EEG Beta functional connectivity decrease in the left amygdala correlates with the affective pain in fibromyalgia: A pilot study

Soline Makowka; Lliure-Naima Mory; Michael Mouthon; Christian Mancini; Adrian G Guggisberg; Joelle Nsimire Chabwine

doi:10.1371/journal.pone.0281986

. 2023 Feb 21;18(2):e0281986. doi: 10.1371/journal.pone.0281986

EEG Beta functional connectivity decrease in the left amygdala correlates with the affective pain in fibromyalgia: A pilot study

Soline Makowka ^1,^#, Lliure-Naima Mory ^1,^2,^#, Michael Mouthon ¹, Christian Mancini ¹, Adrian G Guggisberg ³, Joelle Nsimire Chabwine ^1,^2,^*

Editor: Claudia Sommer⁴

PMCID: PMC9943002 PMID: 36802404

Abstract

Fibromyalgia (FM) is a major chronic pain disease with prominent affective disturbances, and pain-associated changes in neurotransmitters activity and in brain connectivity. However, correlates of affective pain dimension lack. The primary goal of this correlational cross-sectional case-control pilot study was to find electrophysiological correlates of the affective pain component in FM. We examined the resting-state EEG spectral power and imaginary coherence in the beta (β) band (supposedly indexing the GABAergic neurotransmission) in 16 female patients with FM and 11 age-adjusted female controls. FM patients displayed lower functional connectivity in the High β (Hβ, 20–30 Hz) sub-band than controls (p = 0.039) in the left basolateral complex of the amygdala (p = 0.039) within the left mesiotemporal area, in particular, in correlation with a higher affective pain component level (r = 0.50, p = 0.049). Patients showed higher Low β (Lβ, 13–20 Hz) relative power than controls in the left prefrontal cortex (p = 0.001), correlated with ongoing pain intensity (r = 0.54, p = 0.032). For the first time, GABA-related connectivity changes correlated with the affective pain component are shown in the amygdala, a region highly involved in the affective regulation of pain. The β power increase in the prefrontal cortex could be compensatory to pain-related GABAergic dysfunction.

Introduction

Fibromyalgia (FM) is one of the most frequent chronic pain disease, reaching up to 4% of frequency in the general population, and affecting more significantly females than males [1]. The clinical constellation characterizing FM combines widespread chronic pain, fatigue, mood disorders, cognitive deficits and sleep disturbances. Despite active research on underlying mechanisms and etiologies, FM remains a poorly understood disease condition.

As in any other chronic pain syndrome, central sensitization plays an important role in FM [2], which could be reflected by altered brain dynamics in several areas involved in nociception observed in functional connectivity (FC) studies [3]. A recent resting-state fMRI study found transient functional connectivity changes related to ongoing pain intensity, driven by central sensitization in brain areas responsible for pain regulation in FM patients [4]. However, the study did not discriminate between different dimensions of pain, which could have contributed to further understand mechanisms subtending observed connectivity modifications. Knowing the prominent impact of emotional disturbances in FM, we made the hypothesis that the affective pain component would be related to connectivity changes in emotional pain-regulating brain regions [4].

Chronic pain induces significant modifications in neurotransmitter pathways, the most remarkable among them being an over-excitatory state owing to a decrease in the inhibitory input [5] and/or excessive excitatory neurotransmission [6]. Accordingly, substantial decrease in brain GABAergic signaling is reported in chronic pain patients [7], brain inhibition being mainly driven by GABAergic interneurons [8]. Furthermore, we recently showed reduction in EEG markers of the GABAergic neurotransmission, namely beta (β) oscillations, in chronic neuropathic pain [9]. Thus, we further assumed that expected power and FC changes in FM would be measurable through decrease in the EEG β oscillatory band.

This pilot study is part of a larger project investigating β EEG oscillations considered to be indicators of the GABAergic neurotransmission in chronic pain clinical models as a contribution to the mechanistic approach of pain characterization and therapy. In this particular investigation, the aim was to assess EEG FC changes in the β frequency domain occurring in FM patients and relate observed modifications to the affective component of pain.

Materials and methods

Study design

This correlational, cross-sectional case-control pilot study included FM patients and age- and sex-adjusted healthy participants (2018–2020). Approval by the Ethical Committee of Vaud (CER-VD) was obtained under the number PB_2016–00739 (initial number 331/15). Each participant signed an informed consent form prior to any data collection and received a financial compensation thereafter.

Patients were recruited mainly through neurologists, rheumatologists and pain specialists from Fribourg Hospital, and through Swiss FM associations using web-based and flyer advertisements, while advertisements for controls targeted middle-aged adult hobby associations. Participants (cases and controls) were adult (≥18 y) females [1] and right-handed [9]. The diagnosis of FM had to be made by the specialists and meet internationally admitted criteria (see below). Exclusion criteria consisted in: existence of central nervous system lesion or disease (including epilepsy and parasomnia), significant cognitive impairment, coexistence of any other type of pain (patients) or any pain (controls), and surgery involving any nervous system structure less than six months before inclusion [9].

Data collection

Each participant was interviewed following a standardized questionnaire (age, sex, marital status, profession and education, treatments, relevant medical history) and underwent a brief neurological examination in order to exclude abnormalities potentially related to a central nervous system lesion or disease.

Three additional specific questionnaires (the FM Rapid Screening Tool (FiRST) [10], the Symptoms Severity Score (SSS) and the Widespread Pain Index (WPI) of the 2010 American College of Rheumatology criteria (ACR 2010) were used to confirm FM (FiRST > 5, SSS ≥ 7 and WPI ≥ 5 or SSS ≥ 9 and WPI 3–6 [11]).

Pain evaluation (FM patients) included pain intensity using the Visual Analogue Scale (VAS) on the day of evaluation (VAS_d) and on average over the week before [12], considering VAS_d≥3 as significant [9]. The VAS shows good statistical qualities for evaluation of chronic pain patients [13–15], and is frequently used in FM studies [16,17]. The Short-form 2 McGill Pain Questionnaire allowed differentiation between the sensory (SF-MPQ-2_sensory) and the affective (SF-MPQ-2_affective) components of pain (both scores re-scaled /10) [18]. The SF-MPQ-2 has been developed to be used in chronic pain populations and has excellent reliability and validity [18]. It has also been often used in FM studies [17,19–21]. The Hospital Anxiety and Depression Scale (HADS) determined existence of anxiety and depression [22], whereas the Insomnia Severity Index (ISI) was compiled for insomnia assessment [23]. HADS is a reliable instrument for screening clinically significant anxiety and depression as well as their severity [22,24], frequently used in FM studies [25–27]. Finally, ISI is a reliable and valid instrument to quantify insomnia severity [23,28]. It has already been used in chronic pain population [29,30], including in FM patients [31–33].

The EEG data were recorded using a high density 64-channel EEG recording system (BIOSEMI ActiveTwo, Amsterdam, Netherlands) at a sampling rate of 1024 Hz. All EEG recordings were obtained before noon [9] in a quiet dark room shielded by a Faraday cage. Participants were requested to sit down with eyes closed, minimizing eye blinks and body movements. The recording lasted 24 minutes. To avoid sleepiness due to the length of the recording, participants were maintained seated, and acoustic sounds were used two times during the recording.

EEG data

Raw EEG data were down-sampled to 512 Hz, and band-pass filtered between 0.5 and 40 Hz. Bad EEG channels were excluded by visual inspection using Cartool software for data visualization, while careful manual artifact-rejection was performed to exclude eye movements and blinks, body movements and electrode drifts. Only the first five minutes of artifact-free data of the recording was retained for the analysis.

Preprocessed data were referenced to the Cz electrode and segmented into non-overlapping 1-second epochs. Analyses were performed in MATLAB (The MathWorks), using the toolbox NUTMEG [34,35]. The lead-potential was computed using a boundary element head model [36,37], with the Helsinki BEM library [34] and the NUTEEG plugin of NUTMEG. The head model was based on the Montreal Neurological Institute template brain, and solution points were defined in the gray matter with 10 mm grid spacing.

EEG epochs were Hanning-windowed, Fourier transformed, and projected to gray matter voxels, using an adaptive filter (scalar minimum variance beamformer) [38] and the δ (0.5 to 3.5 Hz), θ (3.5 to 7.5 Hz), α (7.5 to 12.5 Hz), Low β (Lβ, 13–20 Hz) and High β (Hβ, 20–30 Hz) frequency bands were defined. The β band (13–30 Hz) [39], supposedly indicating brain GABAergic activity, was divided into Lβ and Hβ sub-bands following our previous observations [9,40,41], whereas the δ band was considered as a control frequency.

The absolute source spectral power was computed as the absolute squared signal amplitude, whereas the relative power was obtained by normalizing the power in each band to the mean power of all bands and dividing by their standard deviation; thus obtaining z-scores.

FC was assessed in source space (i.e. after source localization) as the statistical dependency between reconstructed activities at the different solution points. Analysis of FC was conducted as described previously [34,42]. We used the absolute imaginary component of coherence as index of FC and calculated the weighted node degree (WND) for each solution point as the sum of its coherence with all other cortical solution points [43]. In order to minimize EEG signal-to-noise ratio influence on FC, we normalized WND values using z-scores by subtracting the mean WND value of all voxels of the subject from the imaginary component of coherence values at each voxel and by dividing by the standard deviation over all voxels [44,45].

Statistical analysis

Statistical non-parametric mapping was used to compare patients to controls at all solution points of the cortex. Correction for multiple testing was obtained by defining a cluster-size threshold based on the cluster size distribution obtained after random reversions of original data [46]. This voxel-wise analysis revealed the topography of contrasts, which was complemented by anatomical region of interest (ROI) defined with the Julich atlas [47], and the later thereafter compared between patients and controls using an unpaired t-test.

Associations between EEG and clinical data were analyzed with Spearman correlation test (more robust to detect outliers than Pearson correlation). Data are all presented as mean (SD) and the level of significance admitted at p<0.05 (95% confidence interval).

Voxel-wise statistics were performed with the toolbox NUTMEG, the remaining analyses with the Statistics toolbox of MATLAB (The MathWorks) [34,35].

Results

General data

In total, 16 patients and 11 controls (51.8(8.5) and 54.2(4.6) years) were included for the analysis, as shown in the selection flowchart (Fig 1). FM patients complained of the typical widespread pain at moderate intensity the day of evaluation (VAS_d 4.75(2.84)) and during the last week before assessment (6.38(2.11)). The SF-MPQ-2_affective and SF-MPQ-2_sensory scores were similar (5.88(2.87) and 5.29(2.33), respectively). Patients reported moderate insomnia (ISI 17.75(5.27)), anxiety (HADS anxiety sub-score 10.50(4.12)) and depression (HADS depression sub-score 10.94(3.07)), while controls had normal scores (respective p <0.001, <0.005 and <0.001). Information regarding the patient’s medications are detailed in Table 1.

Table 1. FM patient’s medications.

TREATMENT	Type of treatment	n/16
	NSAID*	7
	Antidepressants	7
	Physical and alternative	5
	Antimigrainous	4
	Benzodiazepines	3
	Opiates	2
	Other drugs	5
	None	3

Open in a new tab

*NSAID: Nonsteroidal Anti-Inflammatory Drugs.

Spectral power analysis

Absolute source power values displayed no difference between FM patients and controls, while a significant cluster of higher Lβ relative power was observed in FM patients in the left prefrontal cortex (PFC) (Fig 2A), exclusively correlated with the VAS_d (ρ = 0.54, p = 0.032) (Fig 2B). FM patients with VAS_d≥3 displayed higher relative Lβ power than those with VAS_d<3 (p = 0.028). Neither difference nor correlation were seen in the δ band.

Fig 2 — A voxel-wise analysis of the entire cortex (see methods for details) revealed a significant cluster of increased Lβ band (13–20 Hz) relative power in FM patients compared to controls (respective mean(SD)) of -0.56(0.10) and -0.69(0.06)) in the prefrontal cortex (red color, p<0.05, cluster corrected) **(A)**. This increase correlated with the ongoing pain intensity (VAS_d) **(B)**. Grey dots correspond to patients with VAS_d<3 whereas black dots are related to patients with significant pain (VAS_d≥3).

Functional connectivity

A significant decrease in Hβ FC was noticed in FM patients compared to controls in the left mesiotemporal area (Fig 3A), with a trend to correlation with the SF-MPQ-2_affective (ρ = 0.45, p = 0.082) (Fig 3B). Within the left mesiotemporal area, the basolateral amygdala (BLA) displayed a significant decrease in Hβ FC (p = 0.039; Fig 3C and 3D), and a significant correlation with the SF-MPQ-2_affective (ρ = 0.50, p = 0.0495), with no impact of ongoing pain. No FC difference appeared in Lβ and δ frequencies, and the Hβ FC did not correlate with the other clinical scores.

Discussion and conclusion

The most remarkable result of this study is the decrease of FM patients’ Hβ FC in the BLA and its selective correlation with the affective pain component. For the first time, a clear anatomo-clinical basis for the affective dysfunction in FM can be demonstrated, involving a brain area eloquent for the expression and control of emotions, as well as the modulation of the affective dimension of pain [48]. Furthermore, the BLA receiving all sensory (including the nociceptive) inputs, is believed to add an emotional valence to the latter before conveying them onto the central nucleus of the amygdala, its main output region [49]. Differences and correlations to pain descriptors confined to the β oscillatory domain indicate a GABAergic dysfunction in FM [50]. Moreover, in accordance with previous hypotheses [9], these data further suggest Hβ modifications as an indicator of pain-related affective dysfunction involving the BLA (rich of GABAergic interneurons [51]) in FM.

