Lower Resting-State Alpha Reactivity in Chronic Widespread Pain Compared to At-Risk and Healthy Individuals

Abstract

Background

Chronic widespread pain (CWP), the hallmark of Fibromyalgia (FM), is poorly understood. Altered spectral power of cortical oscillations during rest have been identified in chronic pain, yet it is unknown whether people at high risk of future CWP show FM-like Electroencephalogram (EEG) features. This cross-sectional observational study is the first to examine spectral changes in individuals at high risk of CWP.

Methods

Differences in resting-state oscillatory power in FM (n = 19), At-Risk (AR; n = 21) and Healthy Control (HC; n = 17) groups were examined. Risk factors included non-CWP pain, somatic symptoms, illness behaviour and sleep problems. Patients were not on centrally acting medication. EEG recordings were obtained in eyes-open and closed conditions (6 min of each) to assess alpha reactivity (eyes-closed minus eyes-open). Group differences in scalp-averaged spectral power were examined in several frequency bands, and topographic analysis was performed on alpha.

Results

Alpha reactivity was significantly lower in FM compared to AR and HC groups. Topographic analysis of alpha found that this interaction peaked in right-frontal electrodes, where there was a positive (uncorrected) correlation between alpha reactivity and morning salivary cortisol levels in AR individuals, not present in FM.

Conclusions

As expected, alpha reactivity differed between groups; however, contrary to our expectations, AR did not show FM-like resting alpha physiology, but were instead similar to HC. The (uncorrected) positive association of right-frontal alpha reactivity and cortisol hints at a pattern related to resilience to CWP in high-risk individuals, which larger, perhaps longitudinal studies may confirm.

Keywords

chronic widespread pain fibromyalgia alpha reactivity resting-state EEG neurophysiology pain biomarkers at-risk

Introduction

Chronic widespread pain (CWP), the hallmark of Fibromyalgia (FM), is prevalent worldwide.^1,2 FM pathogenesis is incompletely understood, and pharmaceutical interventions have limited efficacy. Identification of reliable biomarkers underlying FM development could support earlier diagnosis and prevention strategies.³ Non-CWP pain, somatic symptoms, illness behaviour and sleep problems place individuals at high risk of future CWP.⁴ Higher cortisol levels in FM and in individuals with these risk factors, compared to low-risk controls,⁵ has been previously reported, but the underlying brain mechanisms remain unclear.

Several abnormalities in nociceptive processing within the central nervous system (CNS) have been identified in chronic pain.⁶ FM and osteoarthritis pain have been linked to heightened electroencephalographic (EEG) activity in the insula during experimental pain anticipation, while anticipatory frontal activity was related to poorer coping in both patient groups.⁷ It remains unknown whether such changes reflect baseline neural activity, and whether individuals at high risk of CWP show similar patterns.

A candidate neurophysiological signature for CWP is altered cortical oscillations. A better understanding of these changes could inform FM prevention, including targets for neurofeedback.⁸ Several groups have demonstrated an inverse relationship between alpha power and experimental pain in healthy individuals.^9–14 Alpha might therefore represent an inhibitory process linked to pain resilience. In FM patients relative to controls, studies have identified lower resting-state alpha power,^15–18 which was related to higher FM pain intensity,^15,19 and increases following pain-reduction interventions.^20,21 Systematic reviews suggest changes in alpha, theta and other frequency bands in CWP and FM compared to healthy controls, though findings are inconsistent and often confounded by centrally acting medication.^22,23

Alpha reactivity – the reduction in alpha power from eyes-closed to eyes-open rest – is a sensitive marker of cortical responsiveness.^24,25 Reduced alpha reactivity has been linked to neuropathic pain and may indicate impaired sensory gating or inhibitory control.^26,27 However, alpha reactivity has not been examined in FM or in individuals at high risk of CWP.

Here, we examined resting-state spectral changes in the absence of centrally acting medication. We analysed spectral power across frequency bands and compared eyes-open and eyes-closed conditions to assess alpha reactivity. Given the evidence reviewed above, we hypothesised reduced alpha reactivity in FM relative to controls. Based on the idea that alpha changes might underlie the transition to CWP, we also hypothesised abnormal – likely reduced – alpha reactivity in the at-risk group relative to low-risk controls.

Methods

Written informed consent was obtained from all participants. We used the STROBE reporting guideline²⁸ to draft this manuscript, and the STROBE reporting checklist²⁹ when editing, included in Supplemental materials.

