1

Proposed journal section: Disorders of the Nervous System

Title: When the brain expects pain: Common neural responses to pain anticipation are

related to clinical pain and distress in fibromyalgia and osteoarthritis

Abbreviated title: Pain anticipation in fibromyalgia and osteoarthritis

Authors:

(Corresponding author) Christopher A Brown, PhD. Research Associate, Human Pain

Research Group, The University of Manchester, Manchester Academic Health

Science Centre, Clinical Sciences Building, Salford Royal NHS Foundation Trust,

Salford, M6 8HD, United Kingdom

Email: christopher.brown@manchester.ac.uk

Tel: 0161 206 4528

Wael El-Deredy, PhD. Senior Lecturer, School of Psychological Sciences, The

University of Manchester, Zochonis Building, Brunswick Street, Manchester, M13

9PL, United Kingdom.

Email: wael.el-deredy@manchester.ac.uk

Tel: 0161 275 2566

Anthony KP Jones, MD. Professor of Neuro-Rheumatology, Human Pain Research

Group, University of Manchester, Clinical Sciences Building, Salford Royal NHS

Foundation Trust, Salford, M6 8HD, United Kingdom

Email: anthony.jones@manchester.ac.uk

2

Tel: 0161 206 4266

Number of pages: 33

Number of figures: 2

Number of tables: 3

Number of words: Abstract 239, Introduction 494, Discussion 1821, Whole

manuscript 5895 (including abstract and figure legends).

Keywords: Event-related potentials; electroencephalography; expectancy; chronic

pain; human.

3

Abstract

Supra-spinal processes in humans can exert a top-down enhancing effect on

nociceptive processing in the brain and spinal cord. Studies have begun to suggest

such influences occur in conditions such as fibromyalgia (FM), but it is not clear if

this is unique to FM pain or common to other forms of chronic pain, such as that

associated with osteoarthritis (OA). We assessed top-down processes by measuring

anticipatory evoked-potentials and their estimated sources, just prior (<500ms) to

laser heat pain stimulation, between 16 patients with FM, 16 patients with OA and 15

healthy participants (HPs) using whole-brain Statistical Parametric Mapping. Clinical

pain and psychological coping factors (pain catastrophizing, anxiety, and depression)

were well matched between the patient groups such that these did not confound our

comparisons between FM and OA patients. For the same level of heat pain, insula

activity was significantly higher in FM than the other two groups during anticipation,

and correlated with the intensity and extent of reported clinical pain. However, the

same anticipatory insula activity also correlated with OA pain, and to the number of

tender points across the two patient groups, suggesting common central mechanisms

of tenderness. Activation in dorsolateral prefrontal cortex (DLPFC) was reduced

during anticipation in the both patient groups, and was related to less effective

psychological coping. Our findings suggest common neural correlates of pain and

tenderness in FM and OA that are enhanced in FM but not unique to this condition.

Introduction

4

Fibromyalgia (FM) is characterized by chronic widespread pain and tenderness

(Wolfe et al 1990a), but the mechanisms of FM pain remain poorly understood.

Conversely, it is often assumed that osteoarthritic (OA) pain arises solely from

peripheral mechanisms in affected joints. Yet, in OA there is a poor relationship

between radiographic evidence of joint damage and pain (Bedson and Croft 2008).

Many patients also report pain at multiple sites irrespective of tissue damage (Natvig

et al 2000) and there is evidence of central sensitization (Lee et al 2011). There may

therefore be overlapping central mechanisms in FM and OA.

Investigations into FM have largely focused on possible central contributions to pain

(Yunus 1992;Clauw 2009). Abnormalities have been identified in spinal mechanisms

(Staud and Smitherman 2002;Price et al 2002;Staud 2002) and in diffuse noxious

inhibitory controls (Lautenbacher and Rollman 1997;Staud et al 2003), which rely on

spinal and supraspinal pathways. Brain-imaging has shown augmented responses to

experimental pain stimuli compared with pain-free controls (for a review, see Gracely

and Ambrose 2011) consistent with either central augmentation or lack of inhibition.

FM is associated with psychological co-morbidities such as anxiety and depression

(Thieme et al 2004), pain catastrophizing (Hassett et al 2000), and cognitive

impairments (Baumstark et al 1993), which are, to an extent, shared in patients with

OA (Edwards et al 2011). Pain catastrophizing has been associated with increased

activity in brain areas related to anticipation of pain (Gracely et al 2004). Both

catastrophizing (Sullivan et al 2001;Seminowicz and Davis 2006) and anticipation

(Koyama et al 2005;Brown et al 2008a;Brown et al 2008b) have been shown to

augment pain and its neural processes. However, it is not clear whether supraspinal

5

processing abnormalities are unique to patients with FM or are just related to

psychological factors that can also occur in conditions such as OA.

In this study we compared central processing of pain in groups of patients with OA

and FM (comparable in clinical pain levels and catastrophizing) relative to pain-free

controls. We used electroencephalography (EEG) with source estimation to measure

the neural generators of anticipatory and pain-evoked responses to experimental acute

laser pain. The high temporal resolution of EEG provides an advantage over types of

neuroimaging that are reliant on slow haemodynamic responses or blood flow.

Previously, we showed in a healthy population (Brown et al 2008a) and in patients

with musculoskeletal pain (Brown and Jones 2013) that ‘late’ anticipatory responses,

within half a second prior to pain onset, can be reliably localized to pain processing

regions. These localized responses correlate with expectancy and pain ratings (Brown

et al 2008b) and provide unique picture of the brain state in preparation for pain. In

the present study we hypothesized that FM and OA pain would be associated with

common abnormalities in anticipatory neural networks, suggesting shared top-down

influences on pain, in brain regions known to activate during pain anticipation and be

modified by a psychosocial intervention (insular, mid-cingulate and dorsolateral

prefrontal cortices) (Brown and Jones 2013).

Materials and Methods

The research study was approved by Salford Local Research Ethics Committee in the

United Kingdom. The study conforms with the Code of Ethics of the World Medical

Association (Declaration of Helsinki). 47 right-handed participants were recruited. All

6

subjects gave written informed consent to take part in the study. 16 patients had a

diagnosis of fibromyalgia (FM, age 48.6 ± 8.6 (mean ± SD)), 16 of osteoarthritis (OA,

age 54.3 ± 9.8), and 15 were pain-free healthy participants (HP, age 46.3 ± 7.3). The

gender of the participants was well matched, with only one male in each of the FM

and HP groups and two in the OA group. Age was significantly different between

patient groups, and age was therefore used as a nuisance variable for all statistical

analyses.

FM and OA patients fulfilled the American College of Rheumatology (ACR) criteria

for the diagnosis of FM (Wolfe et al 1990b) and OA (Altman et al 1986;Arnett et al

1988). While new criteria for FM currently exist (Wolfe et al 2011), data collection

for this study began prior the new criteria being available and so these were not used.

