Neurophysiological Correlates of Adaptation and Interference during Asymmetrical Bimanual Movements

Journal Pre-proofs Research Article Neurophysiological correlates of adaptation and interference during asymmetrical bimanual movements Phillip C. Desrochers, Alexander T. Brunfeldt, Florian A. Kagerer PII: DOI: Reference:

S0306-4522(20)30074-9 https://doi.org/10.1016/j.neuroscience.2020.01.044 NSC 19503

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Neuroscience

Received Date: Revised Date: Accepted Date:

17 August 2019 28 December 2019 29 January 2020

Please cite this article as: P.C. Desrochers, A.T. Brunfeldt, F.A. Kagerer, Neurophysiological correlates of adaptation and interference during asymmetrical bimanual movements, Neuroscience (2020), doi: https://doi.org/10.1016/ j.neuroscience.2020.01.044

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Neurophysiological correlates of adaptation and interference during asymmetrical bimanual movements Phillip C. Desrochersa, Alexander T. Brunfeldta, Florian A. Kagerera,b * aDepartment

of Kinesiology, Michigan State University, East Lansing, MI 48824, USA

bNeuroscience

Program, Michigan State University, East Lansing, MI 48824, USA

* Corresponding author: [email protected]

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Abstract In this study, we investigated brain dynamics during interference between hands during bimanual movements. Participants performed a bimanual center-out reaching task in which a visuomotor rotation was applied to the right hand while the left hand did not receive visual feedback of its movements. This manipulation resulted in interference from the adapting right hand to the kinesthetically guided left hand. Electroencephalography (EEG) recordings during the task showed that spectral power in the high and low beta frequency bands was elevated early in exposure, but decreased throughout learning. This may be representative of error-based updating of internal models of movement. Additionally, coherence, a measure of neural functional connectivity, was elevated both within and between hemispheres in the beta frequencies during the initial presentation of the visuomotor rotation, and then decreased throughout learning. This suggests that beta oscillatory neural activity may be marker for transmission of conflicting motor information between hemispheres, which manifests in interference between the hands during asymmetrical bimanual movements.

Keywords: Bimanual coordination; neuromotor control; spectral power; coherence; neural crosstalk; EEG

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Introduction Bimanual actions are a significant part of everyday life. While the adult neuromotor system is usually capable of performing simple bimanual tasks without problems, more complicated movements, particularly those with spatial incongruencies can produce interference between the hands (Franz et al., 1991; Swinnen, 2002; Wenderoth et al., 2004; Kagerer, 2015) . In these tasks, interference is a process by which the action of one hand influences the action of another hand. Interference between the upper limbs can be induced by asking participants, for example, to move their hands with different amplitudes (Wenderoth et al., 2005; Kovacs and Shea, 2010; Pan and Van Gemmert, 2019), spatial directions (Franz et al., 1991; Wenderoth et al., 2004), relative phases (Kovacs and Shea, 2011), or by generating different forces (Kennedy et al., 2016, 2017). Interference during bimanual coordination has been well studied (Semjen et al., 1995; Obhi and Goodale, 2005; Albert and Ivry, 2009; Casadio et al., 2010; Kagerer, 2015, 2016; Kennedy et al., 2017), but little is known about the underlying neurophysiological mechanisms by which interference may occur. During complex bimanual actions, lateralized motor plans may influence contralateral motor plans and neural activity in motor areas controlling the opposite hand. For example, Kagerer (Kagerer, 2015) showed that when one hand was exposed to a visuomotor rotation during a bimanual simultaneous center-out reaching task, the visually occluded contralateral hand deviated from a straight trajectory despite instructions to reach straight towards a target. Importantly, the degree of movement difficulty elicits corresponding levels of interference. During continuous bimanual finger-wagging or circle drawing, interference is minimal when movements are in-phase or anti-phase (Kelso, 2010). Conversely, when movements are incongruous or phase shifted, interference is much more pronounced. Interference can also be increased by requiring movements to be made with additional force or effort (Walter and Swinnen, 1990; Kennedy et al., 2017). These phenomena are well studied using continuous movement paradigms (i.e. continuous circling or repetitive line drawing), but interference during discrete movements, such as target-directed reaches, are far less studied. In this context, previous research indicates that control of continuous movements might involve different neural circuitry from that involved in the control of discrete movements (Hogan and Sternad, 2007). It has been theorized that interference is mediated by the corpus callosum (Kennerley et al., 2002; Diedrichsen et al., 2003; Bloom and Hynd, 2005; Gooijers and Swinnen, 2014). More 3

specifically, neural crosstalk, defined as a process by which motor information of one hemisphere-hand system is sent to the contralateral hemisphere-hand system, has been suggested as a mechanism by which interference may occur (Cattaert et al., 1999; Swinnen, 2002; Hazeltine et al., 2003; Swinnen and Wenderoth, 2004; Houweling et al., 2010; Kennedy et al., 2016, 2017). Studies in non-human primates have shown that the corpus callosum allows for hand-specific information to be projected between the hemispheres, particularly from the supplementary motor area (SMA) and pre-SMA (Liu et al., 2001). Evidence from imaging studies in humans demonstrates increased activity of homologous areas in each hemisphere during interference caused by directional stimuli, particularly in the parietal-premotor network (Wenderoth et al., 2004, 2005, 2006). Additionally, interference is reduced in callosotomy patients, who can accurately perform asymmetrical drawing and force production tasks, presumably due to diminished neural crosstalk (Franz et al., 1996; Diedrichsen et al., 2003). Such findings emphasize the notion that motor information is transmitted between hemispheres and may influence activity in the contralateral hemisphere, manifesting as interference between effectors. Recent work using electroencephalography (EEG) to measure changes in brain dynamics related to motor control has shown spectral power of neural oscillations to be related to voluntary movement and the acquisition of motor skills (Pfurtscheller and Neuper, 1994; Pfurtscheller et al., 1996; Manganotti et al., 1998; Neuper et al., 2006; Chung et al., 2017). Decreased power, known as event-related desynchronization (ERD), is associated with cortical engagement during a motor task and asynchronous firing of underlying neural populations. Conversely, increased spectral power, known as event-related synchronization (ERS), represents more synchronized oscillatory neural activity. In addition to EEG spectral power, EEG coherence has also been related to motor control and skill learning (Ford et al., 1986; Manganotti et al., 1998; Kristeva et al., 2007; Rueda-Delgado et al., 2014; Chung et al., 2017). Coherence measures the degree of shared oscillatory activity between two EEG signals and represents functional connectivity between underlying neural populations. These oscillations are thought to be one mechanism by which information is transmitted between different brain regions (Schnitzler and Gross, 2005). Motor performance and control are mostly commonly associated with changes in neural oscillations at the alpha (8-13 Hz) and beta (13-30 Hz) frequencies ((Pfurtscheller and Neuper, 1994; Deiber et al., 2001); for review, (Rueda-Delgado et al., 2014)), and may reflect planning 4