Participants

Participants were recruited via advertisement through the University and the wider community. Patients with acute or CWP were additionally identified through Rheumatology and Pain clinics in the region. The sample was part of a larger study that included neuroimaging (not reported here), the target sample size for which was between 16 and 20 per group (total N = 48–60), determined by a statistical power analysis to achieve 80% power to detect a medium (f = .25) interaction or a large (f = .40) main effect of group in the 3 (group)×2 (condition) mixed ANOVA (alpha = .05, correlation between repeated measures = 0.5).

All participants were right-handed and aged between 25 and 65. Participants were excluded if they had neurological or morbid psychiatric illness, ischaemic heart disease, peripheral vascular disease, complex regional pain syndrome, or peptic ulcer disease.

FM patients met the American College of Rheumatology criteria for FM.³⁰ Full details of the screening process for AR and low-risk HC groups have been published elsewhere.⁵ Briefly, the AR group reported the following in the previous month: pain that lasted more than twenty-four hours that did not fulfil CWP criteria according to the Manchester definition,³¹ and at least two of the following: two somatic symptoms,³² and a minimum score of four on the illness behaviour subscale of the illness attitude scale³³ and the sleep problem scale.³⁴ The low-risk HC group were free from acute or chronic pain and any other known disease, and individuals were excluded if they reported more than one of the following: two somatic symptoms, and a score of 4 or more on the illness behaviour and sleep problem scales.

All participants were free from analgesic and antidepressant medication. Six FM patients and one AR individual were withdrawn from such medication prior to study commencement, for at least six half-life duration, according to pharmacokinetic properties of the drug [Electronic Medicines Compendium; last accessed 2020]. For full details of medication withdrawal, see Table S1.

Questionnaires and Salivary Cortisol

A series of validated questionnaires were completed on the day of the EEG recording session by all participants except for one FM patient for whom there is missing data. These included: The aforementioned Sleep Problem Scale; the Brief Pain Inventory (BPI) to measure pain severity³⁵; the Hospital Anxiety and Depression Scale³⁶ (HADS); and the twelve-item list of threatening life events.³⁷ Additionally, the Fibromyalgia Impact Questionnaire³⁸ (FIQ) was completed by FM (n = 18), AR (n = 17) and HC (n = 13), and the Pain Catastrophising scale³⁹ (PCS) was completed by FM (n = 14), AR (n = 16) and HC (n = 18).

Morning (8–9 am) and evening (10pm) saliva samples were obtained in FM (n = 17) and AR (n = 14) individuals for the measurement of cortisol levels (see reference⁵ for further details). The majority of FIQ and PCS scores, as well as saliva samples were obtained within four weeks of the EEG recording session.

EEG Data Collection

EEG data were acquired using one of two caps, according to their availability at the time recording sessions took place. Scalp electrodes were placed according to an extended 10–20 system (Brain Cap 64-MR; Brain Products GmbH, Gilching, Germany). The EEG caps differed in the number of scalp electrodes (either 62 or 63). The 62 EEG channels common to both caps were included in the analysis. Electrodes were referenced to FCz for both caps. Data were acquired with a sampling rate of either 500 Hz or 1000 Hz and were down-sampled to the same rate during pre-processing. A 50 Hz notch filter was set to reduce background electrical noise.

Resting-State Recordings

Six minutes of each eyes-open (EO), and eyes-closed (EC) resting-state recordings were obtained, with the order of conditions counterbalanced across participants. Participants were instructed to rest comfortably, and not to think of anything in particular, while refraining from movement or falling asleep. During the EO condition, they were asked to focus their eyes on a fixation cross. All participants confirmed that they had remained awake throughout the recordings.

EEG Analysis

Data Pre-Processing

EEG data were analysed using SPM12 (https://www.fil.ion.ucl.ac.uk/spm/; running in MATLAB R2015a, The MathWorks Inc., 2015), with some EEGLAB⁴⁰ (version 14_1_1b) and FieldTrip⁴¹ functions used for artefact removal and channel interpolation, called via custom scripts (https://github.com/jason-taylor).

Several datasets (HC: n = 7; AR: n = 6; FM: n = 7) were contaminated by high-frequency artefact across all EEG channels. Prior to pre-processing, Independent component analysis (ICA) was conducted on these datasets using EEGLAB's ‘runica’ function. Thirty-two components were extracted from each participant's data after principal component analysis (PCA), and high-frequency components were identified by eye and projected out of the data.

During pre-processing, the data were first converted to SPM12 format and down-sampled to 200 Hz. High (0.5 Hz) and low-pass (100 Hz) filters, and a 49–51 Hz notch filter were applied, where the latter was employed to remove the mains frequency of 50 Hz. Arbitrary epochs of 2000 ms were created, and each epoch was mean centred by subtracting its average. Noisy channels were identified manually, and interpolated using FieldTrip's ‘channelrepair’ function, by producing a weighted average of neighbouring channels. Electrodes were subsequently re-referenced to the common average.