The ability of surface EEG to probe amygdala activity is controversial, given the inherently low signal to noise ratio in deep brain structures. Recent data suggest that high density EEG can reliably sense subcortical electrophysiological activity (including in the amygdala) [52–57]. However, validation with intra-cortical recordings have only been obtained in studies using higher-density EEG montages [53,58], while we used only 64 electrodes. One should also be cautious extrapolating results from patients with coma [57] and epilepsy [56] to patients with fibromyalgia. Nevertheless, our data overall suggest a GABA-mediated functional disturbance of brain activity (pointing to the amygdala) related to the affective dimension of pain in FM. Alterations in brain function have previously been observed in FM studies assessing FC by other methods such as magnetoencephalography [59], or investigating different brain networks such as the default mode network or the salience network [3]. However, none of these studies associated observed electrophysiological changes to specific neurotransmitter pathways or to measures of the affective pain component. Nonetheless, they all add up to the evidence for existence of objective brain dysfunction in FM.

Although Lβ power correlated with ongoing pain intensity as previously observed [9], to our surprise, there was now an increase and a positive correlation, contrary to the previously noticed decrease and negative correlation. Additionally, Lβ power maxima were previously observed in the posterior left-brain area (possibly corresponding to the somatosensory cortical areas) in contrast to the present Lβ power increase in the left PFC. Brain networks involved in pain chronification processes are similar to those implicated in executive functions (engaged in adaptive brain mechanisms) primarily controlled by the PFC [60], the latter being also implicated in pain regulation through abundant connections with the somatosensory system [61]. Considering the decrease in GABA-dependent inhibition occurring in chronic pain, we hypothesize that the increase in Lβ power possibly indicates a compensatory mechanism counteracting chronic pain-related GABAergic dysfunction. Interestingly, the PFC (in particular the dorsolateral PFC) constitutes a primary target for non-invasive brain therapies, such as the transcranial magnetic stimulation (TMS), including in FM [62]. Furthermore, TMS has been reported to have analgesic effects through GABAergic restauration [7]. Thus, the hypothesized “natural” GABA-related compensatory mechanisms would have potential analgesic effect, with possible reinforcement by therapeutic measures. Existing connections between the PFC and the amygdala [63] may finally provide an anatomic link between the identified site of brain dysfunction and the assumed compensatory region.

Previous investigations in FM reported both decrease [50] and increase [64] in GABAergic markers. In this study, Hβ decrease and Lβ increase were observed in different brain areas, associated to different pain descriptors, and differently interpreted (i.e. pathological decrease and compensatory increase), possibly reconciling these apparently conflicting results.

All depicted modifications in EEG markers occurred in left-sided brain areas in right-handed individuals, similar to previous observations in neuropathic pain and healthy populations [9], thereby supporting the concept of lateralization of chronic-pain-related brain modifications.

The specificity of the link between β EEG oscillations and pain can be questioned if we consider on one side, that GABAergic circuits also contribute to other oscillatory bands [7,65]. The current knowledge gives however, good indication for a link between fast EEG oscillations (namely β waves) and GABA concentrations in the brain [66,67]. Moreover, fast EEG oscillations are mainly driven by brain inhibitory interneurons, which are mostly GABAergic [67]. On the other side, we found an association between β waves and pain clinical descriptors, while at the same time, in the literature, β oscillations are linked with attention [68] or communication functions in verbal or non-verbal modalities [69]. Instead of seeing these findings as a contradiction with our results, we rather consider that β oscillations would indicate multifaceted aspects (including possibly the cognitive dimension) of pain. However, more investigations are warranted, to further enlighten this link. Additionally, due to the small number of participants in our research, further confirmation is necessary in larger studies. In addition, the interpretation frame of obtained results regarding the direction of electrophysiological modifications, their localization (i.e. decrease in the amygdala and increase in the PFC), as well as their implications in the mechanistic approach of pain in FM would need further investigation.

In conclusion, this study investigating EEG-measured GABAergic signaling modifications associated with the affective component of pain in FM patients, showed a Hβ FC decrease in the BLA correlated with the affective pain dimension, but an increase in PFC Lβ power associated with ongoing pain intensity. While the FC decrease was interpreted as part of the pathological process, the power increase was assumed to be compensatory, with potential therapeutic application. All disclosed modifications were left-sided, adding up to the emerging concept of left-lateralized changes in (GABAergic) pain-related brain pathways. Finally, given the small sample size and need for further methodological validation, larger and more accurate studies are needed to confirm these preliminary observations.

Acknowledgments

We thank Prof Jean-Marie ANNONI for his helpful and meaningful comments at different steps of this study.

Data Availability

Patients have neither given their consent to share their coded data on a public repository nor to anonymize their data for such purpose, as confirmed by the Ethical Committee of Vaud (CER-VD). Prof Dominique SPRUMONT, the President of the CER-VD (e-mail: dominique.sprumont@vd.ch or secretariat.cer@vd.ch), is ready to answer any query regarding this issue (please mention the study number PB_2016-00739 (331/15) in each related correspondence to the CER-VD).

Funding Statement

A minor part of this study was jointly supported by the University of Fribourg, the Fribourg Hospital and the Quadrimed Fund. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

