Our knowledge of the processes underpinning learning and the accomplishment of motor tasks has significantly advanced over the past 10 years. A notable advancement has been the development of non-invasive electrophysiological procedures like transcranial magnetic stimulation (TMS), which uses powerful magnets placed near the skin to electromagnetically stimulate brain tissue electromagnetically [1]. This method induces a relatively tiny and brief change in the electrical activity, which can be used to stimulate and investigate the operation of the brain's circuits and neural connections. Numerous research have been conducted in recent years to examine the connection between brain activity and muscular reaction. In this regard, research on athletes was crucial for understanding the mechanics behind the performance of motor activities. The corticospinal excitability of karate practitioners was found to be greater than that of sedentary people, showing that exercise alters the interactions and balance between inhibitory and facilitatory circuits, which define the primary motor cortex's (M1) final output. Furthermore, sports-specific neurophysiological configurations may be supported by the strong association between corticospinal excitability and coordination performance [2, 3]. In a different study, the authors discovered that karate practitioners had faster reaction times (RT), lower resting motor thresholds (rMT), shorter motor evoked potential (MEP) latency, and higher MEP amplitude. In addition, they discovered that there was a significant positive linear correlation between RT and rMT, between RT and MEP latency, and between RT and MEP amplitude [4, 5]. The differences between the trained and untrained groups in this study's findings imply that corticomotor projections can become functionally malleable with long-term reinforcement and helpful motor practice [6, 7]. The differences discovered in earlier research may have resulted from training-induced improvements in cortical excitability. In fact, the adult brain can change how it is organized through physiological processes like practicing easy actions repeatedly. However, in a dynamic environment, brain plasticity allows the nervous system to ensure that the muscles are correctly activated to achieve the behavioral objective, and, more recently, a genetic component for brain plasticity has been found [8, 9]. Numerous studies have shown that structured physical training is an excellent strategy to improve motor skills and induce changes at the cortical level [10-12]. In this regard, it has been observed that in physical activity programs aimed at improving coordination skills by observing target movements, an increase in cortical excitability is observed, which could lead to an improvement in learning processes [13, 14]. A recent investigation has shown that simple RT is related to the level of cortical excitability and that increasing the complexity of a movement consequently increases the RT. These results highlight that cortical excitability influences both RT and decision-making processes [15, 16].

Being able to pay attention is a crucial life ability that is directly linked to memory, learning, perception, and executive function. The fundamental job of attention is to choose and select a portion of the available stimuli while simultaneously suppressing irrelevant information. Focused attention is the ability to respond clearly to tactile or visual information. Selective attention is the ability to retain a behavioral or cognitive set in the face of competing or distracting stimuli. In contrast, sustained attention is the capacity to maintain a steady behavioral response throughout continuous and repetitive activities. [1-3]. However, corticospinal differences in excitability may also be problems of previous studies in a neuron-enhanced exciting state of complex neuron tasks. It would be fascinating to find out whether cortical excitability may influence RT in light of the rise in RT associated with stimulus difficulty. Thus, the purpose of this research was to investigate the presence of relationships between cortical excitability, assessed using TMS, and complex RT, assessed using the Posner test. The cortical excitability parameters investigated are rMT (%) and MEP latency, while, the Posner test were performed in order to investigate the RT and the percentages of correct answer [15, 17].

The subjects recruited for the study were invited to the Physiology Laboratory of the University of Foggia. Upon entering the laboratory, each subject was explained the entire experimental procedure, which began after the signing of the informed consent. Before the beginning of the session, the subjects were explained how to carry out the Posner test. To ensure that all study subjects understood how to perform this test, a non-study subject showed them how to perform it. The subjects did not perform Posner tests to avoid a training effect that could have distorted the results. Subsequently, the tests were performed with this temporal sequence: detection of anthropometric parameters, positioning on the chair, identification of the RMT and recording of twenty stimuli for MEP analysis, Posner tests (Fig. 1).

2.5. Statistical Analysis

Statistical analyses were performed by GraphPad 6 Software, Inc., for Windows, version 6.01. The data are presented as mean (M) ± standard deviation (SD), and statistical significance was set at p < 0.05. The Shapiro–Wilk test was used to check the normal distribution of variables. Linear regression analysis was performed to investigate the relationship between the cortical excitability parameters and the results of the Posner test.

Fig. (1). The positive relationship between rMT% and RT. rMT%= resting motor threshold%; RT= reaction time.

No discomfort or adverse effects during the TMS procedure were noticed or reported (Table 1).

Fig. (2). The positive relationship between MEP latency and RT. MEP= motor evoked potential; RT= reaction time.

Fig. (3). The positive relationship between rMT and the percentage of errors committed by the subjects during the execution of the Posner test (p<0.001; R2=0.61; F=28.58). rMT%= resting motor threshold%.

Furthermore, significant correlations emerged between the neurophysiological parameters detected by TMS and the parameters detected by the Posner test. A significant positive correlation (p<0.001) emerged between rMT and RT (Fig. 2). Moreover, a significant positive correlation (p<0.001) emerged between MEP latency and RT (Fig. 3).

