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Background Since the introduction (in 2006) of commercially available portable wireless muscle oximeters, the use of muscle near-infrared spectroscopy (NIRS) technology is gaining in popularity as an application to observe changes in muscle metabolism and muscle oxygenation during and after exercise or training interventions in both laboratory and applied sports settings. Objectives The objectives of this systematic review were to highlight the application of muscle oximetry in evaluating oxidative skeletal muscle performance to sport activities and emphasize how this technology has been applied to exercise and training. Methods Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines were followed in a systematic fashion to search, assess and synthesize existing literature on this topic. The Scopus and MEDLINE/PubMed electronic databases were searched to 1 March 2017. Potential inclusions were screened against eligibility criteria relating to recreationally trained to elite athletes, with or without training programs, who must have assessed physiological variables monitored by commercial oximeters or NIRS instrumentation. Results Of the 14,609 identified records, only 57 studies met the eligibility criteria. This systematic review highlighted a number of key findings in 16 sporting activities. Overall, NIRS information can be used as a marker of skeletal muscle oxidative capacity and for analyzing muscle performance factors. Conclusions Although NIRS instrumentation is promising in evaluating oxidative skeletal muscle performance when used in sport settings, there is still the need for further instrumental development and randomized/longitudinal trials to support the detailed advantages of muscle oximetry utilization in sports science.

While the importance of physical abilities is noncontested to perform in elite sport, more focus has recently been turned toward cognitive processes involved in sport performance. Practicing any sport requires a high demand of cognitive functioning including, but not limited to, decision-making, processing speed, working memory, perceptual processing, motor functioning, and attention. Noninvasive transcranial electrical stimulation (tES) has recently attracted considerable scientific interest due to its ability to modulate brain functioning. Neuromodulation apparently improves cognitive functions engaged in sports performance. This opinion manuscript aimed to reveal that tES is likely an adjunct ergogenic resource for improving cognitive processes, counteracting mental fatigue, and managing anxiety in elite athletes. Nevertheless, the first evidence is insufficient to guarantee its real effectiveness and benefits. All tES techniques could be add-ons to make performance-related cognitive functions more efficient and obtain better results. Modulating inhibitory control through tES over the frontal cortex might largely contribute to the improvement of mental performance. Nevertheless, studies in elite athletes are required to assess the long-term effects of tES application as an ergogenic aid in conjunction with other training methods (e.g., neurofeedback, mental imagery) where cognitive abilities are trainable.

Gerrit Burkhardt and colleagues reported that the use of transcranial direct current stimulation (tDCS) as an additional treatment to stableselective serotonin reuptake inhibitor pharmacotherapy did not elicit further antidepressant effects in patients with major depressive disorder (MDD) [...].

Although endurance running (ER) seems to be a simple repetitive exercise, good ER performance also requires and relies on multiple cognitive and motor control processes. Most of previous neuroimaging studies on ER were conducted using a single MRI modality, yet no multimodal study to our knowledge has been performed in this regard. In this study, we used multimodal MRI data to investigate the brain structural and functional differences between endurance runners (n = 22; age = 26.27 ± 6.07 years; endurance training = 6.23 ± 2.41 years) and healthy controls (HCs; n = 20; age = 24.60 ± 4.14 years). Compared with the HCs, the endurance runners showed greater gray matter volume (GMV) and cortical surface area in the left precentral gyrus, which at the same time had higher functional connectivity (FC) with the right postcentral and precentral gyrus. Subcortically, the endurance runners showed greater GMV in the left hippocampus and regional inflation in the right hippocampus. Using the bilateral hippocampi as seeds, further seed-based FC analyses showed higher hippocampal FC with the supplementary motor area, middle cingulate cortex, and left posterior lobe of the cerebellum. Moreover, compared with the HCs, the endurance runners also showed higher fractional anisotropy in several white matter regions, involving the corpus callosum, left internal capsule, left corona radiata, left external capsule, left posterior lobe of cerebellum and bilateral precuneus. Taken together, our findings provide several lines of evidence for the brain structural and functional differences between endurance runners and HCs. The current data suggest that these brain characteristics may have arisen as a result of regular ER training; however, whether they represent the neural correlates underlying the good ER performances of the endurance runners requires further investigations.