References

1.Wolfe F, Walitt B, Perrot S, Rasker JJ, Häuser W. Fibromyalgia diagnosis and biased assessment: Sex, prevalence and bias. Sommer C, editor. PLoS One. 2018;13: e0203755. doi: 10.1371/journal.pone.0203755 [DOI] [PMC free article] [PubMed] [Google Scholar]
2.Cagnie B, Coppieters I, Denecker S, Six J, Danneels L, Meeus M. Central sensitization in fibromyalgia? A systematic review on structural and functional brain MRI. Seminars in Arthritis and Rheumatism. W.B. Saunders; 2014. pp. 68–75. doi: 10.1016/j.semarthrit.2014.01.001 [DOI] [PubMed] [Google Scholar]
3.Vanneste S, Ost J, Van Havenbergh T, De Ridder D. Resting state electrical brain activity and connectivity in fibromyalgia. PLoS One. 2017;12. doi: 10.1371/journal.pone.0178516 [DOI] [PMC free article] [PubMed] [Google Scholar]
4.Čeko M, Frangos E, Gracely J, Richards E, Wang B, Bushnell MC. Dependence of changes of the default mode network in fibromyalgia patients on current clinical pain. Neuroimage. 2020; 116877. doi: 10.1016/j.neuroimage.2020.116877 [DOI] [PMC free article] [PubMed] [Google Scholar]
5.Henderson LA, Peck CC, Petersen ET, Rae CD, Youssef AM, Reeves JM, et al. Chronic pain: Lost inhibition? J Neurosci. 2013;33: 1754–1782. doi: 10.1523/JNEUROSCI.0174-13.2013 [DOI] [PMC free article] [PubMed] [Google Scholar]
6.Parker RS, Lewis GN, Rice DA, Mcnair PJ. Is Motor Cortical Excitability Altered in People with Chronic Pain? A Systematic Review and Meta-Analysis. Brain Stimulation. Elsevier Inc.; 2016. pp. 488–500. doi: 10.1016/j.brs.2016.03.020 [DOI] [PubMed] [Google Scholar]
7.Barr MS, Farzan F, Davis KD, Fitzgerald PB, Daskalakis ZJ. Measuring GAB aergic inhibitory activity with TMS-EEG and its potential clinical application for chronic pain. Journal of Neuroimmune Pharmacology. Springer; 2013. pp. 535–546. doi: 10.1007/s11481-012-9383-y [DOI] [PubMed] [Google Scholar]
8.Jones EG. Gabaergic neurons and their role in cortical plasticity in primates. Cereb Cortex. 1993;3: 361–372. doi: 10.1093/cercor/3.5.361-a [DOI] [PubMed] [Google Scholar]
9.Teixeira M, Mancini C, Wicht CA, Maestretti G, Kuntzer T, Cazzoli D, et al. Beta Electroencephalographic Oscillation Is a Potential GABAergic Biomarker of Chronic Peripheral Neuropathic Pain. Front Neurosci. 2021;15: 108. doi: 10.3389/fnins.2021.594536 [DOI] [PMC free article] [PubMed] [Google Scholar]
10.Perrot S, Bouhassira D, Fermanian J. Development and validation of the Fibromyalgia Rapid Screening Tool (FiRST). Pain. 2010;150: 250–256. doi: 10.1016/j.pain.2010.03.034 [DOI] [PubMed] [Google Scholar]
11.Wolfe F. New American College of Rheumatology Criteria for Fibromyalgia: A Twenty-Year Journey. Arthritis Care Res (Hoboken). 2010;62: 583–584. doi: 10.1002/acr.20156 [DOI] [PubMed] [Google Scholar]
12.Collins SL, Moore RA, McQuay HJ. The visual analogue pain intensity scale: What is moderate pain in millimetres? Pain. 1997;72: 95–97. doi: 10.1016/s0304-3959(97)00005-5 [DOI] [PubMed] [Google Scholar]
13.Price DD, Mcgrath PA, Rafii A, Buckingham B. The Validation of Visual Analogue Scales as Ratio Scale Measures for Chronic and Experimental Pain. Pain. 1983. [DOI] [PubMed] [Google Scholar]
14.Hawker GA, Mian S, Kendzerska T, French M. Measures of adult pain: Visual Analog Scale for Pain (VAS Pain), Numeric Rating Scale for Pain (NRS Pain), McGill Pain Questionnaire (MPQ), Short-Form McGill Pain Questionnaire (SF-MPQ), Chronic Pain Grade Scale (CPGS), Short Form-36 Bodily Pain Scale (SF-36 BPS), and Measure of Intermittent and Constant Osteoarthritis Pain (ICOAP). Arthritis Care Res. 2011;63. doi: 10.1002/acr.20543 [DOI] [PubMed] [Google Scholar]
15.Hjermstad MJ, Fayers PM, Haugen DF, Caraceni A, Hanks GW, Loge JH, et al. Studies comparing numerical rating scales, verbal rating scales, and visual analogue scales for assessment of pain intensity in adults: A systematic literature review. Journal of Pain and Symptom Management. Elsevier; 2011. pp. 1073–1093. doi: 10.1016/j.jpainsymman.2010.08.016 [DOI] [PubMed] [Google Scholar]
16.Cheatham SW, Kolber MJ, Mokha M, Hanney WJ. Concurrent validity of pain scales in individuals with myofascial pain and fibromyalgia. J Bodyw Mov Ther. 2018;22: 355–360. doi: 10.1016/j.jbmt.2017.04.009 [DOI] [PubMed] [Google Scholar]
17.da Cunha Ribeiro RP, Franco TC, Pinto AJ, Pontes Filho MAG, Domiciano DS, de Sá Pinto AL, et al. Prescribed Versus Preferred Intensity Resistance Exercise in Fibromyalgia Pain. Front Physiol. 2018;9: 1097. doi: 10.3389/fphys.2018.01097 [DOI] [PMC free article] [PubMed] [Google Scholar]
18.Dworkin RH, Turk DC, Revicki DA, Harding G, Coyne KS, Peirce-Sandner S, et al. Development and initial validation of an expanded and revised version of the Short-form McGill Pain Questionnaire (SF-MPQ-2). Pain. 2009;144: 35–42. doi: 10.1016/j.pain.2009.02.007 [DOI] [PubMed] [Google Scholar]
19.Sánchez AI, Martínez MP, Miró E, Medina A. Predictors of the Pain Perception and Self-Efficacy for Pain Control in Patients with Fibromyalgia. Span J Psychol. 2011;14: 366–373. doi: 10.5209/rev_sjop.2011.v14.n1.33 [DOI] [PubMed] [Google Scholar]
20.Geisser ME, Gracely RH, Giesecke T, Petzke FW, Williams DA, Clauw DJ. The association between experimental and clinical pain measures among persons with fibromyalgia and chronic fatigue syndrome. Eur J Pain. 2007;11: 202–207. doi: 10.1016/j.ejpain.2006.02.001 [DOI] [PubMed] [Google Scholar]
21.Harris RE, Gracely RH, McLean SA, Williams DA, Giesecke T, Petzke F, et al. Comparison of Clinical and Evoked Pain Measures in Fibromyalgia. J Pain. 2006;7: 521–527. doi: 10.1016/j.jpain.2006.01.455 [DOI] [PubMed] [Google Scholar]
22.Zigmond AS, Snaith RP. The Hospital Anxiety and Depression Scale. Acta Psychiatr Scand. 1983;67: 361–370. doi: 10.1111/j.1600-0447.1983.tb09716.x [DOI] [PubMed] [Google Scholar]
23.Morin CM, Belleville G, Bélanger L, Ivers H. The insomnia severity index: Psychometric indicators to detect insomnia cases and evaluate treatment response. Sleep. 2011;34: 601–608. doi: 10.1093/sleep/34.5.601 [DOI] [PMC free article] [PubMed] [Google Scholar]
24.Bjelland I, Dahl AA, Haug TT, Neckelmann D. The validity of the Hospital Anxiety and Depression Scale: An updated literature review. J Psychosom Res. 2002;52: 69–77. doi: 10.1016/S0022-3999(01)00296-3 [DOI] [PubMed] [Google Scholar]
25.Nam S, Tin D, Bain L, Thorne JC, Ginsburg L. Clinical utility of the Hospital Anxiety and Depression Scale (HADS) for an Outpatient Fibromyalgia Education Program. Clin Rheumatol. 2014;33: 685–692. doi: 10.1007/s10067-013-2377-1 [DOI] [PubMed] [Google Scholar]
26.Vallejo MA, Rivera J, Esteve-Vives J, Rodríguez-Muñoz MF. Uso del cuestionario Hospital Anxiety and Depression Scale (HADS) para evaluar la ansiedad y la depresión en pacientes con fibromialgia. Rev Psiquiatr Salud Ment. 2012;5: 107–114. doi: 10.1016/j.rpsm.2012.01.003 [DOI] [PubMed] [Google Scholar]
27.Marchi L, Marzetti F, Orrù G, Lemmetti S, Miccoli M, Ciacchini R, et al. Alexithymia and psychological distress in patients with fibromyalgia and rheumatic disease. Front Psychol. 2019;10: 1735. doi: 10.3389/fpsyg.2019.01735 [DOI] [PMC free article] [PubMed] [Google Scholar]
28.Bastien CH, Vallières A, Morin CM. Validation of the insomnia severity index as an outcome measure for insomnia research. Sleep Med. 2001;2: 297–307. doi: 10.1016/s1389-9457(00)00065-4 [DOI] [PubMed] [Google Scholar]
29.Alföldi P, Wiklund T, Gerdle B. Comorbid insomnia in patients with chronic pain: A study based on the Swedish quality registry for pain rehabilitation (SQRP). Disabil Rehabil. 2014;36: 1661–1669. doi: 10.3109/09638288.2013.864712 [DOI] [PubMed] [Google Scholar]
30.TANG NKY, WRIGHT KJ, SALKOVSKIS PM. Prevalence and correlates of clinical insomnia co-occurring with chronic back pain. J Sleep Res. 2007;16: 85–95. doi: 10.1111/j.1365-2869.2007.00571.x [DOI] [PubMed] [Google Scholar]
31.Edinger J, Sanchez Ortuño M, Stechuchak K, Coffman C, Krystal A. Can CBT for insomnia also improve pain sensitivity in fibromyalgia patients?: results from a randomized clinical trial. Sleep Med. 2013;14: e213. doi: 10.1016/j.sleep.2013.11.509 [DOI] [Google Scholar]
32.Aloush V, Gurfinkel A, Shachar N, Ablin JN, Elkana O. Physical and mental impact of COVID-19 outbreak on fibromyalgia patients. Clin Exp Rheumatol. 2021;39: S108–S114. doi: 10.55563/clinexprheumatol/rxk6s4 [DOI] [PubMed] [Google Scholar]
33.Gammoh OS, Al-Smadi A, Tayfur M, Al-Omari M, Al-Katib W, Zein S, et al. Syrian female war refugees: preliminary fibromyalgia and insomnia screening and treatment trends. Int J Psychiatry Clin Pract. 2020;24: 387–391. doi: 10.1080/13651501.2020.1776329 [DOI] [PubMed] [Google Scholar]
34.Guggisberg AG, Dalal SS, Zumer JM, Wong DD, Dubovik S, Michel CM, et al. Localization of cortico-peripheral coherence with electroencephalography. Neuroimage. 2011;57: 1348–1357. doi: 10.1016/j.neuroimage.2011.05.076 [DOI] [PubMed] [Google Scholar]
35.Dalal SS, Zumer JM, Guggisberg AG, Trumpis M, Wong DDE, Sekihara K, et al. MEG/EEG source reconstruction, statistical evaluation, and visualization with NUTMEG. Comput Intell Neurosci. 2011;2011: 17. doi: 10.1155/2011/758973 [DOI] [PMC free article] [PubMed] [Google Scholar]
36.Stenroos M, Mäntynen V, Nenonen J. A Matlab library for solving quasi-static volume conduction problems using the boundary element method. Comput Methods Programs Biomed. 2007;88: 256–263. doi: 10.1016/j.cmpb.2007.09.004 [DOI] [PubMed] [Google Scholar]
37.Sekihara K, Nagarajan SS, Poeppel D, Marantz A. Asymptotic SNR of scalar and vector minimum-variance beanformers for neuromagnetic source reconstruction. IEEE Trans Biomed Eng. 2004;51: 1726–1734. doi: 10.1109/TBME.2004.827926 [DOI] [PMC free article] [PubMed] [Google Scholar]
38.Sekihara K, Nagarajan SS, Poeppel D, Marantz A. Asymptotic SNR of scalar and vector minimum-variance beanformers for neuromagnetic source reconstruction. IEEE Trans Biomed Eng. 2004;51: 1726–1734. doi: 10.1109/TBME.2004.827926 [DOI] [PMC free article] [PubMed] [Google Scholar]
39.Pernet C, Garrido MI, Gramfort A, Maurits N, Michel CM, Pang E, et al. Issues and recommendations from the OHBM COBIDAS MEEG committee for reproducible EEG and MEG research. Nat Neurosci. 2020;23: 1473–1483. doi: 10.1038/s41593-020-00709-0 [DOI] [PubMed] [Google Scholar]
40.Haenschel C, Baldeweg T, Croft RJ, Whittington M, Gruzelier J. Gamma and beta frequency oscillations in response to novel auditory stimuli: A comparison of human electroencephalogram (EEG) data with in vitro models. Proc Natl Acad Sci U S A. 2000;97: 7645–7650. doi: 10.1073/pnas.120162397 [DOI] [PMC free article] [PubMed] [Google Scholar]
41.von Rotz R, Kometer M, Dornbierer D, Gertsch J, Salomé Gachet M, Vollenweider FX, et al. Neuronal oscillations and synchronicity associated with gamma-hydroxybutyrate during resting-state in healthy male volunteers. Psychopharmacology (Berl). 2017;234: 1957–1968. doi: 10.1007/s00213-017-4603-z [DOI] [PubMed] [Google Scholar]
42.Guggisberg AG, Rizk S, Ptak R, Di Pietro M, Saj A, Lazeyras F, et al. Two Intrinsic Coupling Types for Resting-State Integration in the Human Brain. Brain Topogr. 2015;28: 318–329. doi: 10.1007/s10548-014-0394-2 [DOI] [PubMed] [Google Scholar]
43.Newman MEJ. Analysis of weighted networks. Phys Rev E—Stat Physics, Plasmas, Fluids, Relat Interdiscip Top. 2004;70: 9. doi: 10.1103/PhysRevE.70.056131 [DOI] [PubMed] [Google Scholar]
44.Dubovik S, Pignat JM, Ptak R, Aboulafia T, Allet L, Gillabert N, et al. The behavioral significance of coherent resting-state oscillations after stroke. Neuroimage. 2012;61: 249–257. doi: 10.1016/j.neuroimage.2012.03.024 [DOI] [PubMed] [Google Scholar]
45.Mottaz A, Solcà M, Magnin C, Corbet T, Schnider A, Guggisberg AG. Neurofeedback training of alpha-band coherence enhances motor performance. Clin Neurophysiol. 2015;126: 1754–1760. doi: 10.1016/j.clinph.2014.11.023 [DOI] [PubMed] [Google Scholar]
46.Singh KD, Barnes GR, Hillebrand A. Group imaging of task-related changes in cortical synchronisation using nonparametric permutation testing. Neuroimage. 2003;19: 1589–1601. doi: 10.1016/s1053-8119(03)00249-0 [DOI] [PubMed] [Google Scholar]
47.Amunts K, Mohlberg H, Bludau S, Zilles K. Julich-Brain: A 3D probabilistic atlas of the human brain’s cytoarchitecture. Science (80-). 2020;369: 988–992. doi: 10.1126/science.abb4588 [DOI] [PubMed] [Google Scholar]
48.Neugebauer V, Li W, Bird GC, Han JS. The amygdala and persistent pain. Neuroscientist. Neuroscientist; 2004. pp. 221–234. doi: 10.1177/1073858403261077 [DOI] [PubMed] [Google Scholar]
49.Thompson JM, Neugebauer V. Amygdala Plasticity and Pain. Pain Research and Management. Hindawi Limited; 2017. doi: 10.1155/2017/8296501 [DOI] [PMC free article] [PubMed] [Google Scholar]
50.Foerster BR, Petrou M, Edden RAE, Sundgren PC, Schmidt-Wilcke T, Lowe SE, et al. Reduced insular γ-aminobutyric acid in fibromyalgia. Arthritis Rheum. 2012;64: 579–583. doi: 10.1002/art.33339 [DOI] [PMC free article] [PubMed] [Google Scholar]
51.Spampanato J, Polepalli J, Sah P. Interneurons in the basolateral amygdala. Neuropharmacology. Elsevier Ltd; 2011. pp. 765–773. doi: 10.1016/j.neuropharm.2010.11.006 [DOI] [PubMed] [Google Scholar]
52.Seeber M, Cantonas LM, Hoevels M, Sesia T, Visser-Vandewalle V, Michel CM. Subcortical electrophysiological activity is detectable with high-density EEG source imaging. Nat Commun. 2019;10: 1–7. doi: 10.1038/s41467-019-08725-w [DOI] [PMC free article] [PubMed] [Google Scholar]
53.Nahum L, Gabriel D, Spinelli L, Momjian S, Seeck M, Michel CM, et al. Rapid consolidation and the human hippocampus: Intracranial recordings confirm surface EEG. Hippocampus. 2011;21: 689–693. doi: 10.1002/hipo.20819 [DOI] [PubMed] [Google Scholar]
54.Michel CM, Koenig T, Brandeis D, Gianotti LRR, Wackermann J. Electrical neuroimaging. Electrical Neuroimaging. Cambridge University Press; 2009. doi: 10.1017/CBO9780511596889 [DOI] [Google Scholar]
55.Michel CM, Brunet D. EEG source imaging: A practical review of the analysis steps. Front Neurol. 2019;10: 325. doi: 10.3389/fneur.2019.00325 [DOI] [PMC free article] [PubMed] [Google Scholar]
56.Sharma P, Scherg M, Pinborg LH, Fabricius M, Rubboli G, Pedersen B, et al. Ictal and interictal electric source imaging in pre-surgical evaluation: a prospective study. Eur J Neurol. 2018;25: 1154–1160. doi: 10.1111/ene.13676 [DOI] [PubMed] [Google Scholar]
57.De Stefano P, Carboni M, Pugin D, Seeck M, Vulliémoz S. Brain networks involved in generalized periodic discharges (GPD) in post-anoxic-ischemic encephalopathy. Resuscitation. 2020;155: 143–151. doi: 10.1016/j.resuscitation.2020.07.030 [DOI] [PubMed] [Google Scholar]
58.Lopes da Silva FH. Intracerebral Sources Reconstructed on the Basis of High-Resolution Scalp EEG and MEG. Brain Topography. Springer New York LLC; 2019. pp. 523–526. doi: 10.1007/s10548-019-00717-9 [DOI] [PubMed] [Google Scholar]
59.Hsiao FJ, Wang SJ, Lin YY, Fuh JL, Ko YC, Wang PN, et al. Altered insula–default mode network connectivity in fibromyalgia: a resting-state magnetoencephalographic study. J Headache Pain. 2017;18. doi: 10.1186/s10194-017-0799-x [DOI] [PMC free article] [PubMed] [Google Scholar]
60.Yuan P, Raz N. Prefrontal cortex and executive functions in healthy adults: A meta-analysis of structural neuroimaging studies. Neuroscience and Biobehavioral Reviews. Elsevier Ltd; 2014. pp. 180–192. doi: 10.1016/j.neubiorev.2014.02.005 [DOI] [PMC free article] [PubMed] [Google Scholar]
61.Ong WY, Stohler CS, Herr DR. Role of the Prefrontal Cortex in Pain Processing. Molecular Neurobiology. Humana Press Inc.; 2019. pp. 1137–1166. doi: 10.1007/s12035-018-1130-9 [DOI] [PMC free article] [PubMed] [Google Scholar]
62.Tanwar S, Mattoo B, Kumar U, Bhatia R. Repetitive transcranial magnetic stimulation of the prefrontal cortex for fibromyalgia syndrome: a randomised controlled trial with 6-months follow up. doi: 10.1186/s42358-020-00135-7 [DOI] [PubMed] [Google Scholar]
63.Folloni D, Sallet J, Khrapitchev AA, Sibson N, Verhagen L, Mars RB. Dichotomous organization of amygdala/temporal-prefrontal bundles in both humans and monkeys. Elife. 2019;8. doi: 10.7554/eLife.47175 [DOI] [PMC free article] [PubMed] [Google Scholar]
64.Pomares FB, Roy S, Funck T, Feier NA, Thiel A, Fitzcharles MA, et al. Upregulation of cortical GABAA receptor concentration in fibromyalgia. Pain. 2020;161: 74–82. doi: 10.1097/j.pain.0000000000001707 [DOI] [PMC free article] [PubMed] [Google Scholar]
65.Christian EP, Snyder DH, Song W, Gurley DA, Smolka J, Maier DL, et al. EEG-β/γ spectral power elevation in rat: a translatable biomarker elicited by GABA _Aα2/3 -positive allosteric modulators at nonsedating anxiolytic doses. J Neurophysiol. 2015;113: 116–131. doi: 10.1152/jn.00539.2013 [DOI] [PubMed] [Google Scholar]
66.Baumgarten TJ, Oeltzschner G, Hoogenboom N, Wittsack H-J, Schnitzler A, Lange J. Beta Peak Frequencies at Rest Correlate with Endogenous GABA+/Cr Concentrations in Sensorimotor Cortex Areas. Johnson B, editor. PLoS One. 2016;11: e0156829. doi: 10.1371/journal.pone.0156829 [DOI] [PMC free article] [PubMed] [Google Scholar]
67.Gaetz W, Edgar JC, Wang T PL Roberts DJ, Gaetz W. Relating MEG Measured Motor Cortical Oscillations to resting γ-Aminobutyric acid (GABA) Concentration. Neuroimage. 2011;55: 616–621. doi: 10.1016/j.neuroimage.2010.12.077 [DOI] [PMC free article] [PubMed] [Google Scholar]
68.Gao Y, Wang Q, Ding Y, Wang C, Li H, Wu X. Selective Attention Enhances Beta-Band Cortical Oscillation to Speech under “Cocktail-Party” Listening Conditions. 2017;11: 1–10. doi: 10.3389/fnhum.2017.00034 [DOI] [PMC free article] [PubMed] [Google Scholar]
69.Piai V, Roelofs A, Rommers J, Maris E. Beta oscillations reflect memory and motor aspects of spoken word production. Hum Brain Mapp. 2015;36: 2767–2780. doi: 10.1002/hbm.22806 [DOI] [PMC free article] [PubMed] [Google Scholar]

