Articles in PresS. J Neurophysiol (November 4, 2015). doi:10.1152/jn.00497.2015
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Changes in cortical activity measured with EEG during a high intensity cycling exercise.
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Hendrik Enders1, Filomeno Cortese2, Christian Maurer3, Jennifer Baltich1, Andrea B.
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Protzner2,4, Benno M. Nigg1
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Human Performance Laboratory, Faculty of Kinesiology, University of Calgary, Calgary, AB,
Canada 2
Hotchkiss Brain Institute, University of Calgary, Calgary, AB, Canada
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Red Bull Diagnostic and Training Center, Thalgau, Austria
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Department of Psychology, University of Calgary, Calgary, AB, Canada
Running Title: Cortical activity during cycling
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Corresponding author and address:
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Hendrik Enders, e-mail:
[email protected]
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Human Performance Laboratory, Faculty of Kinesiology, University of Calgary
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2500 University Drive NW, Calgary, Alberta, Canada, T2N 1N4
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Phone: (403) 220-2413
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Fax: (403) 284-3553
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Research article submitted to Journal of Neurophysiology
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Copyright © 2015 by the American Physiological Society.
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Abstract
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This study investigated the effects of a high intensity cycling exercise on changes in spectral and temporal aspects of electroencephalography (EEG) measured from ten experienced cyclists. Cyclists performed a maximum aerobic power test on the first testing day followed by a time-toexhaustion trial at 85% of their maximum power output on two subsequent days that were separated by approximately 48 hours. EEG was recorded using a 64 channel system at 500 Hz. Independent component analysis parsed the EEG scalp data into maximally independent components (IC). An equivalent current dipole model was calculated for each IC and results were clustered across subjects. A time-frequency analysis of the identified electrocortical clusters was performed to investigate the magnitude and timing of event-related spectral perturbations. Significant changes (p < 0.05) in electrocortical activity were found in frontal, supplementary motor and parietal areas of the cortex. Overall, there was a significant increase in EEG power as fatigue developed throughout the exercise. The strongest increase was found in the frontal area of the cortex. The timing of event-related desynchronization (ERD) within the supplementary motor area corresponds with the onset of force production and the transition from flexion to extension in the pedalling cycle. The results indicate an involvement of the cerebral cortex during the pedalling task that most likely involves executive control function as well as motor planning and execution.
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Keywords: Electroencephalography, Locomotion, Motor control, Fatigue
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Introduction Coordinated motor skills require the integration of information from peripheral sensors,
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spinal locomotor networks as well as supraspinal commands (Grillner et al., 2008). However,
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only in the last decade, researchers have begun to investigate the function of the human brain
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with respect to voluntary, mostly rhythmic locomotor tasks in real time. Owing the dynamic
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nature of human locomotion, a primary requirement to quantify cortical dynamics during
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locomotion is a time resolution that allows the recording of modulations in cortical activity
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within one cycle of a repeating movement sequence. Classical neuroimaging tools such as
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functional magnetic resonance imaging (fMRI) or positron emission tomography (PET) are not
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well suited to resolve such rapid modulations of activity. Contrary, electroencephalography
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(EEG) is a mobile brain imaging technique with more-than-sufficient time resolution to capture
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cortical dynamics during human locomotion. While historically, EEG has been considered too
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prone to noise and artifacts for dynamic recordings, recent advancements in hardware and
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software have opened the possibility for mobile brain imaging using EEG (Makeig et al., 2009;
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Gramann et al., 2011).
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In the last decade, researchers have begun to study time-dependent cortical dynamics in
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locomotor and exercise-related situations with the majority of studies focusing on upright human
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walking and slow jogging. It was proposed that the low frequency component (0.1 – 2 Hz) of
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EEG recordings can be used to infer the overall time-dependent, linear and angular kinematics of
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the ankle, knee and hip joints during walking (Presacco et al., 2011). Another study has shown
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that specific brain regions such as the primary motor, the parietal, and frontal cortices exhibit
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distinct time-frequency patterns that are closely linked to the overall movement kinematics of the
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gait cycle (Gwin et al., 2011). Similarly, it was shown that task-dependent EEG can be extracted
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during slow to moderate walking speeds (De Sanctis et al., 2012) and that cortical modulations
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seen in EEG differ between active and passive walking conditions (Wagner et al., 2012). More
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recently, Wagner and colleagues showed that providing interactive feedback during walking
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enhances premotor and parietal areas in the brain that are thought to play an important role for
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motor planning and intention (Wagner et al., 2014). These studies demonstrate that cortical
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dynamics respond to changes in task parameters indicating that EEG can be used to study neural
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control during human locomotor tasks. The typical frequency bands that are investigated in
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movement related tasks are theta (θ: 4 – 8 Hz), alpha (α: 8 – 12 Hz), beta (β:12 – 30 Hz) and low
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gamma (γ: 30 – 50 Hz) frequency bands as they are believed to reflect different aspects with
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respect to motor planning, execution and control (Pfurtscheller et al., 2000; Taniguchi et al.,
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2000; Jurkiewicz et al., 2006; Waldert et al., 2008; Gwin et al., 2011). This was supported by a
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recent study that found sustained suppression of cortical oscillations in the α and β band while
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cortical activity in the low γ band seemed to be modulated during the gait cycle phases (Seeber et
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al., 2014) suggesting that these responses characterize different elements of human walking. A
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follow up study found that low and high γ amplitudes are modulated throughout the gait cycle,
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however, their amplitude envelopes were negatively correlated throughout the movement
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(Seeber et al., 2015). The majority of the mentioned studies have been conducted during human
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walking at low intensities. The aim of this study was to apply similar analysis techniques to high
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effort locomotor tasks. Cycling avoids the high impact forces that are typically observed during
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high effort running and has often been used in studies investigating neuromuscular strategies due
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to the well-controlled movement and repeatability of the task (Enders et al., 2013). In fact, it has
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previously been used for EEG measurements during high intensity cycling and evidence was
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provided for increased communication between mid/anterior insular and motor cortex (Hilty et
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al., 2011). This increased communication between cortical areas was found at the end of the
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cycling exercise and returned to baseline after a rest period suggesting that (muscle) fatigue
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alters interaction between cortical structures. Expanding upon these findings, the purpose of this
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study was to localize electrical brain activity using EEG during cycling at a constant workload
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and quantify changes in temporal brain activation throughout an exhaustive bout of exercise.
