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. Author manuscript; available in PMC: 2020 Jun 1.
Published in final edited form as: J Physiol. 2019 May 15;597(12):3203–3216. doi: 10.1113/JP277326

Practice modulates motor-related beta oscillations differently in adolescents and adults

James E Gehringer 1,2, David J Arpin 1,2, Elizabeth Heinrichs-Graham 1,3, Tony W Wilson 1,3, Max J Kurz 1,2
PMCID: PMC7105901  NIHMSID: NIHMS1577395  PMID: 31045245

Abstract

There is considerable evidence that there are motor performance and practice differences between adolescents and adults. Behavioural studies have suggested that these motor performance differences are simply due to experience. However, the neurophysiological nexus for these motor performance differences remains unknown. The present study investigates the short-term changes (e.g. fast motor learning) in the alpha and beta event-related desynchronizations (ERDs) associated with practising an ankle plantarflexion motor action. To this end, we utilized magnetoencephalography to identify changes in the alpha and beta ERDs in healthy adolescents (n = 21; age = 14 ± 2.1 years) and middle-aged adults (n = 22; age = 36.6 ± 5 years) after practising an isometric ankle plantarflexion target-matching task. After practice, all of the participants matched more targets and matched the targets faster, and had improved accuracy, faster reaction times and faster force production. However, the motor performance of the adults exceeded what was seen in the adolescents regardless of practice. In conjunction with the behavioural results, the strength of the beta ERDs across the motor planning and execution stages was reduced after practice in the sensorimotor cortices of the adolescents, but was stronger in the adults. No pre-/post-practice changes were found in the alpha ERDs. These outcomes suggest that there are age-dependent changes in the sensorimotor cortical oscillations after practising a motor task. We suspect that these noted differences might be related to familiarity with the motor task, GABA levels and/or maturational differences in the integrity of the white matter fibre tracts that comprise the respective cortical areas.

Introduction

It is well recognized that the brain maintains and updates a real-time internal representation of how the musculoskeletal system performs under various task constraints (Thoroughman & Shadmehr, 1999; Hwang & Shadmehr, 2005; Milner & Franklin, 2005; Smith & Shadmehr, 2005; Kluzik et al. 2008; Huang et al. 2011). This internal model is used to make feedforward predictions about the ideal muscle synergies that are necessary to accurately perform a motor task, but these models are rarely perfect (Shadmehr, 2004; Wolpert, 2007). Improving the internal model through practice is based on the sensory feedback, and knowledge about the success of the final motor performance (Willingham, 1998; Doyon & Benali, 2005). While it is accepted that the internal model is updated in both adults and adolescents, there are differences in the effect of practice between adolescents and adults (Contreras-Vidal et al. 2005; Goble et al. 2005; Hay et al. 2005; Bo et al. 2006; Contreras-Vidal, 2006; King et al. 2009, 2012; Pangelinan et al. 2011, 2013). For adolescents to improve at a level comparable to adults, adolescents require more practice and feedback (Sullivan et al. 2008; Goh et al. 2012). While these practice effect differences are well appreciated, we do not fully understand the neurophysiological nexus for why these differences exist.

A large body of literature has established that there are cognitive processing differences in adolescents (Chuah & Maybery, 1999; Haselen et al. 2000; Yuzawa, 2001; Czernochowski et al. 2005; Ferguson & Bowey, 2005; Mäntylä et al. 2007), and potentially immature cognitive processing contributes to the motor performance differences. It has been hypothesized that these motor performance differences may simply arise from inexperience with the task and may improve with practice (Goble et al. 2005; Contreras-Vidal et al. 2005; Contreras-Vidal, 2006; King et al. 2009, 2012; Pangelinan et al. 2011, 2013). It has also been hypothesized that inexperience may lead to difficulty switching between relevant alternative motor plans and/or greater reliance on online error corrections, as opposed to selecting an appropriate motor plan initially (Hay et al. 2005; Bo et al. 2006). Furthermore, this inexperience may lead to suboptimal updates to the internal model (Goble et al. 2005). Collectively, these hypotheses suggest that the adolescent brain is less efficient at executing motor plans and interpreting the feedback sensory information that returns during and upon the completion of the motor task. Unfortunately, these hypotheses are primarily driven by behavioural data, which cannot be used to fully identify the underlying neurophysiological differences that are responsible for differential motor performance between adolescents and adults.

