ReviewCharacteristics of the athletes' brain: Evidence from neurophysiology and neuroimaging
Introduction
How do athletes control their movements? How does the brain functioning of athletes differ from that of non-athletes? How long must non-athletes train to obtain the brain of an athlete? Such questions have long been important to the field of sports sciences. One approach to answering them involves measuring the brain activities of athletes and comparing these with the results obtained from non-athletes (novices) because sports activities are strongly linked to many neurophysiological characteristics. For instance, flexible adaptation of behavior is required for performing skilled movements under variable and changing environments (Kudo et al., 2000, Kudo and Ohtsuki, 2008). These adaptations include complicated and simultaneous neural activities, such as perception, stimulus discrimination, decision making, multimodal integration, motor preparation, and execution. In addition, attentional state plays an important role in producing temporally and spatially accurate behaviors, and rapid shifting from broad to selective attention constitutes an important contributor to successful performance (Fontani et al., 1999). In these cases, complex brain activities, including the activation of relevant brain structures and the deactivation of irrelevant ones, are required (Kudo et al., 2004, Laurienti et al., 2002). In addition, the athlete's brain offers a good opportunity for studying neuroplasticity because athletes participate in long-term training and practice, often starting very early in childhood, throughout their entire careers. Motor-skill learning relates to the acquisition of a motor ability as a result of repetition or long-term training. Previous studies on non-human brains have shown that repeated or long-term training leads to long-lasting changes in the neuronal firing rate of the primary motor cortex (MI) (Rioult-Pedotti et al., 2000), synaptogenesis (Kleim et al., 2002), and alterations in topography (Nudo et al., 1996). Similar learning-dependent modifications have also been found in sensory cortical areas. For example, monkeys trained to discriminate between auditory stimuli of different frequencies showed an expansion of the area in the auditory cortex responsive to the trained frequency and a subsequent reduction of the area in the auditory cortex responsive to non-trained frequencies (Recanzone et al., 1993). Thus, considerable evidence for learning-dependent changes in non-human brains has been accumulated. However, neuroscientific research on the brains of athletes remains necessary because highly skilled human movements, such as sports-related skills, represent distinguishing characteristics of the most highly developed brain, the human brain. One major challenge facing brain scientists involves achieving a better understanding of the brain functions underlying the acquisition, planning, and execution of such movements.
Recently, several non-invasive recording methods have been used to measure human brain activity. Among these are methods based on neurophysiology, including electroencephalography (EEG), magnetoencephalography (MEG), and transcranial magnetic stimulation (TMS), and methods based on neuroimaging, including functional magnetic resonance imaging (fMRI) and near-infrared spectroscopy (NIRS).
EEG is indispensable for examining the neural activities in the human brain and offers a high temporal resolution on the order of milliseconds. EEG technology captures fluctuations in the electrical voltage of the brain through electrodes placed on the scalp in accordance with the standardized guidelines of the International 10–20 system (Jasper, 1958). EEG data also represent changes in the potential differences between different points on the human scalp and the electric field potentials that arise from excitatory and inhibitory postsynaptic potentials.
MEG offers several theoretical advantages over EEG in localizing cortical sources (brain dipoles) because the magnetic fields recorded on the scalp are less affected by volume currents and anatomical homogeneity. MEG also permits spatial and temporal localization of excited cortical areas on the order of millimeters and milliseconds (reviewed in Hari et al., 2000, Kakigi et al., 2000).
The use of TMS was first reported by Barker et al. (1985), who showed that it was possible to activate the corticospinal tract by exposing the scalp of human participants to a brief magnetic field. This technique has been able to deliver single, paired, and repetitive (rTMS) pulses. TMS of the motor cortex can produce relatively synchronous muscle responses, the motor evoked potentials (MEPs). Considerable information about the functional connectivity within the cerebral cortex has been gained using TMS-induced MEPs. Such information has enhanced understanding of such phenomena as interhemispheric interactions between the dorsal premotor area (PM) and the contralateral MI (Mochizuki et al., 2004, Koch et al., 2006). In addition, because a pulsed magnetic field creates current flow in the brain and can temporarily excite or inhibit specific areas, researchers have used TMS to investigate many areas of cognitive neuroscience, such as perception, attention, learning, plasticity, language, and awareness (reviewed in Hallett, 2000, Walsh and Cowey, 2000).
fMRI, which measures the blood oxygenation level-dependent (BOLD) signal, has been used not only as a tool for mapping brain activity but also as a means of studying the dynamics of neural networks by tracking fMRI response characteristics across various spatial and temporal scales (Logothetis et al., 2001).