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Based on previous observations in cycling (Schneider et al., 2009; Brümmer et al., 2011), we
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hypothesized that electrocortical sources will be located in the motor areas, the frontal cortex and
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the parietal cortex. Furthermore, it was hypothesized that EEG activity within motor areas will
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demonstrate an oscillating pattern of increased and decreased activation throughout the pedalling
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cycle. We further hypothesized that overall EEG activity would increase as fatigue develops
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throughout the exercise duration.
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Materials and methods
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Subjects and ethics approval
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Ten healthy, experienced male cyclists (Table 1) with no history of lower limb injury
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within the past two years and no known neurological or muscular deficits volunteered for this
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study. All subjects provided written informed consent prior to testing. All procedures complied
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with the standards of the Declaration of Helsinki and were reviewed and approved by the
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University of Calgary research ethics review board. Insert Table 1 here
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Experimental protocol
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Each subject performed testing on three different days. All experimental protocols were
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supervised by a Canadian Society of Exercise Physiology Certified Exercise Physiologist
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(CSEP-CEP) and participants were cleared for exercise using a Physical Activity Readiness
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Questionnaire (Par-Q+). Resting blood pressure and heart rate were measured prior to daily
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testing and standardized ceiling levels according to the Canadian Society of Exercise Physiology
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were adhered to. All testing was completed using a Velotron Dynafit Pro cycle ergometer
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(RacerMate Inc., Seattle, WA) that was calibrated for each testing session (Figure 1A). All
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participants used their own pedals (clipless) and corresponding footwear that locked into the
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pedals to accommodate their usual bike racing equipment. The first testing day was used to
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establish the maximum aerobic power (MAP) capacity of each subject based on an incremental
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test. Testing on the second day consisted of a time-to-exhaustion (TTE) test at 85% of the
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individual’s MAP and was used for familiarization with the protocol and equipment. The third
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day was a repeat of the second day and was used for the data analysis. The three testing sessions
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for each subject were separated by 48 ± 1 hours. To increase repeatability of the results, caffeine
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intake was restricted to two hours prior to testing and the subject was asked to maintain a normal
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diet and refrain from any resistance training for the duration of the study.
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Maximum aerobic power (MAP)
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On the first day of testing an incremental cycling exercise test was used to establish blood
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lactate thresholds LT1 and LT2 for each subject according to the double breakaway model
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(Kindermann et al., 1979; Anderson and Rhodes, 1989). Subjects were given twenty minutes of
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rest after the incremental test prior to starting the MAP protocol. The MAP test was started at a
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workload slightly below the LT1 and the workload increased every minute by 25W. The test was
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terminated when the cadence dropped by more than 15 revolutions per minute (RPM) or below
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70 RPM (Figure 1B). Subjects were included in the study if they reached an MAP of at least
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4.5W/kg. Insert Figure 1 here
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Time-to-exhaustion (TTE) trial Prior to the TTE trial, each subject performed a standardized warm-up protocol based on
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three five-minute stages corresponding to resistance values below, at, and above the measured
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LT1. After the warm-up, the TTE trial was performed at a constant workload of 85% of the MAP
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with the same seat and handlebar settings of the ergometer that were used to perform the MAP
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test. On both days subjects were unaware of the workload and the elapsed time of the TTE trial
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to ensure consistency between days. The preferred cadence was freely chosen by the cyclists
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(typically between 90 and 100 RPM) and the test was terminated if the cadence dropped by 15
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RPM or below 70 RPM. Verbal encouragement was provided throughout the test on both days to
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ensure maximum performance.
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EEG Data collection
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Subjects cycled at constant intensity using the ergometer described above. A magnetic
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switch was used to synchronize the data streams and identify the pedalling cycle. The switch sent
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a square wave pulse that was recorded in synchrony with the EEG data in order to identify
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individual pedalling revolutions. EEG was recorded using an active electrode 64-channel (10-20
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positioning) BrainVision actiCHamp system (Brain Products GmbH, Germany). Subjects wore a
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tight fitting electrode cap corresponding to the measured head circumference using a chinstrap to
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reduce movement of the cap (Figure 1A). For each subject nasion, inion and preauricular points
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were used as anatomical landmarks to position the electrode cap. Prior to data collection,
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SuperVisc electrode gel (EasyCap GmbH, Germany) was used to ensure that the impedance was
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less than 20 kΩ for each channel. During acquisition, all EEG channels were referenced to the
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parietal midline (Pz) electrode and a frontal midline (AFz) electrode was used as ground. EEG
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signals were recorded with a sampling frequency of 500 Hz per channel and stored on a
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computer for offline processing. All data processing was performed in Matlab (The Mathworks,
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Natick, MA) using custom written software in combination with scripts based on EEGLab
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13.2.2b, an open source toolbox for the analysis of electrophysiological data sets (Delorme and
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Makeig, 2004).