processes (Deiber et al., 2005). Learning of a complex bimanual sequence resulted in greater ERD in the alpha and low beta frequencies from early learning to late learning (Andres et al., 1999). During a visuomotor adaptation task, frontal, central, and temporal sites initially show an alpha ERD in the spectral power, which then becomes more synchronized over the course of adaptation (Gentili et al., 2011). This change in power over the course of learning potentially reflects the acquisition of a new internal model and an updated visuomotor representation. Internal models are a theoretical neural construct which represent the plan for a given movement and its expected sensory consequences, and are used to ascertain the success and outcome of a movement and correct for perceived error (Wolpert et al., 1995; Kawato, 1999). Conversely, ERS has long been thought of as cortical disengagement or ‘cortical idling’, primarily in the alpha frequency band (Pfurtscheller and Neuper, 1994; Pfurtscheller et al., 1996; Neuper et al., 2006; Gentili et al., 2011). In other words, when not actively engaged in a task, neural populations tend to show similar patterns of oscillatory activity. However, more recent research is starting to show a functional role of ERS in motor control. ERS in the beta band, called beta rebound, is also observed as movement ceases. Research suggests that beta ERS may be related to active inhibition of motor networks (Klimesch et al., 2007) and/or may also reflect postmovement updating of internal models following a perceived motor error (Parkes et al., 2006; Zaepffel et al., 2013; Tan et al., 2014, 2016). Coherence in the alpha and beta band is found between a wide array of sensorimotor areas, including the cerebellum (Pollok et al., 2005a, 2005b, 2007). Coherence between within and between the hemispheres in the beta band increased with greater task difficulty while learning to perform interleaved bimanual sequences; over time, as participants became more familiar with the task, coherence decreased again (Gerloff et al., 1998; Andres and Gerloff, 1999). Conversely, for continuous in-phase and anti-phase movements, coherence in the beta band decreased as cycling frequency increased, particularly for anti-phase movements (Serrien and Brown, 2002). These seemingly contradictory findings suggest that learning and sensorimotor integration may factor heavily into coherence in beta oscillations (Serrien and Brown, 2003). Drawing movements under mirror transformations were associated with a change in intra- and interhemispheric beta coherence in unimanual and bimanual conditions, though findings are equivocal as to whether functional connectivity increases or decreases in response to altered visuomotor states (Serrien, 2009; Serrien and Spapé, 2009). Coherence also changes over 5

the course of motor adaptation. For example, Gentili and colleagues (Gentili et al., 2015) found that coherence decreased across adaptation to a novel sensorimotor task, suggesting a gradual disengagement of frontal areas as the sensorimotor map was updated. Coherence is also important for integration of motor commands between hemispheres in bimanual tasks (Gerloff and Andres, 2002). While we have a better understanding of how spectral power and coherence are related to motor control processes, very little is known as to how changes in these measures may reflect neural processes underlying interference during bimanual tasks. If interference is modulated by neural crosstalk, changes in spectral power and coherence should be detectable between the hemispheres. Furthermore, if interference is modulated by updating of internal models within a hemisphere, changes in power and intra-hemispheric coherence between premotor, motor, and parietal sites should be observed. In the current study, we used a discrete bimanual reaching task in which participants experienced a rotation of visual feedback in their dominant (right) hand while their nondominant (left) hand received no visual feedback. This reliance on kinesthetic feedback left the non-dominant hand susceptible to interference from the adapting dominant hand. Simultaneously, we recorded EEG, time-locked to each reach, and investigated how power and coherence changed in the alpha and beta frequency bands with regards to the interference task. Generally, we hypothesized that this task would be associated with increased ERD and greater coherence within and between hemispheres, particularly when the perturbation was first introduced. We also hypothesized that the asymmetric nature of the bimanual task would be associated with differences in spectral power and coherence between the hemispheres. Experimental Methods Participants We recruited 28 healthy, college-aged students; all were right-handed, healthy, and of normal or corrected vision. They were randomly assigned to either a control or an experimental group which experienced a visuomotor perturbation. All participants provided informed consent and study procedures were approved by the Michigan State University Institutional Review Board. One participant was removed due extremely outlying kinematic values, leaving fourteen participants assigned to the control group (12 female; M = 19.8 years, SD = 1.8) and 13 participants assigned to the visual perturbation group (11 female; M = 19.8 years, SD = 1.2). 6