Participants were excluded from the study if their medical records showed a history of

neurological disorder, morbid psychiatric disorder (including major depression and

anxiety-related disorders confirmed by a psychiatrist) or cardiovascular disease. It

was expected that patients would be recruited with sub-clinical levels of anxiety and

depression that are normal for chronic pain populations. All participants were non-

medicating at the time of the study, having been requested to withdraw from any

analgesic medication for the purpose of the study.

Experimental protocol

Questionnaires were posted to participants to fill out two weeks prior to the

experimental session. The Hospital Anxiety and Depression Scale (HADS, (Zigmond

and Snaith 1983)) was used to assess mood symptoms. It is composed of statements

7

relating to anxiety or depression. The Pain Catastrophising Scale (PCS, (Sullivan et al

1995)) was used as a measure of pain-related psychological coping.

On the day of the experiment, tender point (TP) examinations were first carried out by

a trained research nurse, and scored using the Manual Tender Point Survey according

to a published methodology (Okifuji et al 1997). Due to the lack of availability of a

research nurse on the day, tender points were examined in only 8 out of the 15 healthy

volunteers and 14 out of the 16 OA patients, while all 16 FM patients were examined.

Clinical pain levels (“In general, how severe is your pain?”) was measured using a 0

– 10 visual analogue scale (VAS) ranging from “no pain” to “very severe”. Pain

interference (“How much does the pain interfere with your life?”) was also assessed

using a VAS ranging from “not at all” to “completely”. Healthy volunteers completed

these scales as well as patients.

Neural responses to acute pain

Acute pain was induced using a CO2 laser that specifically activates nociceptors in the

skin (Meyer et al 1976). Heat stimuli of 150ms duration and a beam diameter of

15mm were applied to the dorsal surface of the subjects’ right forearm. Between

stimuli, the laser was moved randomly over an area 3cm x 5cm to avoid habituation,

sensitization or skin damage. Subjects wore protective laser safety goggles during the

experiment.

8

An initial psychophysics procedure was performed using a 0-10 numerical rating

scale, which was anchored such that a level 4 indicated pain threshold, 7 indicated

moderate pain, and 10 indicated unbearable pain. A ramping procedure was repeated

three times to determine an intensity of laser stimulus for each subject at level 7,

corresponding to a moderately painful heat level, as done previously (Brown et al

2008a).

The main experiment consisted of the delivery of 40 moderately painful (level 7) laser

pulses, occurring ten seconds apart. Each laser stimulus occurred after three preceding

auditory anticipation cues spaced one second apart, to ‘count-down’ the onset of the

laser stimulus so that participants could accurately predict it. The first of the auditory

cues was concurrent with a visual cue indicating that the laser stimulus could be

expected in three seconds time (Fig. 1), which was to act as a visual fixation point to

discourage eye movements. Participants were instructed to attend to the intensity of

the pain and to rate it using the same 0-10 numerical scale as used in the

psychophysics testing procedure as detailed above.

Electroencephalographic recordings of anticipatory and pain-evoked responses

EEG recordings were taken from 61 scalp electrodes placed according to an extended

10-20 system (Quik-Cap system, Neuroscan, Inc.). Bandpass filters were set at DC -

70Hz, with a sampling rate of 500Hz and gain of 500. A notch filter was set to 50Hz

to reduce electrical interference. Electrodes were referenced to common average

across all electrodes. The vertical and horizontal electro-oculograms (EOG) were

measured for off-line reduction of blink and eye-movement artifacts.

9

Analysis of self-report measures

Differences were analyzed between groups in self-reported pain and the different

measures of psychological coping. A series of univariate ANOVAs were conducted

on each measure with two-tailed tests, with group (FM, OA, HP) as a fixed factor and

age as a covariate. Pain ratings and the laser energy required to induce a level 7 pain

were also compared in this way. VAS measures of clinical pain, and tender point

scores, were subjected to a non-parametric test (Kruskal Wallis) due to non-normality

of the data in the OA group. P-values were corrected for multiple comparisons using

the False Discovery Rate (FDR) statistic set at q = 0.05 (using Matlab code by

Nichols available at http://www-personal.umich.edu/~nichols/FDR/). The resulting p-

value threshold at this level was p < 0.012. For dependent variables showing

significant group effects in the ANOVA, post-hoc tests were performed on each group

pair. For the parametric ANOVAs, we used the Scheffe test, while for the non-

parametric data (VAS scores and tender points) we used the Mann-Whitney U test.

Pre-processing of EEG data

EEG data were analyzed using the EEGLAB toolbox (v4.515) running on MATLAB

version 7.8. Averaged Event-Related Potentials (ERPs) covering the anticipation and

pain phases of neural activity were created for each participant and each session, after

the removal of linear trends in the data and ocular artifacts (by removing artifactual

components after performing Independent Components Analysis), and filtering at 20

Hz low pass. ERPs were baseline-corrected to either the 500ms interval preceding the

10

visual anticipation cue (for the measurement of anticipatory-evoked responses) or the

500ms preceding the laser stimulus (for measurement of the pain-evoked response).

Three 500ms temporal periods of the anticipatory brain response were extracted for

analysis: a ‘baseline’ period, at -3500ms to -3000ms preceding the laser stimulus and

occurring just prior to the anticipation cue (used for baseline correction of the EEG

data), an ‘early’ period, at -2500ms to -2000ms preceding the laser stimulus and

occurring soon after the anticipation cue, and a ‘late’ period, at –500ms to 0ms

preceding the laser stimulus, as detailed and justified elsewhere (Brown and Jones

2010). The P2 peak of the Laser-Evoked Potential (LEP) was analyzed, as this was

the largest amplitude potential generated post-laser stimulus and the only potential to

be robustly produced across subjects with our laser stimulation parameters. For each

subject and condition, P2 peak latencies were determined at the electrode for which

the P2 peak showed maximum amplitude (Cz). An averaged 20ms window of LEP

data was then extracted, centered on this latency.

Analysis of Event-Related Potential (ERP) data

For each temporal period (early anticipation, late anticipation, P2 peak), we

performed a “scalp-region-of-interest” analysis to avoid the multiple comparisons

problem of individually testing groups effects at every electrode. The voltage at nine

electrodes were extracted and averaged for analysis of ERP amplitudes. The nine

electrodes used included the electrode showing the maximum amplitude over the

whole scalp for that time window when a grand average was created across subjects,

plus the surrounding eight adjacent electrodes. This number of electrodes was

11

regarded as reasonably broad enough to capture the peak of activity in most subjects

(which wasn’t necessarily at the same electrode as in the grand average), while

maintaining a reasonable degree of spatial specificity. For early anticipation the nine

electrodes were centered on FCz, while for late anticipation at the P2 peak the nine

electrodes were centered on Cz. We used a univariate ANOVA (fixed factor: group;

covariate: age) to identify group differences in the anticipatory and pain-evoked

potentials. The ANOVA was performed once for each of the three time periods, and

so results were judged to be statistically significant after correcting for multiple

comparisons using False Discovery Rate (FDR), with a q value of 0.05 and two-tailed

statistics. The resulting p value threshold was p < 0.01.

