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. Author manuscript; available in PMC: 2026 Jan 1.
Published in final edited form as: Motor Control. 2024 Aug 22;29(1):53–68. doi: 10.1123/mc.2023-0033

Post-Movement Beta Rebound in Real and Imagined Movement

Helene M Sisti 1,1, Annika L Beebe 1, Mercedes A Bishop 1, Elias Gabrielsson 1
PMCID: PMC12110600  NIHMSID: NIHMS2079628  PMID: 39179240

Introduction

Brainwaves demonstrate spatial-temporal specificity. However, the functional implications of each remains largely unknown. Regarding motor control, the beta-band (13-30 Hz) is of particular interest because its power in the sensorimotor cortex fluctuates with movement (McFarland et al., 2000; Espenhahn et al., 2017; Pakenham et al., 2020). Movement initiation results in a decrease, more broadly referred to as event-related desynchronization, whereas movement termination results in an increase, more broadly known as event-related synchronization (Pfurtscheller & Lopes da Silva, 1999; Kilavik et al., 2013; Chung et al., 2017). Upon movement termination, beta power localized to sensorimotor cortex increases; this is the Post-Movement Beta Rebound (PMBR). Functionally, the PMBR may reflect inhibition of the motor cortex (Heinrichs-Graham et al., 2017). Additional interpretations include top-down communication, error-correction and sensorimotor integration (Gilbertson et al., 2005, Androulidakis et al., 2007, Buschman et al., 2007, Bressler et al., 2015, Little et al. 2019, Barone and Rossiter, 2021). Further study is necessary to establish the functional significance of the PMBR.

PMBR is modulated by various motor parameters, such as amount of force, rate of force development, and duration of muscle contraction (Fry et al., 2016; Pakenham et al., 2020). Fry et al. (2016) used an isometric task in determining the role of force on PMBR in the sensorimotor cortex. In their paradigm, a person rested the forearm with the palm facing up and was required to flex the wrist joint in order to lift a weight at four different levels of force. They discovered that a greater amount of force results in a greater amplitude of PMBR (Fry et al., 2016). When the rate of force development was manipulated, PMBR was not only higher in amplitude, but also shorter in duration (Fry et al., 2016). However, Pakenham et al. (2020) demonstrated that increasing the duration of a grip-force task significantly reduced the PMBR amplitude. They also used an isometric contraction task. These studies demonstrate that different types of movement result in divergent effects on beta-band activity in movement.

Beta-band modulation in the sensorimotor cortex is one of the frequencies targeted in brain computer interface (BCI) applications (Wolpaw, 2013). In BCIs, users are asked to imagine a specific movement, such as making a fist, in order to control an external device, such as a neuroprosthetic (McFarland et al., 2000). BCIs can empower the severely disabled with the ability to move more independently (Wolpaw et al., 2018, Jadavji et al., 2022). With repeated training trials of the imagined movement, users can improve their ability to move an actuator. This happens, in part, through modulation of the beta-band. The cognitive and neurophysiological parameters that determine how well a person can exert BCI control are largely unknown (McFarland and Wolpaw, 2018). Beta-band is also a target of neuromodulation in clinical populations, including stroke patients where damage is unilateral (Ulanov and Shtyrov, 2022). The contralaterality of the motor system is well known. By analyzing the PMBR in both unimanual and bimanual contexts, greater insight to movement related beta oscillations can be made. Collectively, there is a clear need to elucidate beta-modulation in both real and imagined movement. In so doing, both theoretical interpretations and practical applications can be further developed.

In the present study, we aimed to characterize PMBR with a use of a unimanual and bimanual dial rotation task using both real and imagined movement. We hypothesized that the PMBR would be stronger in actual movement compared to imagined movement. We further hypothesized that there would be differences in the magnitude of the PMBR between unimanual and bimanual movement. To the author’s knowledge, this is the first study to directly compare unimanual and bimanual PMBR of real and imagined movement.