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EEG Data analysis
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Analysis of continuous EEG signals was carried out according to previously used
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methodologies (Gwin et al., 2011; Wagner et al., 2012, 2014; Kline et al., 2014). Briefly, EEG
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signals were band pass filtered (1-50 Hz) in order to remove signal drifts as well as line noise
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and were re-referenced to a common average reference. Channels were removed if one of the
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following criteria were true: (1) channels with standard deviation (SD) larger than 1000 μV, (2)
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channels whose kurtosis was more than 5 SD from the mean and (3) channels that were
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uncorrelated with neighbouring channels (r < 0.4) for more than 0.1% of the time. On average
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51.6 channels were used for further analysis (SD: 1.96, range 49 – 54). Independent component
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analysis (ICA) was used to decompose the signal into maximally independent components (IC)
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(Makeig et al., 1996). ICA was applied to the data set of individual subjects. EEG data
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corresponding to the entire time-to-exhaustion trial (approximately seven minutes) was used for
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the ICA decomposition. ICs representing line noise, movement artifacts and eye activity were
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rejected prior to further analysis (Jung et al., 2000). In order to locate the neural sources in brain
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space, an equivalent current dipole model was computed for individual component scalp maps
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using the DIPFIT plugin within EEGLab (Oostenveld and Oostendorp, 2002). Each IC was
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modeled as a single dipole. We used a boundary element head model based on the Montreal
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Neurological Institute (MNI) template that utilizes the average of 152 MRI scans from healthy,
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young adult individuals. No specific spatial constraints were enforced as spatial accuracy is
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limited without the individual anatomy of subjects. ICs were excluded from further analysis if
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the projection of the dipole to the scalp accounted for less than 80% of the scalp map variance.
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All remaining ICs of all individual subjects (approximately 100 in total) were clustered across all
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subjects using k-means clustering algorithms implemented in EEGLab. The algorithm used the
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dipole location in brain space and the power spectra of each individual IC for clustering purposes
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and was reduced to the first ten principal dimensions (as in Gwin et al., 2011). Clusters and
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dipoles indicating eye movement artifacts were not considered for further analysis. Additionally,
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any components that were located near the lower back part of the head were not considered for
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further analysis as they typically represent EMG activity from neck muscle that is characterized
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by an increase in spectral power above 30 Hz. Lastly, any clusters that were located in deeper
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brain structures near the cerebellum were not considered for analysis as EEG is not able to
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reliable localize neural activity in these areas.
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Spectral analysis of EEG data
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For all individual components in the identified electrocortical clusters we calculated
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power spectra using Welch’s method. The average power spectra for each cluster were compared
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for different stages of the exercise corresponding to the first, middle and last 20% of all pedal
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revolutions corresponding to the fresh, middle and fatigue condition during the time-to-
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exhaustion trial. An analysis of variance (ANOVA) with 3 conditions (fresh, middle, fatigue)
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was computed within the EEGLab toolbox in order to compare power spectra between
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conditions controlling for false discovery rate (Benjamini and Hochberg, 1995) in case of
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multiple comparisons (p < 0.05).
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Time-frequency analysis
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Specifically for electrocortical clusters in the motor areas, we calculated spectrograms for
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each pedal revolution as a function of crank angle. Spectrograms were calculated based on a set
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of sinusoidal wavelets (Delorme and Makeig, 2004). The number of cycles increased slowly with
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increasing center frequency which allows for a better frequency resolution at higher frequencies
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compared to a traditional wavelet approach using constant cycle length. The time-frequency
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spectrograms were time-locked to the start of the pedal revolution and then linearly time warped
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so that the onset and offset of each pedal revolution occurred at the same latencies (Gwin et al.,
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2011; Gwin and Ferris, 2012; Castermans et al., 2014; Seeber et al., 2015). The time warping
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procedure uses event latencies of each individual pedal revolution onset and offset and warps
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these latencies to the average onset and offset latency, thereby, time normalizing each pedal
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revolution. In order to display electrocortical fluctuations within a pedal revolution we expressed
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the time-frequency transformed data relative to the average spectrogram (averaged across the
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time domain of the pedal revolution) similar to previous studies (Gwin et al., 2011; Wagner et
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al., 2012, 2014; Kline et al., 2014). This was done based on the following equation:
= 10 ∙ log
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Where Yt is the power at time t and Y0 is the average power over the pedalling cycle.
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These fluctuations from baseline are typically referred to as event-related spectral
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perturbations (ERSP). We calculated ERSPs for each component within the motor area cluster. A
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grand average ERSP was obtained by averaging the individual ERSPs. Significant pedalling
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ERSPs were identified using bootstrapping methods available within EEGLab (200 iterations).
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Bootstrapping is based on surrogate data distributions by randomly resampling the observed
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data, re-computing the outcome variable and comparing the experimental data to the generated
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surrogate data distribution. Multiple comparisons were controlled using the false discovery rate
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with a significance level set at 0.05. All non-significant fluctuations from baseline in the ERSP
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plots were masked for visualization in this manuscript. In order to reveal the relative timing of
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brain activity we calculated waveforms representing spectral power fluctuations in the alpha (8-
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12 Hz), beta (12-30 Hz) and low gamma (30-50 Hz) band. ERSPs and waveforms were
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visualized for the first 20% of the exercise (fresh) and the last 20% of the exercise (fatigue).
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These timeframes correspond to approximately 120 – 135 pedal revolutions. Comparisons
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between the ERSP conditions was made within EEGLab using the bootstrapping procedure
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outlined above using the false discovery rate to correct for multiple comparisons.