This study aims to provide a transferable methodology in the context of sport performance modelling, with a special focus to the generalisation of models. Data were collected from seven elite Short track speed skaters over a three months training period. In order to account for training load accumulation over sessions, cumulative responses to training were modelled by impulse, serial and bi-exponential responses functions. The variable dose-response (DR) model was compared to elastic net (ENET), principal component regression (PCR) and random forest (RF) models, while using cross-validation within a time-series framework. ENET, PCR and RF models were fitted either individually (M_I) or on the whole group of athletes (M_G). Root mean square error criterion was used to assess performances of models. ENET and PCR models provided a significant greater generalisation ability than the DR model (p = 0.018 , p < 0.001 , p = 0.004 and p < 0.001 for ENET_I , ENET_G , PCR_I and PCR_G , respectively). Only ENET_G and RF_G were significantly more accurate in prediction than DR (p < 0.001 and p < 0.012). In conclusion, ENET achieved greater generalisation and predictive accuracy performances. Thus, building and evaluating models within a generalisation enhancing procedure is a prerequisite for any predictive modelling.

In the last decade, virtual reality (VR) training has been used extensively in video games and military training to provide a sense of realism and environmental interaction to its users. More recently, VR training has been explored as a possible adjunct therapy for people with motor and mental health dysfunctions. The concept underlying VR therapy as a treatment for motor and cognitive dysfunction is to improve neuroplasticity of the brain by engaging users in multisensory training. In this review, we discuss the theoretical framework underlying the use of VR as a therapeutic intervention for neurorehabilitation and provide evidence for its use in treating motor and mental disorders such as cerebral palsy, Parkinson's disease, stroke, schizophrenia, anxiety disorders, and other related clinical areas. While this review provides some insights into the efficacy of VR in clinical rehabilitation and its complimentary use with neuroimaging (e.g., fNIRS and EEG) and neuromodulation (e.g., tDCS and rTMS), more research is needed to understand how different clinical conditions are affected by VR therapies (e.g., stimulus presentation, interactivity, control and types of VR). Future studies should consider large, longitudinal randomized controlled trials to determine the true potential of VR therapies in various clinical populations.

This study aims to predict individual Acceleration-Velocity profiles (A-V) from Global Navigation Satellite System (GNSS) measurements in real-world situations. Data were collected from professional players in the Superleague division during a 1.5 season period (2019–2021). A baseline modeling performance was provided by time-series forecasting methods and compared with two multivariate modeling approaches using ridge regularisation and long short term memory neural networks. The multivariate models considered commercial features and new features extracted from GNSS raw data as predictor variables. A control condition in which profiles were predicted from predictors of the same session outlined the predictability of A-V profiles. Multivariate models were fitted either per player or over the group of players. Predictor variables were pooled according to the mean or an exponential weighting function. As expected, the control condition provided lower error rates than other models on average ( p = 0.001). Reference and multivariate models did not show significant differences in error rates ( p = 0.124), regardless of the nature of predictors (commercial features or extracted from signal processing methods) or the pooling method used. In addition, models built over a larger population did not provide significantly more accurate predictions. In conclusion, GNSS features seemed to be of limited relevance for predicting individual A-V profiles. However, new signal processing features open up new perspectives in athletic performance or injury occurrence modeling, mainly if higher sampling rate tracking systems are considered.

The beneficial effects of acute exercise on executive function have been well-documented, but the influence of cardiorespiratory fitness on this effect requires further investigations, especially using imaging technique. This study aimed to examine the effects of cardiorespiratory fitness on acute exercise-induced changes on behavioral performance and on functional brain activation. Based on their cardiorespiratory fitness level, 62 participants ranked in the top and bottom of the maximum oxygen consumption (VO max) were finally selected and allocated to high-fit group or low-fit group. Both groups were asked to complete the Stroop task after 30 min of aerobic exercise and chair-seated rest (control session). Among them, 26 participants were randomly selected and asked to undergo the Functional Magnetic Resonance Imaging (fMRI). Behavioral results showed that individuals responded significantly faster after exercise than those in the control session. The fMRI results revealed a significant interaction effects of Group by Session in brain regions including anterior cingulate cortex (ACC) and bilateral dorsal lateral prefrontal cortex (DLPFC). For the ACC, activation in the high-fit group was significantly decreased after aerobic exercise compared to those in the control session; whereas an increased activation was noticed in the low-fit group. Regarding to the bilateral DLPFC, activation in high-fit group was significantly decreased after exercise compared to those in the control session, while no significant differences were found in the low-fit group. In addition, for the post-exercise session, a significant positive correlations between activation of the ACC and left DLPFC in the high-fit group was observed. There was a significant negative correlation between activation of the ACC and reaction time in the congruent condition after exercise in the low-fit group. Findings further clarify the neurophysiological processes of acute exercise-induced changes in cognitive performance as they suggest that cardiorespiratory fitness is an important factor which influences changes in brain activation patterns in response to acute aerobic exercises.