Apparatus and procedure After completing the Edinburgh Handedness Questionnaire Short Form (Oldfield, 1971), participants were seated at a Kinarm Endpoint Robot (Kinarm, Inc., Kingston, Ontario) where they performed a simultaneous bimanual center-out reaching task by moving two cursors controlled by the two robotic manipulanda. Stimuli and cursors were projected onto a reflective surface above the manipulanda, which occluded vision of the hands. When the participant looked down at the reflective surface, the perceived depth and location of experimental stimuli provided a 1:1 virtual workspace. Participants controlled two circular white cursors (0.8 cm diameter), which represented the location of each manipulandum endpoint (Figure 1D). During each trial, participants first placed each hand/cursor into two home positions (1.5 cm diameter) spaced 17 cm from one another. After participants held their hands in the home positions for a random interval between 3000 and 4500 ms, the appearance of two 1.5 cm diameter peripheral targets, located 10 cm either directly forward or backward of the home positions, cued participants to reach from the home position to the targets. Movements of both hands were always in the same direction. Participants were instructed to perform straight and fast reaches with both hands simultaneously. After they had remained in the targets for 2000 ms, the home positions reappeared, cuing participants to move back to the home positions. “Up” or “down” trials were presented pseudorandomly, with neither direction being cued more than twice, consecutively. The reaching task consisted of seven blocks of 60 trials each (Figure 1C). It started with a visual baseline (VBL) block during which participants reached to the targets with full visual feedback of the cursors for both hands. This was followed by the kinesthetic baseline (KBL) block, during which the cursor representing the left hand was removed. Participants were instructed to reach towards the target with their left hand and stop when they thought their hand was in the target. The right-hand cursor remained visible throughout the task. After this, there were four exposure (EXP) blocks, in which the visual perturbation group was exposed to an abrupt visual rotation in the right hand, with the cursor being rotated by 40 degrees clockwise about the home position. The rotation required participants to adjust their reach trajectory in their right hand 40 degrees counterclockwise to move the cursor into the target positions. Over the course of the four EXP blocks, subjects adapted to the rotation, such that early in exposure 7

they showed large directional errors, which decreased across blocks. The control group continued to reach to the targets across 240 trials under the same conditions as during the KBL block (left hand invisible, right hand visible with veridical visual feedback). For both groups, the left-hand cursor feedback remained off throughout all exposure trials. Finally, in the postexposure (P-EXP) block, the right-hand feedback rotation was removed, thus providing participants in the rotated group with veridical visual feedback again. Left hand visual feedback remained off. Kinematic measures To evaluate adaptation in the right and interference in the left hand, we computed the initial directional error (IDE), endpoint error (EE), and root-mean-squared error (RMSE) for both hands for each trial during the reach from the home positions to the targets. IDE was defined as the angle between the target vector and the movement vector to the hand’s location at peak velocity. It represents the feed-forward component of the movement, since peak velocity occurs before any perceived motor error can be integrated in the sensorimotor system. EE was defined as the lateral x displacement between the hand position and the target. EE represents a period in which feedback about the movement is integrated into the sensorimotor system and the participant can correct any perceived reaching error. To measure movement straightness, we computed the RMSE by integrating the root-mean-square of the lateral displacement between the hand position and the closest point on the straight line between the home and target. To account for individual kinematic differences and examine the specific effects of the perturbation on adaptation and interference, each of these measures was standardized to the KBL block using the formula 𝑆𝑇𝑖 = (𝑠𝑖 ― 𝑀𝐾𝐵𝐿)/𝑆𝐷𝐾𝐵𝐿, where STi represents ith standardized score of the ith raw score s. To examine changes in kinematics over the course of the EXP block, we calculated the means for each kinematic measure during the first 20 (Early-EXP) and last 20 (Late-EXP) trials of exposure for statistical comparison. This number of trials was chosen in order to be comparable with the EEG data, which requires enough trials to capture a ‘stationary’ signal. EEG measures EEG was recorded using a 32-channel electrode cap conforming to the international 10/20 coordinate system (Brain Products GmbH., Gilching, Germany). Data were acquired with 8

a BrainAmp amplifier (Brain Products GmbH, Gilching, Germany) at 1000 Hz, downsampled to 250 Hz, filtered with a 0.5-80 Hz 4th order dual-pass Butterworth filter, and referenced to the T9 and T10 electrodes located near the mastoid processes. Using Analyzer software (Brain Products GmbH, Gilching, Germany), eyeblinks were removed using ocular ICA, and the recording was segmented into two distinct windows that were time-locked to the onset of the reach from the home position to the targets. The first, capturing movement planning, was computed from -1100 to -100 ms prior to movement onset (‘planning window’), and the second, capturing movement execution, was computed from -100 prior to 900 ms following movement onset (‘execution window’). This was done to account for nerve conduction velocities relaying movement execution signals from cortex to arm muscles, and for the hand to break the proscribed movement-start velocity threshold of 5 cm/sec. Segments were baseline corrected, and those containing artifacts were rejected. Regions of interest (ROIs) were determined by averaging signals from left frontal (FP1, F3), right frontal (FP2, F4), left central (FC1, C3, FC5), right central (FC2, C4, FC6), left parietal (CP1, P3, CP5), and right parietal (CP2, P4, CP6) electrodes (Figure 1B). To examine changes in spectral power, a fast Fourier transform was applied, and the spectral power was integrated across the alpha (8-13 Hz), low beta (13-22 Hz) and high beta (2230 Hz) frequency bands(Kranczioch et al., 2008; Kropotov, 2009; Rietschel et al., 2011; RuedaDelgado et al., 2014; Gentili et al., 2015). Spectral power for each trial and frequency band was standardized to the KBL block in the same fashion as for the kinematic analysis. Similar to the kinematic measures, to examine changes in spectral power over the course of the EXP block, we calculated the mean spectral power in each frequency band during the first (Early-EXP) and last (Late-EXP) 20 trials of the EXP block for statistical comparison. To examine functional connectivity, magnitude-squared coherence was computed between interhemispheric frontal, central, and parietal ROIs, and between intrahemispheric frontal-central (FC), central-parietal (CP), and frontal-parietal (FP) ROIs by averaging coherence between all combinations of channels between the ROIs. Since the coherence computation takes into account the stability of the signal across a given number of trials (in this case, Early- or Late-EXP), instead of performing a trial-by-trial standardization, we instead performed a baseline correction by subtracting mean KBL coherence from Early-EXP and Late-EXP coherence.

