Abstract

Bimanual coordination requires the functional integration of the activity in various cortical, subcortical, spinal, and peripheral neural structures. We challenged this functional integration by destabilizing bimanual 5:8 tapping through an increase in movement tempo, while measuring brain and muscle activity using magnetoencephalography and electromyography. Movement instabilities were characterized by a drop in frequency locking. Time–frequency analysis revealed movement-related beta amplitude modulation in bilateral motor areas as well as movement-related corticospinal entrainment. Both of these synchronization patterns depended on movement tempo suggesting that the timescale needed for the upregulation and downregulation of beta synchrony in rhythmic tapping poses constraints on motor performance. Bilateral phase locking over movement cycles appeared to be mediated by beta-frequency oscillations and constrained by its phase dynamics. The timescale of beta synchrony thus seems to play a key role in achieving timed phase synchrony in the motor cortex and along the neural axis. Once event-related desynchronization–synchronization cycles cannot be build up properly, inhibition may become inadequate, resulting in a reduction of the stability of performance, which may eventually become unstable.

Introduction

Unraveling the mechanisms that transfer information across the human nervous system forms one of the greatest challenges in neuroscience. Intra- and interhemispheric interactions between bilateral motor areas provide an expedient window into the neural underpinnings of motor control. Interhemispheric interaction comprises both inhibitory and excitatory influences that are mediated through transcallosal pathways (Ferbert et al. 1992). There is accumulating evidence that connections of premotor areas (PM1s) through the anterior portion of the corpus callosum support skilled movements, including bimanual coordination. In addition, ipsilateral primary motor cortex (M1) appears to continuously interact with its contralateral counterpart (Daffertshofer et al. 1999). In fact, many studies have shown that during unimanual movement not only contralateral but also ipsilateral motor areas are active. This ipsilateral activity may map onto its contralateral corticospinal pathway and thus induce bilateral crosstalk, which may become manifest as mirror movements (e.g., Shibasaki and Nagae 1984; Britton et al. 1991). This line of argument highlights the importance of effectively inhibitory interhemispheric interactions as they may serve to suppress unwanted crosstalk (Daffertshofer et al. 1999; Gerloff and Andres 2002; Serrien and Brown 2002).

In the present study, we investigate the role of balancing excitatory and inhibitory interactions in the context of movement instabilities. As movement instabilities, we specifically consider the loss of bimanual coordination during rhythmic performances (Kelso 1995). These can be interpreted as phase transitions and captured mathematically by (simple) bifurcation schemes. Daffertshofer et al. (2005) proposed a mathematical model for the neural dynamics accompanying rhythmic motor performance. In a nutshell, this model describes how effectively inhibitory interhemispheric interactions may result from excitatory transcallosal fibers projecting to M1 and PM1, and PM1 inhibiting M1 via intrahemispheric cortico-cortical connections. The model incorporates oscillators to represent local activity and the interregional interactions (intra and interhemispheric) are given as coupling between these oscillators. Coupling is either excitatory or inhibitory, which causes in-phase or antiphase oscillations, respectively. If intrahemispheric excitation and inhibition are properly balanced, these oscillations cancel each other so that the local power drops and the interhemispheric crosstalk is effectively suppressed. Changes in local activity as measured by local power (or local event-related [de]synchronization, see below) can indicate that intrahemispheric excitation and inhibition are not balanced. The underlying oscillatory processes might be too slow to provide the proper balance, that is, the oscillators' phases cannot be adjusted in time (Daffertshofer et al. 2005). Put differently, the intrahemispheric inhibition fails whenever the phase coupling between M1 and PM1 becomes less stringent. This may yield a greater amount of interhemispheric crosstalk through the aforementioned, excitatory transcallosal fibers and can result in a destabilization of motor performance.

Many empirical studies on neural activity accompanying movement instabilities have focused on in-phase versus antiphase coordination or on-the-beat versus off-the-beat tapping (Jirsa and Fuchs 1998; Kelso et al. 1998; Daffertshofer et al. 2000a, 2000b; Fuchs et al. 2000; Mayville et al. 2001; Serrien and Brown 2002; Aramaki et al. 2006). However, such isofrequency performances barely allow for studying the aforementioned crosstalk as the activities associated with the 2 fingers (or hands) are very hard to distinguish in encephalographic signals. To overcome this drawback, we investigated polyrhythmic tapping, that is, a task in which left and right fingers move rhythmically in a p:q frequency relation (p and q are integers and with p:q in reduced form both p and q are larger than 1). We induced instabilities in the performance of this task by increasing movement tempo (Peper et al. 1995; Meyer-Lindenberg et al. 2002). In polyrhythmic tapping, one can uniquely identify neural activities that correspond to either the left or the right finger (Lang et al. 1990). The left–right crosstalk may hence be seen as the ratio between activities associated with the right and left fingers (Kristeva et al. 1990). In accordance with the model's prediction, we hypothesized that a destabilization of behavior is accompanied by a disbalancing of intrahemispheric excitation/inhibition yielding a loss of effective interhemispheric inhibition. Besides a pronounced bilateral interaction as mere reflection of the increased crosstalk, we expected to find an increased, movement-related activity in the motor cortices, in particular in the hemisphere ipsilateral to the finger displaying the movement instability.

