Skip to main content
NCBI home page
Journal of Neurophysiology logoLink to Journal of Neurophysiology
. 2016 Aug 17;116(5):2210–2220. doi: 10.1152/jn.00337.2016

Interactive effect of acute pain and motor learning acquisition on sensorimotor integration and motor learning outcomes

Erin Dancey 1, Bernadette Murphy 1,✉, Danielle Andrew 1, Paul Yielder 1
PMCID: PMC5102313  PMID: 27535371

Enhanced learning was found when motor skill acquisition took place in the presence of acute capsaicin-induced experimental pain, indicating that pain does not always have negative effects on motor learning, a finding relevant for rehabilitation and skill training. Differential changes in somatosensory evoked potentials (SEPs) were seen between those whose performed the motor skill acquisition during pain vs. control, indicating that SEPs may serve as markers for the early neuroplastic changes accompanying motor learning.

Keywords: somatosensory evoked potentials, motor learning, acute pain, sensorimotor integration

Abstract

Previous work has demonstrated differential changes in early somatosensory evoked potentials (SEPs) when motor learning acquisition occurred in the presence of acute pain; however, the learning task was insufficiently complex to determine how these underlying neurophysiological differences impacted learning acquisition and retention. To address this limitation, we have utilized a complex motor task in conjunction with SEPs. Two groups of 12 participants (n = 24) were randomly assigned to either a capsaicin (capsaicin cream) or a control (inert lotion) group. SEP amplitudes were collected at baseline, after application, and after motor learning acquisition. Participants performed a motor acquisition task followed by a pain-free retention task within 24–48 h. After motor learning acquisition, the amplitude of the N20 SEP peak significantly increased (P < 0.05) and the N24 SEP peak significantly decreased (P < 0.001) for the control group while the N18 SEP peak significantly decreased (P < 0.01) for the capsaicin group. The N30 SEP peak was significantly increased (P < 0.001) after motor learning acquisition for both groups. The P25 SEP peak decreased significantly (P < 0.05) after the application of capsaicin cream. Both groups improved in accuracy after motor learning acquisition (P < 0.001). The capsaicin group outperformed the control group before motor learning acquisition (P < 0.05) and after motor learning acquisition (P < 0.05) and approached significance at retention (P = 0.06). Improved motor learning in the presence of capsaicin provides support for the enhancement of motor learning while in acute pain. In addition, the changes in SEP peak amplitudes suggest that early SEP changes reflect neurophysiological alterations accompanying both motor learning and mild acute pain.

NEW & NOTEWORTHY

Enhanced learning was found when motor skill acquisition took place in the presence of acute capsaicin-induced experimental pain, indicating that pain does not always have negative effects on motor learning, a finding relevant for rehabilitation and skill training. Differential changes in somatosensory evoked potentials (SEPs) were seen between those whose performed the motor skill acquisition during pain vs. control, indicating that SEPs may serve as markers for the early neuroplastic changes accompanying motor learning.

within rehabilitation programs, the concurrent presentation of pain and motor deficits is ubiquitous. Typically, motor deficits are regarded as a consequence of movement-related pain; however, there is evidence that pain affects motor control and has the ability to negatively influence the neuroplasticity associated with motor output (Bank et al. 2013; Hodges and Tucker 2011; Mercier and Leonard 2011). While the presence of acute pain during motor learning may interfere with skill acquisition (Boudreau et al. 2007; Flor 2003; Schweinhardt et al. 2006), our recent studies (Dancey et al. 2014, 2016) demonstrated that motor learning acquisition improved in the presence of acute pain. A limitation of previous work (Andrew et al. 2015; Dancey et al. 2014, 2016) is that learning saturation occurred with these typing tasks as baseline accuracy was high. If the learning task difficulty is not high enough, differences between groups may not be observed and a type II error may be likely (Dancey et al. 2014, 2016). To address this we developed and validated a more difficult motor tracing task. This tracing task has been used by Holland et al. (2015), who demonstrated continued motor learning acquisition throughout the training period with a significant consolidation of motor performance at retention, and by Andrew et al. (2015), who showed that the tracing task was a more effective learning tool than a typing task. The application of a more complex motor tracing paradigm combined with electrophysiological and behavioral measures will allow us to examine the impact of acute pain on motor learning as well as the cortical, subcortical, and cerebellar regions involved.

Motor learning acquisition requires sensorimotor integration (SMI), which is the processing of somatosensory information received from the motor task and integrating this information with the motor command in order to fine-tune and improve motor task performance. Early somatosensory evoked potentials (SEPs) are electrical field potentials generated by different neuronal substrates within the peripheral and central nervous systems induced by electrical stimulation of somatosensory receptors and their axons (Mauguiere 1999). SEPs represent precognitive sensory processing (Cruccu et al. 2008) and can be used to study the early neuroplastic consequences of the interactive effects of acute pain and motor learning acquisition. SEPs offer the highest temporal resolution available in noninvasive investigation (Walsh and Cowey 2000) and include peripheral (N9), spinal (N11, N13), subcortical (N18), and cortical (N20, P25, N24, N30) components for the upper limb. Recent work has found significant changes in spinal (N13) and cortical (N20, N24, P25, N30) SEP peaks after tracing (Andrew et al. 2015) and typing (Dancey et al. 2016) tasks. Studies using experimental muscle pain (Rossi et al. 2003; Schabrun et al. 2013) and acute cutaneous pain (Dancey et al. 2016) have found decreases in early SEP amplitudes in healthy individuals. Additionally, we recently determined that after a motor learning acquisition typing task there was a significant increase in a cortical (N20) SEP peak for a control group that was not observed for a capsaicin-induced pain group, and we hypothesized that acute pain may have negated a change that would otherwise have occurred (Dancey et al. 2016). It has been proposed that motor learning acquisition can reverse the effects of pain and, conversely, that acute pain undermines the capacity for learning (Ferguson et al. 2012). There remains a gap in our understanding of whether early SEP peaks change in the presence of acute cutaneous pain in healthy humans and whether acute pain impacts SEP changes observed in response to a complex motor learning acquisition task, which is addressed by the present study.

Another limitation of several previous studies is that they have not measured retention, even though it is known that an offline or consolidation period is a critical process for learning (Boudreau et al. 2007; Dancey et al. 2014). A few studies have investigated the impact of capsaicin application on retention using a motor adaptation task during a locomotor (Bouffard et al. 2014) or an upper limb reaching (Lamothe et al. 2014) task and found that acute pain during motor learning acquisition had a negative impact on retention despite not having a negative impact on baseline performance measures (Lamothe et al. 2014) or acquisition (Bouffard et al. 2014). More recently, Bilodeau et al. (2016) investigated the effect of heat pain on motor learning of a finger tapping sequence task and found that acquisition and retention were not affected by the presence of pain during training. In addition, our recent work (Dancey et al. 2016) found improved retention for a local pain group compared with a remote pain group. This provides support for improved motor learning retention with mild acute pain, and we hypothesized that local acute pain increased attention to the body part utilized in motor learning acquisition (Dancey et al. 2016). Factors improving or decreasing motor learning acquisition are not necessarily predictive of motor retention (Reis et al. 2009; Richardson et al. 2006), and from a practical perspective it is retention that indicates whether learning has been impacted positively or negatively. It is therefore important to investigate how retention is affected with a complex motor tracing paradigm.

The interactions between pain and motor control are complex, and to date few studies have investigated the effect of acute experimental pain on motor learning acquisition and retention in healthy humans. Inducing acute experimental pain in healthy participants is therefore instrumental in isolating the motor consequences of acute pain and the mechanisms and conditions under which motor learning in the presence of pain becomes either adaptive or maladaptive. We investigated the primary hypothesis that a novel motor learning acquisition task performed during acute pain (capsaicin group) compared with a control group would show differential changes in early SEP peaks. Our secondary hypothesis was that participants performing a novel motor learning acquisition task during acute pain would demonstrate improved accuracy before motor learning acquisition, after motor learning acquisition, and at retention compared with a control group.

METHODS

Methods Overview

Two groups of 12 participants [14 men, 10 women; aged 19–27 yr (mean 20.3, SD 2.5)] were recruited from the student population at the University of Ontario Institute of Technology. Each participant filled out a confidential health history in order to identify any exclusionary medical conditions that could impact normal somatosensation including, but not limited to, recent cervicothoracic injury, neurological conditions, current use of neuroactive or pain medication, or currently suffering from a chronic pain condition.

