Motor-Evoked Pain Increases Force Variability in Chronic Jaw Pain

Accepted Manuscript Title: Motor-Evoked Pain Increases Force Variability in Chronic Jaw Pain Author: Wei-en Wang, Arnab Roy, Gaurav Misra, Derek B. Archer, Margarete C. Ribeiro-Dasilva, Roger B. Fillingim, Stephen A. Coombes PII: DOI: Reference:

S1526-5900(18)30081-6 https://doi.org/10.1016/j.jpain.2018.01.013 YJPAI 3532

To appear in:

The Journal of Pain

Received date: Revised date: Accepted date:

1-11-2017 15-1-2018 22-1-2018

Please cite this article as: Wei-en Wang, Arnab Roy, Gaurav Misra, Derek B. Archer, Margarete C. Ribeiro-Dasilva, Roger B. Fillingim, Stephen A. Coombes, Motor-Evoked Pain Increases Force Variability in Chronic Jaw Pain, The Journal of Pain (2018), https://doi.org/10.1016/j.jpain.2018.01.013. This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.

1

Title: Motor-evoked pain increases force variability in chronic jaw pain Running Title: Increased force variability in chronic jaw pain

Author Names: Wei-en Wang1, Arnab Roy1, Gaurav Misra2, Derek B. Archer1, Margarete C. Ribeiro-Dasilva3, Roger B. Fillingim4, Stephen A. Coombes1

Author Affiliations: 1 Laboratory for Rehabilitation Neuroscience, Department of Applied Physiology and Kinesiology, University of Florida, Gainesville, FL 2 Quantified Habits Inc., Washington DC 3 Department of Restorative Dental Science, University of Florida, Gainesville, FL 4 Department of Community Dentistry and Behavioral Science, University of Florida, Gainesville, Florida

*Corresponding Author: Stephen A. Coombes, Ph.D. University of Florida Laboratory for Rehabilitation Neuroscience Department of Applied Physiology and Kinesiology PO Box 118206 Email: [email protected] Phone: 352-294-1768 Pages:38, Figures: 4, Tables: 3 Words – Abstract: 213, Introduction: 473, Discussion: 1340

Page 1 of 41

2

DISCLOSURES No funding sources were provided. Stephen A. Coombes is co-founder and manager of Neuroimaging Solutions, LLC.

Highlights (a maximum of 85 characters per highlight)

  

Chronic jaw pain is characterized by increased force variability. The accuracy of force production was not compromised in the presence of motorevoked pain. Predictors of force variability shift from trait to state measure of pain as force level increases.

Page 2 of 41

3

ABSTRACT Musculoskeletal pain changes how people move. Although experimental pain is associated with increases in the variability of motor output, it is not clear whether motorevoked pain in clinical conditions is also associated with increases in variability. In the current study, we measured jaw force production during a visually guided force paradigm in which individuals with chronic jaw pain and control subjects produced force at 2% of their maximum voluntary contraction (MVC) (low target force level) and at 15% of their MVC (high target force level). State measures of pain were collected before and after each trial. Trait measures of pain intensity and pain interference, self-report measures of jaw function, and measures of depression, anxiety, and fatigue were also collected. We demonstrated that the chronic jaw pain group exhibited greater force variability compared to controls irrespective of the force level, whereas the accuracy of force production did not differ between groups. Furthermore, predictors of force variability shift from trait measures of pain intensity and pain interference at the low force level to state measures of pain intensity at the high force level. Our observations demonstrate that motor-evoked jaw pain is associated with increases in force variability that are predicted by a combination of trait measures and state measures of pain intensity and pain interference.

Page 3 of 41

4

PERSPECTIVE Chronic jaw pain is characterized by increases in variability during force production which can be predicted by pain intensity and pain interference. This article could help clinicians better understand the long term consequences of chronic jaw pain on the motor system.

Keywords: chronic pain; force variability; motor control; musculoskeletal pain

Page 4 of 41

5

INTRODUCTION Chronic musculoskeletal pain is the most common cause of severe long-term pain and physical disability, affecting over 50 million people in the United States. 9,24 Movement can exacerbate pain, and pain can modify movement.6,20,26,41,50,64,65,75 Understanding how pain leads to changes in motor control is fundamental to understanding the relationship between chronic pain and physical disability. In the current study, we use jaw force production as a model to understand how motor-evoked pain leads to changes in force production in a cohort of patients with chronic jaw pain. Force production is influenced by the variability of synaptic input received by the motoneurons.20,73 Input to the motoneuron is driven by descending signals from the brain combined with afferent input from sensory receptors in the periphery. Force production can therefore be influenced by the activation of nociceptors in the periphery and by activation of the pain processing network in the brain.19,29 In healthy adults, external stimuli are used to evoke acute experimental pain states, and an increase in pain has been associated with decreases in the maximum force a muscle can generate27,61, increases in the variability of shoulder abduction 3, and increases in the variability of finger force production20,73. Acute experimental pain studies have the advantage of controlling state levels of pain before and during movement,20,54,73 but they cannot address the characteristics of persistent motor-evoked pain, which is one of the key symptoms of chronic pain that limits activities of daily living and increases physical disability.28,38 Understanding the relationship between motor-evoked jaw pain and jaw force production is key to understanding the long term consequences of chronic jaw pain on the motor system.

Page 5 of 41

6

Increases in jaw movement variability have been demonstrated in individual with a history of trigeminal neuropathic pain, even after pain remission 4 and there is good evidence that chronic pain is associated with increases in depression 22, anxiety22, and fatigue12, and each of these factors have been associated with changes in motor function.7,11,53 Other evidence supports the position that temporomandibular disorders are associated with changes in motor function,58,61 but previous studies have not addressed how motor-evoked pain in clinical conditions influences motor function. To address this gap in the literature, we assessed motor-evoked pain during visually-guided jaw force production. One advantage of using a visually guided force task is that the amplitude and duration of force production can be precisely manipulated2,40,68 and pain ratings can be assessed before and after each trial. We reasoned that changing the target force of a jaw force production task on a trial by trial basis would allow us to assess the relationship between force production and state and trait pain measurements. We test the hypotheses that individuals with chronic jaw pain will report greater motor-evoked pain and will produce force with greater variability compared to controls. We also examine whether pain intensity, pain interference, and psychological measures predict force production parameters.

Page 6 of 41

7

METHODS Ethics Statement The University of Florida’s Institutional Review Board approved the procedures involved in this study. All individuals read and signed the informed consent prior to participation.

Subjects Seventeen participants with a history of jaw pain, and nineteen pain-free healthy controls were recruited in the study. The recruitment of the jaw pain participants were based on the temporomandibular disorder pain screening questionnaire.25 In summary, cases met the following criteria: (1) pain is always present in jaw and temporal area on either side for the last 30 days; (2) have pain or stiffness in jaw on awakening in the last 30 days; and (3) participants had at least three following activities that would change pain in jaw or temporal area in the last 30 days: (a) chewing hard or tough food; (b) opening the month or moving the jaw forward or to the side; (c) jaw habits such as holding teeth together, clenching, grinding or chewing gum; (d) other activities such as talking, kissing or yawning. Controls were recruited from the local community through flyers. Medication use for each subject is reported in Supplementary Table 1. Exclusion criteria for healthy controls included any history of neurological disease, psychiatric condition, and any history of chronic pain or current acute pain.

