Cardiac-Brain Dynamics Depend on Context Familiarity and Their Interaction Predicts Experience of Emotional Arousal

Physiologic Network-Based Brain-Heart Interaction Quantification During Visual Emotional Elicitation

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

Unveiling the acute neurophysiological responses to strength training: An exploratory study on novices performing weightlifting bouts with different motor learning models

Currently, there is limited evidence regarding various neurophysiological responses to strength exercise and the influence of the adopted practice schedule. This study aimed to assess the acute systemic effects of snatch training bouts, employing different motor learning models, on skill efficiency, electric brain activity (EEG), heart rate variability (HRV), and perceived exertion as well as mental demand in novices. In a within-subject design, sixteen highly active males (mean age: 23.13 ± 2.09 years) randomly performed snatch learning bouts consisting of 36 trials using repetitive learning (RL), contextual interference (blocked, CIb; and serial, CIs), and differential learning (DL) models. Spontaneous resting EEG and HRV activities were recorded at PRE and POST training bouts while measuring heart rate. Perceived exertion and mental demand were assessed immediately after, and barbell kinematics were recorded during three power snatch trials performed following the POST measurement. The results showed increases in alpha, beta, and gamma frequencies from pre- to post-training bouts in the majority of the tested brain regions (p values ranging from < 0.0001 to 0.02). The CIb model exhibited increased frequencies in more regions. Resting time domain HRV parameters were altered following the snatch bouts, with increased HR (p < 0.001) and decreased RR interval (p < 0.001), SDNN, and RMSSD (p values ranging from < 0.0001 to 0.02). DL showed more pronounced pulse-related changes (p = 0.01). Significant changes in HRV frequency domain parameters were observed, with a significant increase in LFn (p = 0.03) and a decrease in HFn (p = 0.001) registered only in the DL model. Elevated HR zones (> HR zone 3) were more dominant in the DL model during the snatch bouts (effect size = 0.5). Similarly, the DL model tended to exhibit higher perceived physical (effect size = 0.5) and mental exertions (effect size = 0.6). Despite the highest psycho-physiological response, the DL group showed one of the fewest significant EEG changes. There was no significant advantage of one learning model over the other in terms of technical efficiency. These findings offer preliminary support for the acute neurophysiological benefits of coordination-strength-based exercise in novices, particularly when employing a DL model. The advantages of combining EEG and HRV measurements for comprehensive monitoring and understanding of potential adaptations are also highlighted. However, further studies encompassing a broader range of coordination-strength-based exercises are warranted to corroborate these observations.

Dynamic Functional Connectivity of Emotion Processing in Beta Band with Naturalistic Emotion Stimuli

While naturalistic stimuli, such as movies, better represent the complexity of the real world and are perhaps crucial to understanding the dynamics of emotion processing, there is limited research on emotions with naturalistic stimuli. There is a need to understand the temporal dynamics of emotion processing and their relationship to different dimensions of emotion experience. In addition, there is a need to understand the dynamics of functional connectivity underlying different emotional experiences that occur during or prior to such experiences. To address these questions, we recorded the EEG of participants and asked them to mark the temporal location of their emotional experience as they watched a video. We also obtained self-assessment ratings for emotional multimedia stimuli. We calculated dynamic functional the connectivity (DFC) patterns in all the frequency bands, including information about hubs in the network. The change in functional networks was quantified in terms of temporal variability, which was then used in regression analysis to evaluate whether temporal variability in DFC (tvDFC) could predict different dimensions of emotional experience. We observed that the connectivity patterns in the upper beta band could differentiate emotion categories better during or prior to the reported emotional experience. The temporal variability in functional connectivity dynamics is primarily related to emotional arousal followed by dominance. The hubs in the functional networks were found across the right frontal and bilateral parietal lobes, which have been reported to facilitate affect, interoception, action, and memory-related processing. Since our study was performed with naturalistic real-life resembling emotional videos, the study contributes significantly to understanding the dynamics of emotion processing. The results support constructivist theories of emotional experience and show that changes in dynamic functional connectivity can predict aspects of our emotional experience.