Source analysis of Event-Related Potential (ERP) data

Sources of anticipatory and pain-evoked potentials were estimated using the imaging

approach to source reconstruction as implemented in SPM8 for MEG/EEG, combined

with custom MATLAB code for batch processing. For each participant, a forward

model was constructed, using an 8196 vertex template cortical mesh, coregistered to

the electrode positions of the standard 10-20 system via three fiducial markers. This

produced ‘voxels’ (equivalent current dipoles) of 2mm x 2mm x 2mm. The lead-field

of the forward model was computed using the three-shell BEM EEG head model

available in SPM8. Source estimates were computed on the canonical mesh using 256

multiple sparse priors per hemisphere including subcortical structures (Friston et al

2008), under group constraints (Litvak and Friston 2008). Source prior smoothness

was set at 1mm. Source estimates were created based on windows of the ERP data of

500ms for pre-anticipation baseline, early and late anticipation (using the time

12

windows described in the pre-processing section above), and 80ms for the P2 peak

centered on the maximum amplitude of the peak for each individual participant. The

resulting images were smoothed at 10mm FWHM, and log-transformed prior to

statistical analysis to improve the normality in the distribution of the data.

Statistical analysis at the group level was performed using conventional SPM t tests.

Statistical tests were performed over all voxels in the brain to enable exploratory

analyses outside of our hypothesized regions of interest. To control for type I errors

over the whole brain, results are reported that were significant at the voxel level after

FDR correction (whole-brain) at pvoxel < 0.05, and only considering cluster sizes

greater than 100 voxels. To view the images and extract clusters as volumes of

interest, statistical parametrical maps were thresholded at pvoxel < 0.001 (uncorrected).

We compared the groups at each of the three time periods individually. The names of

regions of activity were identified using the Automated Anatomical Labeling (AAL)

system (Tzourio-Mazoyer et al 2002).

To test if there were any differences between patients in general and the HP group,

analyses were conducted for each time period as follows. The two paired contrasts

comparing HP patients with the FM group (“FM > HP” and “HP > FM”), were used,

in addition to a further two contrasts comparing the HP group to the OA group (“HP >

OA” and “OA > HP”). Age was added as a covariate for each contrast. These four

contrasts were used to construct two separate conjunction analyses, for the purpose of

identifying the main effect of chronic pain, i.e. common differences in both patient

groups compared to the HP group. Specifically, activations were identified that were

greater in the HP group than in the other two groups (i.e. [HP > FM] + [HP > OA]),

13

and that were greater in the patient groups compared to the HP group (i.e. [FM > HP]

+ [OA > HP]).

To test for differences between the FM group and the other two groups, for each time

period two paired contrasts were defined comparing FM patients with the HP group

(“FM > HP” and “HP > FM”), and a further two contrasts comparing the FM group to

the OA group (“FM > OA” and “OA > FM”), with age as a covariate. These four

contrasts were then used to construct two separate conjunction analyses to test for the

main effects of FM, i.e. common differences in the FM group compared to both OA

and HP groups. The first test was for activations that were greater in the FM group

than in the other two groups (i.e. [FM > HP] + [FM > OA]), while the second was for

for activations that were lesser in the FM group (i.e. [HP > FM] + [OA > FM]).

A final analysis was performed to explain the discrepancy between the FM and OA

groups in the overall size of the anticipatory-evoked potential during late anticipation.

The OA group were contrasted to the FM group [OA > FM] in a single SPM t-test,

with age as a covariate.

Analyses of volumes of interest (VOIs)

We extracted data from VOIs identified in the previous analyses (from the

conjunctive group comparisons) as possible correlates of self-report variables. We

also correlated anticipatory activity across patient groups between different clusters of

brain regions that appeared functionally important. Linear regression was used on the

data pooled across the patient groups. For each VOI, brain activity was initially

14

adjusted for age by regressing age with VOI activity and calculating the residuals of

VOI activity for further regressions. There were five statistical tests per VOI (table 3)

and to control for multiple comparisons the False Discovery Rate (FDR) statistic was

set at q = 0.05. The standardized residuals of the variables tested were tested for

normality to check that the assumptions of linear regression were not violated.

Results

Patient characteristics

The ages of patients in each group were significantly different after applying a

univariate ANOVA (p < 0.04). Because of these differences, age was used as a

covariate in all statistical analyses to remove any variance attributed to this variable

from the results.

Self-report measures

Group comparisons of self-report measures showed that a number of variables

(clinical pain, pain catastrophizing, and anxiety) showed no difference between the

two patient groups, but were significantly greater in these groups compared to the HP

group (table 1). Scores for depression were no different between the two patient

groups, but only the FM group showed significantly higher scores than the HP group.

The number of tender points was significantly higher in the FM group compared to

the HP group, and there was a trend towards a greater number in the OA group

compared to the HP group, and the FM group compared to the OA group.

15

Laser energies, pain scores and evoked potentials

Regarding the acute pain experiment, the levels of laser energy used were no different

between groups, nor were there any significant differences in the resulting pain

ratings (table 1). In comparing the amplitudes of the anticipatory (early, late phases)

and the pain-evoked potential (P2 peak), group effects were only found during late

anticipation (p < 0.01, figure 1). Statistical significance was found in a post-hoc

paired test comparing the FM and OA groups (p < 0.01). The anticipatory response

was of highest amplitude in the OA group, and lowest in the FM group, with HP

group amplitudes being in-between but nearer to the OA group.

Neural sources of evoked potentials

The group effects on source activity during each anticipatory and pain-evoked ERP

are shown in figure 2 and listed in table 2. Significant results were only found during

late anticipation, as follows. In the comparison of the FM group with the other two

groups (figure 2a), the conjunction analysis showed greater activations in the FM

group during late anticipation in the bilateral insula cortices, and the right inferior

temporal gyrus. The conjunction analyses of the two patient groups compared to the

HP group (figure 2b) showed areas with significantly reduced activations in the

patients during late anticipation, including frontal and parietal brain regions consisting

of the left (contralateral) postcentral gyrus, left superior frontal gyrus (supplementary

motor area), and the left middle frontal gyrus (dorsolateral prefrontal cortex). Smaller

clusters were found in the right superior and middle frontal gyrus, right precentral

16

gyrus, bilateral occipito-temporal gyrus, the left (contralateral) insula and neighboring

parietal operculum, and the thalamus. The contrast [OA > FM] during late

anticipation revealed a source in the precuneus bilaterally (figure 2c and table 2).