Methods

Participants

Participants were 18 years of age or older and were students of Norwich University. All completed the Oldfield (1971) handedness questionnaire, as well as questions regarding previous history with activities that require bimanual coordination, including video games, driving manual transmission automobiles, or playing a musical instrument. One subject was left-hand dominant and was not included in the analysis. Participants were fitted with EEG QuikCap and completed behavioral procedures. After data collection was complete, EEG signals were checked for excessive noise in electrodes (e.g., flat lines or large sinusoidal waves that span the entire screen) by visual inspection and were excluded. This resulted in a sample of n=21 for both neurophysiological and behavioral analyses. All procedures were approved by the NU Institutional Review Board Ethics Committee and in accordance with the 1964 Declaration of Helsinki (revised in 2008). All participants signed the Informed Consent form.

Apparatus and task description

Participants were comfortably seated at a table in front of a computer monitor with both lower arms resting on two custom-made adjustable ramps. At the end of each ramp, a dial was mounted on a horizontal support consisting of a flat disc and a vertical peg. The dials were rotated by holding each peg between the thumb and index finger. The wrists rested at the edge of the ramp to maximize comfort and minimize fatigue. Direct vision of both hands and forearms was occluded by a horizontal table-top bench that was placed over the forearms. The two dials controlled movement of a red cursor (a flexible line segment 1 cm long) on the monitor. The left-hand and right-hand dial controlled red cursor movement along the vertical and horizontal axis, respectively. When the left dial was rotated clockwise, the cursor moved up; when it was rotated counterclockwise, the red cursor moved down; and vice versa. The target was a white cursor that moved from the center of the display, along a blue target line, to the periphery; this was the path the red cursor was supposed to follow. To move vertically, only the left dial needed to be rotated. To move horizontally, only the right dial needed to be rotated. To move in any other direction, both dials needed to be rotated.

Experimental procedures

Trial type and sequence were as follows: five trials of left hand only movement, in which participant rotates only left dial to track a vertical line; five trials of right hand only movement, in which participant rotates only right dial to track a horizontal line; twenty trials where the left hand needed to rotate the dial three times for every single rotation of the right hand to track the line. This is referred to as the 3:1 pattern; the resultant line is at an angle close to the vertical axis. Twenty trials where the right hand rotated the dial three times for every single rotation of the left hand. This is the 1:3 pattern; the resultant line is at an angle close to the horizontal axis. Actual movement conditions were counterbalanced within each phase (unimanual and bimanual) so that participants were trained with either of two sequences: LH, RH, 3:1, 1:3, or RH, LH, 1:3, 3:1. The target moved at a steady rate along the line for 10 seconds. Before the trial begins, a stationary green dot is displayed at the starting point, beginning of the line, for 5 seconds. After the trial ends, the screen is black for 5 seconds. The entire intertrial interval (ITI) is 10 seconds. After completing each of the four trial blocks, the participant was asked to complete five motor imagery trials (see Figure 1). Participants were asked to close their eyes and to use visual and kinesthetic cues to imagine the task they just completed. To facilitate both visual and kinesthetic imagery, instructions included phrases such as, “imagine seeing the line, imagine feeling the hands as they rotate the dials.” Mental chronometric measures were collected for each motor imagery trial. To do this, experimenter used a timer and verbally signaled to the participant when to begin. Once they completed each motor imagery trial, participants were asked to communicate this verbally by saying, “Done.” Intertrial interval for imaginary trials was 5 seconds. Only 5 imagined trials were required due to concern regarding experimental fatigue. After the last trial of the block, participants were asked to rate the vividness of both their visual and kinesthetic imagery on a scale from 1 to 10, where 1 is not at all vivid and 10 is extremely vivid.

Figure 1. Experimental Design.

Figure 1

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The four actual movement conditions are illustrated below. The red cursor is controlled by rotation of two dials. The participant moves the left dial to move up or down (Left Hand). They move the right dial to move the cursor horizontally (Right Hand). Moving the two dials simultaneously results in an angle. The blue line is stationary and serves as a visual cue. The white dot is the target which moves at a steady rate along the line. The task is for the participant to follow the moving target by rotating one or both dials at the required speed and direction. After each trial block of actual movement, participants are asked to close their eyes and imagine the task they just completed using visual and kinesthetic mental imagery.