Materials and Methods

Subjects

Thirteen right-handed percussionists (2 female, age 20–25 years), all students or graduates from the Conservatory of Amsterdam, participated in the experiment, which was conducted in full compliance with the guidelines of the Ethical Committee of the Faculty of Human Movement Sciences of VU University Amsterdam. All subjects signed an informed consent form prior to participation. They had completed the course “Advanced rhythms” in which several polyrhythms were extensively practiced. They were paid for their participation.

Procedure

Subjects were invited to tap a 5:8 polyrhythm with their left and right index fingers. Starting with a steady 5:8 performance, movement tempo was increased over 23 plateaus in steps of 0.2 Hz starting with the right finger at ωR = 2.5 Hz (plateau 1) and ending at 6.9 Hz (plateau 23); in plateaus 1–13, a single rhythmic cycle was produced, whereas from plateau 14 onward, 3 rhythmic cycles were produced (Fig. 1). The initial frequency of the left finger was ωL = 5/8 × 2.5 = 1.56 Hz. The beat was acoustically paced; the fast beat was permanently present at the right ear (pitch 660 Hz, tone duration 50 ms), whereas only a single tone of the slow beat (left ear,, pitch 440 Hz, tone duration 50 ms) was presented to indicate the start of a rhythmic cycle at the beginning of a plateau (i.e., always coincident with the fast beat).

Figure 1.

Transition trial scheme. Vertical dashed lines indicate the start of the rhythmic cycles; red solid lines indicate the plateaus (top axis) corresponding to the target tempos (left axis). Each trial lasted for about 80 s (bottom axis); the first 3 rhythmic cycles that served to tune in on the rhythm are not shown.

Figure 1.

Transition trial scheme. Vertical dashed lines indicate the start of the rhythmic cycles; red solid lines indicate the plateaus (top axis) corresponding to the target tempos (left axis). Each trial lasted for about 80 s (bottom axis); the first 3 rhythmic cycles that served to tune in on the rhythm are not shown.

To guarantee steadiness of performance, all trials started with 3 plateaus at ωR = 2.5 Hz with rhythmic 5:8 binaural pacing. These plateaus were not analyzed further as they merely served to tune in on the required polyrhythm. For the magnetoencephalographic (MEG) recordings, subjects sat in an upright position with their arms on an armrest on which force transducers were mounted. The experiment consisted of 5 identical blocks. Between-blocks subjects had a few minutes break during which they stayed in the seat. A block consisted of 2 times 2 initial unimanual tapping trials (first right, then left) followed by 12 trials of bimanual polyrhythmic tapping. In total, we had 5 × 12 = 60 polyrhythmic trials (∼80 s each), 5×2 = 10 unimanual trials for right and left movements (∼80 s each), and 6 resting state trials (30 s each). The unimanual trials fully agreed with the bimanual one, with the only difference that one hand was not moving. In the unimanual and resting state trials, the nonmoving hand(s) was (were) always placed in a relaxed position on the armrest. During all trials, subjects were asked to fixate their gaze at a stationary spot marked as a red cross and located about 2 m in front of them.

Their musical training notwithstanding, all subjects had to follow a 30-min training scheme to properly perform the 5:8 polyrhythm: tapping to audio files with unimanual and bimanual repetitions of series with constant (2.5, 3.3, 4.1, and 4.9 Hz) and increasing (2.7, …, 5.1 Hz) movement tempos at the required frequency ratio. This training scheme was repeated 4 times during the week prior to the experimental session.

Data Acquisition and Preprocessing

Brain activity was recorded using a 151-channel MEG system (CTF Systems Inc., Vancouver) with third-order synthetic gradiometers. The electromyogram (EMG) was assessed from bilateral extensor and flexor digitori with a reference electrode at the left wrist (Ag–AgCl electrodes: Ø 1 cm and about 2 cm interelectrode distance for off-line bipolar montage). Tapping forces were recorded using MEG-compatible force sensors that were mounted on the 2 armrests (Boonstra et al. 2005). All signals, including acoustic stimuli, were online low-pass filtered at 200 Hz prior to digitization at a rate of 625 Hz. All signals were mean-centered and high-frequency noise was eliminated off-line using a second-order bidirectional Butterworth low-pass filter at 200 Hz. In addition, EMG signals were high-pass filtered at 20 Hz to remove movement artifacts and rectified using the Hilbert transform (Myers et al. 2003). To minimize head movement, small pads were positioned between the helmet-shaped opening in the dewar and the subjects' head.