– Insert Table 1 here –

Page 7 of 41

8

Experimental protocol Subjects produced visually guided jaw force against a bite device (Fig.1A). All the subjects completed a total of 2 runs of experimental trials. Each run consisted of 5 trials with alternating force production at low force level (2% of MVC) and at high force level (15% of MVC) (High-Low-High-Low-High & Low-High-Low-High-Low) (Fig.1B showing H-L-H-L-H). Note that the assignment of “low” and “high” levels of force are relative to each other, and that even the “high” force condition is in the lower range relative to an individual’s maximum. The 2% of MVC level was chosen to have a condition in which chronic jaw pain subjects were able to contract their jaw muscles to produce force without evoking a significant increase in pain above baseline levels. The 15% of MVC level was chosen to elicit low to moderate motor-evoked pain while also minimizing the potential effects of malingering pain and fatigue which may be expected at higher force levels. The selected MVC levels were in line with other studies that have investigated force production of the ankle, finger, and hand at levels between 2.5% ~ 30% of MVC.2,8,16,73 The order of each run was pseudorandomized across subjects. A total of 5 trials at the high force level and 5 trials at low force level were completed by each subject. Prior to experimental trials, each subject’s jaw maximum voluntary contraction (MVC) was measured. Subjects were asked to maximally contract the jaw muscles and sustain the contraction for 5 seconds for two consecutive trials, separated by a 30 seconds period of rest. The MVC was calculated as the average of two peak force levels. Each subject then completed a training session to become familiar with the jaw force task. Jaw MVC was measured prior to the study from all subjects. We began collecting a second MVC measurement at the end of the experimental session mid-way through the data-collection period to test for the possibility of fatigue. As such, a second

Page 8 of 41

9

MVC measurement was collected from 8/17 subjects in the jaw pain group and 11/19 subjects in the control group.

– Insert Figure 1 here –

Experimental Task There were two target force levels within each run. At the beginning of each trial, the visual display showed a fixed white target bar and a red force bar against a black background (Fig. 1D). During the rest period (20 seconds), the force bar was red and stationary. Before and after each trial, subjects were asked to give a measure of the jaw pain intensity using a visual analog scale (VAS) ranging from “no pain” to “intolerable pain”. After the baseline pain rating (7.5 seconds), the white bar was separated from the red force bar and moved to the target force location (15 seconds). During the anticipation period, subject could see the designated force level and was ready to produce force. When the red force bar changed color from red to green, subjects were instructed to produce force as quickly as possible to move the green bar to reach the white target bar which was positioned at either 2% or 15% of MVC, and to maintain force at the required target force level while it remained green (30 seconds). Subjects received online visual feedback about their force via the green force bar during the entire force production period. When the force bar turned red, subjects stopped producing force. Following a 2.5 seconds rest period, subjects then assessed the posttask pain intensity (7.5 seconds). The next trial (a low force trial in Fig.1D) began after the post-task pain intensity rating. Hence, subjects were asked to evaluate their jaw pain intensity before and after each trial. Pre-task pain ratings represent a baseline

Page 9 of 41

10

rating of jaw pain while the jaw is at rest. Post-task pain ratings represent the perceived motor-evoked pain during the force task. Timing and components of the 2% of MVC and 15% of MVC trials were identical apart from the different location of the white target bar. Subjects gave their pain intensity ratings by using their left hand to control a scroll wheel to move the slider on the visual analog scale (VAS).

Force measurement Subjects performed the force task in a supine position, with the bite plates held between the upper row and lower row of teeth in the mouth with the base of the device resting on the chest. Experimental trials were completed while functional magnetic resonance images were collected. The fMRI data will be reported elsewhere. Subjects contracted jaw muscles to produce force against bite plates which transmitted force to the custom-built fiber-optic transducer which uses Fiber Bragg grating technology and has a resolution of 0.025 N (Neuroimaging Solutions LLC, Gainesville, FL). Figure 1C shows an example of jaw force produced by a single subject at a target force level of 15% of MVC (red) and 2% of MVC (blue). Signals from the force transducer were transmitted to a sm130 amplifier (Micron Optics, Atlanta, GA) housed in the control room. Custom-built LabVIEW software sampled the force signal at 62.5 Hz and displayed it as a green bar on a magnetically shielded display, which was visible to the subject via a mirror, providing the subject with real-time feedback of their force performance. The display that subjects viewed during the experimental trials is shown in Figure 1D.

Page 10 of 41

11

Statistical Analysis Task-specific pain intensity Mean pre-task and post-task pain intensity values were calculated from all five trials in each force level. To control for between group differences in baseline pain, jaw pain intensity change was also calculated by subtracting the mean post-task pain intensity value from the mean pre-task pain intensity value for each subject and each force level. For each measure, the Shapiro-Wilk test for normality and Levene’s test for equal variance across groups were conducted. Assumptions of normality and equal variance were violated. Therefore, for each measure, two-way (group [jaw pain, control]) Χ (target force level [2% of MVC, 15% of MVC]) repeated measures with nonparametric permutation testing with 5000 iteration was run. Significant interaction effects were followed up with Mann-Whitney U test to compare the chronic jaw pain group and the control group at each force level. All post-hoc t-tests were false discovery rate (FDR) corrected for multiple comparisons. Significance level was set at p < 0.05.

Force Data Force data were analyzed using custom algorithms in LabVIEW. The force-time series data were digitally filtered using a fourth-order Butterworth filter with a 20 Hz lowpass cut-off. Force production was characterized by measures of force amplitude, force variability, accuracy, and regularity. Each measure was calculated for each trial and then averaged separately for low force trials and high force trials. Force amplitude reflected the mean amplitude of force produced by the subject during the trial. Force

Page 11 of 41

12

variability was characterized by the standard deviation of force production, which captures the magnitude of the fluctuations in force around the mean during each trial. Normalized force variability was also calculated using the coefficient of variation to normalize the magnitude of variability (SD) to its corresponding absolute force value (i.e., SD/mean). Force accuracy was characterized by root mean square error (RMSE) which was calculated for each trial using the following equation:

(1) In which s = target force, fi = ith force sample and n = number of samples. Regularity of force production was determined by computing approximate entropy (ApEn) of the signal. ApEn is a regularity statistic in that lower values of ApEn represent a highly regular time series. A perfect sinusoidal signal has ApEn approaching zero, reflecting highly regular and structured signal properties. In contrast, white Gaussian noise has ApEn approaching a value of 2, indicating absence of regularity. ApEn was calculated using the Pincus algorithm.52 (2) where Cm(r) represents the number of times a vector of length m of vector X repeats within r of the standard deviation of the time series. Similarly, C m+1(r) represents the number of times a vector of length m + 1 repeats. Consistent with prior work, the vector length, m was set to 2 and the similarity criterion, r equaled 0.2.52,69 All of the force-dependent variables were calculated from the data extracted from the middle 20 seconds of each trial. The onset of the trial was identified by the time

Page 12 of 41

13

point where the force rose above 2 times the baseline value, while the offset of the trial was identified as the next time point where force fell below 2 times the baseline value. Baseline values were calculated as the mean force amplitude during the 300 ms prior to each 30 seconds trial. Mean force amplitude reflects the average force amplitude during the middle 20 seconds of the trial. The summary statistic for each force-dependent variable was the mean score calculated from all the trials in each force level. A trial was rejected if the subject did not complete the task or did not follow the instructions properly. A total of 97.2% of trials were accepted in the low force condition and a total of 100% of trials were accepted in the high force condition. For each measure, the Shapiro-Wilk test for normality and Levene’s test for equal variance across groups was conducted. Log transformation was applied if the assumption of normality was violated. Due to unequal variance of the force data, separate two-way ANOVAs with mixed model design (group [jaw pain, control]) Χ (target force level [2% of MVC, 15% of MVC]) with non-parametric permutation testing was conducted for each force dependent variable. Significant interaction effects were followed up with post-hoc Mann-Whitney U tests. Significance level was set at p < .05. Given that we ran a total of 3 ANOVAs for the self-report pain intensity measures (i.e. pre-task, post-task, change in pain intensity), and 5 ANOVAs for the force variables (i.e. MVC, SD, CV, RMSE, ApEn), FDR corrections were conducted to adjust p-values. We corrected p-values within each main effect (group, target force level) and interaction (group x target force level) based on the number of pain variables (3) and force variables (5). For example, for the effect of group on pain measures, we corrected based on three p-values (pre-task, post-task, change in pain intensity). Data were analyzed using SPSS 23.0 software (IBM Corp, Armonk, NY), and the R platform (Version 3.3.0) and R packages.34,56

Page 13 of 41

14

Correlation analysis To investigate the relationship between force-dependent variables, trait measures of chronic pain (i.e., duration of pain, disability days, pain intensity, pain interference), self-report measures of jaw function (i.e. JFLS, OBC), psychological measures (i.e., fatigue, anxiety and depression), and state measures of pain intensity (i.e., pre-task, post-task, and pre-post change), the Spearman's Rho rank correlation (rs) was computed at each target force level. The significance of the relationship was tested. A correlation matrix with correlation coefficient was constructed using the R platform and the R packages.17,23,34,47,62 To control for multiple comparisons, all the correlation tests for each force variable were FDR corrected at p < 0.05 significance level.