Neurophysiological procedures

Participants were fitted with a 32-electrode QuikCap (Compumedics, Neuroscan) (Figure 2). The central electrode, Cz, was placed at the midpoint between nasion and anion. Soluble electrolytic gel was inserted into each electrode using a blunt-edge syringe. Gel was inserted until the impedance values were less than 5 kΩ. Once impedance values were less than 5 kΩ, instructions for the behavioral task were administered. Neuroscan Curry8 was used for signal acquisition and offline processing. Triggers were manually delivered to signal the start (keyboard press 1) and end (keyboard press 2) of every trial. Manual triggers are marked in the software with both a time stamp and a visual cue, i.e. a purple vertical line in the data acquisition window. EEG data were collected with a sampling rate of 1,000 Hz.

Figure 2.

Figure 2

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Subject seated at apparatus prepared to begin experiment. The tabletop bench is used to prevent visibility of the hands during training, which can influence learning. The two arm ramps have a dial with a vertical peg used for rotational movement. Participants grasp the tip of the peg between their index finger and thumb to turn the dials. The PC and monitor to the left is used for EEG acquisition (Compumedics NeuroScan Curry 8).

EEG Pre-processing

EEG data were pre-processed using Curry 8. Event triggers executed during the experiment were coded to indicate the experimental condition. There were 8 trial types, indicated by the first digit, as follows: 1 = Left Actual, 2 = Left Imagined, 3 = Right Actual, 4 = Right Imagined, 5 = Bimanual Actual 3:1, 6 = Bimanual Imagined 3:1, 7 = Bimanual Actual 1:3, and 8 = Bimanual Imagined 1:3. The second digit was either 1 for Start of Trial or 2 for End of Trial. Thus, event code “11” would mean “Left Actual Start of Trial” and “82” would indicate “Bimanual Imagined 1:3 End of Trial.” Data were sorted based on trial type and trial start or end. Triggers inspected in Curry for accuracy within 0.10 sec. The precise time of the trigger relative to the start and end of the trial is captured and visualized in Curry 8. Trials with a missing trigger were excluded. Participants with excessive signal contamination determined by visual inspection, i.e. signals in the non-physiological range and occurring across multiple channels, were excluded from EEG analysis. An ipsilateral mastoid reference was used. The following steps were applied to each participant’s data in the same sequence: bandpass filter of 1.0–30 Hz to remove non-physiological artifacts; ocular artifacts removal using Independent Component Analysis; removal of other residual artifacts (e.g., muscular) using a voltage threshold (±100 μV, ±200 ms). We restricted our analysis to the sensorimotor cortices because this is where the PMBR is most evident. This region corresponds with C3 and C4 electrodes, left and right hemisphere, respectively. Conversion of signals from the time to the frequency domain was done by applying the Fast Fourier Transform using Curry 8 software. Data were then exported as .txt files to MS Excel and formatted for statistical analysis in SPSS.

The aim of the study was to characterize changes in beta-band after movement ends, whether the movement was real or imagined. Trials were averaged within each condition, then across participants (Figure 3). Beta-band power was computed for two intervals of two seconds each. The first interval began two sec before movement termination, which was compared to the two sec immediately after movement termination. This temporal interval was selected based on previous bimanual coordination experiments which also focused on beta-band activity in the sensorimotor cortex (Serrien and Brown, 2002). The end of a trial, either real or imagined, was always indicated by a trigger, i.e., keypress 2, recorded on the computer with the EEG acquisition software. For the actual conditions, participants were actively rotating one or both dials. The end of the trial is when the line disappears and goes blank. No further movement is required. The participants rest during this period and wait for the stimulus set to re-appear on the screen. For the imagined conditions, participants were imagining the same behavior with their eyes closed and their hands resting flat on the arm ramps.

Figure 3.