Written informed consent was obtained for all participants, and the study was approved by the University of Ontario Institute of Technology Research Ethics Board. This study was performed according to the principles set out by the Declaration of Helsinki for the use of humans in experimental studies.

Acute pain was induced by applying capsaicin cream, and SMI was assessed by recording early SEPs in humans. The effect of acute pain on signal transmission was assessed by investigating changes in the amplitude of SEPs from baseline, at 20 min after application, and then after the motor learning task (35 min from baseline) (see Fig. 1 for a schematic illustration of the protocol). Before performing the motor learning acquisition task, participants in the capsaicin group received a topical application of capsaicin (0.075% Zostrix) while the control group received a topical control skin lotion (Life Brand, Shopper's Drug Mart). A 5 cm × 10 cm area was marked off on the lateral aspect of the dominant elbow, and then the topical cream or lotion was applied to this 50-cm2 area on the lateral aspect of the right elbow and was massaged into the skin.

Fig. 1.

Fig. 1.

Open in a new tab

Schematic of the protocol.

Outcome Measures

The outcome measures for this study included the amplitude (μV) of the early SEP peaks, motor learning accuracy, and pain (numeric pain rating score).

Motor learning task.

The motor learning tracing task was run through a custom Leap Motion software tool (Leap Motion, San Francisco, CA), required participants to trace sequences of sinusoidal-pattern waves with varying frequency and amplitude using only their thumb on an external wireless touchpad (Logitech, Fremont, CA), and included a pre-motor learning acquisition test, a motor learning acquisition phase, a post-motor learning acquisition test, and a retention test 24–48 h later. The pre-motor learning acquisition, post-motor learning acquisition, and retention tests were ∼4 min in duration, while the motor learning acquisition phase (which occurred between the pre-motor learning acquisition and post-motor learning acquisition tests) was ∼15 min in duration. The traces were formed by a series of dots, and each trial consisted of 500 dots. Each tracing task was comprised of four preselected sinusoidal patterns of varying amplitude and frequency, as determined by a previous study (Holland et al. 2015). Motor error was determined by the software as the average distance of the participant's attempted trace from the presented sinusoidal wave. The training software captured the distance that the participant's cursor dot was from the “perfect” trace and recorded the average distance the cursor was from each dot as it passed the horizontal axis the participant was operating on. The motor error was determined as a percentage that the participant's tracing cursor was from the original “perfect” trace. Before motor learning acquisition, after motor learning acquisition, and at retention, each of the four versions was performed once, while for the motor learning acquisition phase each version was performed three times for a total of 12 traces. Combined flexion and adduction thumb movements were performed, which required the participants to sweep their thumb from left to right, utilizing the abductor pollicis brevis (APB) muscle.

Pain.

Subjective pain was quantified with a Numeric Pain Rating Scale (NPRS) with which participants graded the intensity of their pain from 0 to 10 (Dolphin and Crue 1989). Participants in both groups were asked to rate their pain at baseline, after application (5 min), after application (20 min), after motor learning acquisition (35 min), and after the last round of SEP measurements (45 min) in order to ensure that they were in acute pain for the duration of the experiment.

Stimulation of median nerve to elicit SEPs.

Stimuli consisted of electrical square pulses 0.1 ms in duration delivered at frequencies of 2.47 Hz through Ag/AgCl ECG conductive adhesive electrodes (MEDITRACE 130, Ludlow Technical Products Canada) (impedance <5 kΩ) placed over the median nerve at the wrist of the right hand, with cathode proximal. After the 5-min 2.47 Hz session, stimuli were then delivered at a frequency of 4.98 Hz for 15 min. SEPs were recorded at two different rates in order to record both the N24 and N30 SEP peaks. Using the slower rate of 2.47 Hz does not lead to SEP peak attenuation, while the faster rate, 4.98 Hz, attenuates the N30 SEP peak, allowing for the N24 SEP peak to be accurately identified (Fujii et al. 1994; Haavik and Murphy 2013). The stimulus intensity was increased until motor threshold was attained. Motor threshold was defined as the lowest stimulation intensity that evoked a visible muscle contraction of the APB muscle.

SEP recording parameters.

SEP recoding electrodes (1.8-m-long Traditional Grass Lead, 10-mm disk, 2-mm hole gold cup EEG electrodes, Grass Technologies, Astro-Med) (impedance <5 kΩ) were placed according to International Federation of Clinical Neurophysiologists (IFCN) recommendations with Grass Technologies EEG adhesive conducting paste (type TEN20). Recording electrodes were placed on the ipsilateral Erb's point, over C5 spinous process (Cv5), the anterior neck (tracheal cartilage), 2 cm posterior to contralateral central C3/4 (a parietal site referred to as Cc′), and a frontal site (6 cm anterior and 2 cm contralateral to Cz) (Abbadie et al. 1997; Rossi et al. 2003). The C5 spinous process was referenced to the anterior neck (trachea), while all other electrodes were referenced to the ipsilateral earlobe. A 1.8288-m Traditional Lead, 10-mm disk, 2-mm hole gold cup EEG electrode was also used as a ground and was placed in the mouth of participants. SEPs were recorded at baseline, 20 min after application, and then immediately after the motor tracing acquisition task (45 min from baseline).

The SEP signal was amplified (gain 10,000), filtered (0.2–1,000 Hz), and stored on a laboratory computer for later retrieval. A total of 1,000 sweeps were averaged per stimulation rate with a purpose-written Signal configuration (Cambridge Electronic Design, Cambridge, UK). SEP peak amplitudes were measured according to the IFCN guidelines (Cruccu et al. 2008). We identified and analyzed the peak-to-peak amplitude (μV) and latencies of the following SEP components: the peripheral N9, the spinal N11 and N13, the far-field N18, the parietal N20 and P25, and the frontal N24 and N30 SEP peaks. SEP amplitudes were measured from the averaged traces beginning at the peak of interest to the preceding or succeeding peak of opposite deflection, according to international recommendations (Nuwer et al. 1994) and previous studies in this field (Cheron and Borenstein 1987, 1991; Sonoo et al. 1991). SEP latencies were recorded from the time of stimulation onset to their maximal peak or trough for each of the SEP peaks.

Statistical Analysis

SEP peak amplitudes were normalized to baseline values to account for interparticipant baseline variability and to allow for between-participant comparisons. The Shapiro-Wilk test for normality was run on each SEP peak. The main effect of interest was the interactive effect of pain and motor learning acquisition on SEP peak amplitudes, which was tested with a repeated-measures ANOVA with factors Time (baseline vs. after motor learning acquisition) and Group (control vs. capsaicin). To ensure that the observed interactions were due to the interaction of capsaicin and motor learning acquisition and not simply due to capsaicin application, a separate repeated-measures ANOVA with factors Time (baseline vs. after application) and Group (control vs. capsaicin) was performed on each SEP peak.

The Shapiro-Wilk test for normality was run on the accuracy data. To investigate and compare performance accuracy, a repeated-measures ANOVA with factors Time (before motor learning acquisition vs. after motor learning acquisition vs. retention) and Group (control vs. capsaicin) was performed on the accuracy data.

A Friedman test with pairwise comparisons was run on the capsaicin group NPRS ratings. Statistical analysis was performed with IBM SPSS Statistics for Windows, version 19.0 (IBM, Armonk, NY). Statistical significance was set at P < 0.05. ƞ2 was calculated in SPSS as a measure of effect size, with values of 0.01 representing a small effect size, 0.06 a medium effect size, and 0.14 or greater a large effect size (Fritz et al. 2012).

RESULTS

A total of 24 participants were tested, with 12 participants in the capsaicin group [8 women, 4 men; aged 18–27 yr (mean 20.8, SD 3.3)] and 12 participants in the control group [6 women, 6 men; aged 18–24 yr (mean 22.8, SD 2.0)].

Neurophysiological Data: SEPs

The N9, N30, and P25 SEP peaks were normally distributed. For the N11 and N24 SEP peaks only the capsaicin group (after application) was nonnormally distributed. For the N13 and N20 SEP peaks only the control group (after motor learning acquisition) was nonnormally distributed. For the N18 SEP peak only the capsaicin group (after motor learning acquisition) was nonnormally distributed. All other categories were normal. When only one set of measurements in a repeated-measures design are nonnormally distributed, it is recommended to still run an ANOVA that is robust against departures from normality (Steiner 2008), as conclusions drawn from the ANOVA will be accurate. That is, type I and type II errors will not be inflated if the data are skewed and deviations in kurtosis will only affect power if the sample size is too low (Norman and Streiner 2008). Therefore we conducted a repeated-measures ANOVA on all SEP peaks.

