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Brain and Exercise: A First Approach Using Electrotomography


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Medicine & Science in Sports & Exercise: March 2010 - Volume 42 - Issue 3 - p 600-607
doi: 10.1249/MSS.0b013e3181b76ac8
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In the last 20-30 yr, the promotion of exercise and physical activity has become an important public health message. This can be attributed primarily to the impact of exercise on cardiorespiratory and metabolic parameters and their influence on physical health. In addition, there has been substantial interest in the impact of exercise on brain function, especially because exercise is well known to have a positive influence on mental well being (33,38). To better understand the influence of exercise on neural activity and brain function, the major focus to date has been on exercise-induced changes in brain neurotransmitters (e.g., dopamine and serotonin) (11,30,40). Methods such as positron emission tomography (PET), functional magnetic resonance imaging (fMRI), and near-infrared spectroscopy (NIRS) enable the exploration of regional brain function, yet their use in exercise studies remains relatively rare (7,13,52), probably due to the associated financial, technical, and logistical limitations. fMRI studies have been limited in the main to studies of aerobic or local muscular exercise such as hand-grip exercise (6,55). Obtaining measurements immediately after exercise can be problematic because of the time required to prepare subjects for scanning (fMRI and PET) (13), and the spatial resolution of NIRS is weak (52).

For some time, electroencephalography (EEG) has been the method of choice to examine exercise-induced changes in brain activation (53). It is generally agreed that there is a temporary increase in the EEG alpha activity (7.5-12.5 Hz) after exercise. This increase was first reported in the 1950s (5) and is thought to reflect a state of accumulated relaxation. The responses of other frequencies (delta = 0.5-3.5 Hz, theta = 3.5-7.5 Hz, beta = 12.5-35 Hz, gamma > 35 Hz) have received less attention, although these EEG frequencies are believed to play a major role in changes in central processing, workload, and mood. For example, the beta and the gamma frequency ranges seem to be strongly connected to increases in arousal, attention, or alertness as well as perceptual and information processing (42). When analysis of brain cortical activity after exercise was extended to a wider range of frequencies, changes were of similar magnitude to those observed in the alpha range (16,39). Therefore, no more than an overall cortical activation is apparent after exercise, which makes it difficult or impossible to examine frequency-specific characteristics (e.g., an increase or decrease of arousal should result in changes in the beta frequency only [50]; emotional processes are very tightly connected to changes in frontal alpha activity [14]).

A fundamental limitation of traditional EEG recording procedures is the inverse solution, that is, the challenge in determining activated brain regions by scalp-recorded EEG activity. Approaches such as EEG mapping views or wavelet analysis are restricted to display cortical events as they were recorded on the scalp but do not calculate their three-dimensional origin. Even with multiple electrodes, it has been impossible to obtain a clear localization of the three-dimensional distribution or the origin, power, and orientation of neuroelectrical activity obtained by EEG recordings. In the last recent years, standardized low-resolution brain electromagnetic tomography (sLORETA) has become an accepted tool for the localization of brain cortical activity (26,47,48). sLORETA is a source localization method relying on mathematical models of the bioelectrical generators and the volume conductors within which they lie. It is based on standardized EEG recordings, which are modeled to a probabilistic head model provided by the Montreal Neurological Institute (MNI). Active cortical regions are identified created by allocating the raw sLORETA values of individual voxels to their corresponding Brodmann areas (BA) or cerebral gyri on the basis of the coordinates of the digitized Talairach atlas (51). Apart from sLORETA, there are similar approaches to overcome the inverse solution like brain electrical source analysis (BESA), spatiotemporal regularization map (ST-MAP), multiple-signal classificaton algorithm (MUSIC), and others. By comparing these methods, it gets clear that each has its own specific advantages, but sLORETA was shown to give the most satisfactory results (26). A good overview of the functionality of different system was recently provided by Grech et al. (26). The great advantage of sLORETA and other electrotomographical approaches lies within the possibility to detect three-dimensional changes in neurocortical activity. Traditional approaches in EEG research like mapping views or wavelets are restricted to display cortical events because they were recorded on the scalp but not to calculate their three-dimensional origin.

To sum up, by analyzing standardized EEG signals, sLORETA offers a reliable spatial and temporal detection of brain cortical activity together with a simple, an economical, and a noninvasive way to use. sLORETA is a well-established technique, which is reliable and has been validated against fMRI and PET (2,24,43).

