Abstract
Action observation can facilitate the acquisition of novel motor skills, however, there is considerable individual variability in the extent to which observation promotes motor learning. Here we tested the hypothesis that individual differences in brain function or structure can predict subsequent observation-related gains in motor learning. Subjects underwent an anatomical MRI scan and resting-state fMRI scans to assess pre-observation grey matter volume and pre-observation resting-state functional connectivity (FC), respectively. On the following day, subjects observed a video of a tutor adapting her reaches to a novel force field. After observation, subjects performed reaches in a force field as a behavioral assessment of gains in motor learning resulting from observation. We found that individual differences in resting-state FC, but not grey matter volume, predicted post-observation gains in motor learning. Pre-observation resting-state FC between left S1 and bilateral PMd, M1, S1 and left SPL was positively correlated with behavioral measures of post-observation motor learning. Sensory-motor resting-state FC can thus predict the extent to which observation will promote subsequent motor learning.
Significance Statement Action observation can facilitate motor learning, however like for motor learning through active movement pratice, there is considerable individual variability in the degree to which observation promotes motor learning. Here we show that individual differences in pre-observation brain function can predict subsequent observation-related gains in motor learning. Pre-observation resting-state FC between left S1 and bilateral PMd, M1, S1 and left SPL (assessed using fMRI) predicted observation-related behavioral improvements in motor learning, assessed on the following day. Our results suggest that pre-observation resting-state functional connectivity within the identified sensory-motor network may be used as a biomarker for the extent to which observation promotes motor learning. This kind of information may be useful if visual observation is to be used as a way to boost neuroplasticity and sensory motor recovery for patients undergoing rehabilitation for diseases that impair movement such as stroke.
Introduction
Recent work has shown that action observation can promote motor learning. For example, individuals can learn how to reach in novel robot-imposed force field (FF) environments by observing the movements of a tutor (Mattar and Gribble, 2005). Subjects observed a video of a tutor adapting his reaches to a novel robot-imposed FF applied. Subjects who later performed reaches in the same FF showed a benefit, performing better (straighter) reaches compared to control subjects who did not observe a tutor. Subjects who later performed reaches in the opposite FF performed worse (more curved) reaches than subjects who did not observe. While these results demonstrate that FFs can be partially learned from observation, there is considerable inter-individual variability in the extent to which observation promotes motor learning. Little is known about why this may be. Some individuals may be more predisposed to learning from observation than others, whether from birth, from experience-dependent plasticity, or a combination of these or other individual differences. Here we test the idea that inter-individual differences in brain function or structure underlie the extent to which observation promotes subsequent motor learning.
In a recent review article, Zatorre (2013)discusses findings showing how structural and functional neural connectivity patterns predict individual differences in musical training and speech learning. Other studies have shown similar predictability for a wide array of cognitive abilities including executive function (Barnes et al., 2014; Reineberg et al., 2015), reading (Koyama et al., 2011; Wang et al., 2013), second language acquisition (Chai et al., 2016), visual perceptual discrimination (Baldassarre et al., 2012) and memory recall (King et al., 2015). In the motor domain, Tomassini et al. (2011)demonstrated that individual differences in both functional and structural magnetic resonance imaging (MRI) measures correlate with the acquisition of a novel visuomotor tracking skill through active movement training. Task-based functional activation levels in a network involving prefrontal, premotor, and parietal cortices, as well as basal ganglia and the cerebellum were associated with behavioral measures of active motor learning. Structural differences within the premotor cortex, higher order visual areas, and the cerebellum were also positively correlated with learning abilities (Tomassini et al., 2011). Similarly, using dense-array electroencephalography (EEG), Wu et al. (2014) showed that resting-state functional connectivity (FC) between premotor, primary motor and parietal cortices predicts individual differences in the subsequent learning of a visuomotor tracking task. Together, these studies suggest that functional and structural variations in motor learning-related brain networks can, in part, explain individual differences in the ability to learn novel motor tasks through active movement practice. The results of these studies raise the possibility that individual differences in brain structure or function may also be predictive of motor learning by observing.