PLoS One. doi: 10.1371/journal.pone.0281986.r001

Decision Letter 0

Claudia Sommer

2 Feb 2022

PONE-D-21-31732Beta EEG left amygdala connectivity decreases and correlates with the affective pain in fibromyalgiaPLOS ONE

Dear Dr. Chabwine,

Thank you for submitting your manuscript to PLOS ONE. After careful consideration, we feel that it has merit but does not fully meet PLOS ONE’s publication criteria as it currently stands. Therefore, we invite you to submit a revised version of the manuscript that addresses the points raised during the review process.

Please submit your revised manuscript by Mar 19 2022 11:59PM. If you will need more time than this to complete your revisions, please reply to this message or contact the journal office at plosone@plos.org. When you're ready to submit your revision, log on to https://www.editorialmanager.com/pone/ and select the 'Submissions Needing Revision' folder to locate your manuscript file. Your small sample size is a major concern. If you can increase the sample size, we will be happy to consider a revised version of the manuscript.

Please include the following items when submitting your revised manuscript:

A rebuttal letter that responds to each point raised by the academic editor and reviewer(s). You should upload this letter as a separate file labeled 'Response to Reviewers'.
A marked-up copy of your manuscript that highlights changes made to the original version. You should upload this as a separate file labeled 'Revised Manuscript with Track Changes'.
An unmarked version of your revised paper without tracked changes. You should upload this as a separate file labeled 'Manuscript'.

If you would like to make changes to your financial disclosure, please include your updated statement in your cover letter. Guidelines for resubmitting your figure files are available below the reviewer comments at the end of this letter.

If applicable, we recommend that you deposit your laboratory protocols in protocols.io to enhance the reproducibility of your results. Protocols.io assigns your protocol its own identifier (DOI) so that it can be cited independently in the future. For instructions see: https://journals.plos.org/plosone/s/submission-guidelines#loc-laboratory-protocols. Additionally, PLOS ONE offers an option for publishing peer-reviewed Lab Protocol articles, which describe protocols hosted on protocols.io. Read more information on sharing protocols at https://plos.org/protocols?utm_medium=editorial-email&utm_source=authorletters&utm_campaign=protocols.

We look forward to receiving your revised manuscript.

Kind regards,

Claudia Sommer

Academic Editor

PLOS ONE

Journal Requirements:

When submitting your revision, we need you to address these additional requirements.

1. Please ensure that your manuscript meets PLOS ONE's style requirements, including those for file naming. The PLOS ONE style templates can be found at

https://journals.plos.org/plosone/s/file?id=wjVg/PLOSOne_formatting_sample_main_body.pdf and

https://journals.plos.org/plosone/s/file?id=ba62/PLOSOne_formatting_sample_title_authors_affiliations.pdf

[Note: HTML markup is below. Please do not edit.]

Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

1. Is the manuscript technically sound, and do the data support the conclusions?

The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes. The conclusions must be drawn appropriately based on the data presented.

Reviewer #1: Partly

Reviewer #2: No

**********

2. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #1: Yes

Reviewer #2: No

**********

3. Have the authors made all data underlying the findings in their manuscript fully available?

The PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified.

Reviewer #1: No

Reviewer #2: Yes

**********

4. Is the manuscript presented in an intelligible fashion and written in standard English?

PLOS ONE does not copyedit accepted manuscripts, so the language in submitted articles must be clear, correct, and unambiguous. Any typographical or grammatical errors should be corrected at revision, so please note any specific errors here.

Reviewer #1: Yes

Reviewer #2: Yes

**********

5. Review Comments to the Author

Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters)

Reviewer #1: Title: Must be a pilot study in the title.

Study Design: Must define if it is correlational, or experimental study, etc.

Data collection: Must include the psychometric property of the tests and justify their use.

General data: Define gender: number of women?

Discussion: Describe more about the limitations of the study.

Reviewer #2: Using a 64-channel EEG, Makowka et al. investigated power and functional connectivity (FC) alterations in Fibromyalgia (FM) and related these to clinical parameters including sensory and affective pain components, anxiety, depression, and insomnia. In line with their hypotheses, the authors report decreased high beta connectivity in the basolateral amygdala in FM compared to healthy controls, the extent of which selectively correlated with questionnaire data assessing the affective pain component. Decreased beta band connectivity in the amygdala is thus interpreted as neural mechanism underlying the affective dysregulation seen in FM. In addition, the authors report an increase in relative low beta power in FM located in the left PFC which selectively correlated with pain intensity and might represent a compensatory mechanism.

Overall, the authors address a highly relevant research question using a timely analysis pipeline. By assessing FC patterns in FM, the presented study can contribute to current research efforts focusing on the development of brain-based biomarkers of chronic pain which remains a key challenge at the intersection of cognitive and clinical science. However, major concerns regarding the methodological rigor and especially the sample size of the study should be addressed before a final evaluation can be made.

Major Comments

1. It is questionable whether the sample size of N= 16 patients is sufficient to detect the correlation of interest in a reliable and replicable fashion. Despite the absence of a correction for multiple comparisons several p-values are almost non-significant (e.g., p = 0.049 for the correlation between amygdala connectivity and the affective pain component) and I am very concerned that these findings might represent false positives that would not replicate in another data set. To enhance the confidence in their findings, I strongly recommend that the authors conduct sample size calculations and adjust the sample size accordingly. In addition, the authors should consider correcting for multiple comparisons, e.g., by taking the 5 frequency bands investigated into account.

2. As mentioned in the discussion, EEG is limited in its spatial resolution, and it is highly debatable whether it can provide accurate information regarding deep and focal sources such as the amygdala. The authors cite studies exploring this question, however, the cited papers used 128 and 256 electrodes, respectively and are therefore not directly comparable with the current 64-electrode montage. Consequently, this limitation should be discussed more prominently and maybe also warrants a more cautious choice of title and wording in the abstract, results, and discussion section.

3. Information regarding the medication of included patients is missing and should be added to the manuscript.

4. Several aspects of the EEG data analysis section require further clarification:

• How many data segments were rejected/retained for analysis?

• The investigated beta sub-bands deviate from the conventional canonical frequency bands (e.g., Pernet et al., Nat. Neurosci, 2020). Thus, it would be important to address the question whether findings replicate when performing a re-analysis using the conventional frequency boundaries.

• Is it correct that power analyses were also conducted in source space? If so, this should be stated more clearly.

• Which toolboxes/software were used for the calculation of power, FC, and the weighted node degree?

• Which toolboxes/software packages were used for statistical analyses?

• The FC measure calculated was probably the imaginary component of the complex valued coherency and not coherence (e.g., see Bastos, Front. Syst. Neurosci, 2016)? If so, this should be adjusted throughout the manuscript.

• The FC region of interest analysis should be described in more detail. How were regions of interest (such as the BLA) motivated? Was this done a priori or based on the results of the cluster-based permutation tests?

Minor Comments

1. Were the hypotheses mentioned in the introduction and the analysis plan preregistered?

2. How was the recording duration motivated (20 min seems rather long and might lead to sleep artifacts depending on the seating position) and why was it approximately 20 min implying variations between participants?

3. Previous studies examining FC changes in FM (e.g., Hsiao et al., J. Headache Pain, 2017; Vanneste et al., PLoS One, 2017) could be included in the discussion.

**********

6. PLOS authors have the option to publish the peer review history of their article (what does this mean?). If published, this will include your full peer review and any attached files.

If you choose “no”, your identity will remain anonymous but your review may still be made public.

Do you want your identity to be public for this peer review? For information about this choice, including consent withdrawal, please see our Privacy Policy.

Reviewer #1: No

Reviewer #2: No

[NOTE: If reviewer comments were submitted as an attachment file, they will be attached to this email and accessible via the submission site. Please log into your account, locate the manuscript record, and check for the action link "View Attachments". If this link does not appear, there are no attachment files.]

While revising your submission, please upload your figure files to the Preflight Analysis and Conversion Engine (PACE) digital diagnostic tool, https://pacev2.apexcovantage.com/. PACE helps ensure that figures meet PLOS requirements. To use PACE, you must first register as a user. Registration is free. Then, login and navigate to the UPLOAD tab, where you will find detailed instructions on how to use the tool. If you encounter any issues or have any questions when using PACE, please email PLOS at figures@plos.org. Please note that Supporting Information files do not need this step.

PLoS One. 2023 Feb 21;18(2):e0281986. doi: 10.1371/journal.pone.0281986.r002

Author response to Decision Letter 0

3 Oct 2022

Reviewer #1:

A- Title: Must be a pilot study in the title.

Following the reviewer’s remark and in accordance with the journal’s guidelines, the manuscript’s title has now been changed into: “EEG beta functional connectivity decrease in the left amygdala correlates with affective pain in fibromyalgia: a pilot study” (lines 1-3).

B- Study Design: Must define if it is correlational, or experimental study, etc.

We thank the reviewer for raising our awareness on this important point. In fact, the primary goal of this study was to find an electrophysiological correlate of the affective pain component in the beta band. Thus, this is a correlational cross-sectional case-control study.

We have now better specified the aim and the design of the study respectively in the abstract, in the introduction and in the methods section (lines 27-28, 72, 75).

C- Data collection: Must include the psychometric property of the tests and justify their use.

All tests (VAS, SF-MPQ-2, HADS and ISI) are validated and widely used in chronic pain patients (including those suffering from fibromyalgia). Thus, in general, their psychometric properties are no longer detailed in publications. Nevertheless, we provide below some illustrative literature and have included in the manuscript, as requested by the reviewer, a summarized information regarding the validity of those tests and the most important references supporting their use in chronic pain/fibromyalgia patients (lines 111-122).

McGill Pain Questionnaire the Short-Form-2 (MPQ-SF-2)

- Reliable and valid in diverse chronic pain syndromes with large samples (1, 2);

- Use in fibromyalgia patients (3, 4).

Visual Analog Scale (VAS)

- Good statistical and psychometric properties in chronic pain patients (5-8);

- Use in chronic pain patients, including those suffering from fibromyalgia (9).

Hospital Anxiety and Depression Scale (HADS)

- Valid psychometric properties (10, 11) ;

- Use in fibromyalgia patients (12-14).

Insomnia Severity Index (ISI)

- Reliable and valid in clinical settings (15, 16);

- Use in clinical pain, including in fibromyalgia (17, 18).

Combination of scales:

- SF_MPQ-2 and HADS in fibromyalgia (19);

- SF-MPQ-2 and VAS in fibromyalgia (3, 20) ;

- ISI, HADS, SF-MPQ in chronic pain patients (21).

D- General data: Define gender: number of women?

According to our protocol, the study included only patients of female sex. The reviewer is right in that the use of the term “gender” instead of “sex” could be confusing. Therefore, we now only use the word “sex” in the manuscript (line 30-31, 83, 101).

E- Discussion: Describe more about the limitations of the study.

Study limitations are detailed further in the manuscript in the discussion paragraph (line 283-287). The results of this pilot study need further confirmation in larger settings. In addition, the interpretation frame and raised hypotheses should be further investigated and confirmed. Specifically, the direction of EEG modifications and their localization, as well as their implications in the mechanistic and therapeutic approach of pain in fibromyalgia could be further investigated.

Reviewer #2:

Major Comments

1.a - It is questionable whether the sample size of N= 16 patients is sufficient to detect the correlation of interest in a reliable and replicable fashion.