This study aimed to determine whether sLORETA is able to differentiate the source of changes in brain cortical activity with exercise under condition where typical brain imaging modalities could not be readily applied, that is, before and immediately after treadmill running. In doing so, we also aimed to differentiate the magnitude and the source of the alpha and beta EEG frequencies, both of which usually dominate with exercise. We hypothesized that changes in alpha activity with exercise would be predominantly localized to the frontal areas of the brain, which are strongly associated with emotional processing (14,18,22), and that there would be identifiable changes in the beta frequency range, which would be consistent with the previously described effects of exercise on brain cognitive function (1,34,41).


Participants and procedure.

This study was approved by the University of the Sunshine Coast Human Research Ethics Committee. After providing written informed consent (according to the Declaration of Helsinki), 22 individuals aged 21-45 yr (female n = 8, mean ± SD = 33.75 ± 9.31 yr; male n = 14, mean ± SD = 28.85 ± 6.27 yr) participated in this study. All participants were regular runners and performed a minimum of 2 h of running per week for recreation and health reasons. After a preexercise medical screening, participants underwent an incremental treadmill running test starting at 7.2 km·h−1 and increasing every 3 min by 1.4 km·h−1 until volitional exhaustion. Each stage was separated by 1 min to allow for the sampling of capillary blood from a finger tip for blood lactate analysis (lactate pro portable analyzer; ARKAY Inc., Kyoto, Japan). EEG activity was recorded for 5 min in a supine rest position with eyes closed before exercise (PRE), immediately after exercise (POST), and again 15 min (POST15) after exercise. For study demographics, please refer to Table 1.

Demographics of the study participants.

EEG recording.

A 64-channel portable EEG-System (IT-Med, Usingen, Germany) was used for data acquisition. Thirty minutes before exercise, an EEG-Cap was mounted (Electro-Cap International, Inc., Eaton, OH). This was adapted to the individual's head size and consisted of 19 Ag-AgCl electrodes and one reference electrode (mounted in the triangle of FP1, FP2, and FZ) in the 10-20 system (31). It recorded EEG activity at positions Fp1, Fp2, F3, F4, F7, F8, Fz, C3, C4, Cz, P3, P4, P7, P8, Pz, T7, T8, O1, and O2. The cap was fixed with two straps attached to a belt around the chest to prevent shifting during the exercise trials. The cap was permeable to air to facilitate heat loss during exercise. Distances between electrodes were approximately 5 cm to prevent possible cross talk after exercise due to bridging between electrodes. Each electrode was filled with Electro-Gel (Electro-Cap International, Inc.) to optimize signal transduction. If impedance of an electrode exceeded 10 kΩ, the electrode was excluded from further analysis. The analog signal of the EEG was amplified and converted to digital signals using Braintronics Iso 1064 CE Box (Braintronics B. V., Almere, The Netherlands) and stored at a frequency of 256 Hz on the hard disk of a Neurofile XP EEG-System (IT-Med).

EEG data analysis.

EEG data were processed using Brain Vision Analyzer (Brain Products, München, Germany). After manual artifact detection, high- and low-pass filters were applied so that a frequency range from 0.5 to 49 Hz remained for analysis (time constant = 0.3183 s; 24 dB per octave). Data were then segmented into 4-s sections where an overlap of 10% was accepted. A systematic protocol for excluding artifacts followed that included careful visual inspection of all EEG data and automated exclusion procedures, which were set to gradient threshold < 50 μV. Because it is difficult to recognize artifacts in raw signals at or above 7.5 Hz, data were further checked by spectral analysis. If there was an external interfering signal (e.g., AC/DC at 50 Hz), it would have been present in all channels and therefore made visible by spectral analysis, which was not the case. Segmented data were baseline corrected and exported for further analysis using sLORETA software provided by the KEY Institute for Brain-Mind Research (University Hospital of Psychiatry, Zurich, Switzerland;

Localization of EEG activity-sLORETA analysis.

sLORETA enables the spatial identification and analysis of brain cortical activity via traditional EEG recordings (23,32,46,47). Scalp-recorded cranial EEG activity is evoked by synchronized postsynaptic potentials, which are located in clusters of pyramidal cells (37). sLORETA makes it possible to determine the three-dimensional orientation of these highly synchronized postsynaptic potentials. The software is based on a probabilistic MNI brain volume, which was scanned at a 5-mm resolution. The MNI coordinates were converted to "corrected" Talairach coordinates, then aligned with the Talairach Daemon. Voxels that were unambiguously labeled as cortical gray matter and that fell unambiguously within the brain compartment were retained. This produced 6239 cortical gray matter voxels (46). Coordinates given within this study will refer to the MNI 152 template. Cortical regions are created by allocating the raw sLORETA values of individual voxels to their corresponding BA or cerebral gyri based on the coordinates of the digitized Talairach atlas (51).