Data Analysis

Source Analysis

The cortical motor areas were identified via local event-related desynchronization (ERD) and synchronization (ERS) power differences (i.e., ERD–ERS) in the beta band (20–30 Hz) in the unimanual conditions using a synthetic aperture magnetometry (SAM) beamformer approach (Vrba and Robinson 2001; Cheyne et al. 2006). In brief, weight distributions of sensors were determined to estimate activities in the voxels of a probabilistic brain template (The International Consortium for Brain Mapping [ICBM], see, e.g., Mazziotta et al. 1995, for details). MEG recordings were positioned and scaled to that template using intermittent head position registrations (3 reference coils at nasion and left–right ears, see, e.g., de Munck et al. 2001). SAM was used to compute in every voxel the variance-normalized power in a specific frequency band (pseudo-t values). Subsequently, we computed condition-based difference by subtracting a so-called “active” state from a “control” or baseline state (we note that here these differences were only used to enhance contrast and not to pinpoint active state and the “real” baseline). In detail, the interval from 200 ms (or 300 ms when tapping with the left finger given the longer movement cycle) before the tap to the tap onset (active state) was contrasted with the interval from tap onset to 200 ms (or 300 ms) after the tap onset (control state). The cluster size of voxels with maximal pseudo-t differences was defined as the number of surrounding voxels exceeding 95% of the peak value; the significance of these differences between active and control states was assessed via a paired permutation test across subjects (see below for details). Clusters with less than 10 voxels were discarded. The MEG signals were multiplied and summed using the weight distributions corresponding to the beamformers showing the maximal, significant power differences. Given their location, we denote these projections of the neural signals as M1left and M1right, which thus yielded maximal power differences during right and left finger tapping, respectively. During bimanual movement beta activity in M1left may generally covary with beta activity in M1right (e.g., because of coinciding ERD–ERS cycles in coinciding taps); hence, we used the unimanual controls to uniquely localize sources. We note that we did not find strong ipsilateral beta modulation during unimanual trials.

Spectral Analysis

Power spectra were computed for force signals and M1 projections for each subject, trial, channel, and plateau, using Welch's periodogram method with Hamming windows as tapers (size = one rhythmical cycle). These power spectra were upsampled (via cubic splines) to generate an identical frequency resolution across plateaus. (This approach allows for using a fixed number of tapers for all estimates. We note that here the more conventional padding technique fails because padding size would exceed the data size because the movement frequency spans a fair range.) To remove an overall scaling in amplitude, the power spectra were normalized so that the total power (per subject and trial) was equal to 1. For every subject, trial, channel, and plateau the frequency containing the most power in the spectra of the force signals was identified as the movement frequency (ωR for right tapping and ωL for left tapping). The maximal tapping frequency was defined at the group level as the target frequency corresponding to the last plateau in which the movement frequency matched the target tempo. To this end, the target frequency was compared with the movement frequency across subjects, and tested for equality by a permutation test; if no significant (P < 0.001) difference was found, this was considered a match. These maximal tapping frequencies served to test whether the expected behavioral instabilities were indeed related to coordinative effects, that is, differences in maximal frequencies between bimanual and unimanual conditions imply an effect of bimanual coordination on the critical plateau (see below), which relates to the emergence of movement instabilities.

The power at the movement tempos ωR and ωL and the overlap of the normalized power spectra were used to assess the dynamics around the behavioral instability. The latter provides the similarity Ψxy between 2 spectra Px and Py after rescaling the frequency axis with ρ = p:q 

graphic
see Daffertshofer et al. (2000a, 2000b) for more details. We were primarily interested in the values at the target frequency ratio, that is, at p:q = 5:8. In addition, we also found large spectral power at harmonics (i.e., multiples) of the movement frequency yielding finite spectral overlaps at the corresponding harmonic ratios. The frequency plateau, in which the behavioral transition from steady to unstable performance occurred, was defined as the plateau where the strength of 5:8 frequency locking in the EMGs dropped substantially (referred to as “critical plateau” P0). More specifically, the first plateau in which the actual ratio (given at the highest spectral overlap) differed more than 0.1 from the target ratio 5:8, was considered the transition plateau. To assess neural and behavioral changes during the transition, we evaluated the 6 plateaus preceding the critical plateau P0 with the 6 plateaus following P0, that is, we considered P−6…P−1 as steady and P1…P6 as unstable performance, respectively, and compared their properties (referred to as “relative plateaus”).