Multiple regression analysis To determine whether trait pain measures (i.e., duration of pain, disability days, pain intensity (GCPS), pain interference), self-report measures of jaw function (i.e. JFLS, OBC), psychological measures (i.e., fatigue, anxiety and depression), and state measures of pain intensity (i.e., pre-task, pre-post change) (dependent variables) could predict force measures (independent variable) that were different between groups, a multiple regression analysis was conducted at each target force level using a bidirectional stepwise approach. The model with the lowest AIC (Akaike information criterion) was selected as the best fit model, and the contribution of each independent variable to the best model was calculated. The statistical analyses were performed using the R platform and R packages.57,67

Page 14 of 41

15

RESULTS Group demographics and clinical information are shown in Table 1. The jaw pain group included 12 females and 5 males. The control group included 11 females and 8 males. Sex was not significantly different between groups X2(1, N = 36) = 0.63, p > 0.05). The average age of the jaw pain group was 33.5 ± 12.7 years. The average age of the control group was 29.2 ± 10.4 years. Age was not significantly different between groups (t(34) = -1.14, P > 0.05). All of the subjects in the jaw pain group had jaw pain for a period of at least 6 months. Facial pain days, pain intensity over the last 30 days and interference score were evaluated using the Graded Chronic Pain Scale (GCPS).31 The chronic jaw pain group reported greater disability days (t(16.0) = -6.64, P < 0.05), greater trait levels of pain intensity (t(17.4) = -11.0, P < 0.05) and higher pain interference scores (t(16) = -3.43, P < 0.05) compared to the control group. The characteristic pain intensity subscale of the GCPS reflects an individual’s trait level of pain. The chronic jaw pain group reported greater limitation in jaw function as assessed by the JFLS-20 (mastication subscale: t(16.5) = -6.57, P < 0.05; jaw mobility subscale: t(16.4) = -4.98, P < 0.05; emotional and verbal expression subscale: t(16.0) = -2.92, P < 0.05; Global score: t(16.22) = 4.98, P < 0.05).49 The chronic jaw pain group also reported a higher frequency of parafunctional behaviors, such as clenching teeth, and pencil or pen chewing, as measured by the Oral Behavior Checklist (OBC) (t(34) = -6.42, P < 0.05).39 Psychological measures, including anxiety, depression and fatigue were measured by short forms of the Patient-Reported Outcomes Measurement Information System (PROMIS).55 The chronic jaw pain group reported higher mean scores for anxiety (t(23.06) = -3.28, P < 0.05), depression (t(26.23) = -2.02, P = 0.05) and fatigue (t(34) = -3.51, P < 0.05).

Page 15 of 41

16

State measures of pain intensity The primary aim of the study was to characterize the effect of motor-evoked pain on force production in individuals with chronic jaw pain compared to pain-free healthy controls. Figure 2A shows mean pre-task pain intensity values for the chronic jaw pain group (green) and the control group (blue) at the low force level and the high force level. For the chronic jaw pain group, the pre-task pain intensity was 19.16 (15.8) in the low force level and 17.77 (16.6) in the high force level. Healthy controls reported a mean pre-task pain intensity of 1.59 (3.1) in the low force level and 1.81 (3.0) in the high force level. Consistent with the data shown in Figure 2A, there was a significant main effect of group (p < 0.05), with the chronic jaw pain group reporting higher pre-task pain intensity compared to controls regardless of the target force level on the upcoming trial. Mean pre-task pain intensity did not differ as a function of force level (p > 0.05), and there was no interaction between group and force level (p > 0.05). Figure 2B shows mean post-task pain intensity values for the chronic jaw pain group and the control group at each target force level. For the chronic jaw pain group, post-task pain intensity was 22.71 (16.4) in the low force level and 33.64 (16.7) in the high force level. For the controls, the post-task pain intensity was 1.81 (3.0) in the low force level and 5.44 (7.1) in the high force level. The findings show that post-task pain intensity differed as a function of group (p < 0.05), and force level (p < 0.05). At the low force level, the chronic jaw pain group reported post-task pain intensity that was 20.9 points higher than controls. At the high force level, the chronic jaw pain group reported post-task pain intensity that was 28.2 points higher than the controls. Statistical analyses confirmed this relative increase in difference at the high compared to the low

Page 16 of 41

17

force level as evidenced by a significant interaction (p < 0.05). Post-hoc Mann-Whitney U tests found that the post-task pain intensities were significantly different between the chronic jaw pain group and the controls during the low force level (U = 13.0, P < 0.05) and the high force level (U = 16.5, P < 0.05). Figure 2C shows mean pain intensity change (post-task pain intensity minus pretask pain intensity) in the chronic jaw pain group and the control group for each force level. For the chronic jaw pain group, the pain intensity change was 3.55 (4.3) in the low force level and 15.87 (11.2) in the high force level, and the control group reported a mean pain intensity change of 0.22 (1.2) at the low force level and 4.19 (6.6) at the high force level. Changes in pain intensity were higher at the high force level than at the low force level for both groups. Further, at the high force level, the pain intensity change in the chronic jaw pain group was three times greater than that in the control group. There were significant main effects of group (p < 0.05), and force level (p < 0.05), which were superseded by a significant interaction (p < 0.05). Post-hoc tests found that the pain intensity changes were significantly different between the groups at the low force level (U = 54.0, P < 0.05) and high force level (U = 45.0, P < 0.05).

– Insert Figure 2 here –

Force task The mean MVC of jaw force was 207.3 N (SD = 120.35) in the chronic jaw pain group and 264.4 N (SD = 90.7) in the control group. Although the chronic jaw pain group did have lower MVC values, there was no significant difference in MVC between the two groups (t(34) = 1.62, P > 0.05). The non-significant difference between the groups may

Page 17 of 41

18

be due to the large within group variability. MVC values in the jaw pain group ranged from 27.5 – 521 N. MVC values in the control group ranged from 125 – 415 N. Differences in MVC’s before and after the task in a subset of our subjects were not significantly different for the chronic jaw pain group (t(9) = 0.70, p > 0.05) or the control group: t(10) = -1.36, p > 0.05), suggesting that fatigue was not a significant factor in the current experimental paradigm. Figure 2D shows mean force amplitude after log transformation in the chronic jaw pain group and the control group at each force level. Table 2 reports the raw force data prior to the log transform. Details of statistical tests are also reported in Table 2. For both groups, the target force level was near 2% of MVC at the low force and 15% of MVC at the high force level. There was a significant main effect of target force level (p < 0.05), but no main effect of group was found (p > 0.05). Although there was a significant interaction (p < 0.05), the post-hoc Mann-Whitney U test found no significant difference in mean force amplitude in the high force level (U =151, p > 0.05), while the chronic jaw pain group had slightly higher mean force amplitude compared to controls in the low force level (U = 243.5, p < 0.05). The difference in force amplitude between groups at the low force level was 0.23 MVC%. (jaw pain group: M = 2.22 ± 0.34%; control group: M = 1.99 ± 0.10%). Table 2 shows mean standard deviation of force production in the chronic jaw pain group and the control group at each force level. For the chronic jaw pain group, the SD was 0.25 (0.21) at the low force level and 0.39 (0.52) at the high force level. The SD for the control group was 0.10 (0.05) at the low force level and 0.14 (0.05) at the high force level. Figure 2E shows log transformed SD values for both groups. SD varied as a function of force level (p < 0.05) and group (p < 0.05), and no interaction between group