First, the coordinates of the 19 electrode positions were applied to a probabilistic anatomical template of the Talairach atlas (McConnell Brain Imaging Centre, Montréal Neurological Institute, and McGill University). These Talairach coordinates were then used to compute the sLORETA transformation matrix. After transformation to average reference EEG activity, a minimum of 70 4-s epochs of artifact-free resting EEG were averaged to calculate cross-spectra in sLORETA for delta (0.5-3.5 Hz), theta (3.5-7.5 Hz), alpha-1 (7.5-10 Hz), alpha-2 (10-12.5 Hz), beta-1 (12.5-18 Hz), beta-2 (18-35 Hz), and gamma (35-48 Hz) bands for each subject. Using the sLORETA transformation matrix, cross-spectra of each subject and for each frequency band were then transformed to sLORETA files. This resulted in the corresponding three-dimensional cortical distribution of the electrical neuronal generators for each subject.

To display exercise-induced changes in estimated cortical current density, PRE versus POST and PRE versus POST15 paired samples t-test were computed for sLORETA power at each voxel. Statistical significance was assessed using a nonparametric randomization test (45). To correct for multiple comparisons, a nonparametric single-threshold test was used to define a critical threshold (tcritical). Voxels with values exceeding this threshold have their null hypothesis rejected; that is, there is no difference in EEG power between two tests. The omnibus hypothesis (that all the voxel hypotheses are true) is rejected if a voxel value exceeds the critical threshold for P < 0.05 defined by 5000 randomizations. Voxel-by-voxel t-values in Talairach space are displayed as statistical parametric maps.


Comparison of EEG data recorded before exercise (PRE) and after exercise (POST) localized using sLORETA (tcritical for P < 0.1 = 3.65; P < 0.05 = 3.95; P < 0.01 = 4.63) showed an increase of alpha-1 activity in the left middle frontal gyrus (BA 8; P < 0.01, Fig. 1A and Table 2; t = 4.81**), which was of highest significance at MNI coordinates x, y, z = −40, 20, 50. No further significant changes were obtained for alpha-2 (t = 3.20), beta-1 (t = 3.24), beta-2 (t = 2.98), or gamma activity (t = 3.14). An increase in delta activity that was widely spread across the cortex was observed (t = 5.92**). Theta activity increased (t = 4.28*) immediately after exercise in the left and right temporal regions (BA 21, 22, 37; P < 0.05).

Statistical parametric maps of sLORETA differences comparing PRE and POST measurement changes in alpha-1 activity (A) as well as PRE and POST15 changes in alpha-2 (B), beta-1 (C), and gamma activity (D). Red and yellow colors indicate increased activity in the POST measurement, which was found to significant in BA 8. Blue colors indicate decreased activity in the POST15 measurement, which was found to be significant in BA 20 (alpha-1), BA 20-22, 13, 40-43, 37, 4, 6 (beta-1), and BA 18/19 (gamma). Images depicting statistical parametric maps seen from different perspectives are based on voxel-by-voxelt-values of differences. Structural anatomy is shown in gray scale (L, left; R, right; A, anterior; P, posterior).
Significant differences in alpha-1 activity PRE versus POST, according to voxel-by-voxel analysis with sLORETA.*

Comparing POST15 and PRE measurements (tcritical for P < 0.1 = 3.50; P < 0.5 = 3.78; P < 0.01 = 4.32) revealed a decrease in alpha-2 activity (t = 3.88*), which was localized in only one voxel of the left inferior temporal gyrus at MNI coordinates −55, −5, −40 (BA 20; Fig. 1B). There were considerable reductions in beta-1 activity in the left inferior, middle, and superior temporal gyri (BA 20-22; Fig. 1C and Table 3), which reached highest statistical significance at MNI coordinates −45, −15, −5 (BA 22; t = 4.75**), and gamma activity in the left part of the cuneus (t = 5.40**; MNI −5, −100, 20; BA 18; Fig. 1D and Table 4). No changes from PRE to POST15 were noted for alpha-1 (t = 2.60) or beta-2 activity (t = 3.75). Although the increase delta activity after exercise persisted until POST15 (t = 5.74**), the increase in theta activity, which was noticeable immediately after exercise, was not significant after the 15-min recovery period (t = 3.56).