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EMG data collection and analysis
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While the analysis of muscle activity was not the primary focus of this manuscript,
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surface electromyography (EMG) data were collected to ensure if potential cortical changes were
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associated with altered muscular activation patterns. Details of the data collection and analysis
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techniques is provided in a previously published manuscript that utilized the same techniques
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(Enders et al., 2015). Briefly, muscle activity was collected from muscles spanning the ankle,
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knee and hip joints with a sampling rate of 2000 Hz. Raw EMG signals were separated into
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individual pedal revolutions according to a magnetic pedal switch. In order to obtain temporal
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waveforms, the EMG activity was wavelet transformed (von Tscharner, 2000). The output of the
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wavelet transform is an intensity pattern indicating the EMG activity at each time point for
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specific center frequencies. Summing up the activity across all frequency bands results in the
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total intensity for each time point. These temporal waveforms were normalized to the pedalling
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cycle with a time resolution corresponding to 0.5° of the crank cycle. Grand average waveforms
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were obtained by averaging the trials of each subject corresponding to the first and last 20% of
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the time-to-exhaustion trial.
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Results
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Time-to-exhaustion
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On average, participants were able to produce 85% of their maximum aerobic power for
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7:04 minutes (range: 6:01 – 8:58 minutes). Participants started the trial at a cadence of
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approximately 97 RPM. The cadence significantly dropped to 90 RPM during the last 20% of the
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TTE trial. As per our definition of task failure, a drop in cadence of 15 RPM or below an
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absolute number of 70 RPM, the average cadence for the last 5% of the pedal revolution
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decreased to 86 RPM (Figure 1C).
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Location of clusters in brain space
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Across all subjects, we identified four electrocortical clusters that contained components
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from more than half of our subjects. The corresponding Talairach coordinates (Lancaster et al.,
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2000) and Brodmann areas (BA) are illustrated in Table 2. Two clusters (left and right
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hemisphere) were identified in the parietal cortex with corresponding BA 39 and 7. One cluster
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was identified in the superior frontal gyrus of the frontal cortex corresponding to BA 8. Lastly,
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we identified one cluster in the supplementary motor area (SMA) of the brain in the precentral
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gyrus of the frontal cortex corresponding to BA 6. Additionally, two more clusters were
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identified corresponding to the right frontal and right premotor areas, however, only four and
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five subjects, respectively, contributed to these clusters. According to our inclusion criteria if a
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cluster did not contain dipoles of at least more than half of the tested subjects, it was not included
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in any further analysis. Insert Table 2 here
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Changes with exercise duration All clusters of cortical activity showed a significant increase in EEG power (p < 0.05).
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No differences were found between the fresh and middle portion of the TTE trial and, therefore,
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the results are focused on the fresh and fatigued states. The left frontal cortex showed an increase
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in α-, β- and γ-band while the clusters in the supplementary motor area and left parietal cortex
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only showed a significant increase in the α- and β-band and the right parietal cortex only for the
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α-band (Figure 2). The strongest increase in EEG power was observed in BA 8, followed by
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increases of smaller magnitude in the SMA (BA 6) and the parietal cortex.
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Insert Figure 2 here Spectral fluctuations within motor area The ERSP pattern for BA 6 (SMA) is visualized with warm colors, indicating increased
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brain activity, and cool colors indicating decreased brain activity, with respect to the average
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spectrogram that was considered as baseline (Figure 3). All non-significant modulations of
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spectral power were masked (green) for better visualization. All frequency bands showed an
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oscillating pattern of increasing and decreasing activity throughout the pedalling cycle in both
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conditions. In the fresh condition, significant modulations in α-, β- and γ-band power are
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characterized by two local minima representing event-related desynchronization (ERD), which
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occur before the onset of right and left down stroke, respectively. Modulations of γ-band power
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remain similar in the fatigue condition, with the second negative peak occurring slightly later
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compared to the fresh condition. On the contrary, modulations of α- and β-band power show an
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altered pattern in the fatigue condition with a pronounced ERD occurring prior to the onset of the
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right down stroke, followed by a phase of event-related synchronization (ERS) prior to the left
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down stroke. Overall, there was a larger amplitude modulation in the α- and β-band in the fatigue
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condition. Insert Figure 3 here
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Muscle activation patterns Grand average temporal waveforms of EMG activity were visualized for both conditions
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(Figure 4). The waveforms are in general agreement with previous results investigating muscle
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activity during cycling (Ryan and Gregor, 1992; Baum and Li, 2003; Enders et al., 2015). Most
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muscles showed a trend towards increased mucsle activity, however, across all subjects the
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differences were not statistically different. The overall temporal pattern of muscle recruitment,
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however, seemed very robust despite the development of significant fatigue. Insert Figure 4 here
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Discussion
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Modulation of cortical activity during cycling
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This is the first study to quantify changes in the temporal pattern of electrocortical
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activity during high intensity cycling in a time-to-exhaustion trial. This expands on previous
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work investigating intra-cortical communication during a cycling time-to-exhaustion trial where
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communication between mid/anterior insular and motor cortex significantly increased with the
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development of fatigue (Hilty et al., 2011). Similar to previous studies on gait, the results of our
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study highlight temporal patterns of human brain activity that are spatially localized to the motor
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area within the frontal lobe (BA 6) (Gwin et al., 2011; Wagner et al., 2012). Significant
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modulations from baseline were observed in EEG power for an electrocortical cluster that was
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localized in BA 6 representing the SMA. Significant ERD in EEG power were observed across
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the α, β and γ frequency range for the SMA during the transition from the recovery phase to the
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power production phase for both legs indicating activation and increased neuron excitability for
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these brain areas at these specific latencies (Pfurtscheller and Lopes da Silva, 1999; Neuper and
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Pfurtscheller, 2001). Thus, neural activation within the SMA is greatest when muscles are
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recruited that are critical in transitioning the lower limb from a flexed to an extended position.