Event-Related Analysis

We filtered the MEG projections and the EMG in frequency bands 0–3, 3–6, …, 42–45 Hz, and computed amplitudes and phases using the Hilbert transform. These signals were divided into 1-s windows centered around motor events (taps). Per plateau and frequency band, the dispersion over motor events of the relative phase between M1left and right–left EMG, M1right and right–left EMG, and M1right and M1left was quantified by phase uniformity (Mardia and Jupp 1972). For a discrete set of phases {φ12,…,φN}, their uniformity is defined as 1-circular variance of the phases or 1 − cv with 

graphic
(Uniformity agrees with the more recently employed phase coherence; see, e.g. [Mormann et al. 2000]). As phase uniformity is, by definition, biased by the number of phases N, the uniformity values were transformed to the Rayleigh statistic 2·(1 − cv)2N. To assess the beta modulation and uniformity during the transition, the event-related signals were averaged per plateau Pj (j = −6…6) over all trials and subjects. Because the timing of the local ERD–ERS turned out to depend on the movement frequency, we normalized amplitudes and relative phases by scaling the window such that each window contained 2 movement cycles, and all epochs were of equal length. To identify ERD/ERS, maxima and minima of the amplitude were selected from an interval of 70 ms before to 200 ms after the event (aERS and aERD, respectively), and we computed the amplitude deviations ΔA = aERSaERD. The temporal difference ΔT = tERStERD was quantified by the lag between the ERD and ERS. For the corticospinal synchronization, we integrated epochs of uniformity over its time interval and computed ΔU. Interestingly, these differences dropped with increasing tempo as quantified via an exponential decay of the form e estimated for all the grand averaged measures in bimanual and unimanual trials. To decide whether the data were in agreement with the exponential model, we assessed the goodness of fit by the squared correlation r2 = SSR/(SSE + SSR), where SSR is the sum of the squared residuals and SSE the sum of the squared errors.

Statistics

All values were subjected to a permutation test across subjects (Nichols and Holmes 2002) to assess the hypothesis of a difference in the variable T (e.g., spectral power) between stable and unstable performance. This nonparametric test is based on resampling. In brief, the subjects' difference values for stable and unstable performance Tu,kTs,k are labeled with random signs σi,k, where k indexes all subjects 1,…,N and i indexes all possible permutations across subjects of labelings with +1 or −1. For the ith permutation σi the mean of the differences across all subjects yields the statistic 

graphic
Under the null hypothesis “no difference,” this distribution of means is symmetric (and has mean zero). Small values of the mean suggest that the null hypothesis should be rejected. The P value of this test is given by the portion of ti greater or equal to the test statistic corresponding to the correctly labeled data (with all signs positive).

Results

Source Analysis

Beamformers yielded consistent power differences in the beta band (Fig. 2 and Supplementary Fig. 1). In the 12 left tapping trials, M1right showed the largest event-related decrease (power difference −0.5, P < 0.001, cluster size 34), and in the 12 right tapping trials, M1left showed the largest event-related decrease (power difference −0.1, P = 0.04, cluster size 22). To anticipate, the modulation of beta activity decreased with increasing frequencies, which may explain the weaker result for M1left corresponding to the faster rhythm.

Figure 2.

SAM sources. Axial view of the differential statistical images after setting the threshold to 50% of the largest differences in beta power around the tap onsets overlaid on the brain template (an average of more than 150 MRI scans from different individuals from the ICBM MRI database). Differences in beta power between 200 ms (or 300 ms for performance with the left finger) after (active state) and before (control state) the taps from the unimanual right (left image) and left (right image) conditions. Talairach coordinates of the peak sources were (−29.6,−20.6,52.8) and (30.7,−16.5,51.4), respectively. Both sources were located in the hand–arm area of the contralateral primary motor cortex (Brodmann area 4, see also Supplementary Material).

Figure 2.

SAM sources. Axial view of the differential statistical images after setting the threshold to 50% of the largest differences in beta power around the tap onsets overlaid on the brain template (an average of more than 150 MRI scans from different individuals from the ICBM MRI database). Differences in beta power between 200 ms (or 300 ms for performance with the left finger) after (active state) and before (control state) the taps from the unimanual right (left image) and left (right image) conditions. Talairach coordinates of the peak sources were (−29.6,−20.6,52.8) and (30.7,−16.5,51.4), respectively. Both sources were located in the hand–arm area of the contralateral primary motor cortex (Brodmann area 4, see also Supplementary Material).

Spectral Changes around Transition

All subjects experienced the task as challenging, which was also apparent from the rapid decrease in frequency locking when tempo increased (Fig. 3). One subject (#8) consistently showed transitions at lower movement tempos than the other subjects: The deviation of this subjects' mean transition frequency (over trials) exceeded 2 standard deviations of the group mean; it was therefore considered an outlier. Four subjects (5, 6, 10, and 11) completed the tapping sequence without deviating from the 5:8 frequency relation in several trials; analyses were performed over the remaining 582 trials. On average, performance became unstable between plateaus 12 and 20 (mean: plateau 17, standard deviation: 2). This corresponds to movement tempos around 5.7Hz, which agrees with the previous findings of Peper et al. (1995). The transition was accompanied by a drop in power at both target tempos but most pronounced in the right tapping data at the fast movement tempo.

Figure 3.