BA that showed a distinct decrease (P < 0.01) in beta-1 activity PRE versus POST15.
Significant differences in gamma activity PRE versus POST15, according to voxel-by-voxel analysis with sLORETA.*


This study demonstrated that sLORETA is a valuable and feasible instrument to document and localize changes in brain cortical activity with physical exercise. The results concerning changes in alpha activity are in accordance with prior findings. The first findings from Beaussart et al. (5) showed an increase of around 20% in alpha activity after exercise at a mean maximum heart rate of 200 bpm. Subsequent studies (10,39) confirmed that the increase in alpha activity was of greatest magnitude during the first 5-10 min after exercise, and these changes are assumed to be strongest in frontal brain areas (17). Because recent evidence suggests that EEG activity is constant over a time frame of at least 2 h without any special events (49), we assumed that the changes observed in this study are specifically related to the exercise completed.

Using sLORETA EEG analysis, we determined that the increase in alpha-1 activity immediately after exercise is specifically localized to the left frontal areas (BA 8). This increased activity in frontal regions of the brain is in accordance with previous results using different imaging techniques such as PET (8) and NIRS (52), although those studies only assessed changes in response to lower-intensity aerobic activity (8), and the use of a single position NIRS probe on the left frontal cortex (52) makes it difficult to rule out changes in other areas of the brain. Nevertheless, it is assumed that these changes, which are also supported by previous studies using surface EEG (17,29), are related to emotional effects of exercise as the frontal cortex is strongly connected to emotional processing (14,22). The increase in left frontal lobe activity shown in the present study is particularly interesting and worthy of further investigation because the involvement of left-sided brain regions in approach-related motivation and the greater self-reported happiness are key components of the well-documented model of frontal asymmetry (14,20). Although widely debated, it is suggested that alpha activity is inversely related to cerebral activity. That is, an increase of alpha activity could be interpreted as a reduction in cortical activation. We therefore hypothesized that relatively greater left frontal lobe activity, especially in the lower alpha frequency range (19), may serve as a marker of positive emotions. Indeed, previous studies have reported changes in mood after physical exercise (33,38).

Apart from the changes in frontal alpha activity immediately after exercise, the broad decrease of beta-1 activity, especially in BA 20-22, and a similar decrease of gamma activity in BA 18 and 19 15 min after exercise might be related to a reduction in cortical arousal. A transformation of slow EEG rhythms into faster oscillation is known to occur during arousal and alertness (50). This is thought to be influenced by cholinergic neuromodulation (12), which presumably affects the synchrony of interneuron networks involved in gamma oscillations (9,54). In addition, higher-frequency EEG activity is well known to be associated with the processing of sensory information (25). To follow this line of reasoning, it can be argued that arousal and general sensory noise may be reduced after exhaustive exercise. This might explain previous findings of increased cognitive performance associated with exercise (34,41). Concerning the statistical localization of this decrease in beta-1 activity, further studies should closely examine the effects of exercise on cognitive performance as BA 21/22 are known to be involved in language processing (15) (e.g., Wernicke's area is part of the-mostly left-posterior section of BA 22). Activity within BA 18/19 as part of secondary visual areas also seems to be connected to language processing (15).

Theta oscillations in frontal and limbic brain areas have been shown to be highly correlated with mechanisms of learning and attention as well as retrieval of information (4). This has mainly been described with event-related potentials (theta activity being the most stable component of the P300 response [3]). Although we cannot rule out the possibility that the increase in theta activity is linked with cognitive processing, that the increase in theta activity occurs bilaterally, and that it is located in temporal areas and occurs only immediately postmaximal physical exercise, it is possible that the increase in theta activity was evoked by jaw movements. Combining EEG recordings with EMG recordings of the mastication muscles would enable us to rule this out with more certainty.