In adults, functional magnetic resonance imaging (fMRI), positron emission tomography (PET) and magnetoencephalography (MEG) investigations have examined the neural changes that occur after practising a novel motor task (Shadmehr & Holcomb, 1997; Grafton et al. 2002; Floyer-Lea & Matthews, 2005; Sacco et al. 2006, 2009; Arima et al. 2011; Zhang et al. 2011; van Wijk et al. 2012; Rueda-Delgado et al. 2014; Gehringer et al. 2018). These studies, focusing mainly on upper extremity motor tasks, have shown that the primary motor area, supplementary motor area, prefrontal cortex and parietal cortex exhibit changes in the strength of activation after participants’ practice. The short-term changes seen in these cortical areas have been associated with improved spatial processing, sensorimotor transformations, online error corrections and improved resource allocation (Petersen et al. 1998; Doyon &Ungerleider, 2002; Hikosaka et al. 2002; Kelly & Garavan, 2005; Boonstra et al. 2007; Houweling et al. 2008; Tamas Kincses et al. 2008; Gehringer et al. 2018). Although this work has provided pertinent results demonstrating differences in how cortical activity changes after practice, the roles that these cortical areas play in the planning and execution of a lower extremity motor action after practice have not been identified, especially in adolescents.

Outcomes from electroencephalography (EEG), MEG and invasive electrocorticography (ECoG) experiments have shown that prior to the onset of movement the cortical oscillatory activity in the beta frequency range (15–30 Hz) decreases, and this change is sustained throughout the majority of the movement (Deecke et al. 1983; Pfurtscheller et al. 2003; Kilner et al. 2004; Jurkiewicz et al. 2006; Miller et al. 2010; Tzagarakis et al. 2010; Wilson et al. 2010, 2011, 2014; Heinrichs-Graham & Wilson, 2015; Kurz et al. 2017; Gehringer et al. 2018). This decreased power within the beta frequency band, commonly termed beta desynchronization, is thought to reflect task-related changes in oscillatory activity within local populations of neurons, as they begin to prepare for the specific demands of the pending motor action. The consensus is that this beta event-related desynchronization (ERD) is related to the formulation of a motor plan, because it occurs well before the onset of movement and is influenced by the certainty of the movement pattern to be performed (Tzagarakis et al. 2010, 2015; Grent-’t-Jong et al. 2014; Pollok et al. 2014; Heinrichs-Graham & Wilson, 2015). Typical beta ERD responses involve widespread bilateral activity across the fronto-parietal cortical areas, with the strongest maxima contralateral to the effector producing the motor action and following the basic homuncular topology seen in the pre-/postcentral gyrus. Additional areas of concurrent beta ERD activity often include the premotor area, supplementary motor area, parietal cortices and mid-cingulate (Jurkiewicz et al. 2006; Tzagarakis et al. 2010, 2015; Wilson et al. 2014; Kurz et al. 2016). This pattern of activity has also been observed in adolescents but with distinct differences (Gaetz et al. 2010; Wilson et al. 2010; Cheyne et al. 2014; Kurz et al. 2016). The strength of the beta ERD in adolescents is weaker compared to adults (Gaetz et al. 2010). In addition, adolescents also demonstrate activations in additional areas of the brain, suggesting maturation has an effect on the recruited areas of the sensorimotor network (Wilson et al. 2010; Kurz et al. 2016). Despite the recognition that there are developmental differences in this cortical activity, we still have an incomplete understanding of how practising a motor action relates to these maturational differences in the oscillatory activity.

Overall, there are clear gaps in the scientific literature regarding the impact of practising a motor task on cortical beta oscillations in adolescents. Moreover, it is unclear how these cortical oscillations may change after practice. The objective of the current investigation was to use high-density MEG to identify how practising an ankle plantarflexion target-matching task differentially affects motor-related beta oscillations in adults and adolescents.

Methods

Ethical approval

This experimental work conformed to the standards set by the Declaration of Helsinki, except for registration in a database. The Institutional Review Board at the University of Nebraska Medical Centre reviewed and approved the protocol for this investigation. All of the participants or guardians provided written informed consent and the adolescents provided assent to participate in the investigation.

Subjects

Forty-three subjects with no neurological or musculoskeletal impairments participated in this investigation, with 22 being healthy right-hand dominant adults (mean age = 36.6 years; SD: ±5.0 years, 12 female) and 21 being healthy right-hand dominant adolescents (mean age = 14.0 years; SD: ±2.1 years, 9 female).

MEG data acquisition and experimental protocol

Neuromagnetic responses were sampled continuously at 1 kHz with an acquisition bandwidth of 0.1–330 Hz using an Elekta MEG system (Elekta Oy, Helsinki, Finland) with 306 magnetic sensors, including 204 planar gradiometers and 102 magnetometers. All recordings were conducted in a one-layer magnetically shielded room with active shielding engaged for advanced environmental noise compensation. During data acquisition, the participants were seated upright in a magnetically silent chair and monitored via real-time audio–video feeds from inside the shielded room during the experiment.