Page 18 of 41

19

and force level was observed (p > 0.05). The statistical analyses are consistent with the pattern of data in Figure 2E and show that the chronic jaw pain group had greater SD compared to controls at both force levels and that both groups had higher SD at the high force level compared to the low force level. Table 2 and Figure 2F show the mean CV in the chronic jaw pain group and the control group. For the chronic jaw pain group, the CV was 9.52 (7.5) at the low force level and 1.92 (0.5) at the high force level. For the controls, the CV was 4.51 (1.7) at the low force and 0.86 (0.3) at the high force level. The chronic jaw pain group showed higher CV scores compared to the control group at both force levels. These data are supported by a significant main effect of group (p < 0.05) and force level (p < 0.05). No interaction effect was found (p < 0.05). Table 2 and Figure 2G show that RMSE varied as a function of force level (p < 0.05), with higher RMSE scores at the high as compared to the low force level. No main effect of group (p > 0.05) and no interaction effect was found (p > 0.05). Table 2 and Figure 2H also show ApEn values for both groups. ApEn did not differ as a function of group (p > 0.05) or force level (p > 0.05), and no interaction effect was found (p > 0.05). Together our findings demonstrate that motor-evoked pain in our chronic jaw pain group was associated with an increase in the variability of force production.

– Insert Table 2 here –

Relationship between force variability and pain To investigate the relationship between the force-dependent variables, trait measures of chronic pain, self-report measures of jaw function, psychological measures,

Page 19 of 41

20

and state measures of pain intensity, Spearman's rank-order correlation was run for each force level. Figure 3A shows correlation data for the low force level and Figure 3B shows correlation data for the high force level. In each figure, rows of the matrix on the y-axis represent the four force-dependent variables, and the columns on the x-axis correspond to each self-report questionnaires and pain intensity scores during the force task. The color intensity and the size of the circle is proportional to the strength of the relationship, ranging from -1 (blue) to 1 (red). Circles are only shown when the significance level of the correlation coefficient was achieved after FDR correction (p < 0.05). Figure 3A shows that at the low force level, SD was significantly correlated with trait pain measures, self-report measures of jaw function, psychological measures, and state measures of motor-evoked pain intensity. CV was positively correlated with trait measures of chronic pain, jaw function and state measures of pain intensity. RMSE and ApEn did not correlate with any measures. Correlation coefficients and their corresponding p-values for each pair of variables are reported in Supplementary Table 2. Figure 3B shows that at the high force level SD correlated with the trait pain measures, limitations in jaw function (JLFS), fatigue and anxiety, as well as all state measures of motor-evoked pain intensity. CV was significantly correlated with trait pain measures, limitations in jaw function (JLFS), and pre-task and post-task state measures of pain intensity. In short, the consistent positive relationship between SD and CV with many of the self-report measures of pain intensity, pain interference, and self-report jaw function suggest that force variability is a key feature of chronic jaw pain.

– Insert Figure 3 here –

Page 20 of 41

21

Multiple regression analyses of force variability In the present study, the chronic jaw pain group demonstrated greater force variability compared to controls across the two target force levels based on the ANOVAs (Table 2). Furthermore, the correlation findings revealed that SD and CV were consistently associated with self-report measures (Figure 3). Therefore, to further examine which factors that best predict motor variability during force production, we conducted multiple regression analyses at each target force level for SD and CV. The trait measures of chronic pain (i.e. duration of pain, disability days, pain intensity (GCPS), interference score), self-report measure of jaw function (JFLS, OBC), psychological measures (i.e. fatigue, anxiety, and depression), and the state measures of motor-evoked pain intensity (i.e. pre-task pain intensity and pain intensity change) were used as independent variables. Dependent variables were SD and CV separately at the low force level and at the high force level. Table 3 shows the final models for predicting SD and CV at the low force level and high force level. The model for the SD at the low force level included duration of pain, disability days, pain interference, JFLS, and pain intensity (pre-task and change), which together predicted 50% of the variance in the force variability (R2 adj = 0.50, F(6, 29) = 6.93, p < 0.05). Figure 4A shows the individual contribution of each variable (interference score = 43%, disability days (GCPS) = 17%, JFLS = 17%, pain intensity (pre-task) = 11%, pain intensity (change) = 6%, and duration of pain = 5%). The final model for CV at the low force target level included duration of pain, pain intensity (GCPS), interference score, JFLS, anxiety and depression, which predicted 32% of the

Page 21 of 41

22

variance in the force variability (R2adj = 0.32, F(6, 29) = 3.80, p < 0.05). Figure 4C shows the individual contribution of each variable (pain intensity (GCPS) = 36%, JFLS = 23%, interference score = 15%, depression = 10%, anxiety = 9%, and duration of pain = 7%). The final model for SD at the high force level included duration of pain, pain intensity (GCPS), JFLS, anxiety, and pain intensity (pre-task and change), which predicted 57% of the variance (R2adj = 0.57, F(6, 29) = 8.75, p < 0.05). Figure 4B shows the individual contribution of each variable (pain intensity (GCPS) = 34%, pain intensity (pre-task) = 29%, duration of pain = 15%, JFLS = 11%, anxiety = 7%, and pain intensity (change) = 4%). The final model for CV at the high force level included duration of pain, pain intensity (GCPS), interference score, JFLS, anxiety, and pain intensity (pre-task), which together predicted 48% of the variance (R2adj = 0.48, F(6, 29) = 6.31, p < 0.05). Figure 4D shows the individual contribution of each variable (pain intensity (pre-task) = 28%, pain intensity (GCPS) = 25%, JFLS = 23%, interference score = 11%, anxiety = 7%, and duration of pain = 4%). Based on the analyses, in the low force level, pain interference (dark green bar) and trait levels of pain intensity (GCPS) (red bar) contributed the most across the two models (4A, 4C), while at the high force level, trait levels of pain intensity (GCPS) (red bars) and state levels of pre-task pain intensity (blue bars) together accounted for most of the variance (4B, 4D). Whereas trait levels of pain intensity measured by the GCPS (red bars) explained a significant proportion of variance in force variability across both target force levels, it is notable that the state measure of pre-task pain intensity (blue bars) contributed significantly to the model for the high force level, but was absent (CV) or had a negligible contribution (SD) at the low force level. – Insert Table 3 here –

Page 22 of 41

23

– Insert Figure 4 here – DISCUSSION The goal of the study was to examine the effect of motor-evoked pain on sustained jaw force production in individuals with chronic jaw pain. We report three novel observations. First, our findings showed that compared to controls, individuals with chronic jaw pain exhibited greater variability in force production, irrespective of the target force level. Force amplitude and force error increased with an increase in target force level, and this finding was consistent across groups. Second, force variability positively correlated with pain intensity and pain interference scores. Third, regression analyses extended these findings by demonstrating that at the low force level, force variability was best predicted by trait measures of chronic pain and pain interference as measured by the GCPS. At the high force level, force variability was best predicted by a combination of state and trait measures of pain intensity. Our observations show that motor-evoked pain is positively associated with jaw force variability, and that predictors of force variability are task dependent and shift from trait measures of chronic pain and pain interference at low force levels to state measures of motor-evoked pain intensity at higher force levels.