ICA was performed on all datasets to remove artefacts related to blinks and eye movements. Thirty-two components were extracted following PCA. Independent component time-courses were correlated with pseudo-VEOG, which was computed by averaging channel data from Fp1, Fpz and Fp2, and with pseudo-HEOG, which was calculated by subtracting F7 from F8 channel data. Independent component topographies were then visually inspected, to identify artefact components as those with unusually high correlations with pseudo-EOG channels, or those with typical blink or eye-movement topographies. For almost all participants (n = 52), a single blink/vertical eye-movement component was found (mean Pearson's correlation r = 0.88 (SD = 0.10) with pseudo-VEOG signal). For most participants (n = 46), a single clear horizontal eye-movement component was identified (mean r = 0.78 (SD = 0.12) with pseudo-HEOG signal), and for 4 further participants, the horizontal eye-movement artefact appeared to be split across two components (mean r = 0.59, SD = 0.12). Four further participants appeared to have components that captured a combination of horizontal and vertical eye-movements. After manual inspection, 2 components were removed for 3 of these participants, and 4 components were removed for one of them. Artefact components were projected out of the data using SPM's ‘montage’ function.

Following ICA, epochs that still contained high-amplitude noise were rejected using an absolute channel amplitude threshold of 100 µV.

Spectral Analysis

For each participant, spectral power was computed from 1–45 Hz with a resolution of 1 Hz using Fast Fourier Transform, and a Hanning-tapered window was applied. Data were log-transformed and averaged over epochs, then averaged across all EEG channels in the following frequency bands: delta (1–3 Hz), theta (4–7 Hz), alpha (8–12 Hz), beta (13–30 Hz), and low gamma (31–45 Hz).

Statistical Analyses

Statistical analyses were conducted with IBM SPSS Statistics version 22. Kruskal-Wallis H was used to examine group differences in continuous variables that were not normally distributed according to the Shapiro-Wilk normality test: pain severity, HADSA, HADSD, Sleep Problem Scale, adverse life events, FIQ scores and PCS. Mann-Whitney U post-hoc tests were conducted. To control for multiple comparisons, the alpha criterion of 0.05 was divided by the number of group comparisons to provide an adjusted alpha criterion level (0.05/3 = 0.016).

To follow up a significant interaction in the alpha band from the ANCOVA collapsed over all channels, the spatial distribution over the scalp was explored. A topographic image of average frequency-band log-transformed power was created for each participant, which was then written to a NIFTI image file (32 × 32 pixels) for general linear model (GLM) analysis (mass-univariate pixel-wise analysis in topographic space). The GLM modelled group×condition with age as a covariate, and a family-wise error (FWE) threshold of p_FWE<.05 for peak height was applied for all contrasts.

Correlational Analysis

Spearman's Rho correlations were performed separately in FM and AR groups, between the difference in alpha power between conditions (EC–EO, extracted from the peak of topographic analysis, near electrode F4) and the following measures: log-transformed morning and evening salivary cortisol levels, and the following questionnaire scores demonstrating group differences: pain severity (BPI), sleep problems and FIQ. Participants with missing data were excluded from analyses. Results were corrected for multiple comparisons for FM and AR groups according to the adjusted alpha criterion (0.05/2 = 0.025).

Results

Demographics and Clinical Scores

19 FM patients (17 females; age, 41.1 ± 11.7 years; mean ± SD), 21 At-Risk (AR; 15 females; age 39.0 ± 10.2 years), and 17 pain-free, low-risk, Healthy Controls (HC; 14 females; age 45.0 ± 11.1 years) participated in the study. Groups did not differ in age (χ² = 2.394, p = .302) or sex (χ² = 2.128, p = 0.345). See Table 1 for descriptive statistics for questionnaire scores, as well as details of pain duration and diagnosis in patients.

Table 1.

Mean, Standard Deviation, and Range for Clinical Scores in Healthy Controls (n = 17), At-Risk (n = 21), and Fibromyalgia (n = 18) Groups.