To enhance the confidence in their findings, I strongly recommend that the authors conduct sample size calculations and adjust the sample size accordingly.

The main goal of this pilot study was to find an electrophysiological correlate of the affective pain component in fibromyalgia. Previous studies have reported that functional connectivity correlates with clinical impairments such as motor and cognitive deficits, with correlation coefficients around 0.6 – 0.7 (22-24). Thus, based on these findings, we expected similar correlation coefficients in the present study and performed a power analysis revealing that a sample size of 16 patients would give 80% power to significantly detect comparable correlation (correlation coefficient of 0.65 at p<0.05) (25). An indication on the power calculation was added in the methods section (line 89-93) as well as in the discussion (line 282-283).

1.b In addition, the authors should consider correcting for multiple comparisons, e.g., by taking the 5 frequency bands investigated into account.

This study did not equally and simultaneously explore all the 5 EEG frequency bands. Rather, there was an a priori hypothesis that the expected differences would occur in the beta band, based on the suspected GABAergic mechanisms. There is good evidence in the literature, that brain GABAergic neurotransmission driven by cortical interneurons, is mainly represented in the beta-band (26-30). The delta-band was used as a control band to demonstrate that our findings were specific to the beta band. The remaining frequencies were merely used for computing the relative power of the beta band, i.e., the power of the beta band with regards to the distribution of all bands. Thus, in this respect, there was no need to correct for multiple testing.

We acknowledge however, that we did not control for testing the two sub-bands in the beta frequency range when comparing patients to controls. Nevertheless, the correlation found between one of the two sub-bands (high beta) and the clinical variable of interest (affective pain), in addition to the decrease observed in the same band within an eloquent brain area for that clinical variable (amygdala), suggest that these findings are meaningful. Furthermore, in order to estimate the probability of finding such a difference between groups in any of two frequency bands plus a correlation with a clinical variable by pure chance, we performed a small simulation with random numbers. Out of 2000 iterations of randomly generated numbers (randn function in MATLAB) of the same size as in our real dataset, we obtained a significant difference between groups at any of 2 random vectors AND a correlation with a third random vector in only 8 cases. Thus, the probability that our findings as a whole are due to mere chance can be estimated to about 8/2000, i.e., p=0.004.

1.c - Despite the absence of a correction for multiple comparisons several p-values are almost non-significant (e.g., p = 0.049 for the correlation between amygdala connectivity and the affective pain component) and I am very concerned that these findings might represent false positives that would not replicate in another data set.

As written in the analysis paragraph of the manuscript, correction for multiple testing was performed (line 163). For more clarity, we have now specified the method used for the multiple testing (cluster-size threshold, line 163-165).

The reviewer is right in saying that the correlation p value is close to the limit of significance (<.05). However, the congruence of our findings described above, in addition to the simulation excluding random observations, suggests that this is an observation worth paying attention, opening a new avenue of research in fibromyalgia for further exploration.

The authors cite studies exploring this question, however, the cited papers used 128 and 256 electrodes, respectively and are therefore not directly comparable with the current 64-electrode montage. Consequently, this limitation should be discussed more prominently and maybe also warrants a more cautious choice of title and wording in the abstract, results, and discussion section.

We agree that the use of a 64-electrode montage is not the highest solution for spatial resolution, but constitutes, as published in reference textbooks, the minimal adequate layout for a reliable source reconstruction (31). Furthermore, EEG recordings with less than 64 channels could target the amygdala in clinical populations (32, 33). Finally, from a theoretical perspective, we expect that a reduction of the number of electrodes reduces the spatial sampling, but not per se the ability to capture deep sources. Thus, the source needs to be larger, but not necessarily less deep (34).

Overall, we assume from above-mentioned publications, that this spatial coverage was enough to respond to the question addressed by our manuscript. We have now also added references with similar spatial resolution in the discussion section (line 238).

3. Information regarding the medication of included patients is missing and should be added to the manuscript.

The patients’ medication was added to the manuscript in the form of a table (Table 1, line 185).

4. Several aspects of the EEG data analysis section require further clarification:

4.a How many data segments were rejected/retained for analysis?

The whole recording time lasted 24 minutes, but we retained the first 5 minutes of artifact-free data. We added this information on the method section (line 128-130).

4.b The investigated beta sub-bands deviate from the conventional canonical frequency bands (e.g., Pernet et al., Nat. Neurosci, 2020). Thus, it would be important to address the question whether findings replicate when performing a re-analysis using the conventional frequency boundaries.

The frequency range of interest was the beta band, that we defined between 13 and 30 Hz, as recommended by Pernet et al. (2020) (35). The investigated beta sub-bands (low beta, 13-20 Hz, and high beta, 20-30 Hz), were chosen according to our previous findings (26), as mentioned in the method section (line 146-148). Moreover, those frequency boundaries are often used in EEG research (36, 37).

4.c - Is it correct that power analyses were also conducted in source space? If so, this should be stated more clearly.

This information was mentioned in the result section, but indeed lacked in the methods section. We have now stated it also in the methods section (line 149).

4.d - Which toolboxes/software were used for the calculation of power, FC, and the weighted node degree?

For the EEG data analysis (FC, weighted node degree), this is mentioned in the Methods section of the paper: “Analyses were performed in MATLAB (The MathWorks), using the toolbox NUTMEG” (line 138-139).

The calculation of power was performed using the online Sample Size Calculators from Kohn and Senyak (2021) (25). This reference was added on the method section (line 93).

4.e Which toolboxes/software packages were used for statistical analyses?

Voxel-wise statistics were performed with NUTMEG, the remaining analyses with the Statistics toolbox of Matlab. This information was added on the method section (line 171-172).

4.f The FC measure calculated was probably the imaginary component of the complex valued coherency and not coherence (e.g., see Bastos, Front. Syst. Neurosci, 2016)? If so, this should be adjusted throughout the manuscript.

According to the nomenclature of the original description of the imaginary component of coherence (38), the term “coherence” refers to absolute values, “coherency” to the raw values that range between -1 and 1. As we used the absolute values, we prefer to use the term “coherence”, in accordance with the original terminology (line 154-156).

4.g The FC region of interest analysis should be described in more detail. How were regions of interest (such as the BLA) motivated? Was this done a priori or based on the results of the cluster-based permutation tests?

We did not predefine any region of interest in the analysis presented in the paper. The FC difference in the mesiotemporal lobe came out of the cluster-based permutation test. Based on an extensive literature on the importance of the BLA in affective pain (39-41), we then investigated whether this subregion within the mesiotemporal lobe also revealed a beta-band FC correlate of fibromyalgia, in particular the affective pain component. Indeed, the frequency band for which differences were observed witnessed correlation with our clinical variable of interest, highly suggesting that our results could be clinically meaningful.

Minor Comments

1. Were the hypotheses mentioned in the introduction and the analysis plan preregistered?

No, this study was not preregistered, because it is part of a precedent larger project investigating GABAergic markers of chronic pain, which was not preregistered.

The recording time was exactly of 24 minutes for each participant, thus there was no variation between participants. We corrected the manuscript accordingly (line 128). The recording length was motivated by the will to obtain enough “clean” data (epochs) for the analysis. We agree with the reviewer that this duration is long and keen to sleepiness. For this reason, the participants were maintained seated, we used acoustic sounds two times during the recordings and finally selected only the 5 first minutes of clean recording (line 128-130).

3. Previous studies examining FC changes in FM (e.g., Hsiao et al., J. Headache Pain, 2017; Vanneste et al., PLoS One, 2017) could be included in the discussion.

The two mentioned studies were added and referenced to in the discussion, as additional evidence for objective modifications of brain function in the context of fibromyalgia (42, 43) (line 240-246).

We hope our manuscript will be now found suitable for publication in PLOS One.

Yours sincerely,

Joelle N. CHABWINE, MD, PhD

REFERENCES

1. Dworkin RH, Turk DC, Revicki DA, Harding G, Coyne KS, Peirce-Sandner S, et al. Development and initial validation of an expanded and revised version of the Short-form McGill Pain Questionnaire (SF-MPQ-2). Pain. 2009;144(1-2):35-42.

2. Dworkin RH, Turk DC, Trudeau JJ, Benson C, Biondi DM, Katz NP, et al. Validation of the Short-form McGill Pain Questionnaire-2 (SF-MPQ-2) in acute low back pain. J Pain. 2015;16(4):357-66.

3. Geisser ME, Gracely RH, Giesecke T, Petzke FW, Williams DA, Clauw DJ. The association between experimental and clinical pain measures among persons with fibromyalgia and chronic fatigue syndrome. Eur J Pain. 2007;11(2):202-7.

4. Harris RE, Gracely RH, McLean SA, Williams DA, Giesecke T, Petzke F, et al. Comparison of clinical and evoked pain measures in fibromyalgia. J Pain. 2006;7(7):521-7.

5. Price DD, McGrath PA, Rafii A, Buckingham B. The validation of visual analogue scales as ratio scale measures for chronic and experimental pain. Pain. 1983;17(1):45-56.

6. Hawker GA, Mian S, Kendzerska T, French M. Measures of adult pain: Visual Analog Scale for Pain (VAS Pain), Numeric Rating Scale for Pain (NRS Pain), McGill Pain Questionnaire (MPQ), Short-Form McGill Pain Questionnaire (SF-MPQ), Chronic Pain Grade Scale (CPGS), Short Form-36 Bodily Pain Scale (SF-36 BPS), and Measure of Intermittent and Constant Osteoarthritis Pain (ICOAP). Arthritis Care Res (Hoboken). 2011;63 Suppl 11:S240-52.

7. Hjermstad MJ, Fayers PM, Haugen DF, Caraceni A, Hanks GW, Loge JH, et al. Studies comparing Numerical Rating Scales, Verbal Rating Scales, and Visual Analogue Scales for assessment of pain intensity in adults: a systematic literature review. J Pain Symptom Manage. 2011;41(6):1073-93.

8. Ferraz MB, Quaresma MR, Aquino LR, Atra E, Tugwell P, Goldsmith CH. Reliability of pain scales in the assessment of literate and illiterate patients with rheumatoid arthritis. J Rheumatol. 1990;17(8):1022-4.

9. Cheatham SW, Kolber MJ, Mokha M, Hanney WJ. Concurrent validity of pain scales in individuals with myofascial pain and fibromyalgia. J Bodyw Mov Ther. 2018;22(2):355-60.

10. Bjelland I, Dahl AA, Haug TT, Neckelmann D. The validity of the Hospital Anxiety and Depression Scale. An updated literature review. J Psychosom Res. 2002;52(2):69-77.

11. Zigmond AS, Snaith RP. The hospital anxiety and depression scale. Acta Psychiatr Scand. 1983;67(6):361-70.

12. Nam S, Tin D, Bain L, Thorne JC, Ginsburg L. Clinical utility of the Hospital Anxiety and Depression Scale (HADS) for an outpatient fibromyalgia education program. Clin Rheumatol. 2014;33(5):685-92.

13. Vallejo MA, Rivera J, Esteve-Vives J, Rodriguez-Munoz MF, Grupo I. [Use of the Hospital Anxiety and Depression Scale (HADS) to evaluate anxiety and depression in fibromyalgia patients]. Rev Psiquiatr Salud Ment. 2012;5(2):107-14.

14. Marchi L, Marzetti F, Orru G, Lemmetti S, Miccoli M, Ciacchini R, et al. Alexithymia and Psychological Distress in Patients With Fibromyalgia and Rheumatic Disease. Front Psychol. 2019;10:1735.

15. Morin CM, Belleville G, Belanger L, Ivers H. The Insomnia Severity Index: psychometric indicators to detect insomnia cases and evaluate treatment response. Sleep. 2011;34(5):601-8.

16. Bastien CH, Vallieres A, Morin CM. Validation of the Insomnia Severity Index as an outcome measure for insomnia research. Sleep Med. 2001;2(4):297-307.

17. Aloush V, Gurfinkel A, Shachar N, Ablin JN, Elkana O. Physical and mental impact of COVID-19 outbreak on fibromyalgia patients. Clin Exp Rheumatol. 2021;39 Suppl 130(3):108-14.

18. Gammoh OS, Al-Smadi A, Tayfur M, Al-Omari M, Al-Katib W, Zein S, et al. Syrian female war refugees: preliminary fibromyalgia and insomnia screening and treatment trends. Int J Psychiatry Clin Pract. 2020;24(4):387-91.

19. Sanchez AI, Martinez MP, Miro E, Medina A. Predictors of the pain perception and self-efficacy for pain control in patients with fibromyalgia. Span J Psychol. 2011;14(1):366-73.

20. da Cunha Ribeiro RP, Franco TC, Pinto AJ, Pontes Filho MAG, Domiciano DS, de Sa Pinto AL, et al. Prescribed Versus Preferred Intensity Resistance Exercise in Fibromyalgia Pain. Front Physiol. 2018;9:1097.

21. Tang NK, Wright KJ, Salkovskis PM. Prevalence and correlates of clinical insomnia co-occurring with chronic back pain. J Sleep Res. 2007;16(1):85-95.

22. Dubovik S, Pignat JM, Ptak R, Aboulafia T, Allet L, Gillabert N, et al. The behavioral significance of coherent resting-state oscillations after stroke. Neuroimage. 2012;61(1):249-57.