Force variability in chronic jaw pain Higher standard deviation and coefficient of variation (CV) (normalized standard deviation) of force at both low and high target force levels revealed that individuals with chronic jaw pain performed the task with greater force variability relative to controls. Motor variability is fundamental to understanding how we control, adapt, and learn new movements, with increases in motor variability associated with deficits in motor

Page 23 of 41

24

control.63 Indeed, increases in motor variability during a force task have previously been associated with age8,68, stroke2,35, essential tremor53, and autism70. There is also a long history of studying motor variability in the context of acute experimental pain3,20,37,73 and clinical pain1,29,33,36,37. For instance, pain has been associated with increases in variability within a trial in patients with a history of trigeminal neuropathic pain even after pain remission4, as well as patients with chronic low back pain33. These findings converge to suggest that pain leads to alterations in neuromuscular control which manifest as changes in motor output variability, and our findings are consistent with this position. Defining variability is crucial, however, given that other studies have reported pain-related decreases in variability.1,37 Indeed, an important caveat is whether variability is assessed within a trial or across trials. Whereas pain can increase variability within a trial,4,33,43 pain can also decrease variability between trials.1,37,46 For instance, chronic low back pain patients show a decrease in variability in motor strategy across trials of an isometric trunk extension task,1 and variability in arm and trunk acceleration decreases from trial to trial in patients with chronic neck and shoulder pain.37 Our findings extend the literature by demonstrating that chronic jaw pain leads to within-trial increases in force variability that are consistent with other chronic pain conditions4,33 as well as other neurological conditions including stroke2,35, autism45,71, and essential tremor53. Our observations also demonstrate that adapting our goaldirected visually guided isometric grip-force task to the jaw is a viable approach for assessing motor-evoked jaw pain and force variability in chronic jaw pain.10,42

Pain intensity, pain interference, and force production

Page 24 of 41

25

Consistent with previous work, our observations show that state measures of motor-evoked pain intensity, chronic pain measures, anxiety, and depression were higher in individuals with chronic jaw pain as compared to controls.21,48 Correlation and regression analyses converged to show that force variability was positively related to state measures of motor-evoked pain intensity and trait measures of chronic pain and pain interference derived from the GCPS. The novelty of the current paradigm is that we were able to measure within-trial force production at two precisely controlled target force levels and record motor-evoked pain intensity on a trial by trial basis. Importantly, the regression analyses revealed that the strongest predictors of force variability varied as a function of target force level, with an increase in force level associated with a shift from trait measures of chronic pain intensity and interference to state measures of motorevoked pain intensity. Specifically, force variability at the low force level was best predicted by pain related interference score and trait levels of pain intensity, which are based on a 30-day period, whereas force variability at the high force level was best predicted by a combination of motor-evoked pain intensity immediately prior to each trial of force production as well as trait levels of pain intensity. Positive correlations between pain intensity and the velocity and amplitude of jaw movements have previously been demonstrated in a group of myofascial temporomandibular disorder patients during a gum chewing task.5 Here, we extend these findings by revealing a positive correlation between jaw force variability, pain intensity, and pain interference. Motor-evoked pain has previously been recognized as an important dimension of the pain experience that is more disability-relevant than spontaneous pain,6,38 and our findings are consistent with this position.

Page 25 of 41

26

The motor system can be influenced by a change in pain intensity that varies across time and is non-task-specific, and by motor-evoked pain that is task-specific. Motor-evoked pain may be driven by activation of nociceptors in the periphery or by central sensitization, whereby afferent sensory input associated with the contraction of muscle engages nociceptive circuits at some level of the central nervous system. Increased peripheral/central sensitization at multiple levels of this nociceptive pathway have been implicated in temporomandibular disorders,30,44,59,60,72,74 and may underlie the increase in pain intensity and the increase in force variability during jaw force production in our cohort of chronic jaw pain subjects. In the context of producing jaw force, peripheral sensory receptors are stimulated. The nociceptors in the face area along with other peripheral sensory receptors continuously provide sensory input, via the spinal trigeminal nuclei, to the ventral posterior nucleus of the thalamus, and distribute

information

across

multiple

cortical

regions

including

the

primary

somatosensory cortex and midcingulate cortex.14,15 In addition to the midcingulate cortex being engaged by both pain and motor processes, other regions including the supplementary motor area, cerebellum, and basal ganglia have been implicated in both pain and motor function.32,40,41,51 Together these regions may represent the supraspinal neural network that allows afferent pain signals to change how we execute movement.29 Force production is determined by the integration of supraspinal signals, peripheral input, and properties of the motor neuron pool. Altered afferent input combined with pain-related changes in signals from the brain can reduce motor unit discharge rates18, alter motor unit recruitment,66 increase synaptic noise73, and increase the variability of low frequency components in the output spike train20. These factors may account for the pain-related increase in force variability in chronic jaw pain observed in the current

Page 26 of 41

27

study. Our findings provide evidence of how motor-evoked pain in clinical conditions affects the motor system. Combining measures of force production with the precise measurement of afferent input, brain function, and descending drive will be necessary to determine the mechanisms that underlie the increase in force variability in chronic jaw pain evidenced in the current study. We did not observe a difference in the accuracy of force production at either target force level which is consitent with a previous chronic low back pain study, which showed no difference in spatial accuracy compared to controls when doing a trunk repositioning task.13 However, an oro-facial pain study demonstrated significantly greater error in chronic trigeminal neuropathic pain during a fast jaw opening task but not for slower jaw movements.4 Faster movements may therefore be necessary to drive changes in movement accuracy in individuals with chronic pain. A limitation of the current study was that the low resolution of the visual feedback may account for the difference in force amplitude at the low force level in the chronic jaw pain group. Given that visual gain was held constant across groups, the force bar appeared marginally closer to the target bar in the chronic jaw pain group, who had lower absolute MVC level, and this difference would be most visible at the low force level when the target and force bars are close to each other but not at high force level.

CONCLUSIONS In summary, our findings add new evidence of the effects of motor-evoked pain on force production in chronic jaw pain. Individuals with chronic jaw pain performed the task with greater within-trial variability, which was best predicted by state measures of motor-evoked pain intensity and trait measures of chronic pain as well as pain

Page 27 of 41

28

interference scores. Our observations show that a precisely controlled jaw force paradigm can be used to assess the effects of motor-evoked pain in a cohort of individuals with chronic jaw pain, and when combined with advanced neurophysiological measures, will provide a clinically relevant pain model to assess the neural mechanisms underlying chronic jaw pain.

ACKNOWLEDGEMENTS The authors would like to thank all participants for their time and commitment to this research. This work would not be possible without their participation. We thank members of LRN lab who provided insight and expertise that greatly assisted the research.

Page 28 of 41

29

REFERENCES

1.

Abboud J, Nougarou F, Pagé I, Cantin V, Massicotte D, Descarreaux M: Trunk motor variability in patients with non-specific chronic low back pain. Eur J Appl Physiol Springer Berlin Heidelberg; 114:2645–54, 2014.

2.

Archer DB, Misra G, Patten C, Coombes SA: Microstructural properties of premotor pathways predict visuomotor performance in chronic stroke. Hum Brain Mapp 37:2039–54, 2016.

3.

Bandholm T, Rasmussen L, Aagaard P, Diederichsen L, Rona B: Effects of experimental muscle pain on shoulder-abduction force steadiness and muscle activity in healthy subjects. Eur J Appl Physiol 102:643–50, 2008.

4.

Bhaskaracharya M, Memon SM, Whittle T, Murray GM: Jaw movements in patients with a history of pain: An exploratory study. J Oral Rehabil 42:18–26, 2015.

5.

Brandini DA, Benson J, Nicholas MK, Murray GM, Peck CC: Chewing in temporomandibular disorder patients: an exploratory study of an association with some psychological variables. J Orofac Pain 25:56–67, 2011.

6.

Butera KA, Fox EJ, George SZ: Toward a Transformed Understanding: From Pain and Movement to Pain With Movement. Phys Ther 96:1503–7, 2016.