Performance data. (A) Tapping profiles in a typical trial. The panel shows the left (dark gray) and right (light grea) force signals (ordinate) from 41.5 to 46.5 s during 5 s of steady performance of subject 9. (B) Frequency locking per plateau (y-axis) and frequency ratio (x-axis) in the same trial showing steady performance up to 5.9 Hz (or plateau 18). (C) Box plot of critical plateau P0 (ordinate) for each subject (abscissa) as determined from the spectral overlap at 5:8 in the left:right tapping data (13 subjects, 60 transition trials). The boxes indicate lower and upper quantiles, the midlines indicate medians, and plus signs are outliers. The latter trials were excluded from the analysis; in total, 582 transitions were identified.

Figure 3.

Performance data. (A) Tapping profiles in a typical trial. The panel shows the left (dark gray) and right (light grea) force signals (ordinate) from 41.5 to 46.5 s during 5 s of steady performance of subject 9. (B) Frequency locking per plateau (y-axis) and frequency ratio (x-axis) in the same trial showing steady performance up to 5.9 Hz (or plateau 18). (C) Box plot of critical plateau P0 (ordinate) for each subject (abscissa) as determined from the spectral overlap at 5:8 in the left:right tapping data (13 subjects, 60 transition trials). The boxes indicate lower and upper quantiles, the midlines indicate medians, and plus signs are outliers. The latter trials were excluded from the analysis; in total, 582 transitions were identified.

Figure 4 shows the power spectral densities of taps and M1s, and spectral overlap between tapright and tapleft, and between M1left and M1right. In general, a loss of stability is reflected as a weaker frequency locking as expressed by the drop in spectral overlap, here at 5:8 (see Fig. 4, left panel). These comparisons of spectral densities further show that the performance of the right finger became unstable after the critical plateau as its movement frequency spread around lower frequencies. The performance of the left finger also seemed to become less accurate during the transition. In the M1s, both movement frequencies were found to be present, in particular before the critical plateau P0 (same figure, inner panels). The frequency locking was not confined to 5:8, but the spectral overlap was also large at 1:1 and the harmonics 5:4 (same figure, right panel). We note that the locking at 5:4 can be explained by the presence of harmonics of the movement frequencies in the M1s (Daffertshofer et al. 2000a, 2000b). By comparing the spectral overlap in steady (P−1) and unstable (P1) states, we found that the frequency locking at 5:8 was significantly weaker after P0 in the tapping data (from 0.90 to 0.55, P < 0.001) but not in M1s (P = 0.87). The frequency locking at 1:1 was slightly enhanced after P0 in both tapping signals (from 0.09 to 0.20, P < 0.001) and cortical activity (from 0.92 to 0.94, P = 0.02).

Figure 4.

Spectra and spectral overlap in tapping and contralateral motor cortices. Inner 4 panels: time–frequency plots of normalized spectral power in tapright (top left), tapleft (bottom left), M1left (top right), and M1right (bottom right). Displayed are the averages per channel at different frequencies (ordinate) relative to the critical plateau (abscissa). Note that the timescale of the signals was adjusted with factor 2.5/ωR or 5/8·2.5/ωL in each relative plateau resulting in common target frequencies 2.5 and 5/8·2.5 Hz as an effect of interpolation. Outer panels: frequency locking at different ratios (ordinate) in tapping data (left panel) and cortical sources (right panel) relative to the critical plateau (abscissa). Averages per channel combination over subjects are shown. We note that here we used 8 instead of 3 rhythmical cycles as size of the Hamming taper to improve legibility and guaranteed that the total number of cycles still agreed over time (plateaus) by interpolating the time series prior to estimating the power so that all plateaus contained an identical number of samples (= number during the slowest tempo).

Figure 4.

Spectra and spectral overlap in tapping and contralateral motor cortices. Inner 4 panels: time–frequency plots of normalized spectral power in tapright (top left), tapleft (bottom left), M1left (top right), and M1right (bottom right). Displayed are the averages per channel at different frequencies (ordinate) relative to the critical plateau (abscissa). Note that the timescale of the signals was adjusted with factor 2.5/ωR or 5/8·2.5/ωL in each relative plateau resulting in common target frequencies 2.5 and 5/8·2.5 Hz as an effect of interpolation. Outer panels: frequency locking at different ratios (ordinate) in tapping data (left panel) and cortical sources (right panel) relative to the critical plateau (abscissa). Averages per channel combination over subjects are shown. We note that here we used 8 instead of 3 rhythmical cycles as size of the Hamming taper to improve legibility and guaranteed that the total number of cycles still agreed over time (plateaus) by interpolating the time series prior to estimating the power so that all plateaus contained an identical number of samples (= number during the slowest tempo).