Figure 3

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EEG recording and topographical map at point of movement termination in “Left Actual” condition (mean of 5 trials). The ocular artifact, i.e. an exaggerated eyeblink, is evident in Fp1 and Fp2 electrodes after participants finish tracking the moving target. Artifacts were removed using ICA before statistical analysis. Timeline corresponds with two seconds before and two seconds after movement termination. Vertical purple line signals end of trial, i.e. movement termination.

Statistical analysis

Behavioral Data

To analyze performance, the dependent variable used was the distance between the target and the subject’s cursor at the end of the trial. This was referred to as Finish Offset, consistent with our original report (Sisti et al., 2011). A zero indicates that the cursor was overlapping with the target at the end of the trial. The units are arbitrary and reflect how accurately the subject was able to perform the task. To calculate this measure, the Euclidean distance between the target and the subject’s cursor was computed for every 10 ms of the 10-sec trial. To obtain the mean, the value was divided by 1000. The values were then multiplied by a factor of 10 to ease numerical interpretation. A repeated measures ANOVA was done to determine main effects and interactions for the two unimanual conditions (Left Only vs. Right Only) across the 5 trials. Similarly, a repeated measures ANOVA was done to determine main effect and interaction of Finish Offset between the two bimanual coordination patterns (3:1 vs. 1:3) across the 20 trials. Laterality index revealed one left-hand dominant participant who was removed from all analysis. To assess overall improvement, the absolute difference between the Finish Offset of the first and last trial were computed for all conditions. The outlier criteria was a z score of + 3; values outside this range were not included in the statistical analysis.

EEG Data

Statistical main effects and interactions for beta power (in μV2) of actual, unilateral movement were assessed using a 2 x 2 ANOVA according to the factors Movement Termination (Before vs. After) and Hand (Left vs. Right) in the EEG over sensorimotor cortex (i.e. electrodes C3 and C4). Sensorimotor cortex was selected as the region of interest because this is where the post movement beta rebound effect is most prominent. ‘Before movement termination’ refers to the last two sec of the trial and ‘after movement termination’ refers to the first two sec of the intertrial interval. The selection of two discrete time points immediately before and after movement termination allowed for use of Fast Fourier Transform without violating the assumption of a stationary signal. The ANOVA was first done separately for the left (C3) and right (C4) electrodes. Preliminary analysis revealed no effect of hemisphere. Therefore, C3 and C4 were combined to reflect beta power across the entire sensorimotor cortices. In addition, preliminary analysis revealed no main effect of Hand (Left vs. Right). Therefore, actual movement trials for Left Hand Only and Right Hand Only were collapsed into one group, referred to as Unimanual Condition. Statistical main effects and interactions for beta power of unimanual conditions were then assessed using a 2 x 2 ANOVA according to the factors Movement Termination (Before vs. After) and Movement Execution (Actual vs. Imagined). A similar set of procedures was followed for the bimanual conditions. Finally, a 2 x 2 ANOVA of Condition (Unimanual vs. Bimanual) x Movement Termination (Before vs. After) was done for actual movement, and then repeated for imagined movement.

EEG and Behavioral Data

To determine whether there was a relationship between change in performance and PMBR magnitude, a Pearson’s product moment correlation was conducted for actual movement, bimanual conditions. The first variable was overall improvement; this was calculated by determining the difference of the Finish Offset from the first to last trial. The second variable was task-related power; this was computed by calculating the difference in beta power before and after movement termination. For computation of task-related power, C3 and C4 electrodes (sensorimotor cortices) were collapsed.

Cognitive Data

Mental chronometrics were obtained by calculating the mean time in sec to complete the 5 mental imagery trials for each condition: Left Only, Right Only, 3:1, and 1:3. A repeated measures ANOVA of mean time in sec was used to assess differences across conditions. Mean visual and kinesthetic vividness ratings were calculated for the 5 trials of each condition. To determine whether there were differences across conditions, a 2 x 2 ANOVA of vividness score was done according to the factors coordination pattern (Unimanual vs. Bimanual) and mental imagery type (Visual vs. Kinesthetic).