Regression of VOI clusters and self-report variables

The results of the regression analyses of volume of interest (VOIs) on clinical and

psychological variables (clinical pain, tender point count, anxiety, depression and pain

catastrophising) are shown in table 3 and as scatter plots in figures 2a and 2b. VOI

clusters extracted were the left insula and right insula (more active in FM group

compared to HP and OA groups), a left middle frontal gyrus / superior frontal gyrus /

postcentral gyrus cluster and a right middle frontal gyrus cluster (more active in the

HP group than the two patient groups), and a precuneus cluster (more active in the

OA group than FM group). For all of the following regressions, the standardized

residuals were normally distributed.

Anticipatory activity in the left insula showed significant positive relationships with

clinical pain and the number of tender points when data was pooled across patient

groups (table 3). For average clinical pain scores, these regressions were also

significant within the FM group (r = 0.49, p = 0.02) and the OA group (r = 0.63, p =

0.004) individually. In the regression of the left insula on clinical pain over the patient

groups, controlling for anxiety and catastrophizing by including them as covariates

did not reduce the significance of the relationship. This implies that coping factors

were not responsible for the relationship between anticipatory insula activity and

clinical pain.

17

Conversely, negative correlations were found when regressing the left frontal and

parietal cluster of regions, which were negatively correlated with anxiety and pain

catastrophizing across the patient groups. These correlated within the OA group when

considered separately, although not enough to survive correlation for multiple

comparisons, for anxiety (r = - 0.60, p = 0.03) and pain catastrophizing (r = - 0.57, p

= 0.03). These correlations where not significant within the FM group individually.

The greater correlation in OA patients compared to FM patients is likely a result of

greater variance in predictor variables in the OA group. We also correlated

anticipatory activity across patient groups between two key clusters of brain regions,

the left insula and the left frontal/parietal cluster, but this was not significant (r =

0.15, p = 0.43).

Discussion

In this study, we utilized the high temporal resolution of EEG with source localization

to resolve spatial patterns of activity within pain processing regions in the few

hundred milliseconds prior to experiencing acute pain. While a fMRI study (Burgmer

et al 2011) has investigated differences in pain anticipation between FM patients and

healthy controls, our methodology provides a unique window into possible

abnormalities in pain anticipation by enabling resolution of neural processing

immediately prior to pain experience (not possible with fMRI), and by comparing

with a psychologically well-matched OA group to enable assessment of abnormalities

that may be unique to FM.

18

We identified anticipatory neural substrates of pain and tenderness that are common

in FM and OA, albeit exaggerated in FM, which provide potential common brain

contributors to the mechanisms of chronic pain. Patients with FM had significantly

augmented responses during anticipation of pain in insula cortices compared to both

the OA and HP groups, but this was a difference of extent rather than type: left

(contralateral) insula activity correlated with clinical pain scores and tender points

(but not psychological coping factors) across the patient groups (and within each

patient group for clinical pain scores), suggesting common mechanisms of pain and

tenderness in the insula cortex across patient conditions. Both patient groups,

compared to HPs, had less neural processing during pain anticipation in a left-lateral

frontal/parietal cluster, including dorsolateral prefrontal, superior frontal, and

postcentral (primary somatosensory) cortical regions, which were related to poorer

coping across patient groups.

Group effects in the insula cortices

The increased responses in the insula in FM patients compared to both OA and HP

groups were correlated to symptoms normally associated with FM (clinical pain and

number of tender points) across all patients in the study, regardless of diagnosis, and

in particular within the OA group when considering clinical pain scores. We speculate

that abnormal insula responses represent part of a common mechanism for pain and

tenderness in chronic pain rather than being specific to FM. This reflects the view of

some researchers and clinicians (Croft et al 1996), that FM pain represents one end of

a spectrum chronic pain conditions that is driven by mechanisms that can potentially

19

affect patients with any form of chronic pain, rather than viewing FM as have an

entirely distinct etiology.

There is already support in the literature for the hypothesis of a key involvement of

the insula cortices both at rest and in response to acute pain stimuli in the symptoms

of FM (Cook et al 2004;Harris et al 2009;Napadow et al 2010;Kim et al 2011). The

results of the current study add to this growing body of literature by suggesting that

increases in anticipatory responses in insula cortex may be functionally important.

Anticipatory responses represent top-down processes such as expectancy that can

influence subsequent pain, and are distinct from resting-state activity that is controlled

for by removal of baseline processing in the analysis. The insula is important in

aversive conditioning, a process that augments pain and fear responses to stimuli

through interactions with the medial temporal lobes (Fendt and Fanselow

1999;Buchel and Dolan 2000;Sehlmeyer et al 2009), and contributes to negative

expectancy effects on pain and other aversive stimuli (Sawamoto et al

2000;Sarinopoulos et al 2006;Franciotti et al 2009). Our data is therefore consistent

with a role of the insula in mediating top-down augmentation of pain and tenderness

in FM. Moreover, the augmented anticipatory insula responses in FM cannot be

explained by psychological factors (catastrophizing, anxiety) that are common to FM

and OA patients. This goes against our assumption that top-down effects on pain in

FM would be directly related to factors such as catastrophizing. Rather, it could be

that supraspinal nociceptive pathways in FM are more susceptible to being negatively

conditioned for a given level of psychological distress. This would provide a unique

explanation for the pain and tenderness characteristic of FM beyond that of

psychological risk factors.

20

An alternative explanation for augmentated insula responses is central sensitization,

which is thought to result from facilitatory mechanisms in the brainstem and spinal

cord. We were not able to image neural processes related to central sensitization using

EEG in this study, but such processes are known to result in augmentation of afferent

pain signals reaching the insula cortex (Zambreanu et al 2005). If such facilitation

were taking place, it would be expected that there would be a difference between FM

and OA patients in the laser energy required to generate moderate pain (and possibly

in the amplitude of the pain-evoked potential), which we did not find. Central

sensitization therefore does not appear to explain our results.

Fronto-parietal group effects

Further abnormalities in FM patients compared to healthy volunteers were shared

with OA patients. Reduced activity in a left fronto-parietal cluster was related to

psychological coping factors that were common to both FM and OA groups. Previous

studies have also identified differences between FM and healthy groups in similar

brain regions, including negative correlations of anticipatory activity in

supplementary motor area with the subsequent pain ratings (Burgmer et al 2010),

although greater activity has been found in DLPFC (Burgmer et al 2011) rather than

the reductions found in this study. Methodologically our study was better able to pin-

point activity within a few hundred millisecond of pain, which we have previously

found (Brown et al 2008a) to differ from the earlier anticipatory activity likely to be

resolvable with fMRI due to its poorer temporal resolution. Our results shed further

light on previous data by showing that, largely, abnormalities in frontal responses are

21

not unique to FM patients but rather generic to other forms of chronic pain. Indeed,

previous work has already shown that patients with atypical facial pain have reduced

DLPFC responses to pain compared to pain-free controls (Derbyshire et al 1994).