Questionnaires	HC		AR		FM
	M (SD)	RNG	M (SD)	RNG	M (SD)	RNG
HADSA	1.38 (2.29)	0–7	7.69 (5.43)	0–18	9.06 (3.86)	0–15
HADSD	1.23 (1.48)	0–4	6.06 (5.17)	0–18	7.72 (3.74)	0–13
Adverse Life Events	0.31 (0.63)	0–2	0.51 (1.63)	0–6	0.94 (1.06)	0–3
Sleep Problems	1.85 (2.67)	0–10	8.75 (6.32)	0–20	13.61 (5.48)	0–20
Pain severity (BPI)	0.62 (1.94)	0–7	3.38 (2.55)	0–8	5.94 (1.55)	3–8
FIQ^a	5.59 (12.25)	0–43	37.04 (22.23)	0–73.1	63.67 (12.24)	29.5–82
PCS^b	10.3 (11.69)	0–37	17.81 (13.43)	0–44	23.56 (10.57)	0–40
Years since pain onset	-	-	4.87 (6.83)	0.08–21	12.27 (7.25)	3–25
Years since diagnosis	-	-	-	-	3.88 (5.11)	0.06–20

Abbreviations: HC, Healthy Controls; AR, At-Risk; FM, Fibromyalgia; M, mean; SD, Standard Deviation; RNG, Range; HADSA, Hospital Anxiety and Depression Scale for anxiety, HADSD, Hospital Anxiety and Depression Scale for depression; BPI, Brief Pain Inventory; FIQ, Fibromyalgia Impact Questionnaire; PCS, Pain Catastrophising Scale.

FIQ: HC n = 13, AR n = 17.

PCS: HC n = 14, AR n = 16, FM n = 18.

There were significant group differences in scores for pain severity, HADSA, HADSD, sleep problems, FIQ (all p < .001), PCS (p = .008), and adverse life events (p = .034). Mean scores were highest in FM, middling in AR, and lowest in HC for all questionnaires that demonstrated significant group differences. See Table 2 for Kruskal-Wallis and Mann-Whitney U results.

Table 2.

Results of Kruskal-Wallis H and Mann-Whitney U for Questionnaire Scores in Healthy Controls (n = 17), At-Risk (n = 21), and Fibromyalgia (n = 18).

Questionnaires	Kruskal-Wallis H		Mann-Whitney U with Bonferroni Correction
	All groups		HC & AR		HC & FM		AR & FM
	χ² (2)	P	U	P	U	P	U	P
HADSA	21.87	<0.001*	57.00	<0.001*	22.50	<0.001*	143.00	0.193
HADSD	19.43	<0.001*	70.00	0.001*	29.50	<0.001*	132.00	0.107
Adverse Life Events	6.74	0.034*	109.50	0.019	91.50	0.018	182.50	0.846
Sleep Problems	27.12	<0.001*	38.00	<0.001*	17.00	<0.001*	114.50	0.035
Pain severity (BPI)	30.61	<0.001*	44.00	<0.001*	13.00	<0.001*	84.00	0.003*
FIQ^a	29.84	<0.001*	51.00	0.002*	1.00	<0.001*	38.00	<0.001*
PCS^b	9.78	0.008*	70.00	0.078	46.00	0.002*	101.00	0.137

Abbreviations: HC, Healthy Controls; AR, At-Risk; FM, Fibromyalgia; HADSA, Hospital Anxiety and Depression Scale for Anxiety, HADSD, Hospital Anxiety and Depression Scale for Depression; BPI, Brief Pain Inventory; FIQ, Fibromyalgia Impact Questionnaire, PCS; Pain Catastrophising Scale.

*Significant (Post hoc alpha criterion adjusted significance for multiple comparisons 0.05/3 = 0.016).

FIQ: HC n = 13, AR n = 17, FM = n = 18.

PCS: HC n = 14, AR n = 16, FM n = 18.

The FM group significantly differed from the HC group (FM > HC) in scores for pain severity, HADSA, HADSD, sleep problems, FIQ (all p < .001), and PCS (p = .002), but did not significantly differ in adverse life events (p = .018) after multiple comparison correction. The AR group significantly differed from the HC group (AR > HC) in pain severity, sleep problems, HADSA, HADSD (all p = .001), and FIQ (p = .002), but not in adverse life events (p = .019) following multiple comparison correction, and did not significantly differ in PCS. FM and AR groups significantly differed (FM > AR) in scores for FIQ (p < .001) and pain severity (p = .003), but did not significantly differ in sleep problems (p = .035) after controlling for multiple comparisons, and did not significantly differ in HADSA, HADSD, PCS or adverse life events.