We doubt that previously reported increases in delta activity after exercise reflect changes in brain cortical activity (39). Rather, we proposed that this increase is due to either artifacts of the cardiovascular system or the oscillatory aftereffects of running. There is a peak around 2 Hz in the gravity axis when analyzing oscillations of the center of gravity of the human body during locomotion (35,44). It is assumed that this highly tuned locomotor frequency reflects the intrinsic tempo of a spinal central pattern generator (35). These generators have been established as the basis for locomotor rhythmicity in invertebrates, fish, and cats (27,28). Although their existence in humans can only be inferred from indirect evidence (21,36), it can be assumed that this oscillation is maintained by oscillatory neurons even after exercise. This notion is speculative and requires the inclusion of movement or acceleration data to better establish the relationship between delta activity and oscillatory activity of neurons in the central nervous system. In addition, the possibility that sweat artifact could produce similar low-frequent and high-potential fluctuations cannot be ruled out.

As the primary aim of this study was to determine the usability of sLORETA for examining the effects of exercise, we have only been able to speculate about the relationships between brain activity, cognitive function, and mood. Future studies should combine physiological assessments of brain activity with appropriate psychological assessments. Given the changes noted in beta and gamma activities after exercise, future studies should incorporate tests of cognitive functioning and language processing after exercise. Using a larger number of electrodes would enable a more sensitive localization of brain activity to be established. Finally, although there is evidence that EEG activity is stable over short-term (2 h) measurement periods (49), the inclusion of a control group would have strengthened this study and enabled the changes observed to be more confidently attributed to the effects of the exercise task.

In conclusion, sLORETA is a valuable and comparatively simple noninvasive and inexpensive method for brain imaging in experimental settings where technical and/or organizational limits are present. Although sLORETA is not able to provide localization data with the accuracy of PET or fMRI techniques, it provides a means of investigating the localization of changes in brain cortical activity in response to exercise using simple EEG recordings.

The authors thank the participants for spending some of their valuable study time during the summer semester 2007. They also thank Julia Diehl for her help with data collection. Special thanks go to Dr. Jayne Lucke for proofreading the first version of this manuscript. The comments of two unknown reviewers on the first version of this manuscript were very much appreciated. The results of the present study do not constitute endorsement by the American College of Sports Medicine.

This study was made possible by a grant from the German Space Agency (DLR) 50WB0519 to the first author and a young investigators grant awarded to A. Mierau and S. Schneider by the German Sport University. The authors are also grateful for the support of the University of the Sunshine Coast.