A custom-built, magnetically silent force transducer was developed for this investigation to measure isometric ankle plantarflexion forces (Fig. 1A). This device consisted of a 20 cm × 10 cm air bladder that was inflated to 317 kPa and was integrated within an ankle–foot orthosis. Changes in the pressure of the airbag, due to participants generating an isometric ankle plantarflexion force, were quantified by an air pressure sensor (Phidgets Inc., Calgary, Alberta, Canada) and were converted into units of force offline.

Figure 1. Experimental setup.

Figure 1.

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A, participant seated in the MEG chair with the custom pneumatic ankle force system on their right leg. The device consists of an airbag that is encased in a ridged ankle–foot orthotic. B, visual feedback displayed to the participant. Ankle plantarflexion forces generated by the participant animated the vertical position of a frog on the screen. A successful trial occurred when the participant generated a plantarflexion force that positioned the frog’s mouth at the bug’s position and held it there for 0.3 s.

The experimental protocol involved the participant generating an isometric ankle plantarflexion force with their right leg that matched target forces that varied between 15 and 30% of the participant’s maximum isometric ankle plantarflexion force. The step size between the respective targets was one unit of force. The target force was visually displayed as a moth, and the force generated by the participant was shown as a frog that was animated vertically, based on the isometric force generated (Fig. 1B). The participants were instructed to match the presented targets as fast and as accurately as possible. The distinct target forces were presented in a random order, and a successful match occurred when the bug that represented the target force was inside the frog’s mouth for 0.3 s. The stimuli were shown on a back-projection screen that was ~1 m in front of the participant and at eye-level. Each trial was 10 s in length. The participants started each trial at rest while fixating the centre of the screen for 5 s. After this rest period, the target would appear, prompting the participant to try and produce the matching force value. The target was available to be matched for up to 5 s. Once the target was matched or 5 s elapsed, feedback was given to indicate the end of the trial, and the participant returned to rest and fixated on the centre of the screen while waiting for the next target to appear. Participants performed three blocks of the ankle plantarflexion target-matching task, with each block containing 100 trials. The first and third blocks were performed while recording MEG data, while the second block acted as an extended practice block, where the participant was provided additional information about the accuracy of their target-matching performance via an interactive biofeedback programme. This programme showed the participant the amount of error in their motor action by displaying the distance between the bug and the frog and provided auditory and visual rewards when the participant matched the target faster and had improved accuracy.

MEG coregistration

Four coils were affixed to the head of the participant and were used for continuous head localization during the MEG experiment. Prior to the experiment, the location of these coils, three fiducial points, and the scalp surface were digitized to determine their three-dimensional position (Fastrak 3SF0002, Polhemus Navigator Sciences, Colchester, VT, USA). Once the participant was positioned for the MEG recording, an electric current with a unique frequency label (e.g. 322 Hz) was fed to each of the four coils. This induced a measurable magnetic field and allowed for each coil to be localized in reference to the sensors throughout the recording session. Since the coil locations were also known in head co-ordinates, all MEG measurements could be transformed into a common co-ordinate system. With this co-ordinate system (including the scalp surface points), each participant’s MEG data were coregistered to a structural MRI (MPRAGE) using three external landmarks (i.e. fiducials), and the digitized scalp surface points prior to source space analyses. The neuroanatomical MRI data were aligned parallel to the anterior and posterior commissures, and all data were transformed into standardized space using BESA MRI (Version 2.0; BESA GmbH, Gräfelfing, Germany).