Cerebellum: P25, N18, N24.

p25.

After motor learning acquisition, there was no main effect of Time on the P25 SEP peak amplitude (P = 0.96). After the cream application, there was no main effect of Time on P25 SEP peak amplitude (P = 0.22); however, the interaction effect of Time × Group was significant [F(2,23) = 5.12, P < 0.05, ƞ2 = 0.19], with post hoc ANOVA tests demonstrating that the capsaicin and control groups differed after application [F(1,11) = 5.93, P < 0.05, ƞ2 = 0.35], with the capsaicin group P25 SEP peak decreasing significantly by 15.3% after the application of capsaicin cream [F(1,11) = 5.05, P < 0.05, ƞ2 = 0.32] while there was a nonsignificant 10.0% increase in the P25 SEP peak for the control group (P = 0.28).

n18.

After motor learning acquisition, there was a significant main effect of Time [F(2,23) = 5.66, P < 0.05, ƞ2 = 0.21], and a significant Time × Group interaction effect [F(2,23) = 7.09, P < 0.05, ƞ2 = 0.25]. Post hoc ANOVA tests demonstrated that the capsaicin and control groups differed after motor learning acquisition [F(1,11) = 5.86, P < 0.05, ƞ2 = 0.35], with the capsaicin group SEP peak significantly decreasing by 18.5% after motor learning acquisition [F(1,11) = 17.76, P < 0.01, ƞ2 = 0.62] while the control group showed a nonsignificant 1.7% increase in the N18 SEP peak (P = 0.86). After the application of the cream or lotion, there was no main effect of Time on the N18 SEP peak amplitude (P = 0.59).

n24.

After motor learning acquisition, there was a significant Time effect [F(2,23) = 5.88, P < 0.05, ƞ2 = 0.21] and a significant interaction effect of Time × Group [F(2,23) = 98.92, P < 0.005, ƞ2 = 0.29]. Post hoc ANOVA tests demonstrated that for the N24 SEP peak the capsaicin and control groups differed after motor learning acquisition [F(1,11) = 8.14, P < 0.05, ƞ2 = 0.42], with the control group N24 SEP peak decreasing significantly by 28.9% after motor learning acquisition [F(1,11) = 52.47, P < 0.001, ƞ2 = 0.83] while the capsaicin group showed a nonsignificant 3.0% increase in the N24 SEP peak (P = 0.80). After the cream or lotion application, there was no main effect of Time on N24 SEP peak amplitude (P = 0.19).

Primary somatosensory area: N20.

After motor learning acquisition there was a significant Time effect [F(2,23) = 4.42, P < 0.05, ƞ2 = 0.17] and a significant Time × Group interaction effect [F(2,23) = 4.42, P < 0.05, ƞ2 = 0.35]. Post hoc ANOVA tests demonstrated that the capsaicin and control groups differed after motor learning acquisition [F(1,11) = 14.02 = P < 0.005, ƞ2 = 0.56], with the control group N20 SEP peak significantly increasing by 48.9% after motor learning acquisition [F(1,11) = 11.32, P < 0.05, ƞ2 = 0.51] while there was a nonsignificant 11.5% decrease in the N20 SEP peak for the capsaicin group (P = 0.29). After the cream or lotion application for both groups, there was no main effect of Time on N20 SEP peak amplitude (P = 0.97).

Sensorimotor integration and the motor cortex: N30.

After motor learning acquisition there was a significant main effect of Time [F(2,23) = 23.84, P < 0.001, ƞ2 = 0.52], while the interaction effect of Time × Group was not significant (P = 0.37). After motor learning acquisition the N30 SEP peak increased by 23.8% for the control group and by 16.2% for the capsaicin group. After application of the cream or lotion, there was no main effect of Time on the N30 SEP peak amplitude (P = 0.62).

For the N9, N11, and N13 SEP peaks no significant changes were seen for either group. There were no significant changes in latency data for any SEP peak in either the control group or the capsaicin group (see Table 2).

Table 2.

Average SEP latencies for each peak

SEP Peak Control
 Capsaicin
Before motor learning acquisition After application After motor learning acquisition Before motor learning acquisition After application After motor learning acquisition
N9 10.1 ± 0.5 10.0 ± 0.6 10.2 ± 0.4 9.8 ± 0.6 9.7 ± 0.5 9.8 ± 0.7
N11 11.9 ± 0.7 12.0 ± 0.6 12.0 ± 0.5 11.6 ± 0.7 11.4 ± 0.7 11.5 ± 0.2
N13 13.2 ± 0.8 13.1 ± 0.7 13.3 ± 0.9 13.0 ± 0.6 13.1 ± 0.7 13.1 ± 0.4
N18 18.1 ± 0.4 18.3 ± 0.6 18.4 ± 0.4 17.8 ± 0.9 17.7 ± 1.1 17.4 ± 1.0
N20 20.1 ± 0.7 19.9 ± 0.6 20.4 ± 0.8 19.3 ± 0.9 19.2 ± 1.1 19.5 ± 1.0
N24 23.7 ± 0.6 23.5 ± 0.8 23.6 ± 0.7 24.1 ± 0.8 23.9 ± 0.9 23.8 ± 0.8
P25 24.9 ± 1.0 24.4 ± 1.3 25.0 ± 1.1 25.2 ± 0.8 25.5 ± 1.1 25.4 ± 0.7
N30 31.2 ± 0.8 31.4 ± 0.9 31.1 ± 1.1 30.4 ± 1.2 30.8 ± 1.1 30.5 ± 1.1
Open in a new tab

Data (in ms) are presented as means ± SD.

Figure 2 illustrates the raw data from a representational capsaicin participant indicating SEP peaks, and Figure 3 illustrates the raw data from a representational control participant indicating SEP peaks. The normalized averages for the SEP peaks are illustrated in Fig. 4. Table 1 indicates the mean amplitudes of significant SEP peaks and their associated P values. Table 2 indicates the mean latencies of the SEP peaks.

Fig. 2.

Fig. 2.

Open in a new tab

Raw data from a representational capsaicin participant. Note the significant differences for the P25 SEP peak (P < 0.05) after capsaicin application and the N30 peaks (P < 0.001) after motor learning acquisition, as indicated by asterisks.

Fig. 3.

Fig. 3.

Open in a new tab

Raw data from a representational control participant. Note the significant differences for the N20 (P < 0.05), N24 (P < 0.001), and N30 (P < 0.001) SEP peaks after motor learning acquisition, as indicated by asterisks.

Fig. 4.

Fig. 4.

Open in a new tab

Averaged normalized SEP ratios showing capsaicin vs. control groups after application (A) and after motor learning acquisition (B). A: no significant differences for the control group after application but a significant decrease for the P25 SEP peak (*P < 0.05) for the capsaicin group after application. B: after motor learning acquisition, significantly different changes from baseline are indicated by asterisks for the N20 (P < 0.05), N24 (P < 0.001), and N30 (P < 0.001) SEP peaks for the control group and significantly different changes from baseline are indicated by asterisks for the N18 (P < 0.01) and N30 (P < 0.001) SEP peaks for the capsaicin group. Error bars represent SD.

Table 1.

Significant SEP peak amplitudes and P values

SEP Peak Group Postapplication Mean Postapplication P Value Post-Motor Learning Mean Post-Motor Learning P Value
P25 Control 1.10 ± 0.31 0.28 1.02 ± 0.31 0.96
Capsaicin 0.85 ± 0.24 <0.05 0.98 ± 0.23 0.96
N18 Control 1.10 ± 0.31 0.59 1.02 ± 0.31 0.86
Capsaicin 0.99 ± 0.45 0.59 0.80 ± 0.25 <0.01
N20 Control 1.03 ± 0.13 0.97 1.49 ± 0.50 <0.05
Capsaicin 0.98 ± 0.27 0.97 0.89 ± 0.35 0.29
N24 Control 0.98 ± 0.17 0.19 0.71 ± 0.14 <0.001
Capsaicin 1.20 ± 0.46 0.19 1.03 ± 0.36 0.80
N30 Control 1.04 ± 0.20 0.62 1.24 ± 0.18 <0.001
Capsaicin 1.00 ± 0.23 0.62 1.16 ± 0.22 <0.001
Open in a new tab

Data are presented as means ± SD; significant differences are in bold.

