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Research Article: Study Protocol Systematic Review

Altered local connectivity in chronic pain

A voxel-wise meta-analysis of resting-state functional magnetic resonance imaging studies

Lin, XiaoGuang MDa; Zhen, Dan MDb; Li, HuaLiang MDc; Zhong, JianGuo MDc; Dai, ZhenYu MDd; Yuan, CongHu MDe; Pan, PingLei MDc,f,∗

Author Information
doi: 10.1097/MD.0000000000021378
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Abstract

1 Introduction

Chronic pain (CP) is a public health issue worldwide with an estimated prevalence of 31% in the general population.[1,2] CP is now recognized as a complex multifactorial disease entity. Biological and psychosocial risk factors, comorbidities, and pain-processing mechanisms may contribute to the pathogenesis of CP.[3–5] Converging evidence suggests that central sensitization is a common pathophysiological process that has been implicated in CP conditions,[6] which is associated with brain structural and functional reorganization.[7–13]

Resting-state functional magnetic resonance imaging (RS-fMRI) measures the brain blood oxygenation level-dependent (BOLD) signal during rest, reflecting the intrinsic functional organization of the brain.[14] RS-fMRI enables researchers not only to investigate long-distance interregional functional connectivity (FC), but also to examine local short-range FC in the human brain, which contribute to modeling the human brain connectome.[14,15] Regional homogeneity (ReHo) is a popular approach to depict local FC, which estimates the similarity of the BOLD signal in a single voxel with its nearest neighboring voxels.[16] ReHo has been widely used in many neuropsychological disorders.[17–24]

In recent years, many studies have used ReHo to investigate local FC in CP and bring insights into its pathophysiology. Although expanding our current understanding regarding the neural basis of CP,[25–31] the findings from these studies were mixed and inconsistent. Meta-analysis is therefore required to increase the statistical power to quantitatively elucidate whether there is a consistent and common pattern of local FC alterations in CP. In this study, we performed a coordinate-based meta-analysis (CBMA) using Seed-based d Mapping with Permutation of Subject Images (SDM-PSI)[32] to identify the most consistent ReHo abnormalities in CP.

2 Methods

2.1 Search strategies

We will search the following electronic databases: PubMed, Embase, and Web of Science from each database's inception to June 15, 2019 and updated on March 20, 2020 for records published in English. We will also search China National Knowledge Infrastructure (CNKI), WanFang, and SinoMed databases for records published in Chinese. We will use both general and specific CP keywords: (“pain” OR “chronic pain” OR “chronic widespread pain” OR “chronic musculoskeletal pain” OR “chronic headache” OR “chronic migraine” OR “chronic tension-type headache” OR “trigeminal neuralgia” OR “chronic myofascial pain” OR “chronic burning mouth syndrome” OR “neuropathic pain” OR “temporomandibular disorder∗” OR “chronic neck pain” OR “chronic shoulder pain” OR “chronic thoracic pain” OR “chronic chest pain” OR “chronic back pain” OR “chronic knee pain” OR “chronic ankle pain” OR “chronic limb pain” OR “chronic abdominal pain” OR “chronic visceral pain” OR “chronic pelvic pain syndrome” OR “complex regional pain syndrome” OR “fibromyalgia” OR “chronic fatigue syndrome” OR “osteoarthritis” OR “arthritis” OR “chronic whiplash-associated disorder” OR “chronic epicondylalgia∗” OR “postherpetic neuralgia” OR “ankylosing spondylitis” OR “chronic epigastric pain syndrome” OR “irritable bowel syndrome” OR “inflammatory bowel disease” OR “Crohn disease” OR “dysmenorrhea” OR “chronic bladder pain syndrome” OR “chronic testicular pain” OR “functional dyspepsia” OR “somatoform pain”) AND (“regional homogeneity” OR “ReHo” OR “local connectivity”).

We will also manually examine the reference lists of the included studies and relevant review articles and meta-analyses for additional related studies for potential inclusion. This protocol will follow the Preferred Reporting Items for Systematic review and Meta-Analysis Protocols (PRISMA-P).[33]

2.2 Eligibility criteria

A study will be included if it conforms all of the following criteria: the study should report original data published in a peer-reviewed scientific journal; individuals included in the study should be human patients with CP, which was defined as persistent or recurrent pain that lasted longer than 3 months[34]; the study should use a whole-brain voxel-wise ReHo analysis to investigate local connectivity differences between patients with CP and pain-free control subjects; and the study should report the imaging results in standard Montreal Neurological Institute (MNI) or Talairach space.

We will exclude publications if individuals included in the study did not meet the definition of CP, like those with episodic headaches,[35] acute pain, and experimental pain; sample sizes in each group were <7 subjects; no peak stereotactic coordinates were available; they lacked a direct group comparison between patient with CP and pain-free controls; longitudinal/interventional studies did not report baseline comparison results; the studies only applied regions of interest or surface-based imaging or small volume correction analyses; and they were conference abstracts, editorial letters, case reports, reviews, or meta-analyses. We will select the study with a larger sample size in case of studies that used overlapping samples. The study selection process flowchart will be presented in Fig. 1.