MEG pre-processing, time–frequency transformation and statistics

Using the MaxFilter software (Elekta), each MEG dataset was individually corrected for head motion that may have occurred during the task performance, and subjected to noise reduction using the signal space separation method with a temporal extension (Taulu & Simola, 2006). Artifact rejection was based on a fixed threshold method, supplemented with visual inspection. Essentially, trials that had large gradient or amplitude values (such as those arising from muscular activity in the neck or shoulders) of the magnetic time series were removed prior to time–frequency decomposition. The continuous magnetic time series was divided into epochs of 10.0 s in duration (−5.0 to +5.0 s), with the onset of the isometric force defined as 0.0 s and the baseline defined as −2.0 to −1.4 s. Artifact-free epochs for each sensor were transformed into the time–frequency domain using complex demodulation (resolution: 2.0 Hz, 0.025 s) and averaged over the respective trials. These sensor-level data were normalized using the respective bin’s baseline power, which was calculated as the mean power during the baseline (−2.0 to −1.4 s). This time window was selected for the baseline based on our inspection of the sensor-level absolute power data, which showed that this time window was quiet and temporally distant from the peri-movement oscillatory activity. The specific time–frequency windows used for imaging were determined by statistical analysis of the sensor-level spectrograms across the entire array of gradiometers. Briefly, each data point in the spectrogram was initially evaluated using a mass univariate approach based on the general linear model. To reduce the risk of false positive results while maintaining reasonable sensitivity, a two-stage procedure was followed to control for Type 1 error. In the first stage, one-sample t tests were conducted on each data point, and the output spectrogram of t values was thresholded at P < 0.05 to define time–frequency bins containing potentially significant oscillatory deviations across all participants and conditions. In stage two, time–frequency bins that survived the threshold were clustered with temporally and/or spectrally neighbouring bins that were also below the (P < 0.05) threshold and a cluster value was derived by summing all of the t values of all data points in the cluster. Non-parametric permutation testing was then used to derive a distribution of cluster values, and the significance level of the observed clusters (from stage one) were tested directly using this distribution (Ernst, 2004; Maris & Oostenveld, 2007). For each comparison, at least 10,000 permutations were computed to build a distribution of cluster values.

MEG source imaging

A minimum variance vector beamforming algorithm was employed to calculate the source power across the entire brain volume (Gross et al. 2001). The single images were derived from the cross-spectral densities of all combinations of MEG sensors and the solution of the forward problem for each location on a grid specified by input voxel space. Following convention, the source power in these images was normalized per subject using a separately averaged pre-stimulus noise period of equal duration and bandwidth to the target periods that were identified through the sensor-level statistical analyses (see above; Van Veen et al. 1997; Hillebrand & Barnes, 2005; Hillebrand et al. 2005). Thus, the normalized power per voxel was computed over the entire brain volume per participant at 4.0 mm × 4.0 mm × 4.0 mm resolution. MEG pre-processing and imaging used the Brain Electrical Source Analysis (BESA) software (BESA v6.0; Gräfelfing, Germany).

Time series analysis was subsequently performed on the peak voxels extracted from the grand-averaged beamformer images (see Results). The virtual sensors were created by applying the sensor weighting matrix derived through the forward computation to the pre-processed signal vector, which resulted in a time series with the same temporal resolution as the original MEG recording (Cheyne et al. 2006; Heinrichs-Graham & Wilson, 2016; Heinrichs-Graham et al. 2016). Once the virtual sensors were extracted, they were transformed into the time–frequency domain, and the two orientations for each peak voxel per individual were combined using a vector-summing algorithm. The power of these time courses, relative to baseline, was averaged across the window of interest for each individual to assess group and practice differences in the key oscillatory responses. A 2 × 2 × 2 repeated measures ANOVA (pre-/post-practice × adolescent/adult × planning/execution) at a 0.05 α level was used to determine if there were differences in average beta power, and a repeated measures 2 × 2 ANOVA (pre-/post-practice × adolescent/adult) at a 0.05 α level was used to determine if there were differences in the average alpha power. The post-movement beta rebound responses were not examined because there were significant pre-/post-practice differences in the time that the participants took to match the target, which would have confounded the analyses.

Motor behavioural data

The output of the force transducer was simultaneously collected at 1 kHz along with the MEG data and was used to quantify the participant’s motor performance. The formulation of the motor plan was assumed to be represented by the participant’s reaction time, which was calculated based on the time from when the target was presented to when force production was initiated. The amount of error in the feedforward execution of the motor plan was behaviourally quantified based on the percentage overshoot of the target. The time to match the target was used to quantify the online corrections that were made after the initial motor plan was executed. The online corrections were calculated based on the time difference between the reaction time and the time to reach the target. The coefficient of variation in the force produced while attempting to match the target was also used to evaluate the online corrections that the participants made while trying to match the target. A lower coefficient of variation signified fewer corrections in the force production when attempting to match the target.

Separate repeated measures ANOVAs (age group × pre-/post-practice block) at a 0.05 α level were used to determine if there were differences in the behavioural performance of the participants between the pre- and post-practice blocks and by age group.

Results

Motor behavioural results

Overall, our results showed that participants improved their ability to match the ankle forces that would accurately match the prescribed targets. For the number of targets matched, there was a significant main effect of pre-/post-practice block (Pre: 79 ± 3, Post: 87 ± 2; P < 0.001), which indicated that the participants improved the number of trials that they performed correctly. There was also a main effect of age group (Adolescent: 79 ± 3, Adults: 89 ± 2; P = 0.02), showing that the adults matched more targets. The interaction term was not significant (P > 0.05).