Behavioral Data

Accuracy.

The Shapiro-Wilk test for normality demonstrated that both groups at all time points were normally distributed. The behavioral data demonstrate that motor learning occurred, as both the control [F(1,11) = 79.193, P < 0.001, ƞ2 = 0.88] and capsaicin [F(1,11) = 12.42, P < 0.001, ƞ2 = 0.51] groups improved in accuracy. The interaction effect of Time × Group was significant [F(2,23) = 6.28, P < 0.05, ƞ2 = 0.51], with post hoc ANOVA testing demonstrating that both before motor learning acquisition (which occurred after the capsaicin cream had already been applied) [F(2,23) = 8.32, P < 0.05, ƞ2 = 0.36] and after motor learning acquisition [F(2,23) = 9.49, P < 0.05, ƞ2 = 0.58] the capsaicin group was more accurate than the control group. For the retention session the capsaicin group outperformed the control group, and this approached significance (P = 0.06) (see Fig. 5). Post hoc ANOVA tests on the percent change in motor error demonstrate that there was not a significant difference between the groups after motor learning acquisition (P = 0.31); however, the groups differed significantly from each other at retention (P = 0.036), with the control group showing a 70.5% decrease in motor error and the capsaicin group a 46.0% decrease in motor error at retention relative to pre-motor learning acquisition values.

Fig. 5.

Fig. 5.

Open in a new tab

Percent error by group. Both groups improved in accuracy after motor learning acquisition (P < 0.001), as indicated by a double asterisk. The capsaicin group outperformed the control group before motor learning acquisition (P < 0.05) and after motor learning acquisition (P < 0.05), as indicated by asterisks. Error bars represent SD.

Pain ratings.

The Friedman test on the NPRS ratings demonstrated a significant effect for the capsaicin group [χ2 (df = 4, P < 0.001) = 39.4, ƞ2 = 0.69], with pairwise comparisons indicating that from baseline there was a significant increase in NPRS ratings 20 min after application (P < 0.001), after motor learning acquisition (P < 0.001), and after motor learning acquisition (45 min from baseline) (P < 0.05). The increase 5 min after application of the cream was not significant (P = 0.27). The average NPRS ratings are illustrated in Fig. 6. None of the participants in the control group reported any pain.

Fig. 6.

Fig. 6.

Open in a new tab

Averaged NPRS ratings of participants in the control and capsaicin groups. Significant differences after application for the capsaicin group (P < 0.001) relative to baseline are indicated by asterisks. Error bars represent SD.

DISCUSSION

The results of our study support our hypothesis of differential changes in early cortical SEP peaks evoked after motor learning acquisition, as we observed a decrease in the N18 SEP peak for the capsaicin group whereas the control group had an increase in the N20 SEP peak and a decrease in the N24 SEP peak after motor learning acquisition. In addition, there was an increase in the N30 SEP peaks for both groups after motor learning acquisition, and we found a significant decrease for the P25 SEP peak after the capsaicin intervention. There were significant differences in SEP peaks that represent activity in several pathways related to motor control including the primary somatosensory area (SI) (N20), cerebellum (N18, N24, P25), and motor cortex (MI) (N30), and this highlights the role of these structures in motor learning acquisition and pain processing. Significant improvements in accuracy were observed for both groups, suggesting that motor learning acquisition had occurred. We observed significantly greater accuracy for the capsaicin group (who performed their initial pre-motor learning acquisition session in the presence of pain) compared with the control group. In absolute terms, the capsaicin group continued to outperform the control group after motor learning acquisition, with a strong trend to at retention; however, in relative terms, the control group actually experienced a greater percent learning after motor learning acquisition. This highlights the interactive effect of pain on the magnitude of the improvement. This is in line with our secondary hypothesis that participants performing a novel motor learning acquisition task during acute pain would demonstrate improved accuracy after motor learning acquisition compared with a control group.

Neurophysiological Data

Primary somatosensory area: N20.

The N20 reflects the earliest cortical processing within the SI (Mauguiere 1999) and is known to respond to contralateral tactile stimuli (Hlushchuk and Hari 2006). Our finding of a significant increase in the N20 SEP peaks for the control group after motor learning acquisition demonstrates the role of the SI in motor learning acquisition. This finding is in line with a recent study (Andrew et al. 2015) that demonstrated a significant increase in the N20 SEP peak after 10 min of tracing and 10 min of typing, and it corroborates our previous work in which we found a significant increase in the N20 SEP peak for a control group after a typing task (Dancey et al. 2016). The task used for the Andrew et al. (2015) study and the present study is more complex than the typing tasks used in previous work (Dancey et al. 2014) that did not find an increase in the N20 SEP peak. The performance of a complex finger-tapping task results in additional areas of cortical activation, as measured by functional magnetic resonance imaging (fMRI), compared with a simpler task (Sadato et al. 1996), and the amount of overlapping cortical territories that are altered with learning is greater with fine rather than gross motor skill training (Hluštík et al. 2004).

Cerebellum: P25, N18, N24.

The P25 SEP peak amplitude was significantly decreased after capsaicin application. This peak reflects the process of invasion of the dendrites due to current spread from the cell body along the pyramidal cells of area 3b (Rossini et al. 1987), and therefore cerebellum-induced SEP alterations can be localized within the 3b area of the SI (Molinari et al. 2009). The decrease in the P25 SEP peak after capsaicin application is indicative of the role that the SI and the cerebellum play in somatosensory processing. This finding is in line with our previous work (Dancey et al. 2014, 2016) and with our finding of a significant decrease in the N18 SEP peak after motor learning acquisition for the capsaicin group. The N18 SEP peak originates in the brain stem, between the lower medulla and midbrain-pontine region (e.g., the dorsal column nuclei and/or the accessory inferior olives), reflects activity in the olivo-cerebellar pathways (Sonoo et al. 1991; Nuwer et al. 1994), and has the potential to show changes in cerebellar activity (Noel et al. 1996). Imaging studies have demonstrated significant increases in cerebellar activation with tasks requiring the discrimination of sensory information (Gao et al. 1996) and with the passive manipulation of a limb by an experimenter (Jueptner and Weiller 1998). In addition, previous research suggests that the cerebellum responds to noxious stimuli, as most fMRI studies of pain show activation in the cerebellum (Apkarian et al. 2005; Borsook 2007). Our finding of a significant decrease in the P25 SEP peak after capsaicin application and a decrease in the N18 SEP peak for the capsaicin group after motor learning acquisition supports the role that the cerebellum plays in pain processing, sensorimotor processing, and motor learning acquisition. This is interesting in light of the significant difference in the N20 and N24 SEP peaks after motor learning acquisition for the control group that was not observed for the capsaicin group. The N24 peak reflects activity in the pathway between the cerebellum and the SI and has the potential to show changes in cerebellar activity (Rossi et al. 2003). Source localization has identified the posterior wall of the central sulcus in area 3b of the SI as the site of N24 generation (Waberski et al. 1999). This area receives input from the cerebellar cortex and deep cerebellar nuclei (Molinari et al. 2009). We hypothesize that our finding of a significant decrease after motor learning acquisition for the N24 SEP peak is reflective of the role that cerebellar input plays in this cortical peak. Studies have shown that the cerebellum is associated with motor learning acquisition (Doyon and Ungerleider 2002; Manto and Bastian 2007; Molinari et al. 2007), as animal studies have shown that motor training is associated with increases in synapse number within the cerebellum (Black et al. 1990; Kleim 1994; Kleim et al. 1995) and motor training plays an active role in motor adaption and the behavioral learning of unfamiliar tasks in humans (Doyon et al. 2003). The cerebellum modifies extracerebellar output through inhibition sourced from GABAergic neuron populations (Doyon et al. 2002). Imaging studies confirm that the cerebellum is active during motor sequence tasks (Doyon et al. 2002) and finger-tapping tasks (Olsson et al. 2008; Stoodley et al. 2012; Witt et al. 2008). The resulting increases in activation patterns can come with as little as 5–10 min (Classen et al. 1998) and are more pronounced if the task is novel (Sanes and Donoghue 2000). Our finding is consistent with the work of Baarbé et al. (2014), who demonstrated disinhibition of cerebellar projections to MI after a motor acquisition task and with a study that found a significant decrease in the N24 SEP peak after motor learning acquisition (Andrew et al. 2015).