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This result is in agreement with previously described cortical activation patterns during cycling
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(Jain et al., 2013). Interestingly, a study quantifying cortical dynamics during walking supports
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the notion that modulation of activity in the BA 6 is related to movement with specific phases of
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repetitive locomotor activity (Wagner et al., 2012). They also found a trend for increased neural
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activation in the gamma frequency band in active compared to passive walking. Our results
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during active cycling also show modulation of gamma activity in the SMA supporting the
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suggestion by Wagner and colleagues (2012), that it might be an important contributor to motor
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planning and sensorimotor processing.
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The role of the motor areas in the cycling task used in this experimental protocol can be
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explored in more detail when interpreting the alternating pattern of desynchronization and
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synchronization of EEG power (Figure 3). Desynchronization refers to clusters of neurons that
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are not firing in synchrony and reflect activity distributed across various brain areas. Neural
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desynchronization is often interpreted as active information processing (Pfurtscheller and
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Berghold, 1989; Jurkiewicz et al., 2006). An increase in desynchronization can be indicative of
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an increase in task complexity with a higher demand for information processing (Pfurtscheller
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and Lopes da Silva, 1999). The opposite phenomenon, an increase in EEG power at a given
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frequency, is typically observed due to a synchronized firing of neurons. Although it is generally
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believed that ERS in the alpha and beta band reflects coherent activity and a deactivated
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communication between neural networks (Pfurtscheller and Lopes da Silva, 1999), in the context
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of movement tasks it is often interpreted as movement-related sensory processing (Cassim et al.,
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2001). In summary, ERD reflects active information processing with activity distributed across
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neural networks while ERS reflects a more focal activation pattern indicating a neural cell
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assembly responding in the same way. The results in this study show different responses for the
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alpha and beta compared to the gamma frequency band and will, therefore, be discussed
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separately.
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In the alpha and beta band, during the fresh condition, we see a small ERD that occurs
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during the phases of the right and left down stroke followed by ERS towards the end of the crank
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cycle that is stronger in the beta compared to the alpha band. In both frequency bands this pattern
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is changed during the fatigue condition and pronounced in amplitude. A clear phase of ERD
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during the down stroke of the right leg is followed by a phase of ERS in the second half of the
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cycle. The magnitude of ERD and ERS are increased in both alpha and beta frequency band.
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Specifically, during the fresh condition the phases of ERD, representing an active information
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processing behavior, correspond to the phases when the leg prepares for force production and
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transitions from a flexed to an extended position (see Figure 3).
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The gamma frequency band shows similar behavior to the alpha and beta frequency
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bands during the fresh condition with oscillations of ERD and ERS throughout the pedalling
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cycle. Again, phases of ERD, interpreted as active information processing, correlate well with
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the timing of leg force production. While the temporal pattern seemed to be altered in the alpha
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and beta band, the oscillations in the gamma band remained similar. In both conditions, the
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gamma band shows a pattern of two local minima corresponding to the initiation of power
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production and preparation for the extension phase of the right and left leg.
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The oscillating pattern of ERD and ERS could be interpreted as an alternating behavior in
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the motor areas reflecting a distributed activity and active information processing in preparation
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for leg extension (ERD) followed by a focal activation behavior (ERS) during the main phase of
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force production. This pattern was most pronounced in the gamma frequency band. This
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observation was made in a cluster slightly localized in the left SMA. One may expect a mirror
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effect in the other hemisphere or a cluster that is located over the central midline. Our identified
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cluster contained dipoles located in both the left hemisphere as well as in the midline of BA 6.
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While we did find a cluster in the right part of the premotor cortex, this cluster did not contain
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ICs from more than half of our subjects.
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Overall, we speculate that differences in EEG power modulation between the fresh and
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fatigued state are due to an increased effort and task difficulty that participants need to cope
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with. An alternative option would be a substantial change in neuromuscular strategies of the leg
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muscles to keep up with the cycling task. However, preliminary analysis of EMG data that was
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collected in synchrony with the EEG data does not support the hypothesis of altered lower limb
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neuromuscular strategies as the overall EMG pattern remained similar (Figure 4).
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Spectral power increase in EEG with fatigue
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Our study observed a broadband increase in EEG power, even though for most identified
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clusters this was only true in the α- and β-band. This increase across a broader frequency band is
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in contrast to studies that have investigated different walking conditions and found changes
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limited to the α- and β-band (Wagner et al., 2012, 2014). It is unclear, however, if our fatigue
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protocol directly relates to comparisons of active and passive walking (Wagner et al., 2012) or
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different visual feedback conditions (Wagner et al., 2014). Typically, active movement is
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associated with desynchronization in the α- and β-band and, therefore, it is not surprising to find
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such differences between active and passive movement. Instead our comparison was between
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two conditions that were both based on intense movements. Interestingly, a study that has
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investigated fatigue on a recumbent bicycle ergometer shows results that are very similar to this
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study. They showed that as participants fatigued during the cycling exercise there was a
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broadband increase in spectral power that was significant in the θ (4.5 – 7.99 Hz), low α (8 –
384
10.49 Hz), high α (10.5 – 12.99 Hz), low β 1 (13 – 17.99 Hz) and high β (18 – 30 Hz) frequency
385
bands (Bailey et al., 2008). Another possibility is noise in the acquired EEG that was not
386
removed by our data analysis. While this may partially contribute to our observation, the changes
387
in modulation of ERD and ERS also suggest a response in cortical involvement during the
388
exercise. Limitations with respect to noise and motion artifacts are further discussed at the end of
389
this section.