The correlation of the lower fronto-parietal activity during pain anticipation (found in

patients) with coping factors (pain catastrophising, anxiety) lends itself to the

conclusion that activation of these brain regions during anticipation of acute pain is

adaptive for coping. In a recent study (Brown and Jones 2013), we found that patients

with a mixture of diagnoses (mostly FM and OA) who had participated in a non-

pharmacological intervention showed less deactivation (i.e. greater activity) during

pain anticipation in the left DLPFC as a result. This increase correlated with

improvements in coping variables.

DLPFC is considered part of an executive control network (Fox et al 2005;Buckner et

al 2008), and has been associated with inhibitory effects on negative emotional

responses, for example through cognitive re-appraisal (Ochsner et al 2004;Herwig et

al 2007;Goldin et al 2008), and on conditioned fear processing in the insula and

amygdala (Beauregard et al 2001;Goldin et al 2008). These interactions are thought to

dampen the affective response to pain (Beauregard et al 2001;Lorenz et al 2003).

However, in our study, there was no statistical relationship between anticipatory

response in the insula cortices and that of the DLPFC, nor was there a correlative

relationship between DLPFC and clinical pain within the patient groups.

Further findings

22

An estimated source of the anticipatory potential in the precuneus was found to be

more active in patients with osteoarthritis compared to those with fibromyalgia,

explaining the difference in the evoked-potential amplitude at the scalp. This result

was surprising and not related to our anatomical hypotheses. There was a lack of a

relationship between activity in this region and the clinical/coping self-report

measures, and so at this stage it is difficult to interpret the meaning of this result.

It is interesting to note that precuneus activation is related to memory retrieval

requiring visual imagery (Fletcher et al 1995), and one possibility is that OA patients

evoke more memory-related visual imagery during pain anticipation, although this

cannot be verified with our data. Possible contributions of the precuneus to clinical

pain symptoms warrants further investigation.

Limitations and implications of the study design

While anticipatory responses were the focus of this work, it is of interest to note that

the groups in this study did not differ in their responses to the

experimental laser stimulus, in terms of both the laser energy

required to elicit moderate pain or the amplitude of the resulting P2

peak. This contrasts with some studies that have found allodynia to

laser heat in patients with FM, and greater P2 peak amplitudes

(Gibson et al 1994;Lorenz 1998), although other studies have not be

able to demonstrate such effects for heat stimuli (Norregaard et al

1997) in line with the present data. Differences in methodology

might explain such discrepancies: our protocol was not optimally

designed to pinpoint pain thresholds, but rather laser energies

23

eliciting moderately painful sensations. Also, unlike other studies,

we adjusted the laser energy to be moderately painful for each

participant rather than using fixed laser energy across all

participants. Hence it may not be valid to draw comparisons

between our studies and other studies assessing heat sensitivity.

Another important observation in this study is that, although the

anticipatory responses were related to clinical measures of pain and tenderness, the

experimental pain stimulus activations were not. This can be explained by the fact that

laser heat energies were tailored to each participant. The experiment was designed to

avoid the potential confound of differences in brain responses being due to peripheral

nociception. This involved standardization to similar levels of pain perception across

participants. Hence, the size of the pain-evoked potential would also be individually

adjusted, taking away any potential correlation with distress. The measurement of

anticipation therefore provides a way of comparing top-down influences on pain

across groups of patients and healthy volunteers in a way that is not confounded by

differences in peripheral nociceptive processing.

We did not control for non-specific anticipation (i.e. anticipation not related to the

expected pain level) in this study, for example by providing a contrast of pain

anticipation to the anticipation of non-painful stimuli. It is possible that the results of

this study might arise from generic mechanisms of anticipation rather than those

specific to pain anticipation. This is certainly a question that needs to be explored, and

has implications for whether non-pharmacological treatments (e.g. psychological

24

therapies) should target pain anticipation per se, negative expectancy including but

not limited to pain, or general expectancy (positive or negative) mechanisms.

Conclusion

This study reveals that abnormal anticipatory responses to pain in FM are largely

shared with patients with regional pain (OA), suggesting that these may represent

common brain mechanisms for chronic regional and widespread pain. Lower

anticipatory activity in a left frontal and parietal cluster (including DLPFC) was

related to coping factors (anxiety, catastrophising) rather than pain type. The greater

anticipatory insula activity observed in the FM group was related to clinical pain

symptoms and tenderness regardless of the pain type or psychological factors,

suggesting it may play a role in enhancing pain in OA as well as FM. However, the

causal relationship between clinical pain and pain anticipation responses is not

demonstrated here and clearly warrants further investigation. 

Acknowledgements: The authors declare no conflict of interest including competing

financial interests. This work was supported by Arthritis Research UK [grant number

JO531], and the University of Manchester.

Reference List

Altman,R., Asch,E., Bloch,D., Bole,G., Borenstein,K. & Brandt,K. (1986) The American College of Rheumatology criteria for the classification and reporting of osteoarthritis of the knee. Arthritis Rheum., 29, 1039-1049.

25

Arnett,F.C., Edworthy,S.M., Bloch,D.A., McShane,D.J., Fries,J.F. & Cooper,N.S. (1988) The American Rheumatism Association 1987 revised criteria for the classification of rheumatoid arthritis. Arthritis Rheum., 31, 315-324.

Baumstark,K.E., Buckelew,S.P., Sher,K.J., Beck,N., Buescher,K.L., Hewett,J. & Crews,T.M. (1993) Pain behavior predictors among fibromyalgia patients. Pain, 55, 339-346.

Beauregard,M., Levesque,J. & Bourgouin,P. (2001) Neural correlates of conscious self-regulation of emotion. J Neurosci, 21, RC165.

Bedson,J. & Croft,P.R. (2008) The discordance between clinical and radiographic knee osteoarthritis: A systematic search and summary of the literature. BMC Musculoskeletal Disorders, 9.

Brown,C.A. & Jones,A.K.P. (2013) Psychobiological correlates of improved mental health in patients with musculoskeletal pain after a mindfulness-based pain management program. Clinical Journal of Pain, 29, 233-244.

Brown,C.A. & Jones,A.K.P. (2010) Meditation experience predicts less negative appraisal of pain: Electrophysiological evidence for the involvement of anticipatory neural responses. Pain, 150, 428-433.

Brown,C.A., Seymour,B., Boyle,Y., El-Deredy,W. & Jones,A.K.P. (2008a) Modulation of pain perception by expectation and uncertainty: behavioral characteristics and anticipatory neural correlates. Pain, 135, 240-250.