Here we tested the hypothesis that individual differences in brain function or structure can predict the extent to which individuals will learn to perform a novel sensory-motor task (FF reaching) from observation. Based on our previous work (McGregor and Gribble, 2015; McGregor et al., 2016), we expected that individual differences in brain function and structure within visual and sensory-motor brain networks would be predictive of motor learning by observing. On day 1, subjects performed baseline (no FF) reaches using a robotic arm and then underwent pre-observation anatomical and resting-state functional magnetic resonance imaging (fMRI) scans. Twenty-four hours later, subjects observed a video of a tutor learning to reach in a novel FF, and then performed reaches in a FF as a behavioral assessment of motor learning by observing. We found that pre-observation (day 1) resting-state FC between bilateral dorsal premotor cortex (PMd), primary motor cortex (M1), primary somatosensory cortex (S1) and left superior parietal lobule (SPL) predicts post-observation behavioral measures of motor learning, acquired on day 2. In contrast, individual differences in grey matter volume could not predict subsequent motor learning by observing. Pre-observation sensory-motor resting-state FC can thus explain part of the between-subject variation in motor learning by observing.
Materials and Methods
Subjects
Thirty healthy subjects participated in this study. Fifteen subjects were assigned to a learning group (6 males, mean age 22.87 ± 1.02 (SE) years) and 15 were assigned to a control group (6 males, mean age 22.53 ± 0.86 (SE) years). All subjects were right handed, had normal or corrected-to-normal vision, were naive to force fields, and reported no neurological or musculoskeletal disorders. Subjects provided written informed consent prior to participating. All experimental procedures were approved by the Research Ethics Board at the University of Western Ontario.
Apparatus
Subjects were seated in front of a custom tabletop and grasped the handle of a two degree of freedom robotic arm (IMT2, Interactive Motion Technologies) with the right hand (see Figure 1). The chair height was adjusted such that the subject’s upper arm was abducted approximately 90o from the trunk. An air sled was secured beneath the subject’s right arm to support the arm against gravity. A semi-silvered mirror, mounted horizontally just above the robotic arm, occluded the subject’s vision of his or her own arm and the robotic arm. During the reaching task, a liquid crystal display television (LCD TV) projected visual feedback onto the semi-silvered mirror. Visual feedback included a start position (20-mm blue circle), a single target (20-mm white circle), and a cursor representing hand position (12-mm pink circle).
The reaching task involved guiding the handle of the robotic arm from the start position to the target, which was located 15 cm in front of the start position. Subjects were instructed to move as straight as possible. At the end of each reach, the target changed color to provide feedback about movement time: the target disappeared if the movement time was within the desired range (450-550 ms duration), turned red if the movement was too fast (< 450 ms) or turned green if the movement was too slow (> 550 ms). Following each reach, the robotic arm returned the subject’s hand to the start position.
The robot applied a velocity-dependent force field during the reaching task according to Equation 1: in which x and y are lateral and sagittal directions, Fx and Fy are the applied robot forces, vx and vy are hand velocities, k=14 Ns/m, and d=0 (null field), +1 (right FF) or −1 (left FF).
Reaching Video Stimuli
Each video showed a top-down view of a tutor performing the reaching task described above using her right arm. The tutors in the videos were naive to force fields. The learning video consisted of a series of 30-second clips showing a tutor adapting her reaches to a leftward force field (left FF). These clips showed the gradual progression from curved to straight movements that is indicative of motor learning. The control video consisted of a series of 30-second clips showing a tutor performing reaches in an unlearnable FF in which the direction of the FF varied randomly from trial to trial (left FF, right FF, or null field). These clips showed the tutor performing both high and low curvature movements, but lacked the progressive decrease in movement curvature depicted in the learning video. Thus the control video included similar movements to those shown in the learning video, but did not depict learning. The videos showed 200 reaches each and were 15 minutes in duration (including regular breaks). Video screenshots are shown in Figures 1B and 2A. Note that the dashed trajectories and superimposed labels have been included for demonstrative purposes here and were not shown to subjects in the experiment.