For the time to match the targets, there was a main effect of block (Pre: 2.33 ± 0.10 s, Post: 2.03 ± 0.09 s; P < 0.001), showing that the participants matched the targets faster after practice. There was also a main effect of age group (Adolescent: 2.40 ± 0.10 s, Adults: 1.97 ± 0.09 s; P = 0.017), showing that the adults matched the targets faster. The interaction term was not significant (P > 0.05).

For the target error, there was a main effect of pre-/post-practice block (Pre: 6.02 ± 0.73%, Post: 5.10 ± 0.68%; P = 0.036), showing that the participants had less errors in their force production after practice. There was no main effect of age group (Adolescent: 6.50 ± 0.62%, Adults: 4.66 ± 0.76%; P = 0.18). The interaction term was also not significant (P > 0.05).

For velocity, there was a main effect of pre-/post-practice block (Pre: 48.29 ± 3.54 N s−1, Post: 61.46 ± 5.89 N s−1; P = 0.002) as the participants had a faster velocity of the force production towards the target after practice. There was also a main effect of age group (Adolescent: 45.38 ± 3.64 N s−1, Adults: 63.94 ± 5.57 N s−1; P = 0.038), as the adults had a faster velocity of the force production towards the targets. The interaction term was not significant (P > 0.05).

For the reaction time, there was a main effect of pre-/post-practice (Pre: 0.448 ± 0.020 s, Post: 0.410 ± 0.014 s; P < 0.001), as the participants had faster reaction times after practice. There was also a significant main effect of age group (Adolescent: 0.487 ± 0.021 s, Adults: 0.375 ± 0.008 s; P < 0.001), as the adults responded faster than the adolescents. The interaction term was not significant (P values >0.05).

Sensor-level results

When collapsing the data across the respective age groups and blocks (pre- and post-practice), there were significant alpha (8–14 Hz) and beta (18–32 Hz) ERDs that were present in a large number of sensors near the fronto-parietal region (P < 0.0001, corrected). These responses in the alpha band started near movement onset (0.0 s) and were sustained for approximately 0.6 s afterward (Fig. 2). The responses in the beta band started about 0.3 s before movement onset and were sustained for approximately 0.6 s afterward (Fig. 2). For illustrative purposes, we show the pre- and post-practice time–frequency plots for each age group in Fig. 2, but note that sensor-based statistics were computed by collapsing the data across the practice blocks and age groups. Qualitative inspection of these figures shows that the strength of the alpha and beta ERDs appeared to become weaker after practice in adolescents but strengthen in adults.

Figure 2. Group-averaged time–frequency spectrograms for pre- and post-practice blocks for the adolescent and adult groups.

Figure 2.

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Frequency (Hz) is shown on the y-axis and time (s) is denoted on the x-axis, with 0 s defined as movement onset. The event-related spectral changes during the ankle plantarflexion target-matching task are expressed as percentage difference from baseline (−2.0 to −1.4 s). The MEG gradiometer with the greatest response amplitude was located near the medial primary sensorimotor cortices, contralateral to the ankle used during the task. There was a strong desynchronization in the alpha (8–14 Hz) and beta (18–32 Hz) bands in both the pre- and post-practice blocks. As can be discerned, the strength of the alpha and beta desynchronization became notably weaker in the post-practice MEG session in the adolescents, but stronger in the adults. The colour scale bar for all plots is shown to the far right.

Alpha oscillations

The alpha (8–14 Hz) ERD identified in the sensor-level analysis within the 0.0–0.6 s time window was imaged using a beamformer. This analysis combined the MEG data acquired across the respective pre-/post-practice blocks and used a baseline period of −2.0 to −1.4 s. The resulting images were grand-averaged across pre-/post-practice blocks and age groups and revealed that the alpha ERD response was generated by parietal and occipital cortices (Fig. 3). The local maximums seen in these cortical areas were subsequently used as seeds for extracting virtual sensor time courses (i.e. voxel time courses) from the pre- and post-practice block images per participant. Peaks were found in the parietal and occipital cortices. Separate 2 × 2 mixed-model ANOVAs (pre-/post-practice × adolescent/adult) were conducted on each peak to determine if the average virtual sensor activity during the motor execution stage (0–0.6 s) differed after practice and/or group in the parietal and occipital cortices.

Figure 3. Grand averaged beamformer images of alpha activity (8–1 Hz) from 0.0 to 0.6 s revealed two main clusters in the parietal (A) and occipital cortices (B).

Figure 3.