7.

Buyukdura JS, McClintock SM, Croakin PE: Psychomotor retardation in depression: Biological underpinnings, measurement, and treatment. Prog Neuropsychopharmacol Biol Psychiatry 35:395–409, 2010.

8.

Christou EA: Aging and Variability of Voluntary Contractions. Exerc Sport Sci Rev NIH Public Access; 39:77–84, 2011.

Page 29 of 41

30

9.

Cimmino MA, Ferrone C, Cutolo M: Epidemiology of chronic musculoskeletal pain. Best Pract Res Clin Rheumatol 25:173–83, 2011.

10.

Coombes SA, Corcos DM, Sprute L, Vaillancourt DE: Selective regions of the visuomotor system are related to gain-induced changes in force error. J Neurophysiol 103:2114–23, 2010.

11.

Coombes SA, Higgins T, Gamble KM, Cauraugh JH, Janelle CM: Attentional control theory: Anxiety, emotion, and motor planning. J Anxiety Disord 23:1072–9, 2009.

12.

Craig A, Tran Y, Siddall P, Wijesuriya N, Lovas J, Bartrop R, Middleton J: Developing a model of associations between chronic pain, depressive mood, chronic fatigue, and self-efficacy in people with spinal cord injury. J Pain 14:911– 20, 2013.

13.

Descarreaux M, Blouin J-S, Teasdale N: Repositioning accuracy and movement parameters in low back pain subjects and healthy control subjects. Eur Spine J Springer; 14:185–91, 2005.

14.

Dum RP, Levinthal DJ, Strick PL: The Spinothalamic System Targets Motor and Sensory Areas in the Cerebral Cortex of Monkeys. J Neurosci 29:14223–35, 2009.

15.

Dum RP, Strick PL: Spinal cord terminations of the medial wall motor areas in macaque monkeys. J Neurosci 16:6513–25, 1996.

16.

Enoka RM, Christou EA, Hunter SK, Kornatz KW, Semmler JG, Taylor AM, Tracy BL: Mechanisms that contribute to differences in motor performance between young and old adults. J Electromyogr Kinesiol 13:1–12, 2003.

17.

Erich Neuwirth: RColorBrewer: ColorBrewer Palettes [Internet]. page R package version 1.1-22014. Available from: https://cran.r-

Page 30 of 41

31

project.org/package=RColorBrewer 18.

Farina D, Arendt-Nielsen L, Roatta S, Graven-Nielsen T: The pain-induced decrease in low-threshold motor unit discharge rate is not associated with the amount of increase in spike-triggered average torque. Clin Neurophysiol 119:43– 51, 2008.

19.

Farina D, Negro F: Common Synaptic Input to Motor Neurons , Motor Unit Synchronization , and Force Control. Exerc Sport Sci Rev 43:23–33, 2015.

20.

Farina D, Negro F, Gizzi L, Falla D: Low-frequency oscillations of the neural drive to the muscle are increased with experimental muscle pain. J Neurophysiol 107:958–65, 2012.

21.

Fillingim RB, Ohrbach R, Greenspan JD, Knott C, Dubner R, Bair E, Baraian C, Slade GD, Maixner W: Potential psychosocial risk factors for chronic TMD: Descriptive data and empirically identified domains from the OPPERA casecontrol study. J Pain 12:T46–60, 2011.

22.

Fishbain DA, Cutler R, Rosomoff HL, Rosomoff RS: Chronic pain-associated depression: antecedent or consequence of chronic pain? A review. Clin J Pain 13:116–37, 1997.

23.

Frank E Harrell Jr and with contributions from Charles Dupont and many others.: Hmisc: Harrell Miscellaneous [Internet]. page R package version 4.0-22016. Available from: https://cran.r-project.org/package=Hmisc

24.

Goldberg DS, McGee SJ: Pain as a global public health priority. BMC Public Health BioMed Central; 11:770, 2011.

25.

Gonzalez YM, Schiffman E, Gordon SM, Seago B, Truelove EL, Slade G, Ohrbach R: Development of a brief and effective temporomandibular disorder

Page 31 of 41

32

pain screening questionnaire: reliability and validity. J Am Dent Assoc 142:1183– 91, 2011. 26.

Graven-Nielsen T, Lund H, Arendt-Nielsen L, Danneskiold-Samsøe B, Bliddal H: Inhibition of maximal voluntary contraction force by experimental muscle pain: a centrally mediated mechanism. Muscle Nerve 26:708–712, 2002.

27.

Graven-Nielsen T, Svensson P, Arendt-Nielsen L: Effects of experimental muscle pain on muscle activity and co-ordination during static and dynamic motor function. Electroencephalogr Clin Neurophysiol 105:156–64, 1997.

28.

Hansdottir R, Bakke M: Joint tenderness, jaw opening, chewing velocity, and bite force in patients with temporomandibular joint pain and matched healthy control subjects. J Orofac Pain 18:108–13, 2004.

29.

Hodges PW, Tucker K: Moving differently in pain: A new theory to explain the adaptation to pain. Pain International Association for the Study of Pain; 152:S90– 8, 2011.

30.

Jensen TS, Finnerup NB: Allodynia and hyperalgesia in neuropathic pain: Clinical manifestations and mechanisms. Lancet Neurol 13:924–35, 2014.

31.

Von Korff M, Ormel J, Keefe FJ, Dworkin SF: Grading the severity of chronic pain. Pain 50:133–49, 1992.

32.

Kurth F, Zilles K, Fox PT, Laird AR, Eickhoff SB: A link between the systems: functional differentiation and integration within the human insula revealed by meta-analysis. Brain Struct Funct 214:519–34, 2010.

33.

Lamoth CJC, Meijer OG, Daffertshofer A, Wuisman PIJM, Beek PJ: Effects of chronic low back pain on trunk coordination and back muscle activity during walking: Changes in motor control. Eur Spine J Springer-Verlag; 15:23–40, 2006.

Page 32 of 41

33

34.

Lawrence MA: ez: Easy Analysis and Visualization of Factorial Experiments [Internet]. page R package version 4.4-02016. Available from: https://cran.rproject.org/package=ez

35.

Lodha N, Misra G, Coombes SA, Christou EA, Cauraugh JH: Increased Force Variability in Chronic Stroke: Contributions of Force Modulation below 1 Hz. PLoS One 8:e83468, 2013.

36.

Lund JP, Donga R, Widmer CG, Stohler CS: The pain-adaptation model: a discussion of the relationship between chronic musculoskeletal pain and motor activity. Can J Physiol Pharmacol 69:683–94, 1991.

37.

Madeleine P, Mathiassen SE, Arendt-Nielsen L: Changes in the degree of motor variability associated with experimental and chronic neck–shoulder pain during a standardised repetitive arm movement. Exp Brain Res 185:689–98, 2008.

38.

Mankovsky-Arnold T, Wideman TH, Larivière C, Sullivan MJL: Measures of Spontaneous and Movement-Evoked Pain Are Associated With Disability in Patients With Whiplash Injuries. J Pain 15:967–75, 2014.

39.

Markiewicz MR, Ohrbach R, McCall WD: Oral behaviors checklist: reliability of performance in targeted waking-state behaviors. J Orofac Pain 20:306–16, 2006.

40.

Misra G, Coombes SA: Neuroimaging Evidence of Motor Control and Pain Processing in the Human Midcingulate Cortex. Cereb Cortex 25:1906–19, 2015.

41.

Misra G, Ofori E, Chung JW, Coombes SA, Madeleine P, Mathiassen SE, ArendtNielsen L, Misra G, Ofori E, Chung JW, Coombes SA: Pain-Related Suppression of Beta Oscillations Facilitates Voluntary Movement. Cereb Cortex 27:2592–606, 2016.

42.

Misra G, Paris TA, Archer DB, Coombes SA: Dose-response effect of isometric

Page 33 of 41

34

force production on the perception of pain. PLoS One 9:, 2014. 43.

