions, with more synchrony being associated with more positive temperamental characteristics. Voxel-level PDpulse mapping revealed an "autonomic brain network," including sensory cortices and dorsal attention stream, with significant interactions with peripheral signals. Here, we uncover associations between pulse signal amplitude (presumably of sympathetic origin) and brain resting state, suggesting that interactions between central and autonomic nervous systems are important for characterizing personality and emotions.

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

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

Parasympathetic and sympathetic activity are associated with individual differences in neural indices of selective attention in adults

Multiple theoretical frameworks posit that interactions between the autonomic nervous system and higher-order neural networks are crucial for cognitive and emotion regulation. However, few studies have directly examined the relationship between measures of autonomic physiology and brain activity during cognitive tasks, and fewer studies have examined both the parasympathetic and sympathetic autonomic branches when doing so. Here, 93 adults completed an ERP auditory selective attention task concurrently with measures of parasympathetic activity (high-frequency heart rate variability; HF-HRV) and sympathetic activity (preejection period; PEP). We focus on the well-studied N1 ERP component to test for associations with baseline values of HF-HRV and PEP. Individuals with higher resting HF-HRV and shorter resting PEP showed larger effects of selective attention on their ERPs. Follow-up regression models demonstrated that HF-HRV and PEP accounted for unique variance in selective attention effects on N1 mean amplitude. These results are consistent with the neurovisceral integration model, such that greater parasympathetic activity is a marker of increased selective attention, as well as other theoretical models that emphasize the role of heightened sympathetic activity in more efficient attention-related processing. The present findings highlight the importance of autonomic physiology in the study of individual differences in neurocognitive function and, given the foundational role of selective attention across cognitive domains, suggest that both parasympathetic and sympathetic activity may be key to understanding variability in brain function across a variety of cognitive tasks.

Role of Sex Hormones in the Control of Vegetative and Metabolic Functions of Middle-Aged Women

Aims: In women's life, menopause is characterized by significant physiological changes often associated with an increase in body mass and obesity-associated sicknesses. Numerous researches described interdependencies of estrogen deficiency, aging, and resting energy expenditure (REE) downfall in the obesity correlated with the menopause. The aim of this study was to determining whether healthy, obese menopausal women underwent HRT treatment, showed changes in their REE, autonomic asset, and assessment of oxidative stress in comparison with obese pre- and post-menopausal women.

Methodology: In this study, we measured the body composition, the REE, the oxidative stress, the diet assimilation, and the autonomic nervous system activity in three groups: pre-menopause women (n = 50), post-menopause women following hormone-replacement therapy (HRT; n = 50), and post-menopause women not following HRT (n = 50).

Results: In the group with HRT a significant increase of the sympathetic activity and REE was described. Finally this group showed a notable increment of oxidative stress compared with the others, and utilizing BIA instrument, the free fat mass was increased respect to the fat mass of obese women.

Conclusion: The study highlights the importance of the HRT-related physiological changes that influence body weight in menopause women. This results are important because have a practical implications for prevention and/or treatment of the obesity.

Brain Oscillations Elicited by the Cold Pressor Test: A Putative Index of Untreated Essential Hypertension

Objective

Essential hypertension is associated with reduced pain sensitivity of unclear aetiology. This study explores this issue using the Cold Pressor Test (CPT), a reliable pain/stress model, comparing CPT-related EEG activity in first episode hypertensives and controls.

Method

22 untreated hypertensives and 18 matched normotensives underwent 24-hour ambulatory blood pressure monitoring (ABPM). EEG recordings were taken before, during, and after CPT exposure.

Results

Significant group differences in CPT-induced EEG oscillations were covaried with the most robust cardiovascular differentiators by means of a Canonical Analysis. Positive correlations were noted between ABPM variables and Delta (1–4 Hz) oscillations during the tolerance phase; in high-alpha (10–12 Hz) oscillations during the stress unit and posttest phase; and in low-alpha (8–10 Hz) oscillations during CPT phases overall. Negative correlations were found between ABPM variables and Beta2 oscillations (16.5–20 Hz) during the posttest phase and Gamma (28.5–45 Hz) oscillations during the CPT phases overall. These relationships were localised at several sites across the cerebral hemispheres with predominance in the right hemisphere and left frontal lobe.

Conclusions

These findings provide a starting point for increasing our understanding of the complex relationships between cerebral activation and cardiovascular functioning involved in regulating blood pressure changes.

Combining electroencephalographic activity and instantaneous heart rate for assessing brain-heart dynamics during visual emotional elicitation in healthy subjects

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 time-varying 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 theθband (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.

Brain Oscillations in Sport: Toward EEG Biomarkers of Performance

Brain dynamics is at the basis of top performance accomplishment in sports. The search for neural biomarkers of performance remains a challenge in movement science and sport psychology. The non-invasive nature of high-density electroencephalography (EEG) recording has made it a most promising avenue for providing quantitative feedback to practitioners and coaches. Here, we review the current relevance of the main types of EEG oscillations in order to trace a perspective for future practical applications of EEG and event-related potentials (ERP) in sport. In this context, the hypotheses of unified brain rhythms and continuity between wake and sleep states should provide a functional template for EEG biomarkers in sport. The oscillations in the thalamo-cortical and hippocampal circuitry including the physiology of the place cells and the grid cells provide a frame of reference for the analysis of delta, theta, beta, alpha (incl.mu), and gamma oscillations recorded in the space field of human performance. Based on recent neuronal models facilitating the distinction between the different dynamic regimes (selective gating and binding) in these different oscillations we suggest an integrated approach articulating together the classical biomechanical factors (3D movements and EMG) and the high-density EEG and ERP signals to allow finer mathematical analysis to optimize sport performance, such as microstates, coherency/directionality analysis and neural generators.