23. Allaman L, Mottaz A, Kleinschmidt A, Guggisberg AG. Spontaneous Network Coupling Enables Efficient Task Performance without Local Task-Induced Activations. J Neurosci. 2020;40(50):9663-75.

24. Guggisberg AG, Rizk S, Ptak R, Di Pietro M, Saj A, Lazeyras F, et al. Two intrinsic coupling types for resting-state integration in the human brain. Brain Topogr. 2015;28(2):318-29.

25. Kohn M, Senyak J. Sample Size Calculators. Website. 2021.

26. Teixeira M, Mancini C, Wicht CA, Maestretti G, Kuntzer T, Cazzoli D, et al. Beta Electroencephalographic Oscillation Is a Potential GABAergic Biomarker of Chronic Peripheral Neuropathic Pain. Front Neurosci. 2021;15:594536.

27. Baumgarten TJ, Oeltzschner G, Hoogenboom N, Wittsack HJ, Schnitzler A, Lange J. Beta Peak Frequencies at Rest Correlate with Endogenous GABA+/Cr Concentrations in Sensorimotor Cortex Areas. PLoS One. 2016;11(6):e0156829.

28. Christian EP, Snyder DH, Song W, Gurley DA, Smolka J, Maier DL, et al. EEG-beta/gamma spectral power elevation in rat: a translatable biomarker elicited by GABA(Aalpha2/3)-positive allosteric modulators at nonsedating anxiolytic doses. J Neurophysiol. 2015;113(1):116-31.

29. Barr MS, Farzan F, Davis KD, Fitzgerald PB, Daskalakis ZJ. Measuring GABAergic inhibitory activity with TMS-EEG and its potential clinical application for chronic pain. J Neuroimmune Pharmacol. 2013;8(3):535-46.

30. Jones EG. GABAergic neurons and their role in cortical plasticity in primates. Cereb Cortex. 1993;3(5):361-72.

31. Michel C, Koenig T, Brandeis D, Gianotti L, Wackermann J. Electrical Neuroimaging. Cambridge: Cambridge University Press. 2009:page 83.

32. De Stefano P, Carboni M, Pugin D, Seeck M, Vulliemoz S. Brain networks involved in generalized periodic discharges (GPD) in post-anoxic-ischemic encephalopathy. Resuscitation. 2020;155:143-51.

33. Sharma P, Scherg M, Pinborg LH, Fabricius M, Rubboli G, Pedersen B, et al. Ictal and interictal electric source imaging in pre-surgical evaluation: a prospective study. Eur J Neurol. 2018;25(9):1154-60.

34. Michel CM, Brunet D. EEG Source Imaging: A Practical Review of the Analysis Steps. Front Neurol. 2019;10:325.

35. Pernet C, Garrido MI, Gramfort A, Maurits N, Michel CM, Pang E, et al. Issues and recommendations from the OHBM COBIDAS MEEG committee for reproducible EEG and MEG research. Nat Neurosci. 2020;23(12):1473-83.

36. von Rotz R, Kometer M, Dornbierer D, Gertsch J, Salome Gachet M, Vollenweider FX, et al. Neuronal oscillations and synchronicity associated with gamma-hydroxybutyrate during resting-state in healthy male volunteers. Psychopharmacology (Berl). 2017;234(13):1957-68.

37. Haenschel C, Baldeweg T, Croft RJ, Whittington M, Gruzelier J. Gamma and beta frequency oscillations in response to novel auditory stimuli: A comparison of human electroencephalogram (EEG) data with in vitro models. Proc Natl Acad Sci U S A. 2000;97(13):7645-50.

38. Nolte G, Bai O, Wheaton L, Mari Z, Vorbach S, Hallett M. Identifying true brain interaction from EEG data using the imaginary part of coherency. Clin Neurophysiol. 2004;115(10):2292-307.

39. Corder G, Ahanonu B, Grewe BF, Wang D, Schnitzer MJ, Scherrer G. An amygdalar neural ensemble that encodes the unpleasantness of pain. Science. 2019;363(6424):276-81.

40. Seno MDJ, Assis DV, Gouveia F, Antunes GF, Kuroki M, Oliveira CC, et al. The critical role of amygdala subnuclei in nociceptive and depressive-like behaviors in peripheral neuropathy. Sci Rep. 2018;8(1):13608.

41. Thompson JM, Neugebauer V. Amygdala Plasticity and Pain. Pain Res Manag. 2017;2017:8296501.

42. Hsiao FJ, Wang SJ, Lin YY, Fuh JL, Ko YC, Wang PN, et al. Altered insula-default mode network connectivity in fibromyalgia: a resting-state magnetoencephalographic study. J Headache Pain. 2017;18(1):89.

43. Vanneste S, Ost J, Van Havenbergh T, De Ridder D. Resting state electrical brain activity and connectivity in fibromyalgia. PLoS One. 2017;12(6):e0178516.

PLoS One. doi: 10.1371/journal.pone.0281986.r003

Decision Letter 1

Claudia Sommer

16 Nov 2022

PONE-D-21-31732R1EEG Beta functional connectivity decrease in the left amygdala correlates with the affective pain in fibromyalgia : a pilot studyPLOS ONE

Dear Dr. Chabwine,

Please submit your revised manuscript by Dec 31 2022 11:59PM. If you will need more time than this to complete your revisions, please reply to this message or contact the journal office at plosone@plos.org. When you're ready to submit your revision, log on to https://www.editorialmanager.com/pone/ and select the 'Submissions Needing Revision' folder to locate your manuscript file.

Please include the following items when submitting your revised manuscript:

A rebuttal letter that responds to each point raised by the academic editor and reviewer(s). You should upload this letter as a separate file labeled 'Response to Reviewers'.
A marked-up copy of your manuscript that highlights changes made to the original version. You should upload this as a separate file labeled 'Revised Manuscript with Track Changes'.
An unmarked version of your revised paper without tracked changes. You should upload this as a separate file labeled 'Manuscript'.

We look forward to receiving your revised manuscript.

Kind regards,

Claudia Sommer

Academic Editor

PLOS ONE

[Note: HTML markup is below. Please do not edit.]

Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

1. If the authors have adequately addressed your comments raised in a previous round of review and you feel that this manuscript is now acceptable for publication, you may indicate that here to bypass the “Comments to the Author” section, enter your conflict of interest statement in the “Confidential to Editor” section, and submit your "Accept" recommendation.

Reviewer #1: All comments have been addressed

Reviewer #2: (No Response)

**********

2. Is the manuscript technically sound, and do the data support the conclusions?

Reviewer #1: Partly

Reviewer #2: Partly

**********

3. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #1: Yes

Reviewer #2: Yes

**********

4. Have the authors made all data underlying the findings in their manuscript fully available?

Reviewer #1: No

Reviewer #2: No

**********

5. Is the manuscript presented in an intelligible fashion and written in standard English?

Reviewer #1: Yes

Reviewer #2: Yes

**********

6. Review Comments to the Author

Reviewer #1: Considering that this is a pilot study, it can be accepted.

However, the larger study must overcome the limitations pointed out by the second reviewer.

Reviewer #2: Evaluation

The authors have addressed several of my concerns and added relevant information to the methods section. However, my two main concerns remain, namely (1) that the sample size might be too low for robust results and (2) that 64-channel EEG recordings may not provide the resolution needed to accurately detect functional connectivity changes in the amygdala. Thus, it is crucial to transparently communicate these limitations in the manuscript.

Sample size:

The sample size limitation is now prominently communicated by describing the study as a pilot study in the title. In addition, the authors report the a posteriori power calculation I mentioned in the first review. However, the presented power calculation is problematic because the underlying effect size estimate of r = 0.65 was derived from previous studies examining very different (patient) populations (patients with a stroke in two studies and healthy participants in one study), and thus, might likely not be appropriate. In absence of a better estimate, I recommend deleting the corresponding paragraph from the manuscript.

Spatial resolution:

Indeed, EEG source reconstruction is possible with 64 electrodes, but the relevant question in this context is whether 64 electrodes suffice to reliably detect connectivity changes in the amygdala which is both a deep and a small structure. The authors list two studies examining amygdala activity in clinical populations with less than 64 EEG channels. However, these studies examined different populations (comatose patients and patients with epilepsy) and did not validate their approach (e.g., through simultaneous intracortical recordings). Such validation approaches have yielded promising evidence for high-density EEG (256 electrodes) and MEG (see Lopes da Silva, Brain Topogr, 2019 for a commentary), but I am not aware of similar publications for 64-channel EEG. Thus, in absence of convincing validation studies, it is important that this limitation is not downplayed (page 11, line 240), but prominently discussed, e.g., on page 11 and 13.

Minor comments:

• Page 6, line 132: specify that the first five minutes of artifact-free data were retained

• Page 11, line 239: delete “s” (“recent data”)

• Page 13, line 285: further confirmation “is” necessary instead of “would be”

**********

7. PLOS authors have the option to publish the peer review history of their article (what does this mean?). If published, this will include your full peer review and any attached files.

If you choose “no”, your identity will remain anonymous but your review may still be made public.

Do you want your identity to be public for this peer review? For information about this choice, including consent withdrawal, please see our Privacy Policy.

Reviewer #1: Yes: Susana Cardoso

Reviewer #2: No

**********

PLoS One. 2023 Feb 21;18(2):e0281986. doi: 10.1371/journal.pone.0281986.r004

Author response to Decision Letter 1

23 Dec 2022

Answers to the Reviewers, second round of revisions

Dear Prof Sommer,

We thank you for the opportunity to revise a second time our manuscript and improve it according to the reviewers’ new remarks. We also thank the reviewers for carefully reading all our answers and modifications made to the manuscript, as well as for their comments and advices that are clearly and positively oriented to enrich the manuscript and make it more accurate. We addressed their remarks at our best. In addition, we took this opportunity to review the whole manuscript in search of remaining typos and other minor errors (these modifications are highlighted in orange). In this respect, we removed the reference 11, which was not any more cited (page 16, line 344), while overlooked double citations were removed (page 20, line 449 and line 466; page 21 line 469 and line 482; page 23 line 526).

Reviewer #1 acknowledged that our answers addressed all his queries. Our answers to reviewer #2 are listed below. Corrections in the manuscript are highlighted in blue. Page and line references mentioned below are always related to “track-change” versions of the manuscript.

Reviewer #2: Evaluation

1- Sample size:

We removed the paragraph related to the power computation (page 4 line 88-and changed the sentence related to it in the discussion:

Page 13, line 287-288: Additionally, due to the small number of participants in our research, further confirmation is necessary in larger studies.

2- Spatial resolution:

We agree with the reviewer that both cited EEG studies evaluating electrical activity in the amygdala with less than 64 electrodes have not been validated with intracortical recordings. Therefore, we have toned down our interpretation of our findings, as follows:

Page 11, line 236-242: The ability of surface EEG to probe amygdala activity is controversial, given the inherently low signal to noise ratio in deep brain structures. Recent data suggest that high density EEG can reliably sense subcortical electrophysiological activity (including in the amygdala) [53-58]. However, validation with intra-cortical recordings have only been obtained in studies using higher-density EEG montages [54,59], while we used only 64 electrodes. One should also be cautious extrapolating results from patients with coma [58] and epilepsy [57] to patients with fibromyalgia.

Page 14, line 299-301: Finally, given the small sample size and need for further methodological validation, larger and more accurate studies are needed to confirm these preliminary observations.

4 - Page 6, line 132: specify that the first five minutes of artifact-free data were retained

The mention of this information was initially put in the wrong section (page 6, line 127-128). Now, we have deleted it on this location and displaced it to the correct place (page 6, line 134-135).

5- Page 11, line 239: delete “s” (“recent data”)

Corrected (page 11, line 237)

6- Page 13, line 285: further confirmation “is” necessary instead of “would be”

Corrected (page 13, line 303-304)

We hope we have satisfactorily addressed the reviewer’s concerns and that our manuscript will be now found suitable for publication in PLOS One.

Yours sincerely,

Joelle N. CHABWINE, MD, PhD

PLoS One. doi: 10.1371/journal.pone.0281986.r005

Decision Letter 2

Claudia Sommer

7 Feb 2023

EEG Beta functional connectivity decrease in the left amygdala correlates with the affective pain in fibromyalgia : a pilot study

PONE-D-21-31732R2

Dear Dr. Chabwine,

We’re pleased to inform you that your manuscript has been judged scientifically suitable for publication and will be formally accepted for publication once it meets all outstanding technical requirements.

Within one week, you’ll receive an e-mail detailing the required amendments. When these have been addressed, you’ll receive a formal acceptance letter and your manuscript will be scheduled for publication.

An invoice for payment will follow shortly after the formal acceptance. To ensure an efficient process, please log into Editorial Manager at http://www.editorialmanager.com/pone/, click the 'Update My Information' link at the top of the page, and double check that your user information is up-to-date. If you have any billing related questions, please contact our Author Billing department directly at authorbilling@plos.org.

If your institution or institutions have a press office, please notify them about your upcoming paper to help maximize its impact. If they’ll be preparing press materials, please inform our press team as soon as possible -- no later than 48 hours after receiving the formal acceptance. Your manuscript will remain under strict press embargo until 2 pm Eastern Time on the date of publication. For more information, please contact onepress@plos.org.