390
Interaction of electrocortical clusters
391 392
The four identified clusters of electrical activity in specific cortical domains (prefrontal, premotor and posterior parietal cortices) were consistently active in the majority of subjects in
393
this study. Common activation between these areas indicate a sequence of cognitive processing
394
that includes executive control, motor planning and execution as well as sensorimotor
395
integration. Indeed, previous research suggests that activation in the prefrontal cortex area relates
396
to the executive behavioural control function (Tanji and Hoshi, 2008) and motor planning (Wise
397
et al., 1997; Rizzolatti and Luppino, 2001; Tanji, 2001). Information about motor planning may
398
be passed on to the SMA and premotor cortex as it is well documented that these areas have
399
strong connections to the prefrontal cortex (Rizzolatti and Luppino, 2001; Chouinard and Paus,
400
2006), specifically the dorsolateral prefrontal cortex (Lu et al., 1994). Thus, the output of the
401
prefrontal cortex likely targets specific premotor areas. It has been proposed that the prefrontal
402
dependent motor areas (anterior parts of the premotor cortex and SMA) receive higher order
403
cognitive information regarding motor plans and motivation (Rizzolatti and Luppino, 2001)
404
which supports the executive control function of the frontal areas. Interestingly, the largest
405
response in neural activity accompanying fatigue is seen in the frontal areas. We speculate that
406
this is consistent with an increase in executive control to keep up with the challenging task
407
demand. However, this speculation needs careful evaluation in a future study.
408
In our analysis no cluster was directly associated to activity in the primary motor cortex.
409
Several technical as well as functional explanations may describe this observation. It has to be
410
noted that EEG has a limited spatial resolution limiting the exact location of activity within the
411
motor areas. Further, individual anatomy varies across participants and this study did not utilize
412
individual MRI head models which would improve source localization. However, it is well
413
known that strong interconnections between premotor and primary motor areas exist. It suggests
414
that information that is processed and passed on to the premotor cortex is directed further to the
415
primary motor cortex (Künzle, 1978; Chouinard and Paus, 2006) and the spinal cord (Dum and
416
Strick, 1991; Münchau et al., 2002). In fact, the majority of cortico-cortical connections of the
417
primary motor cortex are with BA 6 (Leichnetz, 1986; Stepniewska et al., 1993) implying that
418
reciprocal information processing between premotor and primary areas plays a crucial role in
419
motor execution. It is further known that both premotor and primary motor areas have strong
420
connections to the spinal cord through corticospinal neurons that are activated during movement
421
(Dum and Strick, 1991; Quallo et al., 2012).
422
Lastly, the connectivity between parietal areas and motor areas (specifically the SMA and
423
premotor cortex) has been demonstrated in many different scenarios. For a detailed review of
424
this network the reader is referred to more in depth literature (Desmurget and Sirigu, 2009).
425
Studies link activity in the parietal cortex to motor awareness and movement intention prior to
426
movement (Desmurget et al., 2009), coordinative aspects of movement, and functioning as an
427
interface between sensorimotor and visually guided aspects of movements (Taira et al., 1990;
428
Buneo and Andersen, 2006). The fact that the posterior parietal cortex plays a role in sensory and
429
motor function led to the proposal that it is heavily involved in sensory-motor interaction.
430
Interestingly, the parietal cortex has typically been identified in studies focusing on real-time
431
EEG recordings during repetitive locomotor tasks (Gwin et al., 2011; Wagner et al., 2014).
432
Specifically, a premotor-parietal network has been identified during rhythmic walking which is
433
believed to represent motor planning and intention and has been shown to increase activity in the
434
presence of task feedback (Wagner et al., 2014). In summary, connectivity between parietal and
435
premotor areas seems to be crucial for motor planning as well as sensory motor integration.
436
Increased EEG power was observed in this area which might suggest the importance of motor
437
planning and the integration of sensory input towards the end of an intense exercise.
438
Limitations: Muscle drive vs. sensory afferent integration
439
The spectral analysis performed in this study adds to the emerging understanding that the
440
cortex may play a more important role during rhythmic movement activity than previously
441
thought. It confirms similar findings of alternating latencies of event-related desynchronization
442
and synchronization within a movement cycle (Gwin et al., 2011; Wagner et al., 2012, 2014; Jain
443
et al., 2013). However, solely based on the spectral analysis it remains unknown if the observed
444
cortical activation patterns are directly involved in the transmission of motor commands to
445
facilitate muscle activation. Another possibility, a hypothesis that was previously discussed
446
(Gwin et al., 2011), is the integration of sensory afferent signals that are used to modulate
447
efferent corticospinal transmission. Most likely, the observed activity reflects involvement in
448
both descending efferent pathways as well as sensory information processing. In order to
449
investigate this proposal in more detail, a coherence analysis should be used in order to address
450
the idea of an anatomical coupling between cortical and muscular signals during movement
451
(Petersen et al., 2012).