Experimental Design
The experimental design is shown in Figure 1B. All subjects (n=30) participated in three sessions. For each subject, the sessions were held at the same time on three consecutive days. On day 0, subjects were familiarized with the reaching task by performing 50 practice movements in a null field (no force applied by the robot). On day 1, subjects performed 200 baseline reaches in the null field and then walked to the imaging facility for a fMRI scan session (see below for scan session details). The scan session began approximately 20 minutes following the completion of the reaching task and lasted 1 hour. Data collected during the day 1 scan session were used to estimate pre-observation resting-state FC between 10 visual and sensory-motor brain areas (see ROIs below) and to estimate whole-brain grey matter volume. On day 2, subjects performed the observational motor learning task. Subjects watched either the learning video or the control video while seated in front of the robotic arm. The video was played on a LCD TV positioned above the robotic arm and was projected onto a semi-silvered mirror surface. To ensure subjects paid attention during the video, we instructed subjects to count the number of correctly-timed reaches in the video (indicated by the target disappearing upon the completion of a reach) and to report the final tally to the experimenter following the video. Note that subjects were not told to pay attention to any particular part of the movement trajectory or arm, nor were they told that the robot would be applying forces to the arm. Reported tallies were analyzed to verify that subjects attended to the video, but these data were not incorporated into the behavioral or neuroimaging analyses. Approximately 80 minutes after video observation, we assessed motor learning by observing by having subjects perform 100 reaches while the robotic arm applied a rightward FF (right FF).
During the 80 minutes between video observation and the motor learning test on day 2, both groups underwent a second fMRI scan session identical to the day 1 fMRI scan session. Data from the second fMRI scan session were not used in any of the analyses presented here since the main objective of the current study was predicting motor learning by observing based on pre-observation (day 1) neuroimaging data. See McGregor and Gribble (2015) for details of FC changes from day 1 to day 2, and how they relate to observation-related gains in motor learning.
We assessed motor learning behaviorally by having subjects perform reaches in a right FF, which was the opposite FF to what was depicted in the learning video. The better subjects learned about the observed left FF, the worse their performance would be in the right FF. The idea is that during observation, subjects learn something about the compensatory pattern of muscle forces (i.e., rightward compensation) that is required to counteract the left FF. Subjects continue to use a learned pattern of muscle forces even when the force environment is unexpectedly changed, resulting in after-effects (e.g.,Shadmehr and Mussa-Ivaldi, 1994). As is the case in this study, after-effects are especially large if the environment is changed such that it is the opposite of the learned environment. This is because the subject compensates rightward (persistence of the learned pattern of muscle forces) and the robotic arm also pushes the hand to the right. Therefore, we expected that those subjects who better learned about the observed left FF would perform more highly curved reaches when first exposed to the right FF (Cothros et al., 2006; Brown et al., 2009; McGregor and Gribble, 2015; McGregor et al., 2016). We chose to use this interference paradigm to assess motor learning by observing because it tends to be a more sensitive measure compared to testing subjects in the same FF that they observed.
The overall aim of this study was to assess if pre-observation resting-state FC or grey matter volume can predict the extent to which subjects will learn from observation. While both the learning and control groups underwent resting-state scans on day 1, we did not perform neuroimaging analyses on the data from the control group. This is because the control group did not observe learning in the control video and so motor learning by observing did not occur. We have included the control group here as a basis for comparing behavior in the final motor learning test to demonstrate the motor learning by observing effect.
Imaging Procedure
Neuroimaging data were acquired by a 3-Tesla Siemens Magnetom Tim Trio imaging system using a 32-channel head coil. All subjects underwent resting-state scans on day 1 to assess functional connectivity prior to the observational motor learning task on day 2. The fMRI scan session lasted 1 hour. The scan session began with two 8-minute resting-state runs during which subjects were instructed to relax with their eyes closed. The resting-state runs were separated by a 5-minute anatomical scan during which subjects were instructed to fixate their gaze on a crosshair projected onto a screen. Subjects then performed two 6-minute functional localizer tasks: an action observation network localizer and a motor localizer task. We selected 10 a priori regions of interest (ROIs) known to be involved in action observation and/or motor learning (see below). The two localizer tasks allowed us to determine the coordinates of each ROI for use in the functional connectivity analysis described below.