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Time series data were extracted from the peak voxel in these clusters and are plotted with power changes relative to the baseline shown in a percentage scale on the y-axis and time on the x-axis in seconds. There were age-related differences in the alpha event-related desynchronization (ERD) during motor execution (0–0.6 s) in both the parietal and occipital cortices. The time window that was used in the beamformer analysis and subjected to statistical analyses is denoted by the grey shading. There were no pre-/post-practice differences in the alpha ERDs during motor execution (0–0.6 s) in the parietal and occipital cortices. The bar graphs represent the average relative power during motor execution separated by age group (0–0.6 s).

For the parietal cortex, there was no significant main effect of pre-/post-practice in the alpha ERD response indicating that it was not affected by practice (P = 0.43, Fig. 3A). There was a significant main effect of age group, suggesting the adults had stronger alpha ERDs in the parietal cortex (P = 0.001). The interaction term was not significant (P = 0.075).

The results for the occipital cortex were similar, as there were no differences in the alpha ERDs after practice (P = 0.082, Fig. 3B). There was a significant main effect of age group, suggesting the adults had stronger alpha ERDs in the occipital cortices (P = 0.003). The interaction term was not significant (P = 0.487). Hence, the alpha ERD in the parietal and occipital cortices was not affected by practice, but did differ with age.

Beta oscillations

The beta (18–32 Hz) ERD identified in the sensor-level analysis between −0.3 and 0.3 s was imaged using a beamformer. Once again, this analysis combined the data acquired across the respective pre-/post-practice blocks and used a baseline period of −2.0 to −1.4 s. The resulting images were grand-averaged across pre-/post-practice blocks and age groups and indicated that the beta ERD was more centred on the leg region of the sensorimotor cortices (Fig. 4), with additional bilateral clusters seen in the occipital cortices (Fig. 5). As with the alpha analysis, the local maximums of these responses were next used as seeds for extracting virtual sensors from the pre- and post-practice data blocks separately (per participant), and the virtual time courses from the two occipital peaks were averaged to create a single time series. Since the beta response extended across movement onset (i.e. 0.0 s), we conducted separate 2 × 2 × 2 mixed-model ANOVAs (pre-/post-practice block × age group × time window) to determine if the average neural activity during the motor planning (−0.3 to 0 s) and execution stages (0–0.3 s) changed after practice in the sensorimotor and occipital cortices.

Figure 4. Grand averaged beamformer images of beta activity (18–32 Hz) from −0.3 to 0.3 s revealed a main cluster in the sensorimotor cortices.

Figure 4.

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Time series data were extracted from the peak voxel in this cluster, and are plotted as in Fig. 3. The beta event-related desynchronization (ERD) response was stronger in adults relative to adolescents across the motor planning and execution (−0.3 to 0.3 s) stages. There was also a significant interaction of age group by pre-/post-practice block. Post hoc analysis showed that adolescents had a significantly weaker beta ERD and adults had significantly stronger ERD in the left sensorimotor cortices after practice. The bar graphs represent the average relative power across the motor planning and execution phases (grey areas) separated by age group and pre-/post-practice block (−0.3 to 0.3 s). Note that we did not examine the post-movement beta responses because we had no hypotheses about these, and because there were significant behavioural differences between pre- and post-practice, which would have biased any analyses.

Figure 5. Grand averaged beamformer images of beta activity (18–32 Hz) from −0.3 to 0.3 s revealed a main cluster in the occipital cortices.

Figure 5.

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Time series data were extracted from the peak voxel in these clusters, and are plotted as in Fig. 3. Bilateral peaks were found in the occipital cortex and were averaged to create a single time series. There were significant group effects whereby older adults exhibited a stronger beta event-related desynchronization (ERD) in the occipital cortex (grey shading). There were no pre-/post-practice differences. The bar graphs represent the average relative power across the motor planning and execution stages separated by age group (−0.3 to 0.3 s)

For the sensorimotor cortices, there was a main effect of time window (P = 0.001), which indicated that the strength of the beta ERD was stronger during the motor execution stage. There was also a main effect of age group (P < 0.001), which revealed that the adults had a stronger beta ERDs compared to the adolescents across the motor planning and execution stages. There was no main effect of pre-/post-practice main effect (P = 0.70). However, there was a significant interaction between age group and pre-/post-practice (P = 0.003). Follow-up post hoc analyses showed that independent of the planning/execution time windows, the beta ERD was significantly stronger in the sensorimotor cortices of the adults after practice (P = 0.004, Fig. 4), while the beta ERD was significantly weaker in the adolescents after practice (P = 0.01, Fig. 4).

