Associations between Heart Rate Variability and Brain Activity during a Working Memory Task: A Preliminary Electroencephalogram Study on Depression and Anxiety Disorder

Neuro-cardiac-guided transcranial magnetic stimulation: Unveiling the modulatory effects of low-frequency and high-frequency stimulation on heart rate

Transcranial magnetic stimulation (TMS) is pivotal in the field of major depressive disorder treatment. Due to its unsatisfied response rate, an increasing number of researchers have turned their attention towards optimizing TMS site localization. Since the influence of TMS in reducing heart rate (HR) offers insights into its regulatory impact on the autonomic nervous system, a novel approach, called neurocardiac-guided TMS (NCG-TMS), has been proposed to pinpoint the brain region eliciting the maximal individual reduction in HR as a personalized optimal stimulation target. The present study intends to systematically explore the effects of stimulation frequency, left and right hemispheres, stimulation positions, and individual differences on HR modulation using the NCG-TMS method. In experiment 1, low-frequency TMS was administered to 30 subjects, and it was found that low-frequency NCG-TMS significantly downregulated HR, with more significant effects in the right hemisphere than in the left hemisphere and the prefrontal cortex than in other brain areas. In experiment 2, high-frequency NCG-TMS stimulation was administered to 30 subjects, showing that high-frequency NCG-TMS also downregulated HR and had the greatest modulatory effect in the right prefrontal region. Simultaneously, both experiments revealed sizeable individual variability in the optimal stimulation site, which in turn validated the feasibility of the NCG-TMS method. In conclusion, the present experiments independently replicated the effect of NCG-TMS, provided an effect of high-/low-frequency TMS stimulation to downregulate HR, and identified a right lateralization of the HR modulation effect.

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.

ECG and EEG based detection and multilevel classification of stress using machine learning for specified genders: A preliminary study

Mental health, especially stress, plays a crucial role in the quality of life. During different phases (luteal and follicular phases) of the menstrual cycle, women may exhibit different responses to stress from men. This, therefore, may have an impact on the stress detection and classification accuracy of machine learning models if genders are not taken into account. However, this has never been investigated before. In addition, only a handful of stress detection devices are scientifically validated. To this end, this work proposes stress detection and multilevel stress classification models for unspecified and specified genders through ECG and EEG signals. Models for stress detection are achieved through developing and evaluating multiple individual classifiers. On the other hand, the stacking technique is employed to obtain models for multilevel stress classification. ECG and EEG features extracted from 40 subjects (21 females and 19 males) were used to train and validate the models. In the low&high combined stress conditions, RBF-SVM and kNN yielded the highest average classification accuracy for females (79.81%) and males (73.77%), respectively. Combining ECG and EEG, the average classification accuracy increased to at least 87.58% (male, high stress) and up to 92.70% (female, high stress). For multilevel stress classification from ECG and EEG, the accuracy for females was 62.60% and for males was 71.57%. This study shows that the difference in genders influences the classification performance for both the detection and multilevel classification of stress. The developed models can be used for both personal (through ECG) and clinical (through ECG and EEG) stress monitoring, with and without taking genders into account.

A rationale for considering heart/brain axis control in neuropsychiatric disease

Neuropsychiatric diseases (NPD) represent a significant global disease burden necessitating innovative approaches to pathogenic understanding, biomarker identification and therapeutic strategy. Emerging evidence implicates heart/brain axis malfunction in NPD etiology, particularly via the autonomic nervous system (ANS) and brain central autonomic network (CAN) interaction. This heart/brain inter-relationship harbors potentially novel NPD diagnosis and treatment avenues. Nevertheless, the lack of multidisciplinary clinical approaches as well as a limited appreciation of molecular underpinnings has stymied progress. Large-scale preclinical multi-systemic functional data can therefore provide supplementary insight into CAN and ANS interaction. We here present an overview of the heart/brain axis in NPD and establish a unique rationale for utilizing a preclinical cardiovascular disease risk gene set to glean insights into heart/brain axis control in NPD. With a top-down approach focusing on genes influencing electrocardiogram ANS function, we combined hierarchical clustering of corresponding regional CAN expression data and functional enrichment analysis to reveal known and novel molecular insights into CAN and NPD. Through ‘support vector machine’ inquiries for classification and literature validation, we further pinpointed the top 32 genes highly expressed in CAN brain structures altering both heart rate/heart rate variability (HRV) and behavior. Our observations underscore the potential of HRV/hyperactivity behavior as endophenotypes for multimodal disease biomarker identification to index aberrant executive brain functioning with relevance for NPD. This work heralds the potential of large-scale preclinical functional genetic data for understanding CAN/ANS control and introduces a stepwise design leveraging preclinical data to unearth novel heart/brain axis control genes in NPD.

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.