Spectral Analysis

Scalp-averaged log-power spectra are shown for each group and condition in Figure 1(a), and frequency band-averaged means and standard deviations are presented in Table 3. In the group×condition ANOVAs, no significant main effects of group were found in any frequency band. There were significant main effects of condition in theta (F(1,53) = 11.352, p = .001, η_p² = 0.176; EC > EO), and in alpha frequency bands (F(1,53) = 12.581, p = .001, η_p² = 0.192; EC > EO). Furthermore, there was a significant interaction between group and condition – i.e., the effect of particular interest – in the alpha band (F(2,53) = 4.066, p = .023, η_p² = 0.133; see Figure 1b).

Figure 1.

Whole-array spectral and alpha-band results. (a) Line graphs illustrating mean log-transformed spectral power by group (top: HC; middle: AR; bottom: FM) and condition (blue: eyes closed; orange: eyes open) for integer frequencies from 1 to 45 Hz. Shading indicates +/- one standard error of the mean (SEM). (b) Error-bar and scatter plot showing the individual averages (circles) and group means (short black horizontal line) +/- SEMs (error bar whiskers) averaged over frequencies in the alpha band (8–12 Hz). Data are averaged over all EEG channels. HC = Healthy Controls (n = 17); AR = At-Risk (n = 21); FM = Fibromyalgia (n = 19).

Table 3.

Group Means and Standard Deviation for Eyes Open and Closed in Each Power Band (log(µV²)) for Healthy Controls (n = 17), At-Risk (n = 21), and Fibromyalgia (n = 19).

		HC		AR		FM
Band	Condition	M	SD	M	SD	M	SD
Delta	Open	−1.077	0.274	−1.057	0.462	−1.054	0.356
Delta	Closed	−1.113	0.349	−0.917	0.432	−1.045	0.399
Theta	Open	−2.070	0.369	−1.899	0.604	−1.903	0.480
Theta	Closed	−1.843	0.432	−1.547	0.683	−1.773	0.540
Alpha	Open	−1.907	0.624	−1.592	0.879	−1.664	0.676
Alpha	Closed	−1.083	0.674	−0.840	0.830	−1.281	0.682
Beta	Open	−2.977	0.493	−2.844	0.672	−2.782	0.427
Beta	Closed	−2.912	0.358	−2.854	0.551	−2.928	0.562
Low Gamma	Open	−4.030	0.703	−3.880	0.756	−3.804	0.510
Low Gamma	Closed	−4.247	0.603	−4.198	0.577	−4.244	0.548

Abbreviations: HC, Healthy Controls; AR, At-Risk; FM, Fibromyalgia; M, mean; SD, Standard Deviation.

Post-hoc tests on the difference in alpha power between conditions (EC–EO, ‘alpha reactivity’) demonstrated significant group differences: HC > FM (t(34) = 2.521, p = .0166 surviving Bonferroni correction (adjusted alpha criterion = 0.05/3 = 0.0167), d = 0.823), and AR > FM comparisons (t(38) = 2.284, p = .0280 (not significant after Bonferroni correction), d = 0.709), but there was no significant difference in the HC versus AR comparison (t(36) = 0.415, p = .680, d = 0.133). Further post-hoc tests conducted to compare group differences in alpha power during eyes closed and open conditions separately did not reveal any significant differences; however, the difference between AR and FM groups during eyes closed approached significance: AR > FM (t(38) = 1.821, p = .077). Analysis removing four outliers (3 AR based on eyes-open alpha; 1 FM based on eyes-closed alpha) produced similar but stronger results (see Supplementary Methods and Results and Figure S1).

Topographic Analysis

The scalp-averaged alpha reactivity differences were explored further in topographic analyses. The main effect of condition (EC > EO) was significant in every pixel, showing that alpha reactivity was present on the entire scalp array when collapsing over group. The ‘full’ interaction (3 group×2 condition) produced no significant results at p_FWE<.05. A ‘reduced’ interaction was run to find where HC and AR together differed from FM across conditions. The reduced interaction produced three clusters: A large cluster of 75 pixels with a right-frontal peak nearest to electrode F4 (peak F(1107) = 11.53, p_FWE=.020), a smaller left-frontal cluster of 41 pixels with peak nearest F5 (peak F(1107) = 10.59, p_FWE=0.028), and a very small posterior-midline cluster of 3 pixels with peak near POz (peak F(1107) = 9.47, p = .046; see Figure 2). All three clusters showed alpha activity that was greater for closed than open, and this alpha reactivity appeared larger for HC and AR than for FM (see Figure 2a; note that the reduced interaction contrast required a difference between conditions between these sets of groups, but it was agnostic as to the direction). An analysis with the four outliers identified above removed found the same pattern of results, but additionally, the full group-x-condition interaction was significant (see Supplementary Methods and Results and Figure S2).

Figure 2.