For the the action observation network localizer task, subjects viewed intact and scrambled video clips of a tutor performing reaches while holding the robotic arm (ten 36-s interleaved blocks). Intact video clips showed a top-down view of a tutor performing straight reaching movements in a null field (no forces applied by the robot). For the baseline condition, subjects viewed scrambled versions of these video clips in which only the start and target positions remained in their original locations. Scrambling the videos allowed us to preserve the low-level motion features such as movement direction and velocity while removing such movement features as shoulder and elbow joint rotations and the hand path (Malfait et al., 2010). During the action observation network localizer task, subjects were instructed to count the number of correctly-timed movements the tutor performed and to report the final tally to the experimenter at the end of the video. This was done to verify that subjects attended to the video. Reported tallies were not incorporated into the behavioral or neuroimaging analyses.
For the motor localizer task, subjects performed interleaved blocks of arm movement and rest (ten 36-s blocks). During movement blocks, subjects slowly moved their right forearm along the frontal plane in a cyclic manner (90o elbow flexion). Color-coded visual cues were used to pace movements at a frequency of 0.1 Hz.
Image Acquisition
Whole-brain functional data were acquired with a T2-weighted EPI sequence (TR = 3,000 ms, TE = 30 ms, 90o flip angle, 3-mm isotropic voxels, 80×80×50 matrix, iPAT acceleration factor = 2). T1-weighted anatomical images were collected with a MPRAGE sequence (TR = 2,300 ms, TE = 2.98 ms, 9o flip angle, 1-mm isotropic voxels, 192×240×256 matrix). For each subject, a field map was acquired at the beginning of the scan session using a gradient echo sequence (TR = 531 ms, TE = 4.92 ms/7.38 ms, 60o flip angle, 3-mm isotropic voxels, 80x80x50 matrix).
Behavioral Data Analysis
During the reaching task, the position and velocity of the robotic handle were sampled at 600 Hz and stored for offline analysis. Positional data were low-pass filtered at 40 Hz. The start and end of each trial were defined using a threshold of 5% of the peak tangential hand velocity. Movement curvature was quantified for each trial as the maximum perpendicular deviation of the hand (PD) from a straight line connecting the start and target locations (Mattar and Gribble, 2005).
We calculated a behavioral motor learning by observing score for each subject. Motor learning by observing scores were calculated as the mean PD of the first 3 reaches in the right FF minus the mean PD of the last 50 reaches in the baseline null field. This approach allowed us to examine the extent to which observing the left FF interfered with subjects’ initial performance in the right FF compared to control subjects who did not observe the tutor undergoing learning. As in our previous work (Cothros et al., 2006; Brown et al., 2009; McGregor et al., 2016), we expected that motor learning by observing would primarily affect initial performance in the right FF, after which motor learning through active movement in the right FF would occur for both groups.
Functional Connectivity Analysis
We carried out a whole-brain seed-based correlation analysis to examine if inter-subject differences in resting-state FC could predict the amount of motor learning by observing that subjects would achieve on the following day. Neuroimaging data analyses were performed only on the data from the learning group using FSL version 5.04 (FMRIB’s Software Library, https://www.fmrib.ox.ac.uk/fsl). Image preprocessing steps for the functional connectivity analysis included the removal of the first 2 volumes in each functional run, slice-timing correction, motion correction, spatial smoothing using a 6-mm kernel, and high-pass temporal filtering (100 s). Field map distortion correction and affine coregistration of functional and anatomical images were performed using boundary-based registration (BBR) in FLIRT. Subjects’ images were registered to MNI space (MNI’s 152-brain T1 template, 2-mm isotropic voxel size) using a 12-DOF affine registration.