Brown,C.A., Seymour,B., El-Deredy,W. & Jones,A.K.P. (2008b) Confidence in beliefs about pain predicts expectancy effects on pain perception and anticipatory processing in right anterior insula. Pain, 139, 324-332.

Buchel,C. & Dolan,R.J. (2000) Classical fear conditioning in functional neuroimaging. Curr Opin.Neurobiol., 10, 219-223.

Buckner,R.L., ndrews-Hanna,J.R. & Schacter,D.L. (2008) The Brain's Default Network. Annals of the New York Academy of Sciences, 1124, 1-38.

Burgmer,M., Petzke,F., Giesecke,T., Gaubitz,M., Heuft,G. & Pfleiderer,B. (2011) Cerebral activation and catastrophizing during pain anticipation in patients with fibromyalgia. Psychosomatic Medicine, 73, 751-759.

Burgmer,M., Pogatzki-Zahn,E., Gaubitz,M., Stober,C., Wessoleck,E., Heuft,G. & Pfleiderer,B. (2010) Fibromyalgia unique temporal brain activation during experimental pain: A controlled fMRI Study. Journal of Neural Transmission, 117, 123-131.

Clauw,D.J. (2009) Fibromyalgia: An Overview. American Journal of Medicine, 122, S3-S13.

Cook,D.B., Lange,G., Ciccone,D.S., Liu,W.C., Steffener,J. & Natelson,B.H. (2004) Functional imaging of pain in patients with primary fibromyalgia. The Journal of Rheumatology, 31, 364-378.

26

Croft,P., Burt,J., Schollum,J., Thomas,E., Macfarlane,G. & Silman,A. (1996) More pain, more tender points: is fibromyalgia just one end of a continuous spectrum? Annals of the Rheumatic Diseases, 55, 482-485.

Derbyshire,S.W., Jones,A.K., Devani,P., Friston,K.J., Feinmann,C., Harris,M., Pearce,S., Watson,J.D. & Frackowiak,R.S. (1994) Cerebral responses to pain in patients with atypical facial pain measured by positron emission tomography. J Neurol.Neurosurg.Psychiatry, 57, 1166-1172.

Edwards,R.R., Calahan,C., Mensing,G., Smith,M. & Haythornthwaite,J.A. (2011) Pain, catastrophizing, and depression in the rheumatic diseases. Nature Reviews Rheumatology, 7, 216-224.

Fendt,M. & Fanselow,M.S. (1999) The neuroanatomical and neurochemical basis of conditioned fear. Neurosci Biobehav.Rev, 23, 743-760.

Fletcher,P.C., Frith,C.D., Baker,S.C., Shallice,T., Frackowiak,R.S.J. & Dolan,R.J. (1995) The mind's eye - Precuneus activation in memory-related imagery. Neuroimage, 2, 195-200.

Fox,M.D., Snyder,A.Z., Vincent,J.L., Corbetta,M., Van Essen,D.C. & Raichle,M.E. (2005) The human brain is intrinsically organized into dynamic, anticorrelated functional networks. Proceedings of the National Academy of Sciences of the United States of America, 102, 9673-9678.

Franciotti,R., Ciancetta,L., la Penna,S., Belardinelli,P., Pizzella,V. & Romani,G.L. (2009) Modulation of alpha oscillations in insular cortex reflects the threat of painful stimuli. Neuroimage, 46, 1082-1090.

Friston,K., Harrison,L., Daunizeau,J., Kiebel,S., Phillips,C., Trujillo-Barreto,N., Henson,R., Flandin,G. & Mattout,J. (2008) Multiple sparse priors for the M/EEG inverse problem. Neuroimage, 39, 1104-1120.

Gibson,S.J., Littlejohn,G.O., Gorman,M.M., Helme,R.D. & Granges,G. (1994) Altered Heat Pain Thresholds and Cerebral Event-Related Potentials Following Painful Co2-Laser Stimulation in Subjects with Fibromyalgia Syndrome. Pain, 58, 185-193.

Goldin,P.R., McRae,K., Ramel,W. & Gross,J.J. (2008) The Neural Bases of Emotion Regulation: Reappraisal and Suppression of Negative Emotion. Biological Psychiatry, 63, 577-586.

Gracely,R.H., Geisser,M.E., Giesecke,T., Grant,M.A., Petzke,F., Williams,D.A. & Clauw,D.J. (2004) Pain catastrophizing and neural responses to pain among persons with fibromyalgia. Brain, 127, 835-843.

Gracely,R.H. & Ambrose,K.R. (2011) Neuroimaging of fibromyalgia. Best Practice &amp; Research Clinical Rheumatology, 25, 271-284.

Harris,R.E., Sundgren,P.C., Craig,A.D., Kirshenbaum,E., Sen,A., Napadow,V. & Clauw,D.J. (2009) Elevated insular glutamate in fibromyalgia is associated with experimental pain. Arthritis & Rheumatism, 60, 3146-3152.

27

Hassett,A., Cone,J., Patella,S. & Sigal,L. (2000) The role of catastrophizing in the pain and depression of women with fibromyalgia syndrome. Arthritis.Rheum., 43, 2493-2500.

Herwig,U., Baumgartner,T., Kaffenberger,T., Bruhl,A., Kottlow,M., Schreiter-Gasser,U., Abler,B., Jancke,L. & Rufer,M. (2007) Modulation of anticipatory emotion and perception processing by cognitive control. Neuroimage, 37, 652-662.

Kim,S.H., Chang,Y., Kim,J.H., Song,H.J., Seo,J., Kim,S.H., Han,S.W., Nam,E.J., Choi,T.Y., Lee,S.J. & Kim,S.K. (2011) Insular cortex is a trait marker for pain processing in fibromyalgia syndrome--blood oxygenation level-dependent functional magnetic resonance imaging study in Korea. Clinical and experimental rheumatology, 29, S19-S27.

Koyama,T., McHaffie,J.G., Laurienti,P.J. & Coghill,R.C. (2005) The subjective experience of pain: where expectations become reality. Proc.Natl.Acad.Sci.U.S A, 102, 12950-12955.

Lautenbacher,S. & Rollman,G.B. (1997) Possible deficiencies of pain modulation in fibromyalgia. Clinical Journal of Pain, 13, 189-196.

Lee,Y., Nassikas,N. & Clauw,D. (2011) The role of the central nervous system in the generation and maintenance of chronic pain in rheumatoid arthritis, osteoarthritis and fibromyalgia. Arthritis Research & Therapy, 13, 211.

Litvak,V. & Friston,K. (2008) Electromagnetic source reconstruction for group studies. Neuroimage, 42, 1490-1498.

