Resistance-induced brain activity changes during cycle ergometer exercises

Electrocortical activity during resistance exercises in healthy young adults-a systematic review

Introduction

Resistance training (RT) is known to induce both peripheral and central adaptations, resulting in enhanced strength, sports performance, and health benefits. These adaptations are specific to the training stimuli. The acute cortical mechanisms of single sessions resistance exercise (RE) are not yet understood. Therefore, this review investigates the electrocortical activity during acute RE regarding the specific RE stimuli.

Methods

A systematic literature search was conducted across three databases, focusing on the acute electrocortical activity associated with the muscle contraction type, load, and volume of RE in healthy young adults.

Results

Out of an initial 1,332 hits, 19 studies were included for data synthesis. The findings from these studies show that the RE load, contraction type, and volume during RE significantly affect brain activity. The current literature exhibits methodological heterogeneity attributed to variations in study quality, differences in the location of cortical sources, the cortical outcome parameter and the use of diverse training interventions.

Discussion

Despite inconsistencies in the current literature, this review highlights the need to investigate time and frequency-specific characteristics when examining electrocortical activity during RE. More research is necessary to further explore the acute cortical mechanisms related to resistance exercise. Future research could improve our understanding of acute neural responses to RE and provide insights into mechanism underlying more long-term neuroplastic adaptations to RT.

Absolute beta power in exercisers and nonexercisers in preparation for the oddball task

BACKGROUND

 High levels of physical conditioning are associated with improvements in cognitive performance. In this sense, electroencephalographic (ECG) correlates are used to investigate the enhancing role of physical exercise on executive functions. Oscillations in the β frequency range are proposed to be evident during sensorimotor activity.

OBJECTIVE

 To investigate the ECG changes influenced by aerobic and resistance exercises performed in an attention task by analyzing the differences in absolute β power in the prefrontal and frontal regions before, during, and after the oddball paradigm in practitioners and nonpractitioners of physical exercise.

METHODS

 There were 15 physical activity practitioners (aged 27 ± 4.71) and 15 nonpractitioners (age 28 ± 1.50) recruited. A two-way analysis of variance (ANOVA) was implemented to observe the main effect and the interaction between groups and moments (rest 1, pre-stimulus, and rest 2).

RESULTS

 An interaction between group and moment factors was observed for Fp1 (p < 0.001); Fp2 (p = 0.001); F7 (p < 0.001); F8 (p < 0.001); F3 (p < 0.001); Fz (p < 0.001); and F4 (p < 0.001). Electrophysiological findings clarified exercisers' specificity and neural efficiency in each prefrontal and frontal subarea.

CONCLUSION

 Our findings lend support to the current understanding of the cognitive processes underlying physical exercise and provide new evidence on the relationship between exercise and cortical activity.

Myoelectric, Myo-Oxygenation, and Myotonometry Changes during Robot-Assisted Bilateral Arm Exercises with Varying Resistances

Robot-assisted bilateral arm training has demonstrated its effectiveness in improving motor function in individuals post-stroke, showing significant enhancements with increased repetitions. However, prolonged training sessions may lead to both mental and muscle fatigue. We conducted two types of robot-assisted bimanual wrist exercises on 16 healthy adults, separated by one week: long-duration, low-resistance workouts and short-duration, high-resistance exercises. Various measures, including surface electromyograms, near-infrared spectroscopy, heart rate, and the Borg Rating of Perceived Exertion scale, were employed to assess fatigue levels and the impacts of exercise intensity. High-resistance exercise resulted in a more pronounced decline in electromyogram median frequency and recruited a greater amount of hemoglobin, indicating increased muscle fatigue and a higher metabolic demand to cope with the intensified workload. Additionally, high-resistance exercise led to increased sympathetic activation and a greater sense of exertion. Conversely, engaging in low-resistance exercises proved beneficial for reducing post-exercise muscle stiffness and enhancing muscle elasticity. Choosing a low-resistance setting for robot-assisted wrist movements offers advantages by alleviating mental and physiological loads. The reduced training intensity can be further optimized by enabling extended exercise periods while maintaining an approximate dosage compared to high-resistance exercises.