We selected 10 a priori regions of interest (ROIs) known to be involved in action observation and/or motor learning. ROIs were used in the whole-brain functional connectivity analysis described below. These regions included left supplementary motor cortex, dorsal premotor cortex, ventral premotor cortex, primary motor cortex, primary somatosensory cortex, visual area V5/MT, superior parietal lobule, inferior parietal lobule, putamen, and right cerebellum. The coordinates of each ROI were determined based on block-design analyses of the action observation network and motor localizer tasks described above. For each localizer, the task-induced response was assessed with a per-subject GLM. Data from all 15 subjects were then included in a mixed-effects analysis (Z > 2.3, p < 0.05, cluster-based thresholding) for each localizer. The seed coordinates were chosen as the peak activated voxel within each of the 10 brain areas listed above. ROIs consisted of all voxels within a 6-mm radius of the activation peaks. Table 1 shows the coordinates of the activation peaks used for each ROI.
Functional connectivity analyses were performed using both resting-state runs acquired on day 1. We analysed both resting-state runs together, as well as separately (see Results). Following preprocessing, a bandpass filter of 0.01 − 0.1 Hz was applied to the resting-state data (Biswal et al., 1995; Damoiseaux et al., 2006). Mean-based intensity normalization was performed (mean value of 10,000) to remove global intensity differences between runs (Damoiseaux et al., 2006). We carried out seed-based correlation analyses on each subject’s resting-state runs using FILM (FMRIB’s Improved General Linear Model). This allowed us to assess FC between each ROI and the rest of the brain on day 1. The mean time series of each ROI was used as the predictor of interest in the GLM. Nuisance regressors included the temporal derivative of the mean ROI time series, 6 rigid body motion parameters obtained from motion correction, mean global signal, mean white matter signal and mean CSF signal. For each ROI, the results of the subject-level analyses were then entered into a mixed-effects group-level analysis. We also included in our group-level analysis a nuisance regressor modelling the average PD of the last 50 reaches in the null field for each subject. This was done because subjects had performed 200 reaches in the null field prior to the fMRI scan session on day 1. Even though the robot did not apply force to the subject’s hand during null field reaches, subjects likely underwent some degree of motor learning as they learned the inertial properties of the robotic arm. We included a nusiance regressor modelling subjects’ behavioral performance in the null field to account for the potential effects of previous experience in the null field.
Group-level analysis results were thresholded based on Gaussian random field theory, using maximum height thresholding with a corrected significance level of p=0.005 (voxelwise thresholding, corrected for familywise error). We applied a Bonferroni correction for the number of ROIs used; therefore, our corrected significance threshold of p=0.005 reflects p=0.05/10 ROIs. These analyses resulted in 10 Z-score maps (one per ROI) showing areas that, on average, exhibited FC with the seed region across subjects. FC was defined as the temporal correlation (Fisher Z-transformed correlation coefficient) between the seed region time series and the average time series of all target clusters.
For each ROI, we computed the correlation between day 1 resting-state FC and day 2 motor learning by observing behavioral scores. We again applied a Bonferroni correction for the number of ROIs used; therefore, we considered statistically significant only those correlations for which p < 0.005 (i.e., p=0.05/10 ROIs).
Voxel-Based Morphometry Analysis
We carried out a whole-brain voxel-based morphometry (VBM) analysis to test for inter-subject differences in grey matter volume across the whole brain (measured on day 1) that could predict motor learning by observing scores on day 2. This analysis was carried out on the T1-weighted images from the learning group using FSL-VBM v1.1. First, each subject’s anatomical image was brain-extracted, grey-matter segmented, and transformed to MNI space using an affine registration. The resulting anatomical images were then averaged and flipped along the x-axis to generate a left-right symmetric, study-specific template. Grey matter images were then smoothed using a 3-mm Gaussian kernel. The VBM analysis was carried out using a voxelwise GLM model. The predictor of interest modelled the subjects’ motor learning by observing scores. Two nuisance regressors were also inluded, one modelled the grey matter grand mean across all subjects, and the second modelled each subject’s unnormalized total brain volume. Each subject’s total brain volume was estimated prior to standard space normalization using FSL’s SIENAX tool. The voxelwise GLM model was applied using non-parametric permutation (50,000 iterations) to correct for multiple comparisons with a significance threshold of p=0.005.