We hypothesize that acute pain may have negated the changes in cortical SEP peaks (N20 and N24) that occur in the pain-free condition after motor learning acquisition. We hypothesize that the neuroplasticity associated with pain and with motor learning acquisition share neural mechanisms and interact with each other (Ferguson et al. 2012). This corroborates previous research that demonstrated that performing an attention-demanding task attenuates the impact of negative stimuli (Erber and Tesser 1992; Erthal et al. 2005; Glynn et al. 2002; Morrow and Nolen-Hoeksema 1990), increases pressure pain thresholds in healthy participants (Volz et al. 2012), and suppresses the activity in limbic regions by frontal cortical regions (Drevets and Raichle 1998). Pain fibers project to the SI and may produce inhibition of the MI via thalamocortical or cortico-cortical inhibitory inputs (Massion 1976).

Sensorimotor integration and the motor cortex: N30.

Current evidence suggests that the frontal N30 peak reflects the activation within a complex supraspinal network linking the thalamus, premotor areas, basal ganglia, and MI (Cebolla et al. 2011; Kanovsky et al. 2003) and is thought to reflect SMI (Rossi et al. 2003). Primate (Strick and Preston 1982; Tanji and Wise 1981) and human (Balzamo et al. 2004) intracortical recordings led to the hypothesis that the N30 SEP peak is generated at the MI. In contrast, there are topographic (Valeriani et al. 1998, 2000) and intracerebral (Barba et al. 2001, 2005) studies that support that this peak is generated in SI. Cebolla et al. (2011) determined that the N30 peak is generated by network activity in the MI as well as the premotor and prefrontal cortex through the use of standardized weighted low-resolution brain electromagnetic tomography (swLORETA). The amplitude of the N30 SEP peak was significantly increased after motor learning acquisition for both groups. Our finding of significant increases in the N30 peak after motor learning acquisition for both groups is consistent with previous work demonstrating significant changes in the N30 peak after repetitive motor activity (Haavik Taylor and Murphy 2007; Murphy et al. 2003) and motor learning acquisition (Andrew et al. 2015; Dancey et al. 2014, 2016).

Behavioral Data

Significant increases in accuracy were observed for both groups, suggesting that motor learning acquisition had occurred. There was an effect of pain on the magnitude of improvement, as the capsaicin group outperformed the control group significantly before motor learning acquisition and after motor learning acquisition and approached significance at retention. Previous studies have shown motor learning deficits in association with acute experimental pain in both animal (Ferguson et al. 2006; Hook et al. 2008) and human (Boudreau et al. 2007; Flor 2003; Schweinhardt et al. 2006) models. We observed an increase in learning accuracy before motor learning acquisition (performed in the presence of capsaicin) and after motor learning acquisition for the capsaicin group, which is in line with our previous research (Dancey et al. 2014, 2016). This work differed from our previous work (Dancey et al. 2014, 2016), as the present study utilized a different task (tracing vs. typing) that was more complex and had lower baseline accuracy. It is significant that in the studies showing an impaired acquisition of the task in the presence of pain, the motor task in itself evoked pain (Boudreau et al. 2007; Hook et al. 2008) and therefore impacted the ability to perform the motor learning acquisition task. The painful stimulation used in the present study and our previous work (Dancey et al. 2014, 2016) and used by another study that demonstrated no impact of pain on motor learning acquisition and retention (Bilodeau et al. 2016) induced cutaneous pain unrelated to movement. This may help to explain why there was not an adverse effect of pain on motor learning acquisition outcomes, as acute pain typically elicits motor responses that protect from further damage that may impair motor learning acquisition (Bank et al. 2013).

Our results suggest that there may be differing effects of pain on motor learning acquisition plasticity. Research indicates that the representation of muscles affected by pain is altered in the sensorimotor system and that the level of ongoing pain and the associated neuroplastic changes can be reversed by motor learning acquisition (Pleger et al. 2005). The reestablishment of sensorimotor representations and reduced pain after motor learning acquisition are also in line with the results of sensory discrimination training in phantom limb pain patients (Flor et al. 2001). There is an interdependence of sensory and motor systems, and the effects of motor learning on pain may be due to cortico-thalamic loops, producing inhibition on sensory systems. Although it has been argued that pain may interfere with learning-induced motor plasticity (Boudreau et al. 2007), other studies indicate that pain may improve motor performance and learning acquisition (Dancey et al. 2014, 2016) or have no effect if the quality of movement is maintained (Bouffard et al. 2014; Ingham et al. 2011).

Research demonstrates that neuroplasticity accompanying motor learning acquisition is mediated by changes in attention (Conner et al. 2003; McGaughy et al. 2002; Rosenkranz and Rothwell 2004; Stefan et al. 2004), as the learning of motor tasks depends on attentional resources (Hazeltine et al. 1997; Nissen et al. 1987). We hypothesize that improved motor learning acquisition outcomes for the capsaicin group are due to attention to the region of the body undergoing learning (Conner et al. 2003; McGaughy et al. 2002; Rosenkranz and Rothwell 2004; Stefan et al. 2004). Growing evidence demonstrates that affective processing is modulated by attention and cognitive regulation (Ochsner and Gross 2005) and that stress leads to a narrowing of attention (Callaway 1959; Callaway and Dembo 1958), decreasing the processing of task-irrelevant stimuli (Chajut and Algom 2003). Previous work has found that the application of tactile-proprioceptive noise improved sensorimotor performance (Mendez-Balbuena et al. 2012) and that an intermediate level of input noise of one sensory modality (tactile noise) enhances the brain evoked response of another sensory modality (visual evoked potentials) (Mendez-Balbuena et al. 2015). In addition, Passmore (2014) had participants recreate the components of Morse code patterns and found that when paresthesia stimulation was present under transfer conditions, performance was significantly better than for the no-stimulation group. These results indicate that a secondary stimulus may draw increased attentional resources toward discerning the meaningful stimulus (Mendez-Balbuena et al. 2012, 2015; Passmore 2014). Cognitive load studies confirm that under high-load conditions there is decreased activation in brain regions associated with emotion (amygdala) and increased activation in executive control areas (prefrontal cortex) (Erthal et al. 2005; Okon-Singer et al. 2007; Van Dillen et al. 2009).

Conclusions

This work provides supportive evidence for SMI areas in motor learning acquisition as demonstrated by significant differences in the N30 SEP peak amplitude after motor learning acquisition for both groups and for the N20 and N24 SEP peaks (control group) and the N18 SEP peak (capsaicin group). A significant decrease in the P25 SEP peak was found after the application of capsaicin cream, demonstrating the effect of acute pain on SEP peaks. As there were significant differences in SEP peaks that represent activity in the cerebellum (N18, N24, P25), an important direction for future work is to investigate changes in excitability between the cerebellum and MI after motor learning acquisition performed in the presence of pain using transcranial magnetic stimulation techniques that measure cerebellar inhibition (Baarbé et al. 2014) to see whether pain changes excitability in the cerebellum to MI pathway. In addition, the findings of improved motor learning acquisition during acute pain may have been caused through attentional mechanisms or through an increase in arousal during the painful stimulation; an important direction for future work is the comparison of the effects of local vs. remote acute pain relative to the muscle(s) performing a complex motor learning acquisition task. In addition, as pain can be viewed as a sensory perturbation that improves motor learning acquisition it would be interesting to explore whether motor learning acquisition in the presence of tactile noise also leads to significant differences in SEP peaks when compared with a control group. The results of this study help to explain why activation of the motor system through therapeutic exercise (focusing on movement) can assist in decreasing pain. As motor learning acquisition is accompanied by pain in a variety of settings, the effect of pain on learning and neuroplasticity is important to consider to ensure that therapeutic interventions lead to adaptive and not maladaptive changes.

GRANTS

The authors acknowledge the following organizations for support and funding: Natural Sciences and Engineering Research Council of Canada (NSERC), Canada Foundation for Innovation, Ontario Ministry of Research and Innovation, and the University of Ontario Institute of Technology.