Kind regards,

Claudia Sommer

Academic Editor

PLOS ONE

Additional Editor Comments (optional):

Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

Reviewer #2: All comments have been addressed

**********

2. Is the manuscript technically sound, and do the data support the conclusions?

Reviewer #2: Yes

**********

3. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #2: Yes

**********

4. Have the authors made all data underlying the findings in their manuscript fully available?

Reviewer #2: No

**********

5. Is the manuscript presented in an intelligible fashion and written in standard English?

Reviewer #2: Yes

**********

6. Review Comments to the Author

Reviewer #2: (No Response)

**********

7. PLOS authors have the option to publish the peer review history of their article (what does this mean?). If published, this will include your full peer review and any attached files.

If you choose “no”, your identity will remain anonymous but your review may still be made public.

Do you want your identity to be public for this peer review? For information about this choice, including consent withdrawal, please see our Privacy Policy.

Reviewer #2: No

**********

PLoS One. doi: 10.1371/journal.pone.0281986.r006

Acceptance letter

Claudia Sommer

10 Feb 2023

PONE-D-21-31732R2

EEG Beta functional connectivity decrease in the left amygdala correlates with the affective pain in fibromyalgia: a pilot study

Dear Dr. Chabwine:

I'm pleased to inform you that your manuscript has been deemed suitable for publication in PLOS ONE. Congratulations! Your manuscript is now with our production department.

If your institution or institutions have a press office, please let them know about your upcoming paper now to help maximize its impact. If they'll be preparing press materials, please inform our press team within the next 48 hours. Your manuscript will remain under strict press embargo until 2 pm Eastern Time on the date of publication. For more information please contact onepress@plos.org.

If we can help with anything else, please email us at plosone@plos.org.

Thank you for submitting your work to PLOS ONE and supporting open access.

Kind regards,

PLOS ONE Editorial Office Staff

on behalf of

Prof. Dr. Claudia Sommer

Academic Editor

PLOS ONE

Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Data Availability Statement

[pone.0281986.ref001] 1.Wolfe F, Walitt B, Perrot S, Rasker JJ, Häuser W. Fibromyalgia diagnosis and biased assessment: Sex, prevalence and bias. Sommer C, editor. PLoS One. 2018;13: e0203755. doi: 10.1371/journal.pone.0203755 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0281986.ref002] 2.Cagnie B, Coppieters I, Denecker S, Six J, Danneels L, Meeus M. Central sensitization in fibromyalgia? A systematic review on structural and functional brain MRI. Seminars in Arthritis and Rheumatism. W.B. Saunders; 2014. pp. 68–75. doi: 10.1016/j.semarthrit.2014.01.001 [DOI] [PubMed] [Google Scholar]

[pone.0281986.ref003] 3.Vanneste S, Ost J, Van Havenbergh T, De Ridder D. Resting state electrical brain activity and connectivity in fibromyalgia. PLoS One. 2017;12. doi: 10.1371/journal.pone.0178516 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0281986.ref004] 4.Čeko M, Frangos E, Gracely J, Richards E, Wang B, Bushnell MC. Dependence of changes of the default mode network in fibromyalgia patients on current clinical pain. Neuroimage. 2020; 116877. doi: 10.1016/j.neuroimage.2020.116877 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0281986.ref005] 5.Henderson LA, Peck CC, Petersen ET, Rae CD, Youssef AM, Reeves JM, et al. Chronic pain: Lost inhibition? J Neurosci. 2013;33: 1754–1782. doi: 10.1523/JNEUROSCI.0174-13.2013 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0281986.ref006] 6.Parker RS, Lewis GN, Rice DA, Mcnair PJ. Is Motor Cortical Excitability Altered in People with Chronic Pain? A Systematic Review and Meta-Analysis. Brain Stimulation. Elsevier Inc.; 2016. pp. 488–500. doi: 10.1016/j.brs.2016.03.020 [DOI] [PubMed] [Google Scholar]

[pone.0281986.ref007] 7.Barr MS, Farzan F, Davis KD, Fitzgerald PB, Daskalakis ZJ. Measuring GAB aergic inhibitory activity with TMS-EEG and its potential clinical application for chronic pain. Journal of Neuroimmune Pharmacology. Springer; 2013. pp. 535–546. doi: 10.1007/s11481-012-9383-y [DOI] [PubMed] [Google Scholar]

[pone.0281986.ref008] 8.Jones EG. Gabaergic neurons and their role in cortical plasticity in primates. Cereb Cortex. 1993;3: 361–372. doi: 10.1093/cercor/3.5.361-a [DOI] [PubMed] [Google Scholar]

[pone.0281986.ref009] 9.Teixeira M, Mancini C, Wicht CA, Maestretti G, Kuntzer T, Cazzoli D, et al. Beta Electroencephalographic Oscillation Is a Potential GABAergic Biomarker of Chronic Peripheral Neuropathic Pain. Front Neurosci. 2021;15: 108. doi: 10.3389/fnins.2021.594536 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0281986.ref010] 10.Perrot S, Bouhassira D, Fermanian J. Development and validation of the Fibromyalgia Rapid Screening Tool (FiRST). Pain. 2010;150: 250–256. doi: 10.1016/j.pain.2010.03.034 [DOI] [PubMed] [Google Scholar]

[pone.0281986.ref011] 11.Wolfe F. New American College of Rheumatology Criteria for Fibromyalgia: A Twenty-Year Journey. Arthritis Care Res (Hoboken). 2010;62: 583–584. doi: 10.1002/acr.20156 [DOI] [PubMed] [Google Scholar]

[pone.0281986.ref012] 12.Collins SL, Moore RA, McQuay HJ. The visual analogue pain intensity scale: What is moderate pain in millimetres? Pain. 1997;72: 95–97. doi: 10.1016/s0304-3959(97)00005-5 [DOI] [PubMed] [Google Scholar]

[pone.0281986.ref013] 13.Price DD, Mcgrath PA, Rafii A, Buckingham B. The Validation of Visual Analogue Scales as Ratio Scale Measures for Chronic and Experimental Pain. Pain. 1983. [DOI] [PubMed] [Google Scholar]

[pone.0281986.ref014] 14.Hawker GA, Mian S, Kendzerska T, French M. Measures of adult pain: Visual Analog Scale for Pain (VAS Pain), Numeric Rating Scale for Pain (NRS Pain), McGill Pain Questionnaire (MPQ), Short-Form McGill Pain Questionnaire (SF-MPQ), Chronic Pain Grade Scale (CPGS), Short Form-36 Bodily Pain Scale (SF-36 BPS), and Measure of Intermittent and Constant Osteoarthritis Pain (ICOAP). Arthritis Care Res. 2011;63. doi: 10.1002/acr.20543 [DOI] [PubMed] [Google Scholar]

[pone.0281986.ref015] 15.Hjermstad MJ, Fayers PM, Haugen DF, Caraceni A, Hanks GW, Loge JH, et al. Studies comparing numerical rating scales, verbal rating scales, and visual analogue scales for assessment of pain intensity in adults: A systematic literature review. Journal of Pain and Symptom Management. Elsevier; 2011. pp. 1073–1093. doi: 10.1016/j.jpainsymman.2010.08.016 [DOI] [PubMed] [Google Scholar]

[pone.0281986.ref016] 16.Cheatham SW, Kolber MJ, Mokha M, Hanney WJ. Concurrent validity of pain scales in individuals with myofascial pain and fibromyalgia. J Bodyw Mov Ther. 2018;22: 355–360. doi: 10.1016/j.jbmt.2017.04.009 [DOI] [PubMed] [Google Scholar]

[pone.0281986.ref017] 17.da Cunha Ribeiro RP, Franco TC, Pinto AJ, Pontes Filho MAG, Domiciano DS, de Sá Pinto AL, et al. Prescribed Versus Preferred Intensity Resistance Exercise in Fibromyalgia Pain. Front Physiol. 2018;9: 1097. doi: 10.3389/fphys.2018.01097 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0281986.ref018] 18.Dworkin RH, Turk DC, Revicki DA, Harding G, Coyne KS, Peirce-Sandner S, et al. Development and initial validation of an expanded and revised version of the Short-form McGill Pain Questionnaire (SF-MPQ-2). Pain. 2009;144: 35–42. doi: 10.1016/j.pain.2009.02.007 [DOI] [PubMed] [Google Scholar]

[pone.0281986.ref019] 19.Sánchez AI, Martínez MP, Miró E, Medina A. Predictors of the Pain Perception and Self-Efficacy for Pain Control in Patients with Fibromyalgia. Span J Psychol. 2011;14: 366–373. doi: 10.5209/rev_sjop.2011.v14.n1.33 [DOI] [PubMed] [Google Scholar]

[pone.0281986.ref020] 20.Geisser ME, Gracely RH, Giesecke T, Petzke FW, Williams DA, Clauw DJ. The association between experimental and clinical pain measures among persons with fibromyalgia and chronic fatigue syndrome. Eur J Pain. 2007;11: 202–207. doi: 10.1016/j.ejpain.2006.02.001 [DOI] [PubMed] [Google Scholar]

[pone.0281986.ref021] 21.Harris RE, Gracely RH, McLean SA, Williams DA, Giesecke T, Petzke F, et al. Comparison of Clinical and Evoked Pain Measures in Fibromyalgia. J Pain. 2006;7: 521–527. doi: 10.1016/j.jpain.2006.01.455 [DOI] [PubMed] [Google Scholar]

[pone.0281986.ref022] 22.Zigmond AS, Snaith RP. The Hospital Anxiety and Depression Scale. Acta Psychiatr Scand. 1983;67: 361–370. doi: 10.1111/j.1600-0447.1983.tb09716.x [DOI] [PubMed] [Google Scholar]

[pone.0281986.ref023] 23.Morin CM, Belleville G, Bélanger L, Ivers H. The insomnia severity index: Psychometric indicators to detect insomnia cases and evaluate treatment response. Sleep. 2011;34: 601–608. doi: 10.1093/sleep/34.5.601 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0281986.ref024] 24.Bjelland I, Dahl AA, Haug TT, Neckelmann D. The validity of the Hospital Anxiety and Depression Scale: An updated literature review. J Psychosom Res. 2002;52: 69–77. doi: 10.1016/S0022-3999(01)00296-3 [DOI] [PubMed] [Google Scholar]

[pone.0281986.ref025] 25.Nam S, Tin D, Bain L, Thorne JC, Ginsburg L. Clinical utility of the Hospital Anxiety and Depression Scale (HADS) for an Outpatient Fibromyalgia Education Program. Clin Rheumatol. 2014;33: 685–692. doi: 10.1007/s10067-013-2377-1 [DOI] [PubMed] [Google Scholar]

[pone.0281986.ref026] 26.Vallejo MA, Rivera J, Esteve-Vives J, Rodríguez-Muñoz MF. Uso del cuestionario Hospital Anxiety and Depression Scale (HADS) para evaluar la ansiedad y la depresión en pacientes con fibromialgia. Rev Psiquiatr Salud Ment. 2012;5: 107–114. doi: 10.1016/j.rpsm.2012.01.003 [DOI] [PubMed] [Google Scholar]

[pone.0281986.ref027] 27.Marchi L, Marzetti F, Orrù G, Lemmetti S, Miccoli M, Ciacchini R, et al. Alexithymia and psychological distress in patients with fibromyalgia and rheumatic disease. Front Psychol. 2019;10: 1735. doi: 10.3389/fpsyg.2019.01735 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0281986.ref028] 28.Bastien CH, Vallières A, Morin CM. Validation of the insomnia severity index as an outcome measure for insomnia research. Sleep Med. 2001;2: 297–307. doi: 10.1016/s1389-9457(00)00065-4 [DOI] [PubMed] [Google Scholar]

[pone.0281986.ref029] 29.Alföldi P, Wiklund T, Gerdle B. Comorbid insomnia in patients with chronic pain: A study based on the Swedish quality registry for pain rehabilitation (SQRP). Disabil Rehabil. 2014;36: 1661–1669. doi: 10.3109/09638288.2013.864712 [DOI] [PubMed] [Google Scholar]

[pone.0281986.ref030] 30.TANG NKY, WRIGHT KJ, SALKOVSKIS PM. Prevalence and correlates of clinical insomnia co-occurring with chronic back pain. J Sleep Res. 2007;16: 85–95. doi: 10.1111/j.1365-2869.2007.00571.x [DOI] [PubMed] [Google Scholar]

[pone.0281986.ref031] 31.Edinger J, Sanchez Ortuño M, Stechuchak K, Coffman C, Krystal A. Can CBT for insomnia also improve pain sensitivity in fibromyalgia patients?: results from a randomized clinical trial. Sleep Med. 2013;14: e213. doi: 10.1016/j.sleep.2013.11.509 [DOI] [Google Scholar]

[pone.0281986.ref032] 32.Aloush V, Gurfinkel A, Shachar N, Ablin JN, Elkana O. Physical and mental impact of COVID-19 outbreak on fibromyalgia patients. Clin Exp Rheumatol. 2021;39: S108–S114. doi: 10.55563/clinexprheumatol/rxk6s4 [DOI] [PubMed] [Google Scholar]