Modeling Brain Functional Connectivity Patterns during an Isometric Arm Force Exertion Task at Different Levels of Perceived Exertion: A Graph Theoretical Approach

The perception of physical exertion is the cognitive sensation of work demands associated with voluntary muscular actions. Measurements of exerted force are crucial for avoiding the risk of overexertion and understanding human physical capability. For this purpose, various physiological measures have been used; however, the state-of-the-art in-force exertion evaluation lacks assessments of underlying neurophysiological signals. The current study applied a graph theoretical approach to investigate the topological changes in the functional brain network induced by predefined force exertion levels for twelve female participants during an isometric arm task and rated their perceived physical comfort levels. The functional connectivity under predefined force exertion levels was assessed using the coherence method for 84 anatomical brain regions of interest at the electroencephalogram (EEG) source level. Then, graph measures were calculated to quantify the network topology for two frequency bands. The results showed that high-level force exertions are associated with brain networks characterized by more significant clustering coefficients (6%), greater modularity (5%), higher global efficiency (9%), and less distance synchronization (25%) under alpha coherence. This study on the neurophysiological basis of physical exertions with various force levels suggests that brain regions communicate and cooperate higher when muscle force exertions increase to meet the demands of physically challenging tasks.

ECG Approximate Entropy in the Elderly during Cycling Exercise

Approximate entropy (ApEn) is used as a nonlinear measure of heart-rate variability (HRV) in the analysis of ECG time-series recordings. Previous studies have reported that HRV can differentiate between frail and pre-frail people. In this study, EEGs and ECGs were recorded from 38 elderly adults while performing a three-stage cycling routine. Before and after cycling stages, 5-min resting-state EEGs (rs-EEGs) and ECGs were also recorded under the eyes-open condition. Applying the K-mean classifier to pre-exercise rs-ECG ApEn values and body weights revealed nine females with EEG power which was far higher than that of the other subjects in all cycling stages. The breathing of those females was more rapid than that of other subjects and their average heart rate was faster. Those females also presented higher degrees of asymmetry in the alpha and theta bands (stronger power levels in the right frontal electrode), indicating stressful responses during the experiment. It appears that EEG delta activity could be used in conjunction with a very low ECG frequency power as a predictor of bursts in the heart rate to facilitate the monitoring of elderly adults at risk of heart failure. A resting ECG ApEn index in conjunction with the subject's weight or BMI is recommended for screening high-risk candidates prior to exercise interventions.

Differences in Physiological Signals Due to Age and Exercise Habits of Subjects during Cycling Exercise

Numerous studies indicated the physical benefits of regular exercise, but the neurophysiological mechanisms of regular exercise in elders were less investigated. We aimed to compare changes in brain activity during exercise in elderly people and in young adults with and without regular exercise habits. A total of 36 healthy young adults (M/F:18/18) and 35 healthy elderly adults (M/F:20/15) participated in this study. According to exercise habits, each age group were classified into regular and occasional exerciser groups. ECG, EEG, and EMG signals were recorded using V-AMP with a 1-kHz sampling rate. The participants were instructed to perform three 5-min bicycle rides with different exercise loads. The EEG spectral power of elders who exercised regularly revealed the strongest positive correlation with their exercise intensity by using Pearson correlation analysis. The results demonstrate that exercise-induced significant cortical activation in the elderly participants who exercised regularly, and most of the p-values are less than 0.001. No significant correlation was observed between spectral power and exercise intensity in the elders who exercised occasionally. The young participants who exercised regularly had greater cardiac and neurobiological efficiency. Our results may provide a new exercise therapy reference for adult groups with different exercise habits, especially for the elders.

EEG-derived brain graphs are reliable measures for exploring exercise-induced changes in brain networks

The interaction of acute exercise and the central nervous system evokes increasing interest in interdisciplinary research fields of neuroscience. Novel approaches allow to monitor large-scale brain networks from mobile electroencephalography (EEG) applying graph theory, but it is yet uncertain whether brain graphs extracted after exercise are reliable. We therefore aimed to investigate brain graph reliability extracted from resting state EEG data before and after submaximal exercise twice within one week in male participants. To obtain graph measures, we extracted global small-world-index (SWI), clustering coefficient (CC) and characteristic path length (PL) based on weighted phase leg index (wPLI) and spectral coherence (Coh) calculation. For reliability analysis, Intraclass-Correlation-Coefficient (ICC) and Coefficient of Variation (CoV) were computed for graph measures before (REST) and after POST) exercise. Overall results revealed poor to excellent measures at PRE and good to excellent ICCs at POST in the theta, alpha-1 and alpha-2, beta-1 and beta-2 frequency band. Based on bootstrap-analysis, a positive effect of exercise on reliability of wPLI based measures was observed, while exercise induced a negative effect on reliability of Coh-based graph measures. Findings indicate that brain graphs are a reliable tool to analyze brain networks in exercise contexts, which might be related to the neuroregulating effect of exercise inducing functional connections within the connectome. Relative and absolute reliability demonstrated good to excellent reliability after exercise. Chosen graph measures may not only allow analysis of acute, but also longitudinal studies in exercise-scientific contexts.