452
Limitations: Movement-related artifacts
453
Movement-related and specifically gait-related artifacts have been a central issue to the
454
debate about the interpretation of EEG data recorded during locomotor activities. Following the
455
recommendation of previous reports, we excluded any components that were clearly indicative
456
of electromyographic artifacts (neck, jaw, facial). Additionally, we recorded the data in an
457
electrically shielded chamber to minimize electromagnetic interference from power lines and
458
devices. Due to the nature of the experimental protocol, we had to address the issue of
459
perspiration. Sweating typically results in very slow drifts of the EEG signal. We found these
460
drifts to be well below 1 Hz and, thus, we were able to remove these drifts using the applied
461
filters. Additionally, sweating was relatively constant throughout the exercise as subjects started
462
to sweat during the warm up period. Therefore, electrode impedance did not change significantly
463
throughout the TTE trial. Participants were not allowed to stand up from the saddle to minimize
464
head and upper body movement as much as possible. However, some movement still occurs
465
during an intense cycling trial and while we have addressed common noise issues from an
466
experimental and computational side, there is a remaining issue as artifact sources were shown to
467
greatly vary across subjects and efforts (Kline et al., 2015) making it difficult to reject artifacts
468
based on templates and models. Therefore, most likely, many reports of EEG during locomotion
469
contain a mixture of true electrocortical and artifact signals which should be kept in mind by the
470
reader.
471
Acknowledgements We would like to thank Jessi Temple for the data collection and supervision of the
472 473
exercise physiology portion of the study protocol as well as Elias Tomaras for technical
474
assistance. We would also like to acknowledge the time commitment and effort of all
475
participants who contributed to this study.
476
Grants
477
H.E. is supported by Vanier Canada (Natural Sciences and Engineering Research
478
Council), the Killam foundation as well as the CREATE program of the Natural Sciences and
479
Engineering Research council. J.B. is supported by Vanier Canada (Canadian Institute of Health
480
Research). A.B.P. is supported by Natural Sciences and Engineering Research Council of
481
Canada (NSERC), Canadian Foundation for Innovation Leaders Opportunity Fund, and Alberta
482
Enterprise and Advanced Education Research Capacity Program, Alberta Alignment Grants.
483
Disclosures
484 485 486 487 488
There are no professional relationships with companies or manufacturers to disclose for all authors.
489
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Figure captions
622
Figure 1: The top panel (A) shows a schematic drawing of the experimental setup. Participants
623
were equipped with a tight fitting 64 channel EEG system and several electrodes to record lower
624
limb electromyography (not used in this study). Cables were securely attached to the subjects
625
back to avoid artifacts due to cable movement. The middle panel (B) shows the cadence of a
626
representative subject for a complete time-to-exhaustion trial (gray line). The black line shows
627
the cadence when applying a moving average with a window size of 50 pedal revolution. The
628
dashed black line shows the cadence that the participant voluntarily chose to start the trial. The
629
dotted line shows the corresponding cut off cadence to define task failure. The areas with the
630
horizontal lines depict the time frame corresponding to the fresh and fatigue condition used for
631
the analysis. The shaded gray area corresponds to the last 5% of the trial ultimately prior to task
632
failure. The bottom panel (C) shows the average cadence across all subjects for the fresh and
633
fatigue phase as well as immediately before task failure. * denotes a significant difference
634
compared to the fresh condition (p < 0.01). † denotes a significant difference compared to the
635
fresh and fatigue condition (p < 0.01).
636
Figure 2: Scalp map projections from the dipole clusters and power spectra for electrocortical
637
sources corresponding to frontal cortex (BA 8, top left), SMA (BA 6, bottom left), right parietal
638
cortex (BA 7, top right), and left parietal cortex (BA 39, bottom right). The color scale indicates
639
the strength of the cluster average scalp projection to each electrode. The traces indicate the fresh
640
(black) and fatigue (gray) conditions with shaded gray areas indicating the 95% confidence
641
intervals. The MRI images show the dipole clusters in the sagittal and coronal planes.
642
Figure 3: ERSP plot (A) of BA 6 showing modulations in EEG spectral power throughout the
643
pedalling cycle (x-axis) as a function of frequency (y-axis, log scaled) with warm and cool colors
644
indicating increased and decreased activity with respect to the average spectrogram, respectively.
645
The left column shows data for the fresh condition (first 20%) and the middle column for the
646
fatigue condition (last 20%). The right column shows the difference (fresh minus fatigue)
647
between the two conditions with any non-significant differences set to zero. The plots below (B)
648
show the time-dependent brain activation across the pedaling cycle in the α, β and low γ
649
frequency bands. The shaded pink and white areas indicate the down stroke (power production
650
phase) of the right and left leg, respectively. The onset of right down stroke (RDS) and left down
651
stroke (LDS) corresponds to the right and left leg passing the top dead centre and is indicated by
652
the arrows below the x-axis. (For interpretation of the references to color in this figure legend,
653
the reader is referred to the web version of this article.)
654
Figure 4: Grand average EMG waveforms in the fresh (black) and fatigue (gray) condition for
655
six lower limb muscles. The x-axis shows the crank cycle with zero referring to the top dead
656
centre (TDC). The y-axis shows the EMG activity normalized to the mean activity of each
657
muscle across the pedalling cycle in the fresh condition.