Alpha-band topographical statistical parametric mapping (SPM) results. (a) Error-bar and scatter plot (formatting as in Figure 1 (b)) of log-transformed alpha power extracted from the peak pixel (near electrode F4) in the right-frontal cluster illustrated in the SPM shown in (b). SPM in (b) shows the unthresholded (greyscale underlay) and thresholded (pFWE<.05; overlay) F-statistic map of the reduced interaction contrast (HC & AR vs FM)×(eyes closed vs open). HC = Healthy Controls (n = 17) AR = At Risk (n = 21); FM = Fibromyalgia (n = 19).

To probe the interaction, data extracted from the right-frontal peak were submitted to post-hoc tests to see whether alpha reactivity (EC > EO log power values, adjusted for age) was significant within each group. Both HC (t(16) = 5.516, p < .001, d = 1.338) and AR (t(20) = 6.187, p < .001, d = 1.350) showed significant right-frontal alpha reactivity (EC > EO), whereas FM did not (t(18) = 1.916, p = .071, d = 0.439). To see whether alpha reactivity differed between groups, post-hoc t-tests were computed to compare groups on the EC–EO difference. Differences in right-frontal alpha reactivity were found for AR > FM (t(38) = 2.879, p = .007, d = .912) and HC > FM (t(34) = 2.432, p = .020, d = .812), though only the AR > FM effect survived correction for multiple comparisons (0.05/3 = 0.0167). The HC versus AR difference was not significant (t(36) = 0.425, p = .673, d = .139).

EEG-Clinical Variable Correlations

There was a significant positive correlation between right frontal-alpha reactivity (EC–EO) and log-transformed morning salivary cortisol levels in the AR group (rho=0.552, p = .041 uncorrected, n = 14), but this did not survive correction for multiple comparisons (0.05/2 = 0.025; see Figure 3). A test for differences between correlations (AR: rho=0.552, n = 14 vs FM rho=−0.115, n = 17) did not reach significance (z = 1.807, p = .071).

Figure 3.

Alpha reactivity-cortisol correlations. Scatterplots and trendlines show the rank of alpha power difference (closed – open, extracted from topographic peak near electrode F4), against the rank of morning cortisol, for At Risk (AR; n = 14; squares) and Fibromyalgia (FM; n = 17; circles).

There were no significant correlations between right frontal alpha reactivity and evening salivary cortisol levels, or questionnaire scores for pain severity, sleep problems or FIQ in the AR group. There were no significant correlations between right frontal alpha reactivity and any of the cortisol or questionnaire measures in the FM group.

Discussion

We explored resting-state spectral power in healthy controls, patients with chronic pain (Fibromyalgia), and those at risk for chronic pain. Clear group differences in alpha reactivity (eyes closed vs open) were found between groups, with reactivity lower in FM than in HC as expected. However, contrary to our prediction that the AR group would show FM-like alpha reactivity, we found the opposite pattern: Alpha reactivity in AR was similar to HC and greater than FM. Topographic analysis identified the same pattern of results (alpha reactivity in HC and AR greater than in FM) in a large cluster with a right frontal peak nearest F4, as well as in smaller left frontal and midline posterior clusters. The right-frontal alpha reactivity showed a nominal positive correlation (not surviving correction for multiple comparisons) with morning salivary cortisol levels in AR but not FM.

Studies in healthy individuals have reported decreased global alpha power from eyes closed to open resting-state conditions.^24,42–44 This change in alpha power, termed alpha reactivity²⁵ is inversely related to arousal.^24,45,46 Longitudinal research has identified reduced alpha reactivity among the earliest predictors of neuropathic pain.^26,27 Our study is the first to report lower alpha reactivity in FM compared to At-Risk and Healthy Control groups. A systematic review of EEG resting-state studies in FM found reduced alpha power in patients compared to controls; however, data were mostly acquired during either eyes closed or open conditions, not both.²³ Our findings of lower alpha reactivity in FM expand on this and are consistent with previous reports of reduced low-frequency reactivity (delta, theta, beta) in sub-acute and chronic neuropathic pain – though patients were on centrally-acting medication in contrast to the present study.^26,27,47,48

We further observed a nominal positive correlation (that did not survive correction for multiple comparisons) between right-frontal alpha reactivity and morning salivary cortisol levels, accounting for individual differences in the At-Risk group only. It is noteworthy that the right-frontal peak was near electrode F4, which has been used as a stimulation site to target the right dorsolateral prefrontal cortex (dlPFC) in transcranial magnetic stimulation (TMS) studies.^49,50 Low-frequency repetitive TMS over the right dlPFC in FM and chronic regional pain patients led to pain reduction, and improved descending pain inhibition, which has been proposed as a protective mechanism against chronic pain development.^51–53 Deficient endogenous pain inhibition has been reported in FM and was suggested to result from lower resting-state alpha power.^7,23,54