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Data analysis Statistical analyses were performed in RStudio 1.1.4 using the ‘afex’ package. To determine the effect of adaptation and interference in the kinematic measures, we used a 2 (Group: control vs. rotated) x 2 (Time: early-EXP vs. late-EXP) repeated-measures ANOVA for each hand. To examine changes in EEG spectral power during the task, we first performed six planned contrasts based on our a priori hypotheses for the two movement windows in each frequency band. First, we hypothesized that when the rotated group was exposed to the visuomotor perturbation, increased engagement of neural populations engaged in the adaptation would result in lower power than the control group in Early-EXP, which would then increase over time (Gentili et al., 2011). As such, three planned contrasts probed power differences between the rotated group and control group at Early- and Late-EXP and between Early- and Late-EXP in the rotated group alone. Second, we hypothesized that the asymmetric interference task would result in asymmetric distribution of power between the hemispheres in the rotated group, but not in controls. As such, two planned contrasts compared the power between the two groups in the left and right hemispheres, and between the two hemispheres in the rotated group alone. We then investigated overall effects using an omnibus 2 (Group) x 2 (Time) x 2 (Hemisphere: Left vs. Right) x 3 (Region: Frontal, Central, Parietal) repeated-measures ANOVA in each frequency band for the planning and execution windows. Effects present in the ANOVA not accounted for by the planned contrasts were then investigated post hoc across time and groups at each ROI with an examination of the simple effects and Tukey’s HSD. To examine changes in EEG coherence, planned contrasts were performed based on a priori hypotheses for the intra- and inter-hemispheric connections for the two movement windows in each frequency band. We hypothesized that 1) the rotated group would display greater coherence than the control group in Early-EXP, but not in Late-EXP. As such, three planned contrasts probed differences in interhemispheric coherence of the rotated group and control group at Early- and Late-EXP and between Early- and Late-EXP in the rotated group alone. We also hypothesized that 2) intrahemispheric coherence would be greater in the rotated group as compared to the controls. Further, we hypothesized that 3) intrahemispheric coherence would be greater in the adapting left hemisphere as opposed to the interfered-with right hemisphere. As such, we performed planned contrasts that compared coherence between the two groups within the left and right hemispheres, and between hemispheres in the rotated group. For 10

intrahemispheric connections, we used a 2 (Group) x 2 (Time) x 2 (Hemisphere) x 3 (Region: Frontal-Central (FC), Central-Parietal (CP), Frontal-Parietal (FP)) repeated measures ANOVA, and a 2 (Group) x 2 (Time) x 3 (Region: Frontal, Central, Parietal) repeated-measures ANOVA was computed for interhemispheric connections in each frequency band for the two windows. In all ANOVAs, Group was considered the primary independent variable of interest, with Time, Hemisphere, and Region providing additional insight. Significant effects were further investigated across time and groups with examination of simple effects and Tukey HSD tests at each connection. When the assumption of sphericity was violated, a Huynh-Feldt correction was applied. Effect sizes were reported as generalized eta-squared (ηg2) for the omnibus ANOVAs and simple effects (Olejnik and Algina, 2003; Bakeman, 2005). Finally, we examined whether the neurophysiological dynamics predicted the interference observed in the left hand, as measured by IDE, via simple linear regression at each ROI in each frequency band and movement window. (insert Figure 1 about here) Results Movement Kinematics We first examined kinematics of the adapting right hand in the rotated group. When the perturbation began, participants initially made large, curved movements with their right hand. As expected, over the course of the EXP blocks participants adapted to the rotation and by the end they moved relatively straight to the targets. This was supported by significant group and time main effects for right hand IDE and RMSE (all p ≤ 0.0001, ηg2 ≥ 0.55), and significant Group x Time interactions (all p ≤ 0.001, ηg2 ≥ 0.65). Taken together, movements of the right hand in the rotated group showed higher IDE and RMSE at the beginning of exposure than at the end, while the control group performed the task with negligible reaching error. We then compared movements of the nonvisible left hand between the two groups. A 2 (Group) x 2 (Time) ANOVA revealed a significant main effect of Group in IDE (F(1, 25) = 5.15, p ≤ 0.05, ηg2 = 0.13), indicating larger IDE in the rotated group in Early-EXP (mean (SD): Rotated = 0.74 (0.76); Control = 0.34 (0.26)) and Late-EXP (mean (SD): Rotated = 1.02 (0.94); Control = 0.44 (0.51)), compared to the controls. Thus, the initial movement trajectory of the 11

left hand deviated from a straight line between the targets more in the rotated group than in the control group. No interference effects were found for EE, suggesting that interference mostly occurred during the initial part of the reach. Analysis of RMSE revealed a significant main effect of group (F(1, 25) = 4.56, p ≤ 0.05, ηg2 = 0.10), in which the nonvisible left hand of the rotated group had a more curved trajectory than controls in both Early-EXP (mean (SD): Rotated = 1.31 (2.04); Control = 0.16 (0.48)) and Late-EXP (mean (SD): Rotated = 0.59 (0.66); Control = 0.27 (0.68)). These results show that the right-hand perturbation induced directional interference in the nonvisible left hand in the rotated group (Figure 2). In further analyses, we chose IDE to be the representative measure of interference, as it was the best measure of directional reaching error with the most robust effects. (insert Figure 2 about here) EEG Spectral Power Planning Window In the alpha band, planned contrasts examining how the groups differed across Early- to Late-EXP and hemispheres revealed no significant effects. The ANOVA showed that the Group x Time x Region x Hemisphere interaction approached significance, though the effect size was negligible (F(1.86, 46.42) = 2.71, p = 0.08, ηg2 = 0.0006). Visual inspection of spectral power across the experiment showed that the rotated group had less power than the controls in Earlybut not Late-EXP in the central and parietal ROIs. In the low beta band, planned contrasts showed no significant main effects. Similarly, no meaningful relationships were found in the omnibus ANOVA. Meanwhile, planned contrasts in the high beta band revealed significantly higher power for the rotated group than the control group in both the left (t(25) = -2.30, p ≤ 0.05) and right hemispheres (t(25) = -2.01, p ≤ 0.05). The omnibus ANOVA showed a significant main effect of group (F(1,25) = 4.79, p ≤ 0.05, ηg2 = 0.06), with overall higher power in the rotated group. Subsequent analysis of the simple effects at each ROI showed significant main effects of group and time in the left and right parietal ROIs, with greater power in the rotated group both Early- and Late-EXP while decreasing across exposure (all p ≤ 0.05). We also found a trend for group differences for the Left Central and Right Central ROIs (both ROI p = 0.06). Similar to the parietal ROIs, power was higher in the 12