658
Tables Table 1: Subjects’ characteristics including lactate thresholds (LT 1 and LT2) and 85% of maximum aerobic power (MAP) that was used for the time-to-exhaustion trial. Subject ID Age Weight MAP MAP LT 1 LT 2 85% MAP # [years] [kg] [W] [W/kg] [W] [W] [W] 35 77.4 425 5.5 250 300 360 1 23 82.5 386 4.7 200 250 330 2 22 68.2 437 6.4 250 300 370 3 29 71.4 438 6.1 250 300 370 4 30 62.2 360 5.8 175 225 310 5 31 72.4 351 4.8 150 225 300 6 35 67.0 450 6.7 275 325 380 7 18 75.1 430 5.7 225 300 365 8 25 65.8 413 6.3 225 275 350 9 27 75.5 413 5.5 225 275 350 10 27.5 71.8 410.3 5.8 222.5 277.5 348.5 Mean 5.6 6.1 33.8 0.7 38.1 34.3 26.9 SD
659
Table 2: Talairach coordinates for identified clusters (geometric mean) Cluster Nearest Gray Talairach Matter coordinates 1 Brodmann Area Frontal cortex, BA 8 (-21, 48, 52) superior frontal gyrus Frontal cortex, BA 6 (-23,-19,70) precentral gyrus Parietal cortex, BA 7 (38, -60, 60) superior parietal lobule Parietal cortex, BA 39 (-48, -65, 46) inferior parietal lobule 1 Determined using the Talairach client (www.talairach.org) (Lancaster et al., 2000) 660
A
EEG cap Pedal switch
EMG electrodes
Controlled power output Data acquisition boxes (EMG & EEG)
B
Cadence [RPM]
100 80 60 40 20 0 0
Fresh 1
Fatigue 2
3 4 Time [min]
5
6
7
C Cadence [RPM]
100
*
†
80 60 40 20 0
Fresh
Fatigue
Task Failure
(last 5% of pedal revolutions)
Frontal (BA 8)
9 Subjects (10 ICs)
7 Subjects (9 ICs)
Parietal Right (BA 7)
Fresh Fatigue
15
Power(10*log10(μV2))
Power(10*log10(μV2))
20
15 10
10 5 0 -5 5
0 -5
10 15 20 25 30 35 40 45 Frequency (Hz)
5
6 Subjects (8 ICs)
10 15 20 25 30 35 40 45 Frequency (Hz)
Parietal Left (BA 39) Power(10*log10(μV2))
Supplementary Motor Area (BA 6) Power(10*log10(μV2))
8 Subjects (9 ICs)
5
15
15
10
10
5
0 5
10 15 20 25 30 35 40 45 Frequency (Hz)
-3
-2
-1
0
1
2
3
5 0 -5 5
10 15 20 25 30 35 40 45 Frequency (Hz)
Frontal (BA 8)
9 Subjects (10 ICs)
7 Subjects (9 ICs)
Parietal Right (BA 7)
Fresh Fatigue
15
15
Power(10*log10(μV2))
Power(10*log10(μV2))
20
10
10 5 0 -5 5
-5 5
10 15 20 25 30 35 40 45 Frequency (Hz)
Parietal Left (BA 39)
6 Subjects (8 ICs) Power(10*log10(μV2))
Power(10*log10(μV2))
0
10 15 20 25 30 35 40 45 Frequency (Hz)
Supplementary Motor Area (BA 6)
8 Subjects (9 ICs)
5
15
15
10
10 5 0 5
10 15 20 25 30 35 40 45 Frequency (Hz)
-3
-2
-1
0
1
2
3
5 0 -5 5
10 15 20 25 30 35 40 45 Frequency (Hz)
Fresh Frequency (Hz)
12 8
3
25 50 75 100 Pedal Cycle (%) -0.5
α
0
B
Alpha 8−12Hz
0.5
0.5
0
0
−0.5
−0.5
−1
−1
ERSP (dB)
1
Beta 12−30Hz
Beta 12−30Hz
1
1
0.5
0.5
0
0
−0.5
−0.5
−1
−1 Low Gamma 30−50Hz
Low Gamma 30−50Hz 1
1
0.5
0.5
0
0
−0.5
−0.5
−1
−1
RDS
50
75 100 LDS
Pedal Cycle (%)
25 50 75 100 Pedal Cycle (%)
-0.5
1
25
0
0.5
Alpha 8−12Hz
0
12 8 3
25 50 75 100 Pedal Cycle (%)
0 ERSP (dB)
ERSP (dB)
0
30
β 12 8
Difference
50
γ
30
ERSP (dB)
Frequency (Hz)
30
3
Fatigue
50
Frequency (Hz)
A 50
0
25 RDS
50
75 100 LDS
Pedal Cycle (%)
0 ERSP (dB)
0.5
Fresh Frequency (Hz)
12 8
3
25 50 75 100 Pedal Cycle (%) -0.5
α
0
B
Alpha 8−12Hz
0.5
0.5
0
0
−0.5
−0.5
−1
−1
ERSP (dB)
1
Beta 12−30Hz
Beta 12−30Hz
1
1
0.5
0.5
0
0
−0.5
−0.5
−1
−1 Low Gamma 30−50Hz
Low Gamma 30−50Hz 1
1
0.5
0.5
0
0
−0.5
−0.5
−1
−1
RDS
50
75 100 LDS
Pedal Cycle (%)
25 50 75 100 Pedal Cycle (%)
-0.5
1
25
0
0.5
Alpha 8−12Hz
0
12 8 3
25 50 75 100 Pedal Cycle (%)
0 ERSP (dB)
ERSP (dB)
0
30
β 12 8
Difference
50
γ
30
ERSP (dB)
Frequency (Hz)
30
3
Fatigue
50
Frequency (Hz)
A 50
0
25 RDS
50
75 100 LDS
Pedal Cycle (%)
0 ERSP (dB)
0.5
EMG Activity (rel)
3 2 1 0
0
90
0 (TDC) 90 Crank Angle (degrees)
180
EMG Activity (rel)
Vastus Medialis EMG Activity (rel)
3 2 1 0 Biceps Femoris
2
3 2 1 0 180
Soleus
Vastus Lateralis
4
EMG Activity (rel)
EMG Activity (rel)
EMG Activity (rel)
Med. Gastrocnemius
2 1 0 Rectus Femoris
3
Fresh Fatigue
2 1 0 180
90
0 (TDC) 90 Crank Angle (degrees)
180