Results

Behavioral

Behavioral results are summarized in Table 1. For the unimanual condition, a repeated measures ANOVA revealed a significant main effect of trial [F(1,4)=7.53, p<.001]. Finish Offset decreased from 35.09 ± 5.48 on Trial 1 to 10.08 ± 1.57 on Trial 2, where it reached asymptote. There was no main effect of Coordination Pattern (Left Only vs. Right Only) [F(1,4)=0.09, p=0.75] and no interaction of Trial by Coordination Pattern [F(1,4)=0.07, p=0.99]. For the bimanual condition, a repeated measures ANOVA revealed a significant main effect of learning [F(1,19)=5.11, p<.001] across the 20 trials. Finish Offset decreased from 45.95 ± 6.44 on Trial 1 to 16.57 ± 1.83 by Trial 20 (Figure 4). There was no main effect of Coordination Pattern (3:1 vs. 1:3) [F(1,19)=0.03, p=.84] and no interaction of Trial by Coordination Pattern [F(1,19)=0.93, p=.54].

Table 1:

ANOVA Summary Tables for Behavioral and EEG Analysis

Behavioral Results: Learning was evident for both unimanual and bimanual patterns within a single session.
Unimanual Conditions
Dependent Variable: Average Finish Offset df F Sig.
Trial 4 7.536 <0.001
Coordination Pattern 1 0.095 0.758
Trial * Coordination Pattern 4 0.073 0.990
 
Bimanual Conditions
Dependent Variable: Average Finish Offset df F Sig.
Trial 19 5.114 <0.001
Coordination Pattern 1 0.038 0.846
Trial * Coordination Pattern 19 0.932 0.543
EEG Results: Post-movement beta rebound was evident for unimanual and bimanual
movement. There was a significant interaction of real vs. imagined movement for both
unimanual and bimanual coordination patterns.
Unimanual Conditions
Dependent Variable: Beta power in sensorimotor cortices df F Sig.
Movement Termination (Before vs. After) 1 125.239 <0.001
Movement Execution (Actual vs. Imagined) 1 27.477 <0.001
Movement Termination x Movement Execution 1 63.097 <0.001
 
Bimanual Conditions
Dependent Variable: Beta power in sensorimotor cortices df F Sig.
Movement Termination (Before vs. After) 1 57.585 <0.001
Movement Execution (Actual vs. Imagined) 1 7.167 0.008
Movement Termination x Movement Execution 1 12.409 <0.001
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Figure 4.

Figure 4

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Error decreased significantly across 20 trials for both bimanual coordination patterns, i.e. when the left hand was rotating the dial 3 times for every 1 rotation of the right (3:1) and when the left hand rotated the dial 1 time for every 3 rotations of the right (1:3).

EEG

EEG results of changes in beta power (in μV2) before and after movement termination were analyzed for unimanual and bimanual conditions. ‘Before movement termination’ refers to the last two seconds of the trial and ‘After movement termination’ refers to the first two seconds of the intertrial interval. For unimanual conditions, a 2 x 2 ANOVA of Movement Termination (Before vs. After) and Movement Execution (Actual vs. Imagined) of beta power in sensorimotor cortex (C3-C4) revealed a significant main effect of Movement Termination [F=125.39, p<.001]. There was also a significant main effect of Movement Execution (Actual vs. Imagined Movement) [F=27.47, p<.001]. There was a significant interaction of Movement Termination x Movement Execution [F=63.09, p<.001]. For bimanual conditions, a 2 x 2 ANOVA of Movement Termination (Before vs. After) and Movement Execution (Actual vs. Imagined) of beta power in sensorimotor cortex (C3-C4) revealed a significant main effect of Movement Termination [F=57.58, p<.001]. There was also a significant main effect of Movement Execution (Actual vs. Imagined Movement) [F=7.16, p=.008] and a significant interaction of Movement Termination x Movement Execution [F=12.40, p<.001]. For Imagined Movement, a 2 x 2 ANOVA of Condition (Unimanual vs. Bimanual) x Movement Termination (Before vs. After) revealed a significant main effect of Movement Termination [F=13.61, p<.001]. Neither a main effect of condition [F=0.67, p=0.41] nor an interaction of Condition x Movement Termination (F=0.46, p=0.49] was found.