We further assessed the relative “contribution” of the movement frequencies to the transition by comparing the power in M1 at ωR and ωL (Fig. 5). Between P−6 to P−5, there were no differences, and this trend continued up to the comparison between P−2 and P−1. From P−1 to P0, there was a drop in power at the contralateral movement frequency in both M1s (P = 0.02 for M1left and P = 0.004 for M1right). This was followed by a sharp power increase in M1right at the ipsilateral movement frequency ωR (P = 0.02). (We note that a power drop in M1right after P1 does not imply the coordination in P2 was more or less stable than in P1; the performance in P2 just deviated more from auditory target than in P1. In general, P0 defines the point at which a coordination pattern looses stability. That point is often accompanied by an increase in fluctuations, which reduce as soon as a new, stable coordination pattern emerges. Admittedly, we could not find clear-cut switches to other long-lasting, stable coordination, which we did in earlier studies [e.g., Peper et al. 1995; Daffertshofer et al. 2005]; nonetheless, we interpret the local increase in power as indicative for a phase transition.) Power also increased at ωR in M1left (P = 0.06). That is, the power of the right movement frequency dominated more after the transition, especially in M1right. Hence, the increase in 1:1 frequency locking was caused by the relative increase at the fast movement frequency in M1right.

Figure 5.

Cortical power at the movement frequencies. The mean (±standard error of the mean) of the power (ordinate) at the contralateral (dark gray) and ipsilateral (light gray) movement frequencies in the motor cortices (over all transitions) is displayed relative to the critical plateau (abscissa). Left panel: power at ωR (dark) and ωL (light) in M1left. Right panel: power at ωR (dark) and ωL (light) in M1right.

Figure 5.

Cortical power at the movement frequencies. The mean (±standard error of the mean) of the power (ordinate) at the contralateral (dark gray) and ipsilateral (light gray) movement frequencies in the motor cortices (over all transitions) is displayed relative to the critical plateau (abscissa). Left panel: power at ωR (dark) and ωL (light) in M1left. Right panel: power at ωR (dark) and ωL (light) in M1right.

Event-Related Changes: Beta Amplitude and Corticospinal Uniformity

The event-related amplitude at different frequency plateaus revealed a pronounced modulation, particularly in the beta band (see Fig. 6, left panels and Supplementary Fig. 3). Event-related patterns were also visible in the uniformity of the relative phases of M1 and EMG, and were again most pronounced in the beta band. This corticospinal beta uniformity showed increases and decreases comparable with the M1 amplitude modulation (see Fig. 6, right panels). Patterns in uniformity between M1left and M1right displayed also event-related structures but varied substantially over subjects. That is, local beta synchronization (i.e., ERS–ERD cycles) was supplemented by more global synchronization (between M1s or between M1 and EMG) that revealed similar event-related modulations. In the alpha band (∼10 Hz), a high uniformity was consistently found, but here, the changes were not time locked to the motor event; M1left/right uniformity was therefore not further analyzed. As anticipated above, the beta amplitude modulation in M1 was clearly dependent on movement frequency.

Figure 6.

Event-related modulation and corticospinal uniformity. (A) The amplitudes of M1left (left) and M1right (right) after narrow-band filtering for each frequency (ordinate) and time relative to the event (abscissa). Grand mean–removed average over subjects, trials, and plateaus. Note the difference in scale between the panels. (B) Uniformity of the relative phases of M1left and right EMG (left) and the relative phase of M1right and left EMG (right). Grand mean–removed averages over subjects, trials, and plateaus. Each amplitude and uniformity epoch was upsampled per frequency plateau to match the largest movement cycle.

Figure 6.

Event-related modulation and corticospinal uniformity. (A) The amplitudes of M1left (left) and M1right (right) after narrow-band filtering for each frequency (ordinate) and time relative to the event (abscissa). Grand mean–removed average over subjects, trials, and plateaus. Note the difference in scale between the panels. (B) Uniformity of the relative phases of M1left and right EMG (left) and the relative phase of M1right and left EMG (right). Grand mean–removed averages over subjects, trials, and plateaus. Each amplitude and uniformity epoch was upsampled per frequency plateau to match the largest movement cycle.

In the unimanual trials, the maximal tapping frequency was on average 0.8 Hz higher than in the bimanual trials. This difference became most apparent from plateau 14 (5.1 Hz) onward. There the movement frequency did not match the target frequency in the bimanual trials (P < 0.01), whereas in the unimanual trials, that mismatch occurred at plateau 18 (5.9 Hz). By contrast, for the performance with the left finger, the movement frequency matched the target frequency across plateaus.

With respect to movement tempo, the timescale of the modulation as well as its amplitude tended to decrease. The temporal difference ΔT between ERD and ERS decreased from about 300 ms in the slowest movement frequency (1.6 Hz) to about 90 ms in the fastest movement frequency (5.1 Hz) for bimanual and unimanual conditions (Fig. 7, left panels). That decrease was nearly linear for the initial 10 frequencies, but the rate of decrease decreased as movement tempo increased. The extent of amplitude modulation ΔA also dropped; however, this decrease was clearly nonlinear (Fig. 7, right panels). The parameters resulting from fitting the exponential functions to ΔA and ΔT did not differ between M1left and M1right. We also could not find any difference between parameters in bimanual and unimanual trials. Lateralization effects in the strength of amplitude modulation were not present in the bimanual condition. Interestingly, beta modulation was almost absent in the ipsilateral M1 throughout unimanual trials in agreement with the absence of significant power changes in the SAM analysis. Although the patterns in corticospinal uniformity appeared movement tempo dependent when comparing left and right tapping (e.g., Fig. 6, right panels), their minima/maxima did not necessarily coincide with ERD/ERS. The modulation of corticospinal uniformity ΔU, however, decreased with increasing frequency because all exponents were positive: b = 0.02 (r = 0.32) and b = 0.01 (r = 0.17) in the bimanual condition and b = 0.007 (r = 0.12) and b = 0.007 (r = 0.12) in the unimanual condition for right EMG/M1left and left EMG/M1right, respectively.