We also performed a linear regression to investigate the possible relationship between cortical excitability and MEP latency with the percentage of errors committed by the subjects during the execution of the Posner test. The results show a significant positive correlation (p<0.001) between rMT and the percentage of errors (Fig. 4). The results also show significant positive correlation (p<0.05) between MEP latency (ms) and the percentage of errors (Fig. 5). The results of linear regression are shown in Tables 1 and 2.

Fig. (4). The positive relationship between MEP latency and the percentage of errors committed by the subjects during the execution of the Posner test (p<0.05; R2=0.26; F=6.37). MEP= motor evoked potential.

Fig. (5). The significant positive correlation (p<0.05) between MEP latency (ms) and the percentage of errors.

In this study, we tested the possible relationships between cortical excitability, with the related electrophysiological parameters, and the times of complex RT detected by a neuropsychological test often used to evaluate attention [25-27]. The main results of the study showed that subjects who showed lower rMT were able to respond faster and they also showed a significantly lower error rate compared to subjects who showed higher motor activation thresholds. We also had similar results with regard to the neurophysiological parameters related to TMS [28]. In fact, the subjects who showed shorter MEP latency were able to respond faster and also showed a percentage of errors significantly lower than the subjects who showed longer MEP latency [29]. In daily life, we constantly witness the control of actions and motor responses to ensure that they are suitable for multiple contests [23, 24], and these situations reflect the so-called concept of the “perception-action cycle”, introduced by Fuster (2003), which highlights the continuous interaction of perceptual neural networks with the executive component, presented by the motor cortex, which interacting with each other are able to perform the movements correctly [30]. As previously described, one of the most important consequences of these relationships is the ability to make a response that is appropriate to the perceived stimulus [26]. The results of our study seem to be in differential agreement with other studies in which the importance of neural networks has emerged. Which are able to analyze information correctly, and establish mature predictions on the sensory inputs of the neural network in order to be able to make quick and precise answers [31, 32]. After the meetings with real stimuli, the neural networks modify the synaptic weights in order to make better predictions of the environment in the future [27]. As widely shown in the literature, quantifying the RT and the percentage of correct responses of a motor task allows us to obtain information about the specific cognitive functions directly involved [28, 29]. Moreover, as showed in a previous study, lower RT is correlated to greater neural activations in young subjects who, therefore study, are able to obtain high performances in complex motor tasks. An important recent review has highlighted that the use of brain stimulation protocols during the execution of different motor tasks is able to reduce RT [1].

According to a previously published study, the frontal lobe regulates cognitive performance and the M1 cortex and frontal lobe have a physically hierarchical connection. The M1 and cognitive regions of the monkeys' frontal lobes showed single-cell activity both before and during the execution of coordinated movement, according to other studies [33-37]. They demonstrated complex physiological connections and linkages between the frontal lobe and M1 cortex [34-38]. According to Friston (2002), precise anatomical and functional connectivity underpins the context-dependent interactions across many brain areas that support sensory, motor, or cognitive functions [35-39]. This supports the theory that the frontal lobe, which regulates cognitive function, and the motor cortex are physically and functionally connected. According to this study, there seems to be a relationship between cortical excitability and RT, as shown in our investigation. Furthermore, positive effects on cognition related to RT were evident after applying brain stimulation protocols and in particular, a significant reduction in RT during learning and memory and executive functions / tasks was observed [30]. However, the situation is different when the response is not in series but carried out in parallel with the processing of the stimulus [33-36]. In other words, there are differences between simple RT and the RT that provide choices. In this regard, Ortiz et al., have shown that in subjects who perform the movement of one of the two hands linked to the type of stimuli, the potentials of premovement occurred first contralateral to the hand that had to move more frequently, even before the movements of the other hand. In this study, the authors could suggest that response selection and motor pre-activation occur in parallel [37, 38].

In conclusion, to the extent that we are aware, our study seems to show a relationship between neurophysiological parameters, RT and percentage of correct answers, unlike what has already been demonstrated in a previous study [39, 40]. Indeed, in this case, we have observed that improved cortical excitability also influences decision-making processes. Therefore, in the future, other studies could be conducted to evaluate the possibility of introducing different subjects to non-invasive brain stimulation protocols, such as TMS, to increase cortical excitability in order to obtain an improvement in performance. Furthermore, it would be advisable to increase the number of subjects and evaluate the same parameters in different populations to confirm the results obtained. For example, studies could be carried out to verify possible relationships, linked ages, between the parameters analyzed in this study. Another aspect to take into consideration is that in our study, we analyzed only two parameters (rMT and MEP latency) that can provide us with indications about cortical excitability. It would be advisable to carry out further studies to confirm the integrity of our data, analyzing other parameters and using other instruments (EEG and functional magnetic resonance) in order to provide us with ever more precise data regarding the relationships between excitability and complex reaction times. In fact, since cortical excitability is defined as the intensity of a cortical neuron's response to a given stimulation, reflects neuronal reactivity and response specificity, and is thus a fundamental aspect of human brain function [32], expanding the field of study can undoubtedly result in interesting data that can improve public health and give us in-depth knowledge about brain function.