DISCLOSURES

No conflicts of interest, financial or otherwise, are declared by the author(s).

AUTHOR CONTR IBUTIONS

E.D., B.M., D.A., and P.Y. conception and design of research; E.D. and D.A. performed experiments; E.D. analyzed data; E.D., B.M., and P.Y. interpreted results of experiments; E.D. prepared figures; E.D. and B.M. drafted manuscript; E.D., B.M., D.A., and P.Y. edited and revised manuscript; E.D., B.M., D.A., and P.Y. approved final version of manuscript.

REFERENCES

  1. Abbadie C, Trafton J, Liu H, Mantyh PW, Basbaum AI. Inflammation increases the distribution of dorsal horn neurons that internalize the neurokinin-1 receptor in response to noxious and non-noxious stimulation. J Neurosci 17: 8049–8060, 1997. [DOI] [PMC free article] [PubMed] [Google Scholar]
  2. Andrew D, Yielder P, Murphy B. Do pursuit movement tasks lead to differential changes in early somatosensory evoked potentials related to motor learning compared with typing tasks? J Neurophysiol 113: 1156–1164, 2015. [DOI] [PubMed] [Google Scholar]
  3. Apkarian AV, Bushnell MC, Treede RD, Zubieta JK. Human brain mechanisms of pain perception and regulation in health and disease. Eur J Pain 9: 463–484, 2005. [DOI] [PubMed] [Google Scholar]
  4. Baarbé J, Yielder P, Daligadu J, Behbahani H, Haavik H, Murphy B. A novel protocol to investigate motor training-induced plasticity and sensorimotor integration in the cerebellum and motor cortex. J Neurophysiol 111: 715–721, 2014. [DOI] [PubMed] [Google Scholar]
  5. Balzamo E, Marquis P, Chauvel P, Regis J. Short-latency components of evoked potentials to median nerve stimulation recorded by intracerebral electrodes in the human pre-and postcentral areas. Clin Neurophysiol 115: 1616–1623, 2004. [DOI] [PubMed] [Google Scholar]
  6. Bank P, Peper C, Marinus J, Beek P, Hilten J. Motor consequences of experimentally induced limb pain: a systematic review. Eur J Pain 17: 145–157, 2013. [DOI] [PubMed] [Google Scholar]
  7. Barba C, Valeriani M, Colicchio G, Mauguiere F. Short and middle-latency median nerve (MN) SEPs recorded by depth electrodes in human pre-SMA and SMA-proper. Clin Neurophysiol 116: 2664–2674, 2005. [DOI] [PubMed] [Google Scholar]
  8. Barba CA, Taggert J, Morgan AS, Guerra J, Bernstein B, Lorenzo M, Gershon A, Epstein N. A new cervical spine clearance protocol using computed tomography. J Trauma 51: 652–657, 2001. [DOI] [PubMed] [Google Scholar]
  9. Bilodeau MC, Roosink M, Mercier C. Effect of local versus remote tonic heat pain during training on acquisition and retention of a finger-tapping sequence task. Exp Brain Res 234: 475–482, 2016. [DOI] [PMC free article] [PubMed] [Google Scholar]
  10. Black JE, Isaacs KR, Anderson BJ, Alcantara AA, Greenough WT. Learning causes synaptogenesis, whereas motor activity causes angiogenesis, in cerebellar cortex of adult rats. Proc Natl Acad Sci USA 87: 5568–5572, 1990. [DOI] [PMC free article] [PubMed] [Google Scholar]
  11. Borsook D. Pain and motor system plasticity. Pain 132: 8–9, 2007. [DOI] [PMC free article] [PubMed] [Google Scholar]
  12. Boudreau S, Romaniello A, Wang K, Svensson P, Sessle BJ, Arendt-Nielsen L. The effects of intra-oral pain on motor cortex neuroplasticity associated with short-term novel tongue-protrusion training in humans. Pain 132: 169, 2007. [DOI] [PubMed] [Google Scholar]
  13. Bouffard J, Bouyer LJ, Roy JS, Mercier C. Tonic pain experienced during locomotor training impairs retention despite normal performance during acquisition. J Neurosci 34: 9190–9195, 2014. [DOI] [PMC free article] [PubMed] [Google Scholar]
  14. Callaway E. The influence of amobarbital (amylobarbitone) and methamphetamine on the focus of attention. Br J Psychiatry 105: 382–392, 1959. [DOI] [PubMed] [Google Scholar]
  15. Callaway E, Dembo D. Narrowed attention: a psychological phenomenon that accompanies a certain physiological change. AMA Arch Neurol Psychiatry 79: 74–90, 1958. [PubMed] [Google Scholar]
  16. Cebolla AM, Palmero-Soler E, Dan B, Chéron G. Frontal phasic and oscillatory generators of the N30 somatosensory evoked potential. Neuroimage 54: 1297–1306, 2011. [DOI] [PubMed] [Google Scholar]
  17. Chajut E, Algom D. Selective attention improves under stress: implications for theories of social cognition. J Pers Soc Psychol 85: 231–248, 2003. [DOI] [PubMed] [Google Scholar]
  18. Cheron G, Borenstein S. Specific gating of the early somatosensory evoked potentials during active movement. Electroencephalogr Clin Neurophysiol 67: 537–548, 1987. [DOI] [PubMed] [Google Scholar]
  19. Cheron G, Borenstein S. Gating of the early components of the frontal and parietal somatosensory evoked potentials in different sensory-motor interference modalities. Electroencephalogr Clin Neurophysiol 80: 522–530, 1991. [DOI] [PubMed] [Google Scholar]
  20. Classen J, Liepert J, Wise SP, Hallett M, Cohen LG. Rapid plasticity of human cortical movement representation induced by practice. J Neurophysiol 79: 1117–1123, 1998. [DOI] [PubMed] [Google Scholar]
  21. Conner JM, Culberson A, Packowski C, Chiba AA, Tuszynski MH. Lesions of the basal forebrain cholinergic system impair task acquisition and abolish cortical plasticity associated with motor skill learning. Neuron 38: 819–829, 2003. [DOI] [PubMed] [Google Scholar]
  22. Cruccu G, Aminoff M, Curio G, Guerit J, Kakigi R, Mauguiere F, Rossini P, Treede R, Garcia-Larrea L. Recommendations for the clinical use of somatosensory-evoked potentials. Clin Neurophysiol 119: 1705–1719, 2008. [DOI] [PubMed] [Google Scholar]
  23. Dancey E, Murphy B, Andrew D, Yielder P. The effect of local versus remote experimental pain on motor learning and sensorimotor integration using a complex typing task. Pain 157: 1682–1695, 2016. [DOI] [PubMed] [Google Scholar]
  24. Dancey E, Murphy B, Srbely J, Yielder P. The effect of experimental pain on motor training performance and sensorimotor integration. Exp Brain Res 232: 2879–2889, 2014. [DOI] [PubMed] [Google Scholar]
  25. Dolphin NW, Crue BL Jr. Pain: clinical manual for nursing practice. Clin J Pain 5: 363, 1989. [Google Scholar]
  26. Doyon J, Penhune V, Ungerleider LG. Distinct contribution of the cortico-striatal and cortico-cerebellar systems to motor skill learning. Neuropsychologia 41: 252–262, 2003. [DOI] [PubMed] [Google Scholar]
  27. Doyon J, Song AW, Karni A, Lalonde F, Adams MM, Ungerleider LG. Experience-dependent changes in cerebellar contributions to motor sequence learning. Proc Natl Acad Sci USA 99: 1017–1022, 2002. [DOI] [PMC free article] [PubMed] [Google Scholar]
  28. Doyon J, Ungerleider LG. Functional anatomy of motor skill learning. In: Neuropsychology of Memory, edited by Squire LR, Schacter DL. New York: Guilford, 2002, p. 225–238. [Google Scholar]
  29. Drevets WC, Raichle ME. Reciprocal suppression of regional cerebral blood flow during emotional versus higher cognitive processes: implications for interactions between emotion and cognition. Cogn Emot 12: 353–385, 1998. [Google Scholar]
  30. Erber R, Tesser A. Task effort and the regulation of mood: the absorption hypothesis. J Exp Soc Psychol 28: 339–359, 1992. [Google Scholar]