[pone.0281986.ref033] 33.Gammoh OS, Al-Smadi A, Tayfur M, Al-Omari M, Al-Katib W, Zein S, et al. Syrian female war refugees: preliminary fibromyalgia and insomnia screening and treatment trends. Int J Psychiatry Clin Pract. 2020;24: 387–391. doi: 10.1080/13651501.2020.1776329 [DOI] [PubMed] [Google Scholar]

[pone.0281986.ref034] 34.Guggisberg AG, Dalal SS, Zumer JM, Wong DD, Dubovik S, Michel CM, et al. Localization of cortico-peripheral coherence with electroencephalography. Neuroimage. 2011;57: 1348–1357. doi: 10.1016/j.neuroimage.2011.05.076 [DOI] [PubMed] [Google Scholar]

[pone.0281986.ref035] 35.Dalal SS, Zumer JM, Guggisberg AG, Trumpis M, Wong DDE, Sekihara K, et al. MEG/EEG source reconstruction, statistical evaluation, and visualization with NUTMEG. Comput Intell Neurosci. 2011;2011: 17. doi: 10.1155/2011/758973 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0281986.ref036] 36.Stenroos M, Mäntynen V, Nenonen J. A Matlab library for solving quasi-static volume conduction problems using the boundary element method. Comput Methods Programs Biomed. 2007;88: 256–263. doi: 10.1016/j.cmpb.2007.09.004 [DOI] [PubMed] [Google Scholar]

[pone.0281986.ref037] 37.Sekihara K, Nagarajan SS, Poeppel D, Marantz A. Asymptotic SNR of scalar and vector minimum-variance beanformers for neuromagnetic source reconstruction. IEEE Trans Biomed Eng. 2004;51: 1726–1734. doi: 10.1109/TBME.2004.827926 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0281986.ref038] 38.Sekihara K, Nagarajan SS, Poeppel D, Marantz A. Asymptotic SNR of scalar and vector minimum-variance beanformers for neuromagnetic source reconstruction. IEEE Trans Biomed Eng. 2004;51: 1726–1734. doi: 10.1109/TBME.2004.827926 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0281986.ref039] 39.Pernet C, Garrido MI, Gramfort A, Maurits N, Michel CM, Pang E, et al. Issues and recommendations from the OHBM COBIDAS MEEG committee for reproducible EEG and MEG research. Nat Neurosci. 2020;23: 1473–1483. doi: 10.1038/s41593-020-00709-0 [DOI] [PubMed] [Google Scholar]

[pone.0281986.ref040] 40.Haenschel C, Baldeweg T, Croft RJ, Whittington M, Gruzelier J. Gamma and beta frequency oscillations in response to novel auditory stimuli: A comparison of human electroencephalogram (EEG) data with in vitro models. Proc Natl Acad Sci U S A. 2000;97: 7645–7650. doi: 10.1073/pnas.120162397 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0281986.ref041] 41.von Rotz R, Kometer M, Dornbierer D, Gertsch J, Salomé Gachet M, Vollenweider FX, et al. Neuronal oscillations and synchronicity associated with gamma-hydroxybutyrate during resting-state in healthy male volunteers. Psychopharmacology (Berl). 2017;234: 1957–1968. doi: 10.1007/s00213-017-4603-z [DOI] [PubMed] [Google Scholar]

[pone.0281986.ref042] 42.Guggisberg AG, Rizk S, Ptak R, Di Pietro M, Saj A, Lazeyras F, et al. Two Intrinsic Coupling Types for Resting-State Integration in the Human Brain. Brain Topogr. 2015;28: 318–329. doi: 10.1007/s10548-014-0394-2 [DOI] [PubMed] [Google Scholar]

[pone.0281986.ref043] 43.Newman MEJ. Analysis of weighted networks. Phys Rev E—Stat Physics, Plasmas, Fluids, Relat Interdiscip Top. 2004;70: 9. doi: 10.1103/PhysRevE.70.056131 [DOI] [PubMed] [Google Scholar]

[pone.0281986.ref044] 44.Dubovik S, Pignat JM, Ptak R, Aboulafia T, Allet L, Gillabert N, et al. The behavioral significance of coherent resting-state oscillations after stroke. Neuroimage. 2012;61: 249–257. doi: 10.1016/j.neuroimage.2012.03.024 [DOI] [PubMed] [Google Scholar]

[pone.0281986.ref045] 45.Mottaz A, Solcà M, Magnin C, Corbet T, Schnider A, Guggisberg AG. Neurofeedback training of alpha-band coherence enhances motor performance. Clin Neurophysiol. 2015;126: 1754–1760. doi: 10.1016/j.clinph.2014.11.023 [DOI] [PubMed] [Google Scholar]

[pone.0281986.ref046] 46.Singh KD, Barnes GR, Hillebrand A. Group imaging of task-related changes in cortical synchronisation using nonparametric permutation testing. Neuroimage. 2003;19: 1589–1601. doi: 10.1016/s1053-8119(03)00249-0 [DOI] [PubMed] [Google Scholar]

[pone.0281986.ref047] 47.Amunts K, Mohlberg H, Bludau S, Zilles K. Julich-Brain: A 3D probabilistic atlas of the human brain’s cytoarchitecture. Science (80-). 2020;369: 988–992. doi: 10.1126/science.abb4588 [DOI] [PubMed] [Google Scholar]

[pone.0281986.ref048] 48.Neugebauer V, Li W, Bird GC, Han JS. The amygdala and persistent pain. Neuroscientist. Neuroscientist; 2004. pp. 221–234. doi: 10.1177/1073858403261077 [DOI] [PubMed] [Google Scholar]

[pone.0281986.ref049] 49.Thompson JM, Neugebauer V. Amygdala Plasticity and Pain. Pain Research and Management. Hindawi Limited; 2017. doi: 10.1155/2017/8296501 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0281986.ref050] 50.Foerster BR, Petrou M, Edden RAE, Sundgren PC, Schmidt-Wilcke T, Lowe SE, et al. Reduced insular γ-aminobutyric acid in fibromyalgia. Arthritis Rheum. 2012;64: 579–583. doi: 10.1002/art.33339 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0281986.ref051] 51.Spampanato J, Polepalli J, Sah P. Interneurons in the basolateral amygdala. Neuropharmacology. Elsevier Ltd; 2011. pp. 765–773. doi: 10.1016/j.neuropharm.2010.11.006 [DOI] [PubMed] [Google Scholar]

[pone.0281986.ref052] 52.Seeber M, Cantonas LM, Hoevels M, Sesia T, Visser-Vandewalle V, Michel CM. Subcortical electrophysiological activity is detectable with high-density EEG source imaging. Nat Commun. 2019;10: 1–7. doi: 10.1038/s41467-019-08725-w [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0281986.ref053] 53.Nahum L, Gabriel D, Spinelli L, Momjian S, Seeck M, Michel CM, et al. Rapid consolidation and the human hippocampus: Intracranial recordings confirm surface EEG. Hippocampus. 2011;21: 689–693. doi: 10.1002/hipo.20819 [DOI] [PubMed] [Google Scholar]

[pone.0281986.ref054] 54.Michel CM, Koenig T, Brandeis D, Gianotti LRR, Wackermann J. Electrical neuroimaging. Electrical Neuroimaging. Cambridge University Press; 2009. doi: 10.1017/CBO9780511596889 [DOI] [Google Scholar]

[pone.0281986.ref055] 55.Michel CM, Brunet D. EEG source imaging: A practical review of the analysis steps. Front Neurol. 2019;10: 325. doi: 10.3389/fneur.2019.00325 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0281986.ref056] 56.Sharma P, Scherg M, Pinborg LH, Fabricius M, Rubboli G, Pedersen B, et al. Ictal and interictal electric source imaging in pre-surgical evaluation: a prospective study. Eur J Neurol. 2018;25: 1154–1160. doi: 10.1111/ene.13676 [DOI] [PubMed] [Google Scholar]

[pone.0281986.ref057] 57.De Stefano P, Carboni M, Pugin D, Seeck M, Vulliémoz S. Brain networks involved in generalized periodic discharges (GPD) in post-anoxic-ischemic encephalopathy. Resuscitation. 2020;155: 143–151. doi: 10.1016/j.resuscitation.2020.07.030 [DOI] [PubMed] [Google Scholar]

[pone.0281986.ref058] 58.Lopes da Silva FH. Intracerebral Sources Reconstructed on the Basis of High-Resolution Scalp EEG and MEG. Brain Topography. Springer New York LLC; 2019. pp. 523–526. doi: 10.1007/s10548-019-00717-9 [DOI] [PubMed] [Google Scholar]

[pone.0281986.ref059] 59.Hsiao FJ, Wang SJ, Lin YY, Fuh JL, Ko YC, Wang PN, et al. Altered insula–default mode network connectivity in fibromyalgia: a resting-state magnetoencephalographic study. J Headache Pain. 2017;18. doi: 10.1186/s10194-017-0799-x [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0281986.ref060] 60.Yuan P, Raz N. Prefrontal cortex and executive functions in healthy adults: A meta-analysis of structural neuroimaging studies. Neuroscience and Biobehavioral Reviews. Elsevier Ltd; 2014. pp. 180–192. doi: 10.1016/j.neubiorev.2014.02.005 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0281986.ref061] 61.Ong WY, Stohler CS, Herr DR. Role of the Prefrontal Cortex in Pain Processing. Molecular Neurobiology. Humana Press Inc.; 2019. pp. 1137–1166. doi: 10.1007/s12035-018-1130-9 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0281986.ref062] 62.Tanwar S, Mattoo B, Kumar U, Bhatia R. Repetitive transcranial magnetic stimulation of the prefrontal cortex for fibromyalgia syndrome: a randomised controlled trial with 6-months follow up. doi: 10.1186/s42358-020-00135-7 [DOI] [PubMed] [Google Scholar]

[pone.0281986.ref063] 63.Folloni D, Sallet J, Khrapitchev AA, Sibson N, Verhagen L, Mars RB. Dichotomous organization of amygdala/temporal-prefrontal bundles in both humans and monkeys. Elife. 2019;8. doi: 10.7554/eLife.47175 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0281986.ref064] 64.Pomares FB, Roy S, Funck T, Feier NA, Thiel A, Fitzcharles MA, et al. Upregulation of cortical GABAA receptor concentration in fibromyalgia. Pain. 2020;161: 74–82. doi: 10.1097/j.pain.0000000000001707 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0281986.ref065] 65.Christian EP, Snyder DH, Song W, Gurley DA, Smolka J, Maier DL, et al. EEG-β/γ spectral power elevation in rat: a translatable biomarker elicited by GABA _Aα2/3 -positive allosteric modulators at nonsedating anxiolytic doses. J Neurophysiol. 2015;113: 116–131. doi: 10.1152/jn.00539.2013 [DOI] [PubMed] [Google Scholar]

[pone.0281986.ref066] 66.Baumgarten TJ, Oeltzschner G, Hoogenboom N, Wittsack H-J, Schnitzler A, Lange J. Beta Peak Frequencies at Rest Correlate with Endogenous GABA+/Cr Concentrations in Sensorimotor Cortex Areas. Johnson B, editor. PLoS One. 2016;11: e0156829. doi: 10.1371/journal.pone.0156829 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0281986.ref067] 67.Gaetz W, Edgar JC, Wang T PL Roberts DJ, Gaetz W. Relating MEG Measured Motor Cortical Oscillations to resting γ-Aminobutyric acid (GABA) Concentration. Neuroimage. 2011;55: 616–621. doi: 10.1016/j.neuroimage.2010.12.077 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0281986.ref068] 68.Gao Y, Wang Q, Ding Y, Wang C, Li H, Wu X. Selective Attention Enhances Beta-Band Cortical Oscillation to Speech under “Cocktail-Party” Listening Conditions. 2017;11: 1–10. doi: 10.3389/fnhum.2017.00034 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0281986.ref069] 69.Piai V, Roelofs A, Rommers J, Maris E. Beta oscillations reflect memory and motor aspects of spoken word production. Hum Brain Mapp. 2015;36: 2767–2780. doi: 10.1002/hbm.22806 [DOI] [PMC free article] [PubMed] [Google Scholar]

PERMALINK

EEG Beta functional connectivity decrease in the left amygdala correlates with the affective pain in fibromyalgia: A pilot study

Soline Makowka

Lliure-Naima Mory

Michael Mouthon

Christian Mancini

Adrian G Guggisberg

Joelle Nsimire Chabwine

Roles

Abstract

Introduction

Materials and methods

Study design

Data collection

EEG data

Statistical analysis

Results

General data

Fig 1. Participant selection procedure.

Table 1. FM patient’s medications.

Spectral power analysis

Fig 2. Low β (Lβ) relative power in fibromyalgia (FM) patients vs. controls.

Functional connectivity

Fig 3. High β (Hβ) functional connectivity (FC) in fibromyalgia (FM) patients vs. controls.

Discussion and conclusion

Acknowledgments

Data Availability

Funding Statement

References

Decision Letter 0

Claudia Sommer

Roles

Author response to Decision Letter 0

Decision Letter 1

Claudia Sommer

Roles

Author response to Decision Letter 1

Decision Letter 2

Claudia Sommer

Roles

Acceptance letter

Claudia Sommer

Roles

Associated Data

Data Availability Statement

ACTIONS

PERMALINK

RESOURCES

Similar articles

Cited by other articles

Links to NCBI Databases