rotated group in Early- and Late-EXP and decreased across exposure. These results show that in both hemispheres, and particularly in central and parietal areas, increased high beta power was evident during the initial exposure to the perturbation in the rotated group (Figure 3). (insert Figure 3 about here) Execution Window In the alpha band, planned contrasts revealed no significant effects. Additionally, no group effects were observed in the ANOVA. A main effect of region was found (F(1.11, 27.71) = 5.65, p ≤ 0.05, ηg2 = 0.03), showing that power was overall lower in the frontal ROIs. In the low beta band, planned contrasts revealed a marginal difference between groups in the left hemisphere, where power was higher for the rotated group compared to controls (t(25) = -0.32, p = 0.09). The ANOVA revealed that the Group x Time x Region x Hemisphere interaction was significant (F(1.89, 47.27) = 3.20, p ≤ 0.05, ηg2 = 0.002), driven by elevated power for the rotated group in central and parietal ROIs in Early-EXP, and overall lower power in the adapting left hemisphere. The analysis of the simple effects in each individual ROI revealed a main effect of group approaching significance in the left frontal ROI (F(1, 25) = 3.43, p = 0.08, ηg2 = 0.06), where the rotated group displayed higher power than the control group in both Early- and LateEXP. In the high beta band, planned contrasts revealed no significant effects, however, the ANOVA revealed a marginal Group x Hemisphere interaction (F(1, 25) = 3.48, p = 0.07, ηg2 = 0.003) and significant Time x Region interaction (F(1.17, 29.21) = 5.52, p ≤ 0.05, ηg2 = 0.02), driven by greater power in the adapting hemisphere at the left central and left parietal ROIs in the rotated group early in learning. Interestingly, the analysis of simple effects revealed a trending main effect of group only in the left frontal ROI (F(1, 25) = 3.31, p = 0.08, ηg2 = 0.06). Taken together, these results showed greater spectral power over the parietal and central regions, particularly in high beta, during movement execution at the outset of asymmetrical reaches which then faded over the course of the exposure period (Figure 4). (insert Figure 4 about here) EEG Coherence 13

Planning Window In the alpha band, planned contrasts revealed no significant effects in the intrahemispheric connections. However, there was a significant Group x Time x Hemisphere interaction, though the effect size was negligible (F(1, 25) = 4.56, p = 0.04, ηg2 = 0.008). Examination of simple effects revealed a Group x Time interaction in the C-P connection of the left hemisphere (F(1, 25) = 4.83, p ≤ 0.05, ηg2 = 0.04), indicating an increase of coherence from Early- to Late-EXP in the rotated group while coherence in the control group decreased. Posthoc tests, however, did not show these differences to be statistically significant. For the interhemispheric connections, planned contrasts revealed no significant effects, and only a main effect of region was found in the ANOVA (F(1.94, 48.44) = 8.67, p ≤ 0.01, ηg2 = 0.10), indicating higher coherence in the central and parietal connections. In the low beta band, planned contrasts for the intrahemispheric connections showed a significant group difference, with greater coherence in the rotated group during Early-EXP (t(25) = -2.33, p ≤ 0.05), but not Late-EXP. Coherence decreased significantly across exposure in this group (t(25) = 3.69, p ≤ 0.01). These effects were captured by a significant Group x Time interaction (F(1, 25) = 5.49, p ≤ 0.05, ηg2 = 0.09). The simple effects revealed significant Group x Time interactions in the F-C connections (F(1, 25) = 6.09, p = 0.02, ηg2 = 0.08) and F-P connections (F(1, 25) = 6.61, p ≤ 0.05, ηg2 = 0.09) of the left hemisphere, and in the F-C connections (F(1, 25) = 10.08, p ≤ 0.01, ηg2 = 0.10) of the right hemisphere, in addition to a trending Group x Time interaction in the right hemisphere F-P connections (F(1, 25) = 3.43, p = 0.08, ηg2 = 0.05). Post-hoc Tukey HSD showed that intrahemispheric coherence in the rotated group decreased significantly across the exposure period in the left F-C, left F-P, and right F-C connections (all p ≤ 0.01). Meanwhile, for interhemispheric coherence, planned contrasts showed a marginal difference between groups at Early-EXP, with greater coherence in the rotated group (t(25) = 1.94, p = 0.06), and a significant difference at Late-EXP, with lower coherence in the rotated group (t(25) = 2.03 p = 0.05). Further, the rotated group’s interhemispheric coherence decreased significantly from Early- to Late-EXP (t(25) = 4.96, p < 0.01); this was not the case for the control group. These effects were reflected in the ANOVA as a significant Group x Time interaction (F(1, 25) = 9.35, p < 0.01, ηg2 = 0.08). Examination of the simple effects showed that