Figure 1
Figure 1:
Study selection process following the PRISMA flowchart. PRISMA = Preferred Reporting Items for Systematic review and Meta-Analysis.

2.3 Data extraction

We will extract the following information from each included study: The name of the first author, publication year, sample size, percentage of women, mean age, patient type, pain intensity (indexed using the visual analog scale [VAS]), pain duration, field strength of MRI scanner, smooth kernel, and statistical threshold for the ReHo analysis, peak coordinates, their corresponding effect sizes (t statistic, z score, P-value, etcetera), and their stereotactic reference space (Talairach or MNI).

2.4 Quality assessment

A 20-point checklist based on previous rs-fMRI meta-analyses[21] will be used for quality assessment of the studies included (details in Table 1).

Table 1
Table 1:
Criteria for objective assessment of methodological quality of individual studies.

Two independent authors (XGL and DZ) will perform the literature search, study selection, data extraction, and quality assessment. Any discrepancies will be resolved by discussion with consensus.

2.5 Main coordinate-based meta-analysis

The SDM-PSI software package (version 6.21, https://www.sdmproject.com/) will be used for this CBMA to identify consistent local connectivity differences between patients with CP and pain-free control subjects. The steps of SDM-PSI have been described in detail elsewhere.[32,36] A recommended statistical threshold of threshold-free cluster enhancement-based family-wise error (FWE) correction for multiple comparisons P < .05, and voxel extent ≥10 will be applied.[32,36]

2.6 Reliability analysis

To test the stability of the results, we will perform a jackknife sensitivity analysis by iteratively repeating the same analysis K – 1 times (K = the number of datasets included), discarding one dataset each time. Clusters that are present in at least 80% of the jackknife folds indicate robustness.

We will extract the information of the peak MNI coordinate reported in the main CBMA to derive standard heterogeneity statistics I2. An I2 < 50% indicates low heterogeneity.

Egger tests for detection of the potential publication bias will be used. An Egger test with P < .05 is considered significant.

2.7 Subgroup analysis

Subgroup CBMA will be performed in clinical subtypes and imaging methodology variables if the corresponding number of the datasets is sufficient (n ≥ 10).

2.8 Meta-regression analysis

Exploratory meta-regression analyses will be performed to estimate which demographic and clinical variables could potentially lead to heterogeneity across studies. All results will be thresholded using the threshold-free cluster enhancement (TFCE)-based FWE corrected threshold (P < .05, voxel extent ≥10).[32,36]

2.9 Ethics and dissemination

Ethics approval is not required because this meta-analysis will be performed using the data based on published studies. The results of this meta-analysis will be published in a peer-reviewed scientific journal.

3 Discussion

Converging evidence suggests that central sensitization is a common pathophysiological process that has been implicated in CP conditions,[6] which is associated with brain structural and functional reorganization.[7–13] RS-fMRI has been widely applied in neuropsychiatric disorders and expanded our understanding of the human brain in the past 2 decades. However, replicability crisis is always a challenge in neuroimaging research.[37] This crisis may be attributed to underpowered small sample sizes, phenotypic heterogeneity in CP conditions, and/or the use of different imaging protocols. Using a CBMA approach, we aimed to identify consistent ReHo alterations in CP. The findings will help to better understand of the commonalities of central mechanisms of this disease entity.