Results
Behavioral Results
Figure 2A shows the behavioral data from the learning and control groups. It can be seen that, on day 1, reaches are straight in the baseline null field condition for both groups. Following video observation on day 2, we assessed motor learning by observing by instructing subjects to perform straight reaches while the robotic arm applied a right FF (the opposite FF to what had been observed in the learning video). The better subjects had learned and retained the observed left FF, the worse their performance would be during their initial performance in the right FF. Indeed, we found that subjects who observed the tutor adapting to a left FF in the learning video exhibited greater PD during initial reaches in the right FF compared to control subjects who observed the tutor performing curved reaches in an unleamable FF. As in previous work (Mattar and Gribble, 2005; Cothros et al., 2006; Brown et al., 2009; Williams and Gribble, 2012; Bernardi et al., 2013; McGregor et al., 2016), the effects of observation are most apparent early in the motor learning test (i.e., the first 10 reaches shown as blocks 1 and 2 in Fig 2A) and diminish as subjects in both the learning and control groups adapt to the right FF. Average motor learning by observing scores are shown in Figure 2B. Motor learning by observing scores reflect the PD of the first 3 reaches in the right FF relative to the subject’s baseline PD in the null field. In Figure 2B, it can be seen that those subjects who observed the tutor undergoing left FF learning exhibited significantly higher motor learning by observing scores compared to control subjects who observed the tutor performing reaches in an unlearnable FF (t(28)=2.58, p < 0.01).
Functional Connectivity Analysis
We performed functional connectivity analyses on the neuroimaging data acquired from the learning group on day 1 to test whether individual differences in pre-observation FC could predict motor learning by observing scores on the following day. Of the 10 ROIs used, only the analysis using the left S1 ROI revealed a network in which pre-observation FC was reliably correlated with day 2 motor learning by observing scores. As shown in Figure 3, day 1 FC between left S1 ROI and clusters in bilateral PMd, bilateral M1, bilateral S1 and left SPL was positively correlated with day 2 motor learning by observing scores (r=0.76, p=0.001). Subjects with greater pre-observation FC among these areas on day 1 went on to achieve higher motor learning by observing scores on the following day. Table 2 shows cluster activation peaks and statistics.
Our computed motor learning by observing score took into account the average PD of a subject’s first 3 reaches in the right FF relative to his or her baseline PD in the null field. To assess the sensitivity of the correlation between FC and motor learning by observing scores, we computed additional motor learning by observing scores to use in our analysis. Additional motor learning scores reflected the average PD of the first 4, 5, 6, 7, 8, 9 or 10 reaches in the right FF minus the average PD of the last 50 reaches in the null field. The correlation between day 1 FC and motor learning by observing scores remained significant for all of the additional measures.
The GLM used for the group-level functional connectivity analyses included a nuisance regressor modeling each subject’s PD in the null field during the last 50 trials. This was done to account for potential effects of subjects having had performed null field reaches before the resting-state scans on day 1. The clusters and correlation with behavioral scores remained significant whether the null field nuisance regressor reflected the average PD of the last 3, 5, 10 or 50 null field reaches or the average PD of the first 3, 5,10 or 50 null field reaches.
It is possible that the correlation between pre-observation FC and the day 2 motor learning scores is due to random chance (e.g. spurious correlations in the BOLD time series) and not due to stable individual differences in functional connectivity. To assess this we repeated the functional connectivity analysis on each the two resting-state runs separately. The resting-state runs were independent, separated in time by a 5-minute anatomical scan. Again using the ROI in left S1, we found consistent spatial patterns of pre-observation (day 1) FC between left S1, bilateral PMd, M1, S1 and left SPL for both individual runs (see Figure 4). Moreover, the correlation between pre-observation (day 1) FC and day 2 motor learning by observing scores was statistically significant for both resting-state run 1 (r=0.75, p=0.001) and run 2 (r=0.63, p=0.01). Therefore, when performed on the each of the two independent resting-state runs, our analysis yielded similar results both in terms of the spatial extent of the clusters and the correlations with day 2 motor learning scores. It is therefore unlikely that our main result arises from a spurious correlation.