Our findings are consistent with the possibility of more effective top-down inhibitory control over pain by frontal regions in our At-Risk group that is absent in the FM group, with cortisol potentially playing a protective role, although this remains speculative given the exploratory, uncorrected correlation and modest sample size. Preferential right-hemispheric, stress-related activation has been shown in humans and animals, and reduced alpha has been related to both experimentally induced pain and psychosocial stress in healthy adults, with some evidence pointing to a role of the prefrontal cortex.^55–57 For example, increased cortisol following noxious stimulation was associated with lower pain unpleasantness, increased pain tolerance, greater prefrontal and reduced pain-related brain activation in an adaptive response in humans and animals,⁵⁸ while research in chronic low back pain has found reduced limbic and greater pain inhibitory mechanisms in those demonstrating a greater cortisol response.⁵⁹

FM pain has been associated with increased morning salivary cortisol, and with corticolimbic activation of the ventromedial prefrontal cortex.^60,61 Increased morning and evening salivary cortisol levels in FM and At-Risk groups compared to controls have been previously reported.⁵ Recently, diffusion orientation complexity (DOC) differences have also been found between FM and At-Risk groups, potentially indicating reduced white matter (WM) microstructural complexity in right-frontal WM tracts in FM.⁶² Though speculative at this point, it may be the case that At-Risk individuals exert elevated corticolimbic regulation of the prefrontal cortex over limbic regions, previously postulated to protect against CWP development.⁵⁹

Collectively, our results, including (tentatively, as it did not survive correction) the relationship between alpha reactivity and cortisol in the At-Risk but not in the FM group, might represent a biomarker of resilience to CWP in high-risk individuals, who potentially demonstrate greater recruitment of pain resilience mechanisms (ie, higher alpha power) in the face of mounting stress. Resilience constitutes a protective factor against future adversity, entailing adequate management of acute and chronic stress, wherein the hypothalamic-pituitary-adrenal axis and related corticolimbic neurocircuitry play a crucial role.^63,64 Future longitudinal studies are warranted to investigate the role of these mechanisms in FM development, and of the right-frontal cortex in the management of pain and stress.

Thalamocortical involvement has been suggested to underlie alpha reactivity in healthy individuals.^24,65 Thalamocortical dysfunction is thought to drive the lack of neural adaptation to altered sensory input when opening the eyes in neuropathic pain^47,48 and might underlie the lower alpha reactivity identified in our FM cohort. The thalamus relays sensory and pain signals to the cortex and has previously demonstrated structural and functional abnormalities in FM, including lower functional connectivity to the periaqueductal grey, a key region of endogenous pain modulation.^66–68 The thalamus provides multimodal input to parts of the cingulate cortex, which mediate endogenous pain inhibitory pathways.⁶⁹ Thalamocortical dysfunction potentially underlying lower alpha reactivity in FM might therefore drive abnormal pain inhibition. The thalamus is structurally connected to the hypothalamus, and through hypothalamic input, inhibits the stress response following chronic stress.^70,71 Reduced resting-state functional connectivity was observed between the medial hypothalamus and thalamus in FM compared to controls, which increased in patients following an exercise intervention and was associated with improved function. Dynamic causal modelling showed that the thalamus exerts influence over the hypothalamus in healthy controls, whereas the reverse connectivity pattern was demonstrated in FM and was partially restored post-intervention.⁷² Future research should investigate the interplay between thalamocortical, pain inhibitory and corticolimbic networks in CWP development.

Recently, a particular mechanism of thalamocortical dysfunction – thalamocortical dysrhythmia (TCD⁷³) – has been discussed as a possible explanation for alpha-band changes in chronic pain. In particular, slowing of individual peak alpha frequency (PAF; sometimes also abbreviated IAF or IPAF) has been found in FM,⁷⁴ widespread pain in urologic chronic pelvic pain syndrome,⁷⁵ chronic neurogenic pain,⁷⁶ and to relate to pain severity Long COVID.⁷⁷ In healthy pain-free controls, pain sensitisation induced by experimental pain paradigms has also been related to lower PAF, suggesting that low PAF could be a risk factor for the development of chronic pain (^78–80; cf^81–83). In our sample, age-adjusted PAF did not differ between groups (see Supplementary Methods and Results; we thank a reviewer for suggesting this analysis).