1. Anish EJ. Exercise and its effects on the central nervous system. Curr Sports Med Rep. 2005;4:18-23.
2. Bai X, Towle VL, He EJ, He B. Evaluation of cortical current density imaging methods using intracranial electrocorticograms and functional MRI. Neuroimage. 2007;35:598-608.
3. Basar E. Brain Function and Oscillations: Volume I: Brain Oscillations. Principles and Approaches. Heidelberg (Germany): Springer; 1998. p. 157.
4. Basar E. Brain Function and Oscillations: Volume II: Integrative Brain Function. Neurophysiology and Cognitive Processes. Heidelberg (Germany): Springer; 1998. pp. 155-60.
5. Beaussart M, Niquet G, Gaudier E, Guislain F. The EEG of boxers examined immediately after combat. Comparative study with the EEG recorded before combat in 52 cases. Rev Obstet Ginecol Venez. 1959;101:422-7.
6. Benwell NM, Mastaglia FL, Thickbroom GW. Changes in the functional MR signal in motor and non-motor areas during intermittent fatiguing hand exercise. Exp Brain Res. 2007;182:93-7.
7. Boecker H, Henriksen G, Sprenger T, et al. Positron emission tomography ligand activation studies in the sports sciences: measuring neurochemistry in vivo. Methods. 2008;45:307-18.
8. Boecker H, Sprenger T, Spilker ME, et al. The runner's high: opioidergic mechanisms in the human brain. Cereb Cortex. 2008;18:2523-31.
9. Borgers C, Epstein S, Kopell NJ. Background gamma rhythmicity and attention in cortical local circuits: a computational study. Proc Natl Acad Sci USA. 2005;102:7002-7.
10. Boutcher SH, Landers DM. The effects of vigorous exercise on anxiety, heart rate, and alpha activity of runners and nonrunners. Psychophysiology. 1988;25:696-702.
11. Buckworth J, Dishman RK. Exercise Psychology. Champaign (IL): Human Kinetics; 2002. pp. 91-114.
12. Buhl EH, Tamas G, Fisahn A. Cholinergic activation and tonic excitation induce persistent gamma oscillations in mouse somatosensory cortex in vitro. J Physiol. 1998;513(Pt 1):117-26.
13. Caglar E, Sabuncuoglu H, Keskin T, Isikli S, Keskil S, Korkusuz F. In vivo human brain biochemistry after aerobic exercise: preliminary report on functional magnetic resonance spectroscopy. Surg Neurol. 2005;64(suppl 2):S53-6; discussion S56-7.
14. Coan JA, Allen JJ. Frontal EEG asymmetry as a moderator and mediator of emotion. Biol Psychol. 2004;67:7-49.
15. Cone NE, Burman DD, Bitan T, Bolger DJ, Booth Jr. Developmental changes in brain regions involved in phonological and orthographic processing during spoken language processing. Neuroimage. 2008;41:623-35.
16. Crabbe JB, Dishman RK. Brain electrocortical activity during and after exercise: a quantitative synthesis. Psychophysiology. 2004;41:563-74.
17. Crabbe JB, Smith JC, Dishman RK. Emotional & electroencephalographic responses during affective picture viewing after exercise. Physiol Behav. 2007;90:394-404.
18. Davidson RJ, Irwin W. The functional neuroanatomy of emotion and affective style. Trends Cogn Sci. 1999;3:11-21.
19. Davidson RJ, Marshall JR, Tomarken AJ, Henriques JB. While a phobic waits: regional brain electrical and autonomic activity in social phobics during anticipation of public speaking. Biol Psychiatry. 2000;47:85-95.
20. Davidson RJ, Schwartz GE, Saron C, Bennett J, Goleman DJ. Frontal versus parietal EEG asymmetry during positive and negative affect. Psychophysiology. 1979;16:202-3.
21. Dietz V. Spinal cord pattern generators for locomotion. Clin Neurophysiol. 2003;114:1379-89.
22. Faw B. Pre-frontal executive committee for perception, working memory, attention, long-term memory, motor control, and thinking: a tutorial review. Conscious Cogn. 2003;12:83-139.
23. Fuchs M, Kastner J, Wagner M, Hawes S, Ebersole JS. A standardized boundary element method volume conductor model. Clin Neurophysiol. 2002;113:702-12.
24. Gamma A, Lehmann D, Frei E, Iwata K, Pascual-Marqui RD, Vollenweider FX. Comparison of simultaneously recorded [H2(15)O]-PET and LORETA during cognitive and pharmacological activation. Hum Brain Mapp. 2004;22:83-96.
25. Gray CM, Singer W. Stimulus-specific neuronal oscillations in orientation columns of cat visual cortex. Proc Natl Acad Sci USA. 1989;86:1698-702.
26. Grech R, Cassar T, Muscat J, et al. Review on solving the inverse problem in EEG source analysis. J Neuroeng Rehabil. 2008;5:25.
27. Grillner S. Neurobiological bases of rhythmic motor acts in vertebrates. Science. 1985;228:143-9.
28. Grillner S, Wallen P. Central pattern generators for locomotion, with special reference to vertebrates. Annu Rev Neurosci. 1985;8:233-61.