Voxel-Based Morphometry Analysis
We carried out a whole-brain VBM analysis on the anatomical images from the learning group. This was done to test if individual differences in grey matter volume could predict subsequent motor learning by observing scores. This analysis yielded no significant results. We tested the sensitivity of this null result to the chosen statistical threshold. No significant clusters survived statistical thresholding at the group level until the p-value threshold was raised to 0.27, at which level clusters survived in left frontal lobe (−32, 54,12) and Broca’s area (−50, 20, 12). When the p-value threshold was raised further to 0.37, a cluster survived which spanned right PMC (54, −8, 52), M1 (54, −10,46), S1 (56, −14,44) and IPL (64, −20,40). However, since none of these clusters survived an appropriopriate statistical threshold, these results are not interpretable. In the context of the dataset here, individual differences in grey matter volume could not account for variability in the extent to which observation promotes motor learning.
Discussion
Here we examined if pre-observation measures of brain function or structure could account for individual differences in the extent to which observation facilitates motor learning. We acquired measures of resting-state FC and grey matter volume using MRI prior to an observational learning task on the following day. We found that pre-observation (day 1) resting-state FC between left S1 and bilateral PMd, bilateral M1, bilateral S1 and left SPL was reliably correlated with behavioral scores of motor learning by observing acquired on day 2. Those subjects who exhibited greater resting-state FC on day 1 achieved greater motor learning by observing scores on day 2. Individual differences in grey matter volume could not predict subsequent motor learning by observing behavioral scores. This finding provides further insight into the neural basis of motor learning by observing.
The finding that pre-observation resting-state FC between S1 and PMd, M1, and SPL predicts subsequent motor learning is consistent with previous work demonstrating that M1 and the somatosensory system play necessary roles in motor learning by observing. Brown et al. (2009) used repetitive transcranial magnetic stimulation (rTMS) to reduce cortical excitability in M1 immediately after subjects observed a FF learning video. A subsequent behavioral assessment showed that reducing M1 excitability following observation disrupted motor learning by observing. rTMS applied to M1 after observation of FF learning reduced the beneficial effect of observing congruent forces, and eliminated the detrimental effect of observing incongruent forces. These results suggest that M1 plays a key role in motor learning by observing.
We have also recently demonstrated that the somatosensory system plays a necessary role in motor learning by observing (McGregor et al., 2016). We used median nerve stimulation to occupy the somatosensory system with unrelated afferent inputs while subjects observed a video of a tutor undergoing FF learning. During observation, subjects received median nerve stimulation to the right arm (the same arm used by the tutor in the video), to the left arm (opposite the arm used by the tutor) or no stimulation. Stimulation disrupted motor learning by observing in a limb-specific manner such that stimulation of the right arm (observed effector) interfered with learning, whereas stimulation applied to the opposite arm did not. This result demonstrated that the somatosensory representation of the observed effector is necessary and therefore must be unoccupied during observation for motor learning by observing to occur. In a follow-up EEG experiment, we showed that S1 cortical activity, as assessed using somatosensory evoked potentials, increased for subjects who observed learning by an amount that positively correlated with subsequent behavioral motor learning scores. These results suggest that observation-induced functional changes in S1 support motor learning by observing (McGregor et al., 2016).
The network identified in the current study overlaps with those identified in neuroimaging studies showing that sensory-motor networks support observational learning. We have previously shown that observing motor learning results in changes in resting-state FC between M1, S1, visual area V5/MT and the cerebellum. Functional connectivity changes within this network were correlated with behavioral measures of motor learning, assessed following the fMRI sessions (McGregor and Gribble, 2015). Cross et al. (2009) showed that observation of dance movement sequences recruits brain areas including premotor and parietal cortices. The authors reported greater activation in premotor and parietal regions when subjects observed movement sequences on which they had been trained (by observation) over the previous 5 days, compared to untrained movement sequences. These studies suggest that the neural substrates of motor learning by observing include premotor cortex, M1, S1 and parietal cortex. This is consistent with the results of the current study in which subjects who exhibited greater pre-observation resting-state FC between S1 and PMd, M1, and SPL later showed the greatest observation-related facilitation of motor learning.