  31. Erthal FS, De Oliveira L, Mocaiber I, Pereira MG, Machado-Pinheiro W, Volchan E, Pessoa L. Load-dependent modulation of affective picture processing. Cogn Affect Behav Neurosci 5: 388–395, 2005. [DOI] [PubMed] [Google Scholar]
  32. Ferguson A, Crown E, Grau J. Nociceptive plasticity inhibits adaptive learning in the spinal cord. Neuroscience 141: 421–431, 2006. [DOI] [PubMed] [Google Scholar]
  33. Ferguson AR, Huie JR, Crown ED, Baumbauer KM, Hook MA, Garraway SM, Lee KH, Hoy KC, Grau JW. Maladaptive spinal plasticity opposes spinal learning and recovery in spinal cord injury. Front Physiol 3: 399, 2012. [DOI] [PMC free article] [PubMed] [Google Scholar]
  34. Flor H. Cortical reorganisation and chronic pain: implications for rehabilitation. J Rehabil Med Suppl 41: 66–72, 2003. [DOI] [PubMed] [Google Scholar]
  35. Flor H, Denke C, Schaefer M, Grüsser S. Effect of sensory discrimination training on cortical reorganisation and phantom limb pain. Lancet 357: 1763–1764, 2001. [DOI] [PubMed] [Google Scholar]
  36. Fritz CO, Morris PE, Richler JJ. Effect size estimates: current use, calculations, and interpretation. J Exp Psychol Gen 141: 2–18, 2012. [DOI] [PubMed] [Google Scholar]
  37. Fujii M, Yamada T, Aihara M, Kokubun Y, Noguchi Y, Matsubara M, Malcolm H. The effects of stimulus rates upon median, ulnar and radial nerve somatosensory evoked potentials. Electroencephalogr Clin Neurophysiol 92: 518–526, 1994. [DOI] [PubMed] [Google Scholar]
  38. Gao JH, Parsons LM, Bower JM, Xiong J, Li J, Fox PT. Cerebellum implicated in sensory acquisition and discrimination rather than motor control. Science 272: 545–547, 1996. [DOI] [PubMed] [Google Scholar]
  39. Glynn LM, Christenfeld N, Gerin W. The role of rumination in recovery from reactivity: cardiovascular consequences of emotional states. Psychosom Med 64: 714–726, 2002. [DOI] [PubMed] [Google Scholar]
  40. Haavik H, Murphy B. Selective changes in cerebellar-cortical processing following motor training. Exp Brain Res 231: 397–403, 2013. [DOI] [PubMed] [Google Scholar]
  41. Haavik Taylor H, Murphy B. Altered cortical integration of dual somatosensory input following the cessation of a 20 min period of repetitive muscle activity. Exp Brain Res 178: 488–498, 2007. [DOI] [PubMed] [Google Scholar]
  42. Hazeltine E, Grafton ST, Ivry R. Attention and stimulus characteristics determine the locus of motor-sequence encoding. A PET study. Brain 120: 123–140, 1997. [DOI] [PubMed] [Google Scholar]
  43. Hlushchuk Y, Hari R. Transient suppression of ipsilateral primary somatosensory cortex during tactile finger stimulation. J Neurosci 26: 5819–5824, 2006. [DOI] [PMC free article] [PubMed] [Google Scholar]
  44. Hluštík P, Solodkin A, Noll DC, Small SL. Cortical plasticity during three-week motor skill learning. J Clin Neurophysiol 21: 180–191, 2004. [DOI] [PubMed] [Google Scholar]
  45. Hodges PW, Tucker K. Moving differently in pain: a new theory to explain the adaptation to pain. Pain 152: S90–S98, 2011. [DOI] [PubMed] [Google Scholar]
  46. Holland L, Murphy B, Passmore S, Yielder P. Time course of corticospinal excitability changes following a novel motor training task. Neurosci Lett 591: 81–85, 2015. [DOI] [PubMed] [Google Scholar]
  47. Hook MA, Huie JR, Grau JW. Peripheral inflammation undermines the plasticity of the isolated spinal cord. Behav Neurosci 122: 233–249, 2008. [DOI] [PMC free article] [PubMed] [Google Scholar]
  48. Ingham D, Tucker KJ, Tsao H, Hodges PW. The effect of pain on training-induced plasticity of the corticomotor system. Eur J Pain 15: 1028–1034, 2011. [DOI] [PubMed] [Google Scholar]
  49. Jueptner M, Weiller C. A review of differences between basal ganglia and cerebellar control of movements as revealed by functional imaging studies. Brain 121: 1437–1449, 1998. [DOI] [PubMed] [Google Scholar]
  50. Kanovsky P, Bares M, Rektor I. The selective gating of the N30 cortical component of the somatosensory evoked potentials of median nerve is different in the mesial and dorsolateral frontal cortex: evidence from intracerebral recordings. Clin Neurophysiol 114: 981–991, 2003. [DOI] [PubMed] [Google Scholar]
  51. Kleim JA. Synaptic Plasticity in the Cerebellum of the Adult Rat Following Complex Motor Skill Acquisition. Champaign, IL: Univ. of Illinois at Urbana-Champaign, 1994. [Google Scholar]
  52. Kleim JP, Bender R, Kirsch R, Meichsner C, Paessens A, Rösner M, Rübsamen-Waigmann H, Kaiser R, Wichers M, Schneweis KE. Preclinical evaluation of HBY 097, a new nonnucleoside reverse transcriptase inhibitor of human immunodeficiency virus type 1 replication. Antimicrob Agents Chemother 39: 2253–2257, 1995. [DOI] [PMC free article] [PubMed] [Google Scholar]
  53. Lamothe M, Roy JS, Bouffard J, Gagné M, Bouyer LJ, Mercier C. Effect of tonic pain on motor acquisition and retention while learning to reach in a force field. PloS One 9: e99159, 2014. [DOI] [PMC free article] [PubMed] [Google Scholar]
  54. Manto M, Bastian AJ. Cerebellum and the deciphering of motor coding. Cerebellum 6: 3–6, 2007. [Google Scholar]
  55. Massion J. The thalamus in the motor system. Appl Neurophysiol 39: 222–238, 1976. [DOI] [PubMed] [Google Scholar]
  56. Mauguiere F. Somatosensory evoked potentials: normal responses, abnormal waveforms and clinical applications in neurological diseases. In: Electroencephalography: Basic Principles, Clinical Applications, and Related Fields, edited by Niedermeyer E. Baltimore, MD: Williams and Wilkins, 1999. [Google Scholar]
  57. McGaughy J, Dalley J, Morrison C, Everitt B, Robbins T. Selective behavioral and neurochemical effects of cholinergic lesions produced by intrabasalis infusions of 192 IgG-saporin on attentional performance in a five-choice serial reaction time task. J Neurosci 22: 1905–1913, 2002. [DOI] [PMC free article] [PubMed] [Google Scholar]
  58. Mendez-Balbuena I, Huidobro N, Silva M, Flores A, Trenado C, Quintanar L, Arias-Carrion O, Kristeva R, Manjarrez E. Effect of mechanical tactile noise on the amplitude of visual evoked potentials: multisensory stochastic resonance. J Neurophysiol 114: 2132–2143, 2015. [DOI] [PMC free article] [PubMed] [Google Scholar]
  59. Mendez-Balbuena I, Manjarrez E, Schulte-Mönting J, Huethe F, Tapia JA, Hepp-Reymond MC, Kristeva R. Improved sensorimotor performance via stochastic resonance. J Neurosci 32: 12612–12618, 2012. [DOI] [PMC free article] [PubMed] [Google Scholar]
  60. Mercier C, Leonard G. Interactions between pain and the motor cortex: insights from research on phantom limb pain and complex regional pain syndrome. Physiother Can 63: 305–314, 2011. [DOI] [PMC free article] [PubMed] [Google Scholar]
  61. Molinari M, Leggio M, Thaut M. The cerebellum and neural networks for rhythmic sensorimotor synchronization in the human brain. Cerebellum 6: 18–23, 2007. [DOI] [PubMed] [Google Scholar]
  62. Molinari M, Restuccia D, Leggio MG. State estimation, response prediction, and cerebellar sensory processing for behavioral control. Cerebellum 8: 399–402, 2009. [DOI] [PubMed] [Google Scholar]