EEG and Behavioral Data

For the 3:1 pattern, no significant correlations were detected between improvement from first to last trial and the magnitude of the PMBR using Pearson’s product moment correlation (R2=.06). For the 1:3 pattern, no significant correlations were detected between improvement from first to last trial and the magnitude of the PMBR using Pearson’s product moment correlation (R2=.09).

Cognitive Results

A repeated measures ANOVA revealed no differences across conditions (Left Only, Right Only, 3:1, and 1:3) in time to complete imagined trials [F(1,3)=0.21, p = 0.89]. Regarding vividness of visual and kinesthetic cues, no differences were detected across conditions (Unimanual vs. Bimanual) [F=0.59, p=0.44] or imagery type (Visual vs. Kinesthetic) [F=0.32, p=0.56]. No interaction was observed [F=0.10, p =0.74].

Discussion

The first hypothesis, that the PMBR would be stronger in actual movement compared to imagined movement, was supported. There was also a difference in magnitude of the PMBR between unimanual and bimanual movement, whereby the effect was more pronounced in the unimanual condition. Importantly, even within imagined movement, the PMBR was evident. There was no correlation between performance improvement and the magnitude of the PMBR. To the authors’ knowledge, this is the first demonstration of PMBR with imagined hand movement of dial rotation.

In the more difficult bimanual coordination pattern in which the non-dominant hand must rotate the dial at a faster rate, 3:1, we found that the beta-band in the left and right sensorimotor cortex demonstrated a strong correlation, regardless of whether it was examined at the start or end of bimanual training. We were expecting to see that there would be little to no correlation within the first several trials of bimanual training and that by the end of the practice session, beta synchronization would strengthen as the left and right hands began to work together. Several explanations exist for why there was a correlation at the start of training that persisted. One possibility is that it reflects the ‘bimanual coupling’ effect (Bozzacchi et al., 2017). This refers to the intrinsic tendency of the two hands to work as a coordinated unit. For instance, the hands naturally swing back and forth during locomotion. Another example of this is if a person is asked to perform a ‘circle-line’ task, in which they must draw a circle with one hand and a line with the other, and to do so simultaneously. Each shape begins to resemble a combination of the two, i.e. an oval; the ‘ovalization index’ is then used as a method to assess the bimanual coupling effect (Franz and Ramachadran, 1998; Garbarini et al., 2015). Another possibility is that the task demands were not high enough to induce ‘decoupling’, or independent functioning of the left and right hand, in the first place. Further, interhemispheric coherence has been determined using a range of computational approaches (Pereda et al., 2005; Sakkalis, 2011). Perhaps the technique used here was not sufficient to detect the changes in question. Lastly, the intrinsic variability of the experimental design may have lead to strong “cross-talk.” In other words, participants were regularly alternating between left and right hand movement, either real or imagined. In the bimanual conditions, one hand was always ‘leading,’ meaning it rotated the dial faster than the other. Regarding the stronger PMBR in unimanual compared with bimanual movement, one might speculate that enhanced inhibition of ipsilateral sensorimotor cortex supports stronger excitation of the contralateral region. As a result, beta power is potentiated. The focus of the present study was not contralateral control, but rather the beta-band in motor imagery. Disambiguating what drives the enhances PMBR in unimanual movement warrants further investigation.

With respect to cognitive measures, the mean duration to complete an imagined trial closely approximated the time it took to complete an actual trial, 10 seconds. Vividness ratings for both visual and kinesthetic cues were similar across conditions and ranked higher than the midpoint across all conditions. Collectively, these data suggest that the approach adopted here, following each actual movement with an imagined version, was sufficient to induce a vivid mental image. This interpretation is based on participants’ self-reports. This strategy could be readily applied to the clinic to facilitate neurorehabilitation, particularly with stroke patients relearning bimanual coordination skills (De Vries and Mulder, 2007). For example, a physical therapist works with a stroke patient to re-learn pouring water into a glass. Rather than focusing solely on physical movements, the therapist could request the patient to practice several imagined trials in between sessions. Visualization is a powerful tool. Imagining the movement coordination pattern required after physically performing the skill could accelerate re-acquisition. Future studies would be needed to directly test this effect.