Figure 7.

Frequency-dependent event-related beta modulation in M1s. Amplitude difference ΔA = aERSaERD (left panels) and temporal difference ΔT = tERStERD (right panels) in ERD and ERS per frequency plateau in M1left (×-markers) and M1right (O-markers). The light shaded markers indicate the plateaus following the mean transition plateau. The solid lines indicate the exponential function of the form ebω resulting from the optimal fitting parameters in the bimanual condition in M1left (red line) and M1right (blue line), which were similar to those in the unimanual condition: for ΔA, these parameters (mean over conditions) were b = 0.23 (range 0.19–0.32) and r = 0.96. For ΔT, these parameters (mean over conditions) were b = 0.05 (range 0.04–0.06) and r = 0.93. Please note that the graphs are plotted in semilog scale.

Figure 7.

Frequency-dependent event-related beta modulation in M1s. Amplitude difference ΔA = aERSaERD (left panels) and temporal difference ΔT = tERStERD (right panels) in ERD and ERS per frequency plateau in M1left (×-markers) and M1right (O-markers). The light shaded markers indicate the plateaus following the mean transition plateau. The solid lines indicate the exponential function of the form ebω resulting from the optimal fitting parameters in the bimanual condition in M1left (red line) and M1right (blue line), which were similar to those in the unimanual condition: for ΔA, these parameters (mean over conditions) were b = 0.23 (range 0.19–0.32) and r = 0.96. For ΔT, these parameters (mean over conditions) were b = 0.05 (range 0.04–0.06) and r = 0.93. Please note that the graphs are plotted in semilog scale.

Averaged over subjects, trials, and steady or unstable performance, ΔA in M1right was greater during steady performance (P < 0.001, see Fig. 8). This decrease in beta modulation from steady to unstable performance correlated with an increase in movement frequency. The modulation of corticospinal uniformity ΔU decreased also from steady to unstable performance for both right EMG versus M1left (P = 0.01) and left EMG versus M1right (P = 0.24), although only the former decrease was significant.

Figure 8.

Event-related beta amplitude modulation and adjusted corticospinal uniformity. (A) Grand average of amplitude (ordinate) in M1left (left) and M1right (right) during steady (dark gray) and unstable performance (light gray) showing a decrease in modulation in M1right during the movement interval (abscissa). (B) Grand average of adjusted uniformity (ordinate) of the relative phase of M1left and right EMG (left) and the relative phase of M1right and left EMG (right) during steady (dark) and unstable performance (light).

Figure 8.

Event-related beta amplitude modulation and adjusted corticospinal uniformity. (A) Grand average of amplitude (ordinate) in M1left (left) and M1right (right) during steady (dark gray) and unstable performance (light gray) showing a decrease in modulation in M1right during the movement interval (abscissa). (B) Grand average of adjusted uniformity (ordinate) of the relative phase of M1left and right EMG (left) and the relative phase of M1right and left EMG (right) during steady (dark) and unstable performance (light).

Discussion

Spectral power in bilateral motor areas at both movement frequencies exposed the expected interhemispheric crosstalk in bimanual performance. A loss of coordination was primarily visible as a power increase in the motor area ipsilateral to the finger (predominantly) undergoing the motor instability. As explained in the “Introduction,” this power increase can be interpreted as a reflection of an improper intrahemispheric phase locking, rendering the interhemispheric inhibition less effective (cf. Daffertshofer et al. 2005). The motor system is bilaterally activated during both bimanual and unimanual movements (Swinnen 2002). The present results suggest why this might be so: Our motor system appears to be inherently designed for bilateral movement, and, as a result, unilateral movements require the suppression contralateral cortical activity (Kristeva et al. 1991; Mayston et al. 1999; Ghacibeh et al. 2007). Homologous motor areas and premotor areas are reciprocally connected through the corpus callosum (Hofer and Frahm 2006), whereas premotor areas may inhibit the activity from the homologous motor cortex (Daffertshofer et al. 2005). When this inhibition falters, residual activity from the opposite hemisphere can be detected. This can also result in the occurrence of mirror movements (Daffertshofer et al. 1999), which are particularly pronounced when fatigued (Duque et al. 2005) or in the case of callosal damage (Dennis 1976; Bonzano et al. 2008). The current study, however, did not allow for discriminating primary and premotor area, so that this interpretation of specific cortical areas remains speculative and should be addressed in future studies.