  63. Morrow J, Nolen-Hoeksema S. Effects of responses to depression on the remediation of depressive affect. J Pers Soc Psychol 58: 519–527, 1990. [DOI] [PubMed] [Google Scholar]
  64. Murphy B, Taylor H, Wilson S, Oliphant G, Mathers K. Rapid reversible changes to multiple levels of the human somatosensory system following the cessation of repetitive contractions: a somatosensory evoked potential study. Clin Neurophysiol 114: 1531–1537, 2003. [DOI] [PubMed] [Google Scholar]
  65. Nissen MJ, Knopman DS, Schacter DL. Neurochemical dissociation of memory systems. Neurology 37: 789–794, 1987. [DOI] [PubMed] [Google Scholar]
  66. Noel P, Ozaki I, Desmedt J. Origin of N18 and P14 far-fields of median nerve somatosensory evoked potentials studied in patients with a brain-stem lesion. Electroencephalogr Clin Neurophysiol 98: 167–170, 1996. [DOI] [PubMed] [Google Scholar]
  67. Norman GR, Streiner DL. Biostatistics: The Bare Essentials. New York: McGraw-Hill, 2008. [Google Scholar]
  68. Nuwer MR, Aminoff M, Desmedt J, Eisen AA, Goodin D, Matsuoka S, Mauguière F, Shibasaki H, Sutherling W, Vibert JF. IFCN recommended standards for short latency somatosensory evoked potentials. Report of an IFCN committee. International Federation of Clinical Neurophysiology. Electroencephalogr Clin Neurophysiol 91: 6–11, 1994. [DOI] [PubMed] [Google Scholar]
  69. Ochsner KN, Gross JJ. The cognitive control of emotion. Trends Cogn Sci 9: 242–249, 2005. [DOI] [PubMed] [Google Scholar]
  70. Okon-Singer H, Tzelgov J, Henik A. Distinguishing between automaticity and attention in the processing of emotionally significant stimuli. Emotion 7: 147–157, 2007. [DOI] [PubMed] [Google Scholar]
  71. Olsson CJ, Jonsson B, Nyberg L. Learning by doing and learning by thinking: an fMRI study of combining motor and mental training. Front Hum Neurosci 2: 5, 2008. [DOI] [PMC free article] [PubMed] [Google Scholar]
  72. Passmore SR, Bosse J, Murphy B, Lee TD. The impact and specificity of nerve perturbation on novel vibrotactile sensory letter learning. Somatosens Mot Res 31: 167–177, 2014. [DOI] [PubMed] [Google Scholar]
  73. Pleger B, Tegenthoff M, Ragert P, Förster AF, Dinse HR, Schwenkreis P, Nicolas V, Maier C. Sensorimotor returning in complex regional pain syndrome parallels pain reduction. Ann Neurol 57: 425–429, 2005. [DOI] [PubMed] [Google Scholar]
  74. Reis J, Schambra HM, Cohen LG, Buch ER, Fritsch B, Zarahn E, Celnik PA, Krakauer JW. Noninvasive cortical stimulation enhances motor skill acquisition over multiple days through an effect on consolidation. Proc Natl Acad Sci USA 106: 1590–1595, 2009. [DOI] [PMC free article] [PubMed] [Google Scholar]
  75. Richardson AG, Overduin SA, Valero-Cabré A, Padoa-Schioppa C, Pascual-Leone A, Bizzi E, Press DZ. Disruption of primary motor cortex before learning impairs memory of movement dynamics. J Neurosci 26: 12466–12470, 2006. [DOI] [PMC free article] [PubMed] [Google Scholar]
  76. Rosenkranz K, Rothwell JC. The effect of sensory input and attention on the sensorimotor organization of the hand area of the human motor cortex. J Physiol 561: 307–320, 2004. [DOI] [PMC free article] [PubMed] [Google Scholar]
  77. Rossi S, della Volpe R, Ginanneschi F, Ulivelli M, Bartalini S, Spidalieri R, Rossi A. Early somatosensory processing during tonic muscle path in humans: relation to loss of proprioception and motor “defensive” strategies. Clin Neurophysiol 114: 1351–1358, 2003. [DOI] [PubMed] [Google Scholar]
  78. Rossini P, Gigli G, Marciani M, Zarola F, Caramia M. Non-invasive evaluation of input-output characteristics of sensorimotor cerebral areas in healthy humans. Electroencephalogr Clin Neurophysiol 68: 88–100, 1987. [DOI] [PubMed] [Google Scholar]
  79. Sadato N, Campbell G, Ibanez V, Deiber M, Hallett M. Complexity affects regional cerebral blood flow change during sequential finger movements. J Neurosci 16: 2691–2700, 1996. [DOI] [PMC free article] [PubMed] [Google Scholar]
  80. Sanes JN, Donoghue JP. Plasticity and primary motor cortex. Annu Rev Neurosci 23: 393–415, 2000. [DOI] [PubMed] [Google Scholar]
  81. Schabrun SM, Jones E, Kloster J, Hodges PW. The temporal association between changes in primary sensory cortex and corticomotor output during muscle pain. Neuroscience 235: 159–164, 2013. [DOI] [PubMed] [Google Scholar]
  82. Schweinhardt P, Lee M, Tracey I. Imaging pain in patients: is it meaningful? Curr Opin Neurol 19: 392–400, 2006. [DOI] [PubMed] [Google Scholar]
  83. Sonoo M, Sakuta M, Shimpo T, Genba K, Mannen T. Widespread N18 in median nerve SEP is preserved in a pontine lesion. Electroencephalogr Clin Neurophysiol 80: 238–240, 1991. [DOI] [PubMed] [Google Scholar]
  84. Stefan K, Wycislo M, Classen J. Modulation of associative human motor cortical plasticity by attention. J Neurophysiol 92: 66–72, 2004. [DOI] [PubMed] [Google Scholar]
  85. Stoodley CJ, Valera EM, Schmahmann JD. Functional topography of the cerebellum for motor and cognitive tasks: an fMRI study. Neuroimage 59: 1560–1570, 2012. [DOI] [PMC free article] [PubMed] [Google Scholar]
  86. Strick PL, Preston JB. Two representations of the hand in area 4 of a primate. I. Motor output organization. J Neurophysiol 48: 139–149, 1982. [DOI] [PubMed] [Google Scholar]
  87. Tanji J, Wise S. Submodality distribution in sensorimotor cortex of the unanesthetized monkey. J Neurophysiol 45: 467–481, 1981. [DOI] [PubMed] [Google Scholar]
  88. Valeriani M, Restuccia D, Barba C, Tonali P, Mauguière F. Central scalp projection of the N30 SEP source activity after median nerve stimulation. Mu scle Nerve 23: 353–360, 2000. [DOI] [PubMed] [Google Scholar]
  89. Valeriani M, Restuccia D, Di Lazzaro V, Le Pera D, Barba C, Tonali P, Mauguiere F. Dipolar sources of the early scalp somatosensory evoked potentials to upper limb stimulation: effect of increasing stimulus rates. Exp Brain Res 120: 306–315, 1998. [DOI] [PubMed] [Google Scholar]
  90. Van Dillen LF, Heslenfeld DJ, Koole SL. Tuning down the emotional brain: an fMRI study of the effects of cognitive load on the processing of affective images. Neuroimage 45: 1212–1219, 2009. [DOI] [PubMed] [Google Scholar]
  91. Volz MS, Mendonca M, Pinheiro FS, Cui H, Santana M, Fregni F. Dissociation of motor task-induced cortical excitability and pain perception changes in healthy volunteers. PloS One 7: e34273, 2012. [DOI] [PMC free article] [PubMed] [Google Scholar]
  92. Waberski TD, Buchner H, Perkuhn M, Gobbelé R, Wagner M, WKücker, Silny J. N30 and the effect of explorative finger movements: a model of the contribution of the motor cortex to early somatosensory potentials. Clin Neurophysiol 110: 1589–1600, 1999. [DOI] [PubMed] [Google Scholar]
  93. Walsh V, Cowey A. Transcranial magnetic stimulation and cognitive neuroscience. Nat Rev Neurosci 1: 73–80, 2000. [DOI] [PubMed] [Google Scholar]
  94. Witt ST, Laird AR, Meyerand ME. Functional neuroimaging correlates of finger-tapping task variations: an ALE meta-analysis. Neuroimage 42: 343–356, 2008. [DOI] [PMC free article] [PubMed] [Google Scholar]

Articles from Journal of Neurophysiology are provided here courtesy of American Physiological Society

RESOURCES