Accumulating evidence points to a functional role for beta-band in movement (Boonstra et al., 2007; Tan et al., 2014; Xifra-Porxas et al. 2019; Shih et al., 2021). This frequency band has been implicated in healthy aging and the acquisition of a range of motor learning tasks, including visuomotor adaptation (Rossiter et al., 2014; Tan et al., 2014; Torrecillos et al., 2015). Movement topology, or the kinematic profile, has been demonstrated to be a key factor in elucidating neural mechanisms of motor control; this was clearly demonstrated by the classic study (Georgopoulos et al., 1986) in which direction of movement was mapped onto neuronal activity in the motor cortex. The present visuomotor tracking paradigm has been used in a range of brain mapping studies which have highlighted the role of the corpus callosum in motor learning (Sisti et al., 2012; Gooijers et a., 2013; Babaeeghazvini et al., 2019; Adab et al., 2020). Regarding the PMBR, additional research is necessary to examine how the trial distribution of real and imagined movements influenced its power. That is, if imagined trials were interspersed with actual trials, instead of being practiced on a ‘blocked’ manner, would the PMBR effect still be observed?

The present visuomotor paradigm required participants to move their hands using periodic, rhythmic motion. This cyclical, continuous motion might facilitate the generation of synchronous waveforms. Considering kinematics may help elucidate the neural basis of movement. For example, Georgopoulos et al. (1986) demonstrated that direction of movement in a reaching task can be predicted by considering the weighted contribution of individual neuronal activity along with the direction of arm movement in 3-D space; the collective neural activity of all relevant cells in the motor cortex, represented as a population vector, was congruent with movement direction. With respect to the dial rotation task used in the present study, it was previously demonstrated that the bimanual coordination constraints evident with discrete movement, could not be applied to continuous movement (Sisti et al., 2011). That is, the HKB-model (Haken et al., 1985) predicts that integer ratios are easier to perform than non-integer ratios, in which 3:1 and 2:1 patterns are both intrinsically favorable relative to the non-integer 3:2 pattern. This is evident in tapping models, but not for continuous movement. In continuous movement, it is the relative velocity between the two hands that predicts performance decrements (Sisti et al., 2011). The key take-away of the present experiment is that the PMBR can be generalized to both unimanual and bimanual dial rotation movements, whether they are real or imagined.

Finally, a dominant model regarding the source localization of EEG signals is that the activity is a result of the EPSPs generated by synchronous activity of pyramidal neurons in the cortex (Schomer and Da Silva, 2012); these cells are arranged in a distinct, laminar manner in the outermost layer of the cortex. Here, we suggest an alternate hypothesis. The force generated by current flowing along the largest white matter tract in the brain may induce synchronicity of the pyramidal cells in the motor cortex. It may be this activity from the corpus callosum that is a key factor in regulating beta changes. Indeed, using optogenetics in the mouse model, it was demonstrated that callosal projections were responsible for effects observed in layer 5 of the auditory cortex (Rock and Apicella, 2015). Beta-band has also been implicated in attentional processes (Sauseng and Klimesch, 2008; Wróbel et al., 2007). Given the attentional demands implicit in this tracking task, they may have contributed to the movement-related changes. Additional experiments that include control for attentional demands will help disambiguate the regulation of cognitive processes from sensory and motor ones. One limitation of the present study is that focus was singular to the broad beta-band. We did not separate out low vs high beta frequencies (approximately 12-17 Hz and 18-30 Hz, respectively), nor did we examine other frequency bands implicated in movement regulation, most notably alpha (3- 12 Hz). Future studies that include analysis of multiple frequency bands and how they are regulated across distal brain regions will be important for understanding the neural dynamics of real and imagined movement.

Figure 5.

Figure 5

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Power of beta (12-30 Hz) increased after movement termination, for both actual and imagined movement. This was evident across unimanual and bimanual movement patterns.

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