NIRS has emerged as a rapidly growing method for non-invasively monitoring the tissue oxygenation and hemodynamics of the brain. Using multiple wavelengths, we can quantify the concentration of the constituents of tissue, including oxy-hemoglobin (HbO2), deoxy-hemoglobin (HHb), water, lipids, and cytochrome oxidase (CtOx) (reviewed in Perrey, 2008).
These recording methods are useful for clarifying human cognitive processing related to visual, auditory, somatosensory (tactile), gustatory, and olfactory stimulation, and human motor processing related to preparing, executing, and imagining behavior. Moreover, these techniques can yield insights into the covert processing involved in sports performance. Indeed, it is generally accepted that athletes are faster, stronger, more accurate, more efficient, more consistent, and more automatic in the performance of their particular sport, and able to jump higher than non-athletes. For example, the beauty of any gymnastic move or the overwhelming power of a javelin throw relies on the ability of the nervous system to ensure that the correct muscles are activated to the proper extent at the right time and in the right sequence (Nielsen and Cohen, 2008). These notions suggest that the neuronal activities in athletes' brains are flexibly modulated through many years of deliberate practice activities (Ericsson and Lehmann, 1996). However, the mechanisms underpinning the plastic adaptive changes in the neuronal circuitries of athletes' brains have remained unclear. Thus, sports scientists need to provide evidence to substantiate this hypothesis.
We have two objectives in this review article. First, we examine recent non-invasive research comparing the brain activities of athletes and non-athletes. To our knowledge, no systematic reviews of the scientific literature on athletes' brains are available even though much research and many research reviews are available on the brains of musicians (Münte et al., 2002, Zatorre et al., 2007). In a review of the literature on musicians' brains, Münte et al. (2002) claimed that professional musicians represent an ideal model for investigating plastic changes in the human brain. We argue that the athletes constitute an additional model to be investigated for this purpose because a variety of abilities or skills, depending on the particular sport (e.g., strength, accuracy, artistry), are required for athletic performance. We focus on four broad areas of research: motor-related neural activity, cognitive processing related to each sensory modality, EEG spectral power, and higher integrative functioning using event-related potentials (ERPs) and fMRI data. Second, based on these findings, we discuss and seek solutions to several problems facing the field of sports sciences.
Section snippets
Motor-related cortical potentials (MRCPs)
Motor-related cortical potentials (MRCPs) are a time-locked measure of spike-triggered EEGs that are recorded preceding self-initiated voluntary movements and that reflect the movement-preparation process (reviewed in Shibasaki and Hallett, 2006). These potentials begin with a slow rising negativity called the Bereitschaftspotential (BP) and progress to a steeper, later negativity starting about 500 ms before movement onset called the negativity slope (NS′). The signal-to-noise ratio of MRCPs
Brain responses to external stimuli
In this section, we examine differences between athletes and non-athletes in brain responses to external visual, somatosensory, and auditory stimuli. To date, most studies have used sensory evoked potentials (EPs) obtained with time-locked EEGs and an averaging method after stimulus onset. In general, one can easily hypothesize that the amplitudes and/or latencies of EPs differ between athlete and non-athlete groups. However, it seems that such differences have not always been observed.
EEG spectral power
During the pre-shot period of target sports such as air-pistol- and rifle shooting, archery, putting, and darts, attending to the target is critical for achieving optimal performance under stressful situations. The consistency and reproducibility attained during the pre-shot by expert athletes and non-athletes differ in these sports (Milton et al., 2007). The spectral power in background EEG has been used to investigate neural activity during the pre-shot period of shooting (Hatfield et al.,
Event-related potentials (ERPs)
ERPs are considered to be direct measures of the neuronal activities in the brain during engagement in specific information-processing tasks. Variations across the scalp in the amplitudes and latencies of ERPs reflect the unique properties of an individual's neural functioning as well as the experimental conditions employed, but ERPs are quite stable within most individuals (O'Connor et al., 1994).
In ERP studies, P300 or P3b is one of the most widely examined components with a parietal
Conclusion
Studies of athletes' brains show that motor-related activities and higher cognitive processing are flexibly modulated by long-term perceptual and motor training. These findings indicate the plasticity of neural activity in the human brain. Moreover, because different cognitive and motor abilities are required for players of different sports, it might be useful to investigate the differences among elite athletes participating in different sports and to examine the correlation between brain
Acknowledgments
This article was supported by grants from the Japan Society for the Promotion of Science for Young Scientists to H.N., and a Grant-in-Aid for Scientific Research (B) (No. 21300215) to K.K.
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