For the occipital cortices, there was a significant main effect of time window (P < 0.001), indicating the power of the beta ERD in the occipital cortices was weaker during motor planning compared to the motor execution stage. There was no pre-/post-practice (P = 0.30) or age (P = 0.14) main effect. However, the time window by age interaction term was significant (P = 0.01), and follow-up post hoc analyses showed that the beta ERD was significantly stronger in the occipital cortices during the motor execution stage for the adults (P = 0.006, Fig. 5).

Discussion

There currently is a substantial knowledge gap in our understanding of how motor-related cortical oscillations are altered by practising a novel motor task. Furthermore, we have limited insight on whether such practice effects are age dependent. We used high-density MEG and advanced beamforming methods to begin to fill this knowledge gap by quantifying changes in the cortical oscillations of adults and adolescents after a short-term practice (e.g. fast-motor learning) session of a goal-directed, isometric, target-matching ankle plantarflexion task. The data-driven approach employed in this investigation revealed that there were notable differences between the adults and adolescents in the strength of the alpha and beta oscillatory activity in the sensorimotor, parietal and occipital cortical areas while generating the ankle plantarflexion force. However, only the beta cortical oscillations in the sensorimotor cortices changed after practice and were different between the two groups. These results imply that such beta oscillatory changes are likely central to the noted differences in the behavioural performance of the adults and adolescents after practice. Further discussion of the implications of our experimental results are discussed in the following sections.

One of our key findings was that the strength of the beta ERD in the leg region of the sensorimotor cortices changed differently in adolescents and adults after practising the motor task. Specifically, the strength of the beta ERDs in the adolescents became weaker after practice, while the strength of the beta ERDs became stronger in adults. The reduced strength of the beta oscillations seen in the adolescents concurs with the numerous neuroimaging studies (e.g. fMRI, EEG and PET) that have shown that the sensorimotor cortical activity is reduced after practising a novel motor task (Haufler et al. 2000; Hillman et al. 2000; Hatfield et al. 2004; Hadipour-Niktarash et al. 2007; Kranczioch et al. 2008; Galea & Celnik, 2009; Reis et al. 2009; Galea et al. 2011; Orban de Xivry et al. 2011; Zhang et al. 2011). This reduced cortical activity after practice may indicate that less cognitive and/or neural resources are required to successfully perform the motor task (Petersen et al. 1998; Poldrack, 2000; Kelly & Garavan, 2005; Gehringer et al. 2018). However, it has also been shown that task familiarity can differentially modulate the magnitude of sensorimotor cortical activity after practice (Hund-Georgiadis & von Cramon, 1999; Perez et al. 2004). Several investigations have shown that participants with minimal familiarity with the task exhibit a reduction in sensorimotor cortical activity after practice, while participants with prior experience with the motor task have an increase in their cortical activity (Hund-Georgiadis & von Cramon, 1999; Perez et al. 2004). These differential responses have been suggested to be related to the various stages of learning. Motor learning occurs in three distinct stages: (1) fast motor learning stage where there are rapid improvements in the task performance after a single practice session, (2) slow motor learning where there are incremental improvements across multiple practice sessions, and (3) offline learning where the motor memories are consolidated and skill stabilization occurs (Robertson et al. 2004; Doyon & Benali, 2005; Fischer et al. 2005; Dayan & Cohen, 2011). The decreased activity is presumed to represent the cortical changes that are associated with the fast motor learning stage where there are rapid improvements in the task performance after a single practice session, while the stronger activity is presumed to represent the cortical changes associated with the slow motor learning stage where there are incremental improvements across multiple practice sessions. We suggest that the different changes seen in the cortical activity between our groups after practice was related to prior familiarity with the motor task. For example, the adults were likely more skilled at performing the fine-motor ankle plantarflexions with their dominant right ankle due to their experience driving automobiles (i.e. pressing the gas pedal).

Our analysis also indicated that the strength of the beta ERD was stronger when adults both planned and executed a motor action relative to the adolescents. These results are well aligned with the previous literature (Gaetz et al. 2010; Heinrichs-Graham et al. 2018). We suspect that the differences in the strength of the cortical oscillations maybe partly related to maturational changes in brain structure, as it has been noted that the thickness of the sensorimotor cortices continues to thin and become refined well into late adolescence (Vandekar et al. 2015). Alternatively, the increased strength of the beta ERD with age could also be attributed to increased γ-aminobutyric acid (GABA) transmission (Gaetz et al. 2011; Rossiter et al. 2014; Heinrichs-Graham & Wilson, 2016; Heinrichs-Graham et al. 2018). The GABA system is still developing through adolescence (Kilb, 2012), and higher GABA levels have been linked to elevated motor-related oscillatory activity (Gaetz et al. 2011; Hall et al. 2011; Muthukumaraswamy et al. 2013), suggesting that adolescents should have lower levels ofmotor-related oscillatory activity.