Central sensitisation – augmentation of nociceptive processing in the CNS – is currently one of the principal explanations underlying FM and CWP.^84,85 We did not find a significant correlation between pain intensity and right-frontal alpha reactivity, in contrast to recent findings of a relationship between higher subjective pain and lower alpha reactivity at central electrodes in chronic low back pain.⁸⁶ In line with central sensitisation, there is evidence of ocular pain related to corneal sensitivity in FM, and increased sensitivity to visual stimuli in FM patients compared to chronic pain and pain-free control groups.^87,88 Our findings of reduced alpha reactivity in FM might therefore be related to increased visual sensitivity, consistent with central sensitisation.

We addressed limitations of previous research by collecting data in both eyes open and closed conditions, and analysing a wide range of frequency bands, in a sample that was not on centrally acting medication.^22,23 A limitation of the study is the relatively small sample size, which was prone to the influence of outliers (see Supplementary Methods and Results). Future work with larger sample sizes may confirm whether the right-frontal alpha reactivity difference is reliable. A further limitation is that EEG and cortisol were measured at different times, which limits the interpretation of the trend cortisol-alpha association to chronic factors rather than to acute changes of state. Future work could measure cortisol and EEG in the same session, perhaps in repeated sessions or in the presence or absence of nociceptive challenge, to establish whether the two measures covary together and whether they relate to pain dynamics.

In conclusion, we observed significantly lower alpha reactivity in FM compared to At-Risk and Healthy Control groups. Topographic analysis produced a right-frontal cluster, where alpha reactivity positively correlated with morning salivary cortisol levels in the At-Risk group but not the FM group (though this correlation did not survive correction for multiple comparisons, so it should be interpreted with caution). Increased alpha reactivity in the presence of raised cortisol in the At-Risk group may represent a pattern reflecting resilience which is lost in established FM. These results have implications for FM prevention strategies and may have relevance to other conditions where chronic stress is a potential mediator.

Supplemental Material

sj-docx-1-eeg-10.1177_15500594261468061 - Supplemental material for Lower Resting-State Alpha Reactivity in Chronic Widespread Pain Compared to At-Risk and Healthy Individuals

Supplemental material, sj-docx-1-eeg-10.1177_15500594261468061 for Lower Resting-State Alpha Reactivity in Chronic Widespread Pain Compared to At-Risk and Healthy Individuals by Nayab Begum, Anthony Jones, Christopher A. Brown, Jonathan N. Rajan, Katherine Wainwright, Ariane Delgado Sánchez, Emily Pye, Timothy Rainey and Jason R. Taylor in Clinical EEG and Neuroscience

Supplemental Material

sj-docx-2-eeg-10.1177_15500594261468061 - Supplemental material for Lower Resting-State Alpha Reactivity in Chronic Widespread Pain Compared to At-Risk and Healthy Individuals

Supplemental material, sj-docx-2-eeg-10.1177_15500594261468061 for Lower Resting-State Alpha Reactivity in Chronic Widespread Pain Compared to At-Risk and Healthy Individuals by Nayab Begum, Anthony Jones, Christopher A. Brown, Jonathan N. Rajan, Katherine Wainwright, Ariane Delgado Sánchez, Emily Pye, Timothy Rainey and Jason R. Taylor in Clinical EEG and Neuroscience

Footnotes

Acknowledgements

We thank the study participants. We thank Professor John McBeth for helpful advice during the early stages of planning the study, Manoj Sivan, Paul Barratt, William Gregory and Fibromyalgia support groups for help with recruitment, and Freya Roberts for help with the manuscript. The authors have no conflicts of interest to declare.

ORCID iDs

Katherine Wainwright

Jason R. Taylor

Ethical Considerations

The regional ethics committee (REC) approved the study (REC: NRES Committee Northwest – Greater Manchester West; REC reference number: 15/NW/0536; IRAS number: 180081), which met guidelines for the Helsinki Declaration of 1975, as revised in 1983.

Consent to Participate

Written informed consent was obtained from all participants.

Consent for Publication

Not applicable.

Author Contributions

NB conceived the research idea, designed the study, carried out data collection and analysis and drafted the manuscript. JRT, AJ and CAB conceived the research idea, designed the study, reviewed and approved the analysis and the manuscript. JNR and TR contributed to data collection. JRT, KW, ADS and EP carried out data analysis. All authors discussed the results and commented on the manuscript.

Funding

This work was funded by a PhD studentship awarded by the University of Manchester to the lead author.

Declaration of Conflicting Interests

The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Data Availability

Anonymised research data available upon reasonable request to the corresponding author.

Supplemental Material

Supplemental material for this article is available online.

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