29. Hall EE, Ekkekakis P, Petruzzello SJ. Regional brain activity and strenuous exercise: predicting affective responses using EEG asymmetry. Biol Psychol. 2007;75:194-200.
30. Hollmann W, Strüder HK. Physical exercise facilitates brain health. Summary and own results. [Körperliche Aktivität fördert Gehirngesundheit und-leistungsfähigkeit: Übersicht und eigene Befunde]. Nervenheilkunde. 2003;9:467-74.
31. Jasper HH. The ten-twenty electrode system of the international Federation. Electroencephalogr Clin Neurophysiol Suppl. 1958;35:371-5.
32. Jurcak V, Tsuzuki D, Dan I. 10/20, 10/10, and 10/5 systems revisited: their validity as relative head-surface-based positioning systems. Neuroimage. 2007;34:1600-11.
33. Karacabey K. Effect of regular exercise on health and disease. Neuro Endocrinol Lett. 2005;26:617-23.
34. Lo Bue-Estes C, Willer B, Burton H, Leddy JJ, Wilding GE, Horvath PJ. Short-term exercise to exhaustion and its effects on cognitive function in young women. Percept Mot Skills. 2008;107:933-45.
35. Macdougall HG, Moore ST. Marching to the beat of the same drummer: the spontaneous tempo of human locomotion. J Appl Physiol. 2005;99:1164-73.
36. Marder E. Moving rhythms. Nature. 2001;410:755.
37. Martin JH. The collective electrical behavior of cortical neurons: the electroencephalogram and the mechanisms of epilepsy. In: Kandel ER, Schwartz JH, Jessel TM, editors. Principles of Neuroscience. London: Prentice-Hall; 1991, pp. 777-91.
38. Martinsen EW. Physical activity in the prevention and treatment of anxiety and depression. Nord J Psychiatry. 2008;62(suppl 47):25-9.
39. Mechau D, Mucke S, Weiss M, Liesen H. Effect of increasing running velocity on electroencephalogram in a field test. Eur J Appl Physiol Occup Physiol. 1998;78:340-5.
40. Meeusen R, De Meirleir K. Exercise and brain neurotransmission. Sports Med. 1995;20:160-88.
41. Mierau A, Schneider S, Abel T, Askew C, Werner S, Struder HK. Improved sensorimotor adaptation after exhaustive exercise is accompanied by altered brain activity. Physiol Behav. 2009;96(1):115-21.
42. Miller R. Theory of the normal waking EEG: from single neurones to waveforms in the alpha, beta and gamma frequency ranges. Int J Psychophysiol. 2007;64:18-23.
43. Mulert C, Jager L, Schmitt R, et al. Integration of fMRI and simultaneous EEG: towards a comprehensive understanding of localization and time-course of brain activity in target detection. Neuroimage. 2004;22:83-94.
44. Murray MP, Drought AB, Kory RC. Walking patterns of normal men. J Bone Joint Surg Am. 1964;46:335-60.
45. Nichols TE, Holmes AP. Nonparametric permutation tests for functional neuroimaging: a primer with examples. Hum Brain Mapp. 2002;15:1-25.
46. Pascual-Marqui RD. Standardized low-resolution brain electromagnetic tomography (sLORETA): technical details. Methods Find Exp Clin Pharmacol. 2002;24(suppl D):5-12.
47. Pascual-Marqui RD, Esslen M, Kochi K, Lehmann D. Functional imaging with low-resolution brain electromagnetic tomography (LORETA): a review. Methods Find Exp Clin Pharmacol. 2002;24(suppl C):91-5.
48. Pascual-Marqui RD, Michel CM, Lehmann D. Low resolution electromagnetic tomography: a new method for localizing electrical activity in the brain. Int J Psychophysiol. 1994;18:49-65.
49. Schneider S, Brummer V, Mierau A, Carnahan H, Dubrowski A, Struder HK. Increased brain cortical activity during parabolic flights has no influence on a motor tracking task. Exp Brain Res. 2008;185:571-9.
50. Steriade M, Amzica F, Contreras D. Synchronization of fast (30-40 Hz) spontaneous cortical rhythms during brain activation. J Neurosci. 1996;16:392-417.
51. Talairach J, Tournoux P. Co-Planar Stereotaxic Atlas of the Human Brain. Stuttgart: Thieme; 1988. p. 122.
52. Thomas R, Stephane P. Prefrontal cortex oxygenation and neuromuscular responses to exhaustive exercise. Eur J Appl Physiol. 2008;102:153-63.
53. Thompson T, Steffert T, Ros T, Leach J, Gruzelier J. EEG applications for sport and performance. Methods. 2008;45:279-88.
54. Tiesinga PH, Fellous JM, Salinas E, Jose JV, Sejnowski TJ. Inhibitory synchrony as a mechanism for attentional gain modulation. J Physiol Paris. 2004;98:296-314.
55. Wong SW, Kimmerly DS, Masse N, Menon RS, Cechetto DF, Shoemaker JK. Sex differences in forebrain and cardiovagal responses at the onset of isometric handgrip exercise: a retrospective fMRI study. J Appl Physiol. 2007;103:1402-11.


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