There are commonalities between the functional network identified in the current study and those functional networks that have been previously reported to predict aspects of motor learning through active movement training. Tomassini et al. (2011) showed that the task-based activation of premotor and parietal cortices (along with prefrontal cortex, basal ganglia and the cerebellum) is associated with higher behavioral measures of motor learning. Wu et al. (2014) have similarly shown that resting-state FC (as measured by high-density EEG) between M1, premotor cortex and parietal cortex can predict skill acquisition. The consistency between predictive functional networks for learning through active movement training and observational motor learning provides evidence in favor of similar neural substrates for these two forms of motor learning.
There is evidence from the motor learning literature that individual differences in brain structure can predict learning through active practice. Tomassini et al. (2011) demonstrated that individual differences in grey matter volume within the cerebellum and higher order visual areas (V2, V3, V5/MT) can also predict behavioral measures of motor learning during a visuo-motor tracking task. While there is evidence for structure-based predictability of motor learning, in the current study we found that this was not the case for motor learning by observing; individual differences in grey matter volume could not account for variability in behavioral scores of motor learning by observing. Future studies investigating grey matter volume correlates of motor learning by observing should have a larger sample size to increase statistical power.
Here we tested if pre-observation measures of brain function or structure could predict subsequent motor learning by observing. We found that pre-observation resting-state FC between S1 and bilateral PMd, M1, and left SPL predicted the extent to which observation would promote motor learning on the following day. Individual differences in grey matter volume could not predict behavioral scores of learning following observation. These results demonstrate that individual differences in resting-state FC among sensory-motor cortical brain areas can explain part of the individual variability in the extent to which observation facilitates motor learning. This finding is consistent with the idea that those individuals who have more ‘primed’ sensory-motor circuits are more predisposed to motor learning through observation. Pre-observation FC within the identified sensory-motor network may be used as a biomarker of the extent to which observation will promote motor learning. Predicting an individual’s predisposition for motor learning by observing could be valuable in a clinical context for planning individualized rehabilitation strategies and improving prognostic accuracy (Stinear, 2010).
The origin of individual variability in pre-observation sensory-motor FC is still unclear. In one scenario, it is possible that the observed individual differences in FC are a reflection of functional variability and not anatomical variability within this network. However, given the close correspondence between anatomical and functional connectivity (e.g.,Fox et al., 2005), another scenario is that the observed differences in FC arise from individual differences in anatomical connectivity. For example, it could be the case that greater structural connectivity between these sensory-motor brain areas results in higher pre-observation sensory-motor FC which, in turn, promotes greater motor learning by observing. Since here we did not acquire images for performing structural connectivity-based analyses (such as diffusion tensor images), we cannot rule out the possibility that individual differences in structural connectivity among sensory-motor brain areas underlies the effect seen here, whereby pre-observation FC predicts motor learning by observing.
However, resting-state FC does not only reflect anatomical connectivity. Indeed, much work has shown that resting-state FC can be shaped by recent experiences. Such “stimulus-rest interactions” have been demonstrated across several domains. For example, exposure to visual stimuli (Lewis et al., 2009) or undergoing active motor learning (Albert et al., 2009) can change resting-state FC. Since resting-state FC is affected by both structure and function, it is likely the case that both of these factors contribute to individual differences in pre-observation sensory-motor FC. While we cannot further pursue this question using the current dataset, this would be an interesting avenue for future research. Another outstanding issue is the stability of these individual differences in pre-observation FC over time. Future research should examine the test-retest reliability of pre-observation FC over longer time periods (e.g., several days or weeks apart) to establish the long-term stability of the FC patterns within the network identified here. This would allow one to better distinguish between within-session patterns from those more permanent structural or functional patterns.
Acknowledgements
This work was supported by the Natural Sciences and Engineering Research Council of Canada, and by the National Institute of Child Health and Human Development R01 HD075740
Footnotes
The authors report no financial interests or conflicts of interests.