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interaction was significant for both the Frontal and Central connections (all p ≤ 0.01), with the rotated group showing significant decreases across learning via Tukey HSD (all p ≤ 0.01). In the high beta band, planned contrasts for the intrahemispheric connections showed that coherence in the rotated group decreased significantly across adaptation (t(25) = 2.71, p ≤ 0.01). The ANOVA revealed a main effect of time (F(1, 25) = 8.34, p ≤ 0.01, ηg2 = 0.14) which appeared to be largely driven by the rotated group. However, there were no significant effects involving group. Planned contrasts for interhemispheric connections showed again that coherence decreased significantly across adaptation for the rotated group (t(25) = 2.71, p ≤ 0.01). The ANOVA showed a trending Group x Time x Region interaction (F(1.54, 38.47) = 3.08, p = 0.07, ηg2 = 0.01) and a significant main effect of time (F(1, 25) = 11.85, p < 0.01, ηg2 = 0.17) which again appeared largely driven by the control group. Examination of the simple effects revealed a trending Group x Time interaction in the Frontal connections (F(1, 25) = 3.32, p = 0.08, ηg2 = 0.08) and significant main effects of time in every ROI (all p ≤ 0.05). Corroborating the results of the planned contrasts, Tukey HSD showed a significant decrease in Coherence across adaptation in the rotated group, but not in the control group, in the Frontal and Central regions (all p ≤ 0.05). Taken together, these results show that in the planning window, both intrahemispheric and interhemispheric connections showed greater connectivity in the rotated group early in the EXP block, which then decreased across EXP. This occurred most strongly in in the low beta frequency, though also occurred in the high beta frequency. Additionally, these patterns of elevated coherence occurred most strongly in connections with and between the frontal ROIs (Figure 5). (insert Figure 5 about here) Execution Window In the alpha band, planned contrasts for the intrahemispheric connections revealed a marginal group difference in the left hemisphere (t(25) = 1.88, p = 0.07), with higher coherence in the control group. However, this was not reflected in the ANOVA, which revealed no significant effects. For the interhemispheric connections, planned contrasts showed no

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significant effects, and the ANOVA showed only a main effect of Region (F(1.58, 39.54) = 3.76, p ≤ 0.05, ηg2 = 0.05), with higher coherence in the central and parietal connections. In the low beta band, planned contrasts for the intrahemispheric connections showed a marginal decrease from Early-EXP to Late-EXP in the rotated group (t(25) = 1.97, p = 0.06). The ANOVA showed only a marginal Group x Time x Hemisphere interaction with a negligible effect size (F(1, 25) = 3.13, p = 0.09, ηg2 = 0.001). For the interhemispheric connections, planned contrasts revealed that coherence decreased significantly across adaptation in the rotated group (t(25) = 2.72, p ≤ 0.01). These effects were not reflected in the ANOVA, which only showed an effect of time (F(1, 25) = 5.88, p = 0.02, ηg2 = 0.05). In the high beta band, intrahemispheric planned contrasts again demonstrated a decrease in coherence across exposure in the rotated group (t(25) = 2.59, p ≤ 0.05). However, the ANOVA showed only significant main effects of Region and Time (each p ≤ 0.05). For interhemispheric connections, planned contrasts showed that coherence decreased significantly across exposure in the rotated group. The ANOVA showed a Group x Time x Region interaction (F(1.70, 42.46) = 3.33, p ≤ 0.05, ηg2 = 0.01) and a main effect of Time (F(1, 25) = 11.98, p ≤ 0.01, ηg2 = 0.14). Analysis of the simple effects revealed a significant Group x Time interaction in the Frontal region (F(1, 25) = 7.59, p = 0.01, ηg2 = 0.10) and a trending Group x Time interaction in the Central region (F(1, 25) = 3.40, p = 0.08, ηg2 = 0.05). Tukey HSD post-hoc tests confirmed that interhemispheric coherence in the rotated group decreased significantly across adaptation in the Frontal and Central regions (all p < 0.01). Altogether, these findings suggest that coherence was elevated during Early-EXP for the rotated group in the execution window in the beta frequency bands. These effects were not as robust as was found in the planning window, suggesting that sharing of sensorimotor information relevant to bimanual coordination may occur most strongly during planning phases of movement (Figure 6). (insert Figure 6 about here) Relationship between brain dynamics and left-hand interference To determine whether brain dynamics predicted reaching error in the left hand, we used simple linear regressions. We found that higher low beta spectral power in left-frontal and right16

frontal ROIs significantly predicted larger IDE in Late-EXP of the execution window (leftfrontal: R2adj = 0.21, F(1, 25) = 8.09, p = 0.01; right frontal: R2adj = 0.11, F(1, 25) = 4.07, p = 0.05). Higher coherence between the frontal ROIs significantly predicted larger IDE in LateEXP in low beta planning (R2adj = 0.17, F(1, 25) = 6.33, p = 0.02) and high beta execution (R2adj = 0.11, F(1, 25) = 4.21, p = 0.05). Higher high beta coherence predicted larger IDE during Early-EXP in the planning window both the frontal ROIs of each hemisphere (R2adj = 0.18, F(1, 25) = 6.65, p = 0.02) and between the frontal and central ROIs within the left hemisphere (R2adj = 0.14, F(1, 25) = 5.39, p = 0.03). These findings suggest that power and coherence in the frontal regions may partly account for the observed interference in the left-hand reaching trajectories.

Discussion In this study we have shown that 1) a visuomotor perturbation of the right hand resulted in spatial interference in the movements of the nonvisible left hand, and 2) this interference was reflected in electrocortical activity. We found that the rotated group showed a significant increase in power in the low beta band during execution and in the high beta band both during planning and execution. In line with our hypotheses, we also found a robust effect of elevated coherence in the rotated group early during exposure to the visuomotor perturbation, which then decreased across the exposure period. This effect was particularly pronounced in the high and low beta bands across both planning and execution and was strongest for intrahemispheric fronto-central and fronto-parietal connections in each hemisphere, and interhemispheric frontal and central connections. Interestingly, and contrary to our hypotheses, we did not find a consistent difference in patterns of neural activity between hemispheres as a result of our asymmetric interference task, though some effects tended to occur more strongly in the adapting left hemisphere. A large body of research has consistently described desynchronization of alpha, and beta frequencies in response to movement preparation and planning (Pfurtscheller and Neuper, 1994; Deiber et al., 2001; Gentili et al., 2011; Rueda-Delgado et al., 2014; Chung et al., 2017). Thus, we expected to observe ERD in these frequency bands reflecting engagement of neural populations involved in adaptation to perturbation. In the alpha band, we observed a drop in power during the planning window, particularly in frontal ROIs, though it was not statistically 17