Lorenz,J. (1998) Hyperalgesia or hypervigilance? An evoked potential approach to the study of fibromyalgia syndrome. Z.Rheumatol., 57, 19-22.

Lorenz,J., Minoshima,S. & Casey,K.L. (2003) Keeping pain out of mind: the role of the dorsolateral prefrontal cortex in pain modulation. Brain, 126, 1079-1091.

Meyer,R.A., Walker,R.E. & Mountcastle,V.B., Jr. (1976) A laser stimulator for the study of cutaneous thermal and pain sensations. IEEE Trans.Biomed.Eng, 23, 54-60.

Napadow,V., LaCount,L., Park,K., As-Sanie,S., Clauw,D.J. & Harris,R.E. (2010) Intrinsic brain connectivity in fibromyalgia is associated with chronic pain intensity. Arthritis & Rheumatism, 62, 2545-2555.

Natvig,B., Rutle,O., Bruusgaard,D. & Eriksen,W.B. (2000) The association between functional status and the number of areas in the body with musculoskeletal symptoms. International Journal of Rehabilitation Research, 23, 49-53.

Norregaard,J., Bendtsen,L., Lykkegaard,J. & Jensen,R. (1997) Pressure and Heat Pain Thresholds and Tolerances in Patients with Fibromyalgia. J Muscoskeletal Pain, 5, 43-53.

Ochsner,K.N., Ray,R.D., Cooper,J.C., Robertson,E.R., Chopra,S., Gabrieli,J.D. & Gross,J.J. (2004) For better or for worse: neural systems supporting the cognitive down- and up-regulation of negative emotion. Neuroimage, 23, 483-499.

28

Okifuji,A., Turk,D.C., Sinclair,J.D., Starz,T.W. & Marcus,D.A. (1997) A standardized Manual Tender Point Survey. I. Development and determination of a threshold point for the identification of positive tender points in fibromyalgia syndrome. Journal of Rheumatology, 24, 377-383.

Price,D.D., Staud,R., Robinson,M.E., Mauderli,A.P., Cannon,R. & Vierck,C.J. (2002) Enhanced temporal summation of second pain and its central modulation in fibromyalgia patients. Pain, 99, 49-59.

Sarinopoulos,I., Dixon,G.E., Short,S.J., Davidson,R.J. & Nitschke,J.B. (2006) Brain mechanisms of expectation associated with insula and amygdala response to aversive taste: implications for placebo. Brain Behav.Immun., 20, 120-132.

Sawamoto,N., Honda,M., Okada,T., Hanakawa,T., Kanda,M., Fukuyama,H., Konishi,J. & Shibasaki,H. (2000) Expectation of pain enhances responses to nonpainful somatosensory stimulation in the anterior cingulate cortex and parietal operculum/posterior insula: an event-related functional magnetic resonance imaging study. J Neurosci, 20, 7438-7445.

Sehlmeyer,C., Sch+¦ning,S., Zwitserlood,P., Pfleiderer,B., Kircher,T., Arolt,V. & Konrad,C. (2009) Human fear conditioning and extinction in neuroimaging: A systematic review. PLoS ONE, 4.

Seminowicz,D.A. & Davis,K.D. (2006) Cortical responses to pain in healthy individuals depends on pain catastrophizing. Pain, 120, 297-306.

Staud,R. (2002) Evidence of involvement of central neural mechanisms in generating fibromyalgia pain. Curr Rheumatol.Rep., 4, 299-305.

Staud,R. & Smitherman,M.L. (2002) Peripheral and central sensitization in fibromyalgia: pathogenetic role. Curr Pain Headache Rep., 6, 259-266.

Staud,R., Robinson,M.E., Vierck Jr,C.J. & Price,D.D. (2003) Diffuse noxious inhibitory controls (DNIC) attenuate temporal summation of second pain in normal males but not in normal females or fibromyalgia patients. Pain, 101, 167-174.

Sullivan,M.J., Rodgers,W.M. & Kirsch,I. (2001) Catastrophizing, depression and expectancies for pain and emotional distress. Pain, 91, 147-154.

Sullivan,M.J.L., Bishop,S.R. & Pivik,J. (1995) The Pain Catastrophizing Scale: Development and Validation. Psychological Assessment, 7, 524-532.

Thieme,K., Turk,D.C. & Flor,H. (2004) Comorbid Depression and anxiety in fibromyalgia syndrome: Relationship to somatic and psychosocial variables. Psychosomatic Medicine, 66, 837-844.

Tzourio-Mazoyer,N., Landeau,B., Papathanassiou,D., Crivello,F., Etard,O., Delcroix,N., Mazoyer,B. & Joliot,M. (2002) Automated anatomical labeling of activations in SPM using a macroscopic anatomical parcellation of the MNI MRI single-subject brain. Neuroimage, 15, 273-289.

29

Wolfe,F., Clauw,D.J., Fitzcharles,M.A., Goldenberg,D.L., H+ñuser,W., Katz,R.S., Mease,P., Russell,A.S., Russell,I.J. & Winfield,J.B. (2011) Fibromyalgia criteria and severity scales for clinical and epidemiological studies: A modification of the ACR preliminary diagnostic criteria for fibromyalgia. Journal of Rheumatology, 38, 1113-1122.

Wolfe,F., Smythe,H.A., Yunus,M.B., Bennett,R.M., Bombardier,C., Goldenberg,D.L., Tugwell,P., Campbell,S.M., Abeles,M., Clark,P., Fam,A.G., Farber,S.J., Fiechtner,J.J., Franklin,C.M., Gatter,R.A., Hamaty,D., Lessard,J., Lichtbroun,A.S. & Masi,A.T. (1990a) The American College of Rheumatology 1990. Criteria for the classification of fibromyalgia. Report of the Multicenter Criteria Committee. Arthritis and Rheumatism, 33, 160-172.

Wolfe,F., Smythe,H.A., Yunus,M.B., Bennett,R.M., Bombardier,C., Goldenberg,D.L., Tugwell,P., Campbell,S.M., Abeles,M., Clark,P., Fam,A.G., Farber,S.J., Fiechtner,J.J., Franklin,C.M., Gatter,R.A., Hamaty,D., Lessard,J., Lichtbroun,A.S., Masi,A.T., McCain,G.A., Reynolds,W.J., Romano,T.J., Russel,I.J. & Sheon,R.P. (1990b) The American College of Rheumatology 1990 criteria for the classification of fibromyalgia: report of the multicenter criteria committee. Arthritis Rheum., 33, 160-172.

Yunus,M.B. (1992) Towards a model of pathophysiology of fibromyalgia: Aberrant central pain mechanisms with peripheral modulation. Journal of Rheumatology, 19, 846-850.

Zambreanu,L., Wise,R.G., Brooks,J.C.W., Iannetti,G.D. & Tracey,I. (2005) A role for the brainstem in central sensitisation in humans. Evidence from functional magnetic resonance imaging. Pain, 114, 397-407.