The ERD–ERS amplitude difference, that is, the strength of local beta modulation was generally larger in M1right than in M1left. This lateralization might be readily attributed to the slower movement tempo (Toma et al. 2002; Houweling et al. 2008). In addition, we found that the temporal relation as well as the amplitude difference between the onsets of ERD and ERS changed with movement tempo, but that change saturated after the tempo that marked the change in performance. That point depended on coordination, as it occurred earlier for the bimanual case than for the unimanual case (5.1 Hz or plateau 14 vs 5.9 Hz or plateau 18, respectively). Admittedly, these neural activities are strictly speaking mere correlates of altered movement tempo and not direct evidence for causes of altered performance. In particular, in view of the well-established relevance of ERD for voluntary movements (e.g., Pfurtscheller and Berghold 1989), however, we here interpret the change in performance as consequences of the change in neural activity.

Complementary modalities (positron emission tomography and functional magnetic resonance imaging) reported a positive, linear relationship between metabolism and movement frequency (Hayashi et al. 2008), whereas metabolism and beta activity and metabolism are inversely related (Ritter et al. 2009). We can support these findings as we also found a drop of overall beta power with increasing frequency (see Supplementary Fig. 2), unlike other, dissonant reports in the magneto- and electroencephalographic literature (Mayville et al. 2001; Serrien and Brown 2002; Toma et al. 2002).

Controlling movement via changes in distributed spectral processing in the beta band may be beneficial in view of its comparably high temporal resolution. The production of a beta cycle requires rapid changes in synchronization within a functional cluster of neuronal ensembles (Pfurtscheller and da Silva 1999). We found that the upregulation and downregulation of beta synchrony was directly related to the movement frequency. Moreover, as frequency increased, both ERD/ERS shifted in time toward the motor event. This finding challenges the notion of ERS as a simple response to recalibrate the underlying network (“rebound”). As the target frequency increases, ERD and ERS start overlapping each other (see Fig. 9), which evidently limits performance. The time required to establish interactions between neural populations poses a very natural constraint on motor performance. On this account, local and global beta modulations not only facilitate but also limit the ability to perform rapid rhythmic tasks. To what degree these time constraints apply to intra and interhemispheric interactions, respectively, remains to be seen. Given that here the ERD/ERS changes coincided with coordinative instability, however, we are tempted to suggest that these time constraints also affect bilateral coordination. Very recent finding of Rizzo et al. (2009) do indeed support the notation of short-term plasticity in (effective) interhemispheric inhibition.

Figure 9.

Event-related beta amplitude modulation. With increasing tempo (left to right), the upregulation and downregulation of movement-related beta activity is challenged, and if ERD/ERS cycles get too close beta modulation vanishes, the movement phase can no longer be stabilized and an instability emerges (right panel).

Figure 9.

Event-related beta amplitude modulation. With increasing tempo (left to right), the upregulation and downregulation of movement-related beta activity is challenged, and if ERD/ERS cycles get too close beta modulation vanishes, the movement phase can no longer be stabilized and an instability emerges (right panel).

In addition to the cortical interactions, long-distant beta synchrony between contralateral M1 and EMG recorded from extensor muscles also revealed a marked entrainment. Interestingly, the corticospinal phase uniformity was stability dependent in that it varied less during unstable performance between both M1left and right EMG and between M1right and left EMG (Boonstra et al. 2007). It appears that the control commands from primary motor cortex to muscle fibers are conveyed via beta modulation of afferent neural signals (which is effective for long-range corticospinal connections), whereas the interhemispheric interactions between M1s occur on a slower timescale as we found transition-related changes in M1–M1 phase coupling at the (ratio of the) movement frequencies. That is, the interhemispheric communication may be mediated by the phase dynamics at the movement frequencies. The latter can be motivated to be closely related to other cognitive functions needed for proper motor control (e.g., rhythm perception), and as such, the actual task properties can be identified from its neural component. On the other hand, the performance needs to be very fast and to a high degree automatic so that cognition appears less important. It might thus also be possible that subcortical structures—the basal ganglia and the cerebellum—are involved, and perhaps, even some learning at the level of the spinal cord could take place (Houweling et al. 2008). Whether this implies that the processes recorded by the MEG reflect prediction of sensory consequences of the movement or directly the sensory feedback remains to be seen (see Baker 2007, for a recent discussion).

In summary, our results strongly underscore the functional relevance of modulations of oscillatory activity in specific frequency ranges. In particular, the timescale of beta synchrony appears to play a key role in achieving timed phase synchrony in the motor cortex and along the neural axis and thus in the stabilization of motor output in general and of bimanual coordination patterns in particular.

Supplementary Material

Supplementary material can be found at: http://www.cercor.oxfordjournals.org/.

Funding

Netherlands Organisation for Scientific Research (NWO grant # 452-04-344 awarded to A.D.).

Conflict of Interest: None declared.

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