significant. However, this finding is consistent with the perturbation engaging frontal cortical regions. Additionally, though we did not observe beta ERD, we did observe beta ERS in our study, which then decreased across exposure. An increase in beta power directly following movement is a common observation in EEG studies of motor control. Increased beta power may be driven by neural populations responsible for updating forward models for movement from one trial to the next. For example, Tan and colleagues (Tan et al., 2014) showed that beta ERS followed a predictable pattern of attenuation from trial to trial that corresponded to the degree of error in the previous trials. The authors postulated that beta ERS mediates error-based learning and updating of internal models following a given discrepancy between predicted and actual sensory consequences of the movement. In further work, they also demonstrated that beta oscillations are related to the degree of uncertainty in a forward model that is updated via Bayesian inference (Tan et al., 2016). In our study, the observed power dynamics occurred most strongly in the parietal ROIs, which supports the role of posterior parietal cortex in motor planning (Snyder et al., 1997; Desmurget et al., 1999; Valyear and Frey, 2015; Freedman and Ibos, 2018). Beta ERS has also been implicated in active inhibition within the motor system (Picazio et al., 2014; Sallard et al., 2014), and may also act as a mechanism by which motor information is communicated between different brain regions (Zaepffel et al., 2013). In our experiment, such inhibitory influence may help to inhibit established motor plans, thus facilitating the generation of new internal models that are shared across hemispheres. Related to our findings regarding beta ERS, we also found robust beta coherence between and within hemispheres in both the planning and execution windows. Beta coherence also predicted the degree of left-hand deviation in our participants between the frontal and right hemisphere fronto-central connections during the early exposure period. This may indicate that the coherent oscillations in the beta frequency band are a mechanism by which motor information is being shared within and between hemispheres in our task, contributing to interference. The early increase and subsequent decrease in coherence may reflect an engagement and functional connection of neural populations planning and controlling movement early in adaptation, and has been observed by others (Gerloff and Andres, 2002; Serrien and Brown, 2003; Gentili et al., 2015). When adapting to the visuomotor perturbation, the motor system likely needs to inhibit previously learned sensorimotor representations and develop or 18

update new internal models to reflect the new sensorimotor environment (Gentili et al., 2015; Kagerer, 2015). Critically, increased beta coherence both within and especially between hemispheres lends support to the notion of neural crosstalk as a mechanism for interference. Additionally, previous studies recording EEG during finger movements have shown that beta coherence is indicative of sensorimotor binding between neural regions, particularly during difficult tasks (Gerloff et al., 1998; Andres and Gerloff, 1999; Gerloff and Andres, 2002). Thus, changes in intrahemispheric coherence may signal an updating of sensorimotor internal models, while changes in interhemispheric coherence in the beta band may reflect neural crosstalk transmitting conflicting information between the hemispheres, contributing to interference. Interestingly, we found very little effect of hemisphere on power and coherence across the majority of our frequency bands, with the exception of greater power in the high beta band in the adapting hemisphere. This suggests that although the adaptation to the visuomotor perturbation is happening for only one hand, both hemispheres may be similarly engaged. This may be due to bilateral formation of motor plans or a shared egocentric representation of the task space between the hemisphere-hand systems. As such, equal contribution of each hemisphere to the asymmetrical motor task, or an inability of the motor system to isolate activity to a single hemisphere during complicated, asymmetrical movements, might also be possible mechanisms by which motor interference occurs. This is supported by our finding of high interhemispheric coherence in the beta frequency bands, particularly between the frontal ROIs. The time course of our power and coherence findings in this study is also important – we observed that beta power and coherence decreased across exposure, indicating that once participants adapted, communication between brain areas via beta oscillations decreased. This mirrors similar findings in unimanual studies(Gentili et al., 2011, 2015), and in bimanual studies as participants learn novel temporal movement patterns (Serrien and Brown, 2002). This finding also aligns with current theories regarding the function of beta oscillations, since the beta frequency band is theorized to be important for updating the forward model (Sallard et al., 2014; Tan et al., 2016). We posit that once participants had adapted to the rotation and updated their internal model compensating for the perceived error, brain activity associated with this updating process was attenuated. It is important to note that interference in the left hand due to the perturbation in the right hand remained elevated in the rotated group throughout the exposure period. This suggests that the interference was not transient, but likely derived from a novel 19

internal sensorimotor representation that, once formed, was maintained for both hemispherehand systems. This study is not without several limitations. First, the interference we observed in the left hand was relatively small, deviating only slightly from a straight reach. The difference to previous studies (Kagerer, 2015, 2016) may lie in the fact that the joysticks used in those studies resist motion with a spring force, whereas the Kinarm robot used in the present study had very little resistance to movement apart from the inertia of the manipulandum segments. The tactile feedback or additional effort required to move spring-loaded manipulandum may contribute to the interference from this task. Second, in the EEG data, it is difficult to parse the effects of the bimanual interference from the effects of visuomotor adaptation alone. In response to unimanual adaptation, prior studies found theta and alpha ERD in response to an abrupt visuomotor perturbation which then increased over time (Gentili et al., 2011). Conversely, in the present study, we found power increases in the beta frequency which we interpreted as related to bimanual control and interference. Additionally, similar to the present study, a prior study examining EEG responses to unimanual adaptation found increased coherence early on in the perturbation which decreased over time, including in the beta frequency(Gentili et al., 2015). However, supporting the importance of beta coherence in bimanual interference shown in this task, we did find that brain dynamics predicted left hand reaching error in some ROIs. These findings should necessarily be interpreted with care however, as we did not show a robust pattern of findings linking power and coherence to interference. This is likely due to the low magnitude of the interference in the left hand of the rotated group, and relatively low variance in interference among participants. Future studies should confirm the importance of beta oscillations in bimanual interference, while also parsing apart the effects of interference and adaptation on beta oscillations. Third, though widely used in EEG studies, magnitude-squared coherence can be susceptible to the influence of volume conduction (i.e., a third oscillatory source may be received by both sensors, artificially inflating the coherence measure between the two sensors). Additionally, magnitude-squared coherence critically depends on both phase and amplitude relationships between signals, which can obscure the interpretation of changes in coherence (i.e., it is difficult to know whether differences are a result of changing phase or changing amplitude). However, this coherence computation was chosen due to its consistent use throughout EEG literature, allowing for comparison between studies, and due to being generally 20