Zigmond,A.S. & Snaith,R.P. (1983) The Hospital Anxiety and Depression Scale. Acta Psychiatrica Scandinavica, 67, 361-370.

30

Table 1: Self-report measure statistics: means (standard deviations), and p values from group effect ANOVAs (for parametric data) or Chi-squared tests (for non-parametric data), with corresponding post-hoc tests. Figures in italics are statistically significant.

Mean (SD) Group effect Post-hoc tests ( P values) HP group FM group OA group Statistic P value HP vs. FM HP vs. OA FM vs. OA

n=15 n=16 n=16 *F or **χ2Age (years) 46.4 (7.3) 48.6 (8.6) 55.0 (8.7) 4.7* 0.014 0.743 0.019 0.100Chronic pain (/10)

Clinical pain 0.4 (0.7) 4.0 (1.7) 4.1 (2.2) 25.0** <0.001 <0.001 <0.001 0.835Pain interference 0.3 (0.7) 4.3 (1.9) 4.1 (2.5) 23.9** <0.001 <0.001 <0.001 0.952

Number of tender points (/18) 2.3 (2.4) 14.1 (3.8) 9.4 (7.3) 15.2** <0.001 <0.001 0.020 0.080Pain Catastrophizing Scale (/52) 6.9 (7.6) 16.3 (7.6) 16.3 (2.3) 5.1* 0.010 0.027 0.031 1.000Hospital Anxiety and Depression Scale

Anxiety (/21) 3.6 (2.3) 8.4 (4.0) 7.5 (3.7) 8.6* 0.001 0.001 0.012 0.766Depression (/21) 1.5 (1.7) 5.9 (3.8) 4.3 (3.7) 7.2* 0.002 0.002 0.078 0.388

Laser pain ratings (/10) 5.8 (0.4) 6.1 (0.9) 6.2 (1.3) 0.3* 0.747Laser energy (W cm-2) 12.4 (2.2) 13.6 (2.1) 13.1 (3.2) 0.9* 0.423

31

Table 2: Group effects on sources of anticipatory potentials. Brain regions are organized by cluster with † denoting regions belonging to the same cluster as the previous entry.

Brain Region L/R

MNI coordinates Cluster level Voxel level

x y zNo.

voxelsp-

ValueT-

Scorep-

ValueEffect of no chronic pain: [HP> FM]+[HP>OA]Late anticipation:

Postcentral gyrus L -30 -16 56 3766 <0.001 6.35 <0.001Postcentral gyrus L -8 -14 60 † † 6.14 <0.001Superior frontal gyrus L -6 32 48 † † 5.73 0.001Superior frontal gyrus L -10 64 22 † † 5.06 0.007Middle frontal gyrus L -36 8 52 † † 4.77 0.013Precentral gyrus R 34 -10 58 1536 0.002 5.98 <0.001Superior frontal gyrus R 18 6 56 † † 5.98 <0.001Occipito-temporal gyrus R 50 -70 4 674 0.016 5.79 <0.001Occipito-temporal gyrus L -28 -72 24 739 0.011 5.06 0.007Parietal operculum L -50 -26 26 380 0.080 4.40 0.037Parietal operculum L -46 -4 10 408 0.068 5.19 0.005Middle frontal gyrus R 34 18 54 980 0.004 4.68 0.018Superior temporal gyrus R -50 -52 12 246 0.186 5.01 0.007

ThalamusL/R 0 -18 12 1355 0.003 4.83 0.018

Effect of FM pain: [FM > HP]+[FM>OA]Late anticipation:

Insula L -40 -8 2 3870 <0.001 5.83 0.005Insula R 36 -10 -8 2351 <0.001 4.65 0.042Inferior temporal gyrus R 58 -8 -28 283 0.146 4.78 0.042

Effect of OA vs. FM pain: [OA>FM]Late anticipation:

Precuneus L -6 -62 48 2748 <0.001 5.54 0.012Precuneus R 12 -76 50 † † 4.62 0.023

32

Table 3: Regression statistics for the comparison of volumes of interest (VOIs) relative to self-report measures across participants in the patient groups. The VOIs were selected on the basis of results from the group effects on pain anticipation. DLPFC: dorsolateral prefrontal cortex; SMA: supplementary motor area; PCG: post-central gyrus. * Covariates used in the multiple regression analyses were HADS-anxiety and pain catastrophising. Significant p values after considering multiple comparisons are italicized.

Dependent variable Independent variableAverage clinical pain

Average clinical pain

Tender points Anxiety Depression

Pain catastro-phizing

With covariate

s *Left DLPFC / SMA / PCG cluster

r coefficient -0.226 0.057 -0.434 -0.385 -0.485p value 0.230 0.774 0.019 0.039 0.007

Right DLPFCr coefficient -0.299 0.234 -0.086 -0.108 -0.164p value 0.096 0.212 0.647 0.564 0.370

Left Insular coefficient 0.558 0.658 0.423 0.073 0.037 0.110p value 0.001 0.001 0.020 0.695 0.843 0.549

Right Insular coefficient 0.403 0.314 0.053 0.103 0.158p value 0.022 0.091 0.776 0.582 0.389

Precuneusr coefficient -0.230 0.011 -0.087 -0.187 -0.043p value 0.206 0.956 0.643 0.313 0.814

33

Figure 1: ERP waveforms and topography plots. (a) Average waveforms for each group

are plotted of the anticipation-evoked potential and pain-avoked potential. The data is the

mean of nine electrodes including Cz and all adjacent electrodes. ERPs are corrected to the

pre-anticipation baseline (-3500ms to -3000ms), with early anticipation, late anticipation,

and P2 peak periods marked. (b) Topoplots displayed are the average of each group, with

ERP data corrected to the pre-anticipation baseline. DLPFC: dorsolateral prefrontal cortex;

SMA: supplementary motor area; PCG: post-central gyrus.

34

Figure 2: ERP source estimates and their correlates during late anticipation (average activity

over -500ms to 0ms). (a) Group effects on ERP sources showing regions of greater activity

in the fibromyalgia group relative to the other two groups (i.e. the conjunction of

fibromyalgia patients vs. with each of the other two groups: FM>OA + FM>HP). Fixation

cross shows most activated voxel. Y-axis units are the log-transformed current source

density. (b) Group effects on ERP sources showing regions of reduced activity in the patient

groups relative to healthy participants (i.e. the conjunction of healthy participants vs. each

patient group: HP>FM + HP>OA). Coordinates of image are chosen to visualize all

activated clusters, and do not denote a region of activation. Y-axis units are the log-

transformed current source density. (c) Greater activity in the osteoarthritis group relative to

the fibromyalgia group (OA>FM). Fixation cross shows most activated voxel.