Mista CA, Christensen SW, Graven-Nielsen T: Modulation of motor variability related to experimental muscle pain during elbow-flexion contractions. Hum Mov Sci 39:222–35, 2015.

44.

Mohn C, Vassend O, Knardahl S: Experimental Pain Sensitivity in Women With Temporomandibular Disorders and Pain-free Controls: The Relationship to Orofacial Muscular Contraction and Cardiovascular Responses. Clin J Pain 24:343–52, 2008.

45.

Mosconi MW, Mohanty S, Greene RK, Cook EH, Vaillancourt DE, Sweeney JA: Feedforward and feedback motor control abnormalities implicate cerebellar dysfunctions in autism spectrum disorder. J Neurosci Society for Neuroscience; 35:2015–25, 2015.

46.

Moseley GL, Hodges PW, Lorimer Moseley G, Hodges PW, Moseley GL, Hodges PW, Lorimer Moseley G, Hodges PW, Moseley GL, Hodges PW, Lorimer Moseley G, Hodges PW: Reduced variability of postural strategy prevents normalization of motor changes induced by back pain: A risk factor for chronic trouble? Behav Neurosci 120:474–6, 2006.

47.

Müller HW and RF and LH and K: dplyr: A Grammar of Data Manipulation [Internet]. page R package version 0.7.32017. Available from: https://cran.rproject.org/package=dplyr

48.

Ohrbach R, Fillingim RB, Mulkey F, Gonzalez Y, Gordon S, Gremillion H, Lim P-F, Ribeiro-Dasilva M, Greenspan JD, Knott C, Maixner W, Slade G: Clinical Findings and Pain Symptoms as Potential Risk Factors for Chronic TMD: Descriptive Data and Empirically Identified Domains from the OPPERA Case-Control Study. J Pain

Page 34 of 41

35

12:T27–45, 2011. 49.

Ohrbach R, Larsson P, List T: The jaw functional limitation scale: development, reliability, and validity of 8-item and 20-item versions. J Orofac Pain 22:219–30, 2008.

50.

Paris TA, Misra G, Archer DB, Coombes SA: Effects of a force production task and a working memory task on pain perception. J Pain 14:1492–501, 2013.

51.

Perini I, Bergstrand S, Morrison I: Where Pain Meets Action in the Human Brain. J Neurosci 33:15930–9, 2013.

52.

Pincus SM: Approximate entropy as a measure of system complexity. Mathematics 88:2297–301, 1991.

53.

Poon C, Robichaud JA, Corcos DM, Goldman JG, Vaillancourt David E. DE: Combined measures of movement and force variability distinguish Parkinson’s disease from essential tremor. Clin Neurophysiol International Federation of Clinical Neurophysiology; 122:2268–75, 2011.

54.

Postorino M, May ES, Nickel MM, Tiemann L, Ploner M: The influence of pain on motor preparation in the human brain. J Neurophysiol 118:2267–74, 2017.

55.

Promis S, Cella D, Yount S, Rothrock N, Gershon R: The Patient-Reported Outcomes Measurement Information. Med Care 45:3–11, 2007.

56.

R Core Team: R: A Language and Environment for Statistical Computing [Internet]. Vienna, Austria: R Foundation for Statistical Computing; 2016. Available from: https://www.r-project.org/

57.

Ripley WNV and BD: Modern Applied Statistics with S [Internet]. Springer; 2002. Available from: http://www.stats.ox.ac.uk/pub/MASS4

58.

Santana-Mora U, Cudeiro J, Mora-Bermúdez MJ, Rilo-Pousa B, Ferreira-Pinho

Page 35 of 41

36

JC, Otero-Cepeda JL, Santana-Penín U: Changes in EMG activity during clenching in chronic pain patients with unilateral temporomandibular disorders. J Electromyogr Kinesiol 19:e543–9, 2009. 59.

Sarlani E, Greenspan JD: Evidence for generalized hyperalgesia in temporomandibular disorders patients. Pain 102:221–6, 2003.

60.

Sarlani E, Greenspan JD: Why Look in the Brain for Answers to Temporomandibular Disorder Pain? Cells Tissues Organs 180:69–75, 2005.

61.

Shimada A, Hara S, Svensson P: Effect of experimental jaw muscle pain on EMG activity and bite force distribution at different level of clenching. J Oral Rehabil 40:826–33, 2013.

62.

Simko TW and V: corrplot: Visualization of a Correlation Matrix [Internet]. 2016. Available from: https://cran.r-project.org/package=corrplot

63.

Slifkin AB, Newell KM: Noise, information transmission, and force variability. J Exp Psychol Hum Percept Perform 25:837–51, 1999.

64.

Svensson P, Houe L, Arendt-Nielsen L: Bilateral experimental muscle pain changes electromyographic activity of human jaw-closing muscles during mastication. Exp Brain Res Springer-Verlag; 116:182–5, 1997.

65.

Trost Z, France CR, Thomas JS: Pain-related fear and avoidance of physical exertion following delayed-onset muscle soreness. Pain International Association for the Study of Pain; 152:1540–7, 2011.

66.

Tucker K, Butler J, Graven-Nielsen T, Riek S, Hodges P: Motor Unit Recruitment Strategies Are Altered during Deep-Tissue Pain. J Neurosci 29:10820–6, 2009.

67.

Ulrike Grömping: Relative Importance for Linear Regression in R: The Package relaimpo. J Stat Softw 17:1–27, 2006.

Page 36 of 41

37

68.

Vaillancourt DE, Newell KM: Aging and the time and frequency structure of force output variability. J Appl Physiol 94:903–12, 2003.

69.

Vaillancourt DE, Slifkin AB, Newell KM: Regularity of force tremor in Parkinson’s disease. Clin Neurophysiol 112:1594–603, 2001.

70.

Wang Z, Kwon M, Mohanty S, Schmitt LM, White SP, Christou EA, Mosconi MW: Increased Force Variability Is Associated with Altered Modulation of the Motorneuron Pool Activity in Autism Spectrum Disorder (ASD). Int J Mol Sci 18:698, 2017.

71.

Wang Z, Magnon GC, White SP, Greene RK, Vaillancourt DE, Mosconi MW: Individuals with autism spectrum disorder show abnormalities during initial and subsequent phases of precision gripping. J Neurophysiol 113:1989–2001, 2015.

72.

Woolf CJ: Central sensitization: Implications for the diagnosis and treatment of pain. Pain International Association for the Study of Pain; 152:S2–15, 2011.

73.

Yavuz UŞ, Negro F, Falla D, Farina D: Experimental muscle pain increases variability of neural drive to muscle and decreases motor unit coherence in tremor frequency band. J Neurophysiol 114:1041–7, 2015.

74.

Younger JW, Shen YF, Goddard G, Mackey SC: Chronic myofascial temporomandibular pain is associated with neural abnormalities in the trigeminal and limbic systems. Pain International Association for the Study of Pain; 149:222– 8, 2010.

75.

Zedka M, Prochazka A, Knight B, Gillard D, Gauthier M: Voluntary and reflex control of human back muscles during induced pain. J Physiol 520 Pt 2:591–604, 1999.