a good measures for functional connectivity. Finally, the absence of a robust ERD in our data was surprising, although we did see non-significant ERD in the alpha band. ERD is modulated by the predictability of the movement, with greater predictability producing greater ERD (Alegre et al., 2003). In our task, which was based on previous work examining bimanual interference (Kagerer, 2015, 2016), both the timing and the direction of the movement was randomly cued, reducing participants’ ability to predict or plan their movements, which may have contributed to a lack of ERD in our study. Understanding neural control of discrete bimanual movements, particularly when the hands are doing different activities, is an important area in motor control research. The ability to use the influence of neural activity from one hemisphere to the other may promote recovery of function through neuroplastic processes (Cauraugh and Summers, 2005). Thus, this research may be relevant for bimanual rehabilitation techniques, particularly for cases such as stroke, in which impairments are often lateralized. Understanding the mechanisms behind bimanual interference may illuminate the neural underpinnings behind mirror movements, which are associated with movement disorders such as Parkinson’s disease and dystonia. These movements are thought to be the result of dysfunctional intra- and intercortical inhibition. In these movements, action of one part of the body seems to cause involuntary activations in another effector (Li et al., 2007; Sitburana and Jankovic, 2008; Sitburana et al., 2009; Cox et al., 2012). Expanding knowledge regarding how motor information is shared between hemispheres, and how the action of one effector may affect the action of another effector may help parse dysfunctional neural processes within these disorders. In summary, in this study we demonstrated that changes in EEG spectral power and coherence, particularly in the beta band, are modulated by an interference task, and are related to interference between the hands during an asymmetrical bimanual movement task. This interference may be due to the generation of novel internal sensorimotor representations that are shared between hemispheres via beta oscillations in response to a visuomotor perturbation. These findings point to a neural mechanism related to interference during asymmetrical bimanual movements. Funding sources: PD was supported by MSU College of Education Practicum Research Support Fellowship, MSU College of Education Summer Research Fellowship, and the MSU 21

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Figure legends Figure 1. Experimental Setup – A) Participants performed reaching movements at a Kinarm robot while EEG was recorded. B) EEG was collected at left and right frontal, central and parietal ROIs. C) Participants performed reaching movements from home positions (orange circles) to peripheral targets (red circles) across VFB, KFB, EXP, and Post-EXP blocks. Gray shaded blocks indicate that visual feedback (white cursors) for the left hand was removed during that block. During the exposure block, the right hand was exposed to a 40° clockwise visual feedback rotation. Interference in the left hand due to the right-hand perturbation was measured. D) illustrates the reaching movement in the control (left panel) and rotated conditions (right panel) at the beginning of the exposure block. Participants could only see the red peripheral targets and white cursors, but not their hands. In the right panel, note the deviating cursor for the right hand. The blue lines represent the robotic manipulanda, the orange circles represent the home positions (participant’s hands and arms are digitally rendered for context). Figure 2. Standardized IDE (in units of standard deviation) for the right and left hands. Shaded areas denote standard error (SE). Each circle represents bins of 20 trials; trial blocks 7-18 represent the exposure phase, while 19-21 represent the post-exposure phase. Elevated directional reaching error in the left hand of the rotated group denotes interference as a result of the perturbed right hand. Note the different y-axis scales for the perturbed group’s right hand (top) and the control group’s right hand (bottom), as well the respective left hand of each group (bottom), due to the different magnitude of IDE for the perturbed right and the interfered-with left hands. Figure 3. Standardized EEG Spectral Power (in units of standard deviation), Planning Window. Regions of interest (A) show mean spectral power values across frequency band, group, and time of adaptation (early vs. late). Bar graphs depict corresponding means ± SE for low beta (B) and high beta (C). Figure 4. Standardized EEG Spectral Power (in units of standard deviation), Execution Window. Regions of interest (A) show mean spectral power values across frequency band, 28

group, and time of adaptation (early vs. late). Bar graphs depict corresponding mean ± SE for low beta (B) and high beta (C). Note the beta rebound for the control group in central and parietal regions, particularly in the left hemisphere. Figure 5. EEG Coherence, Planning Window. Regions of interest (A) show mean coherence across frequency band, group, and phase of adaptation (early vs. late). Bar graphs depict corresponding mean ± SE intrahemispheric coherence for low beta (B) and high beta (C); side panels indicate fronto-central (F-C), centro-parietal (C-P), and fronto-parietal (F-P) connections. Similarly, interhemispheric coherence is shown in (D) and (E). Note the elevated coherence in early-exposure in the beta band for the rotated group. Figure 6. EEG Coherence, Execution Window. Regions of interest (A) show mean coherence values across frequency band, group, and phase of adaptation (early vs. late). Bar graphs depict corresponding mean ± SE coherence for low beta execution (B) and high beta execution (C); side panels indicate fronto-central (F-C), centro-parietal (C-P), and fronto-parietal (F-P) connections. Similarly, interhemispheric coherence is shown in (D) and (E). Note the elevated coherence in Early-EXP in beta band for the rotated group.

Highlights

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Exposing the right hand to a visuomotor perturbation causes interference in the kinesthetically controlled left hand. EEG recordings showed changes in beta power and coherence associated with interference. These changes may represent sharing of an updated internal model during asymmetrical movements.

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