Compared with the adults, our results also showed that the adolescents had weaker beta oscillations in the occipital cortices during the execution stage of the motor task. Previous experimental work has suggested that visual processing within occipital cortices can modulate the strength of activity within the cortical motor network during performance of a visuomotor task (Ledberg et al. 2007; Strigaro et al. 2015). Therefore, it is possible that the reduced beta oscillations seen in occipital cortices might indicate that the neural computations underlying visuomotor transformations during performance were suboptimal for the adolescents. Previous structural imaging has also shown that the behavioural performance of adolescents during a visuomotor task is influenced by the maturation of the optic radiations and the fronto-occipital fasciculus white matter tracts (Scantlebury et al. 2014). Hence, it is possible that the weaker beta oscillations seen in the occipital cortices maybe related to maturation of these white matter fibre tracts.

The alpha ERD in parietal and occipital cortices was also weaker in the adolescents. The location and timing of these neural oscillations were in agreement with the breadth of literature suggesting that activity within these cortical areas supports the visuomotor transformations that are necessary for producing and correcting a motor action (Beurze et al. 2007; Buneo & Andersen, 2006; Della-Maggiore et al. 2004; Gallivan et al. 2011, 2013; Kurz et al. 2016; Valyear & Frey, 2015). The weaker alpha oscillations seen in these cortical areas may imply that adolescents have more difficulty computing these transformations. Nevertheless, the results of the current experiment imply that motor-related alpha oscillations do not appreciably change after short-term practice in adolescents or adults. This is somewhat perplexing since prior MEG and EEG studies have noted that alpha oscillations in sensors/electrodes near the sensorimotor cortices become weaker after practising a motor sequence with the fingers (Leocani et al. 1997; Pollok et al. 2014). We speculate that these discrepancies might reside in the differences between implicit and explicit learning. The finger motor action sequence learned in these previous studies was acquired implicitly, while the ankle motor action learned in this investigation was acquired explicitly.

In conjunction with the noted changes in the sensorimotor beta oscillatory activity, both the adults and adolescents had significant improvements in their motor performance for all of the outcome measurements. However, the adults performed the motor task better as they matched more targets, and matched the targets faster after practice. Previous behavioural work has shown that adolescents require more practice in order to reach motor performance levels that are similar to adults (Sullivan et al. 2008; Goh et al. 2012). Our results appear to follow this notion since the adolescents did not achieve the same performance levels as the adults after practising the same number of trials. Potentially the differences in the extent of changes seen in the adolescents after practice may simply arise from inexperience and the need for more trials to achieve similar outcomes to the adults (Goble et al. 2005; Contreras-Vidal et al. 2005; Contreras-Vidal, 2006; King et al. 2009, 2012; Pangelinan et al. 2011, 2013).

Overall, the results from this investigation showed that the strength of the alpha and beta oscillations seen in the parietal, occipital and sensorimotor cortices during leg motor actions are different in adults and adolescents, and that the strength of beta sensorimotor cortical oscillations change differently after adolescents and adults practice a motor task. We suspect that these noted differences might be related to familiarity with the motor task, GABA levels and/or maturational differences in the integrity of the white matter fibre tracts that connect the involved cortical areas.

Key points.

  • Magnetoencephalography data were acquired during a leg force task in pre-/post-practice sessions in adolescents and adults.

  • Strong peri-movement alpha and beta oscillations were mapped to the cortex.

  • Following practice, performance improved and beta oscillations were altered.

  • Beta oscillations decreased in the sensorimotor cortex in adolescents after practice, but increased in adults.

  • No pre-/post-practice differences were detected for alpha oscillations.

Funding

This work was partially supported by grants from the National Institutes of Health (1R01-HD086245) and the National Science Foundation (NSF 1539067).

Biography

graphic file with name nihms-1577395-b0001.gif

James Gehringer is a PhD Candidate in the Sensorimotor Learning Laboratory at the University of Nebraska Medical Center. James is working on his PhD in the Medical Sciences Interdepartmental Area programme, with a focus on Rehabilitation Sciences. His research focuses on using neuroimaging techniques to quantify how cortical oscillations change after practising motor tasks. The long-term goal ofhis research is to understand what occurs in the brain as someone learns and develop assistive technology that aids in learning motor skills.

Footnotes

Competing interests

The authors declare that they have no competing interests.

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