Page 37 of 41

38

Figure 1. Experimental setup and paradigm. A. The custom-designed bite device. Force was generated against the bite plates held in the subject’s mouth, and was measured by the force transducer. B. An example force time series of one experimental run. The run consisted of 5 trials starting with the high target force level at 15% of MVC and was interleaved with the low target force level at 2% of MVC. C. Average time series of force data across 5 high force trials (red traces) and 5 low force trials (blue traces) for a single subject. The shaded areas represent ± 3 SD of the force data at each time point. D. The visual display that subject viewed during the task. At the beginning of each trial, the subjects rated their baseline intensity of jaw pain (7.5 seconds). Ratings were made using a button box to control a cursor on a visual analog scale (VAS). Following the 7.5-second pre-task pain rating, a white bar was presented with its location indicating 15% or 2% of each subject’s MVC. During the anticipation period (Ant, 15 second), subjects could see the designated level and ready to produce force. When the red force bar turned green, subject produced jaw force to move the green bar upward to the white target bar. The green bar was controlled by the subject and provided real-time visual feedback of force production. After a 30-second force production, a post-task pain rating was made to evaluate the motor-evoked pain intensity during the force production task. Figure 2. State measures of motor-evoked pain intensity and mean force measurements for the chronic jaw pain group and the controls at the low target force and high target force level. Figure A-C. Mean pre-task pain intensity, mean post-task pain intensity, and mean pain intensity change at each target force level. Figure D-H. Mean amplitude of force (% MVC), mean standard deviation (SD, % of MVC), and coefficient of variation of force (CV), mean root mean square error (RMSE), and mean approximate entropy (ApEn) after log transformation during force production at each target force level (2% MVC & 15% MVC). Note that for force amplitude, the two groups showed a similar pattern, and the green line for the jaw pain group is beneath the blue line for the control group. Error bars represent SE. Figure 3. Correlation matrix of the force-dependent variables, with the trait measures of chronic pain, self-report measures of jaw function, psychological measures and state measures of motorevoked pain intensity for the low target force level (A) and high target force level (B). Color depth and size of the circles indicate the correlation strength (Spearman's Rho coefficient, rs). The presence of a circle indicates a significance level < 0.05 after FDR correction was applied to control for multiple comparisons. The blank cell represent coefficient values that were not significant. SD = Standard deviation. CV = Coefficient of variation. RMSE = Root Mean Square Error. ApEn = Approximate Entropy. JFLS = Jaw Function Limitation Scale. OBC = Oral Behavioral Checklist. Disability days, pain intensity (GCPS) and interference score are the subscales from the Graded Chronic Pain Scale (GCPS).

Figure 4. The relative importance of each predictors’ contribution to force variability (SD) in the low and high target force models (A, B) and to CV in the low and high target force level models (C, D) for the chronic jaw pain group and the control group. GCPS = Graded Chronic Pain Scale. JFLS = Jaw Function Limitation Scale. OBC = Oral Behavioral Checklist.

Page 38 of 41

39 Table 1. Group demographics, state measures of chronic pain, and psychological measures for the chronic jaw pain group and the pain-free healthy controls. M = Mean. SD = Standard deviation. JFLS-20 = Jaw Functional Limitation Scale 20-item. GCPS = Graded Chronic Pain Scale. OBC = Oral Behavioral Checklist. PROMIS = Patient-Reported Outcomes Measurement Information System. Control (n = 19)

Jaw pain group (n = 17)

M

SD

M

SD

P-value

Age

29.16

10.43

33.53

12.65

0.26

Gender (Male/Female)

8/11

Duration of pain (years)

0.00

0.00

9.56

9.55

< 0.05

0.05

0.23

86.82

53.09

< 0.05

1.40

3.35

42.84

15.02

< 0.05

0.00

0.00

14.41

17.02

< 0.05

Mastication (0 - 10)

0.05

0.17

2.26

1.37

< 0.05

Jaw mobility (0 - 10)

0.05

0.23

2.34

1.88

< 0.05

Emotional and verbal expression (0-10)

0.00

0.00

0.91

1.65

< 0.05

Global score(0 - 10)

0.04

0.13

1.84

1.48

< 0.05

15.18

7.90

31.94

7.49

< 0.05

Fatigue 8a (0 - 40)

11.68

3.17

17.29

6.27

< 0.05

Anxiety 8a (0 - 40)

13.63

4.85

21.53

8.22

< 0.05

Depression 8a (0 - 40)

9.58

2.62

12.00

4.19

0.05

GCPS Disability days (last 6 months) Characteristic pain intensity (0 - 100) Interference score (0 - 100) JFLS-20

OBC (0 - 100)

5/12

0.50

PROMIS

Page 39 of 41

40 Table 2. Mean state measures of motor-evoked pain intensity (pre-task, post-task, and change), and mean force amplitude (% MVC), force variability (SD & CV), force accuracy (RMSE) and approximate entropy (ApEn) of force production for each group at the low target force level and the high target force level. MVC = maximum voluntary contraction; Force = force amplitude in unit of % MVC; SD = standard deviation of force in unit of % MVC; CV = coefficient of variation of force; RMSE = root mean square error; ApEn = Approximate Entropy. FDR correction (P < 0.05) was applied to control for multiple comparisons (Padj.). Low Force Control

High Force

Jaw Pain

Control

Jaw Pain

Group effect

Force effect

Interaction

M

SD

M

SD

M

SD

M

SD

Padj.

P adj.

Padj.

Pre-task

1.59

3.11

19.16

15.76

1.25

2.73

17.77

16.61

< .05

0.54

0.90

Post-task

1.81

3.02

22.71

16.38

5.44

7.08

33.64

16.67

< .05

< .05

< .05

change

0.22

1.15

3.55

4.34

4.19

6.59

15.87

11.17

< .05

< .05

< .05

Force

1.99

0.10

2.22

0.34

14.88

0.19

14.82

0.30

0.27

< .05

< .05

SD

0.10

0.05

0.25

0.21

0.14

0.05

0.39

0.52

< .05

< .05

0.95

CV

4.51

1.74

9.52

7.50

0.86

0.27

1.92

0.53

< .05

< .05

0.95

RMSE

0.40

0.20

0.53

0.34

0.59

0.30

0.79

0.50

0.24

< .05

0.95

ApEn

1.01

0.26

1.06

0.28

1.08

0.19

0.99

0.21

0.73

0.62

0.52

Page 40 of 41

41 Table 3. Multiple regression coefficients and F statistic tests for the best fit model in prediction of force variability (SD, CV) during force production at the low target force level and the high target force level. SD = standard deviation of force. CV = coefficient of variation of force. β = unstandardized estimated beta; adj.R2 denotes the adjusted proportion of the variance explained by the model. F = F statistic value. GCPS = Graded Chronic Pain Scale. JFLS = Jaw Function Limitation Scale. OBC = Oral Behavior Checklist. Low force Coefficients

SD β (S.E.)

P

High force

CV β (S.E.)

P

SD β (S.E.)

P

CV β (S.E.)

P

Intercept

-1.01 (.05)

< .05

0.64 (.14)

< .05

-0.78 (.11)

< .05

0.12 (.10)

0.24

Duration of pain

-0.01 (.01)

< .05

-0.01 (.01)

0.09

-0.02 (.01)

< .05

-0.01 (.00)

< .05

Disability days Pain intensity (GCPS) Interference score JFLS

0.00 (.00)

< .05

-

-

-

-

-

-

-

-

0.01 (.00)

< .05

0.01 (.00)

< .05

0.01 (.00)

0 .05

0.02 (.00)

< .05

-0.01 (.00)

< .05

-

-

-0.01 (.00)

0.05

-0.21 (.10)

< .05

0.10 (.06)

0.11

-0.10 (.05)

0.06

0.10 (.05)

0.07

OBC

-

-

-

-

-

-

-

-

Fatigue

-

-

-

-

-

-

-

-

Anxiety

-0.02 (.01)

0.09

-0.01 (.01)

0.13

-0.02 (.01)

< .05

Depression Pain intensity (pre-task) Pain intensity (change)

0.03 (.02)

0.16

-

-

-

-

0.01 (.00)

0.09

-

-

0.01 (.00)

< .05

0.01 (.00)

0.10

-0.03 (.02)

0.10

-

-

0.01 (.00)

0.13

-

-

Model Statistics R

2

0.50

0.32

0.57

0.48

F

6.93

3.80

8.75

6.31

p

< .05

< .05

< .05

< .05

adj.

Page 41 of 41