Author contributions

Conceptualization: XiaoGuang Lin, CongHu Yuan, PingLei Pan

Data curation: XiaoGuang Lin, Dan Zhen

Formal analysis: XiaoGuang Lin, HuaLiang Li

Funding acquisition: PingLei Pan

Investigation: XiaoGuang Lin, Dan Zhen, JianGuo Zhong, HuaLiang Li

Methodology: ZhenYu Dai, JianGuo Zhong, PingLei Pan

Project administration: CongHu Yuan, PingLei Pan

Resources: XiaoGuang Lin, Dan Zhen, JianGuo Zhong, HuaLiang Li

Software: ZhenYu Dai, JianGuo Zhong, PingLei Pan

Supervision: CongHu Yuan, PingLei Pan

Validation: PingLei Pan

Visualization: XiaoGuang Lin, Dan Zhen, HuaLiang Li

Writing – original draft: XiaoGuang Lin, Dan Zhen

Writing – review & editing: ZhenYu Dai, CongHu Yuan, PingLei Pan

References

[1]. Duenas M, Ojeda B, Salazar A, et al. A review of chronic pain impact on patients, their social environment and the health care system. J Pain Res 2016;9:457–67.
[2]. Goldberg DS, McGee SJ. Pain as a global public health priority. BMC Public Health 2011;11:1–5.
[3]. Simons LE, Elman I, Borsook D. Psychological processing in chronic pain: a neural systems approach. Neurosci Biobehav Rev 2014;39:61–78.
[4]. Meints SM, Edwards RR. Evaluating psychosocial contributions to chronic pain outcomes. Prog Neuropsychopharmacol Biol Psychiatry 2018;87(pt B):168–82.
[5]. Tunks ER, Crook J, Weir R. Epidemiology of chronic pain with psychological comorbidity: prevalence, risk, course, and prognosis. Can J Psychiatry 2008;53:224–34.
[6]. Arendt-Nielsen L, Morlion B, Perrot S, et al. Assessment and manifestation of central sensitisation across different chronic pain conditions. Eur J Pain 2018;22:216–41.
[7]. Tatu K, Costa T, Nani A, et al. How do morphological alterations caused by chronic pain distribute across the brain? A meta-analytic co-alteration study. Neuroimage Clin 2018;18:15–30.
[8]. Tanasescu R, Cottam WJ, Condon L, et al. Functional reorganisation in chronic pain and neural correlates of pain sensitisation: a coordinate based meta-analysis of 266 cutaneous pain fMRI studies. Neurosci Biobehav Rev 2016;68:120–33.
[9]. Friebel U, Eickhoff SB, Lotze M. Coordinate-based meta-analysis of experimentally induced and chronic persistent neuropathic pain. Neuroimage 2011;58:1070–80.
[10]. Smallwood RF, Laird AR, Ramage AE, et al. Structural brain anomalies and chronic pain: a quantitative meta-analysis of gray matter volume. J Pain 2013;14:663–75.
[11]. Yuan C, Shi H, Pan P, et al. Gray matter abnormalities associated with chronic back pain: a meta-analysis of voxel-based morphometric studies. Clin J Pain 2017;33:983–90.
[12]. Cauda F, Palermo S, Costa T, et al. Gray matter alterations in chronic pain: a network-oriented meta-analytic approach. Neuroimage Clin 2014;4:676–86.
[13]. Davis KD, Flor H, Greely HT, et al. Brain imaging tests for chronic pain: medical, legal and ethical issues and recommendations. Nat Rev Neurol 2017;13:624–38.
[14]. Barkhof F, Haller S, Rombouts SA. Resting-state functional MR imaging: a new window to the brain. Radiology 2014;272:29–49.
[15]. Jiang L, Zuo XN. Regional homogeneity: a multimodal, multiscale neuroimaging marker of the human connectome. Neuroscientist 2016;22:486–505.
[16]. Zang Y, Jiang T, Lu Y, et al. Regional homogeneity approach to fMRI data analysis. Neuroimage 2004;22:394–400.
[17]. Zhen D, Xia W, Yi ZQ, et al. Alterations of brain local functional connectivity in amnestic mild cognitive impairment. Transl Neurodegener 2018;7:1–4.
[18]. Xiao B, Wang S, Liu J, et al. Abnormalities of localized connectivity in schizophrenia patients and their unaffected relatives: a meta-analysis of resting-state functional magnetic resonance imaging studies. Neuropsychiatr Dis Treat 2017;13:467–75.
[19]. Wang J, Zhang JR, Zang YF, et al. Consistent decreased activity in the putamen in Parkinson's disease: a meta-analysis and an independent validation of resting-state fMRI. Gigascience 2018;7:1–3.
[20]. Pan P, Zhan H, Xia M, et al. Aberrant regional homogeneity in Parkinson's disease: a voxel-wise meta-analysis of resting-state functional magnetic resonance imaging studies. Neurosci Biobehav Rev 2017;72:223–31.
[21]. Iwabuchi SJ, Krishnadas R, Li C, et al. Localized connectivity in depression: a meta-analysis of resting state functional imaging studies. Neurosci Biobehav Rev 2015;51:77–86.
[22]. Hao H, Chen C, Mao W, et al. Aberrant brain regional homogeneity in first-episode drug-naive patients with major depressive disorder: a voxel-wise meta-analysis. J Affect Disord 2019;245:63–71.
[23]. Hao H, Chen C, Mao W, et al. Alterations in resting-state local functional connectivity in obsessive-compulsive disorder. J Affect Disord 2019;245:113–9.
[24]. Disner SG, Marquardt CA, Mueller BA, et al. Spontaneous neural activity differences in posttraumatic stress disorder: a quantitative resting-state meta-analysis and fMRI validation. Hum Brain Mapp 2018;39:837–50.
[25]. Chen J, Wang Z, Tu Y, et al. Regional homogeneity and multivariate pattern analysis of cervical spondylosis neck pain and the modulation effect of treatment. Front Neurosci 2018;12:1–9.
[26]. Wang Y, Zhang X, Guan Q, et al. Altered regional homogeneity of spontaneous brain activity in idiopathic trigeminal neuralgia. Neuropsychiatr Dis Treat 2015;11:2659–66.
[27]. Yuan J, Cao S, Huang Y, et al. Altered spontaneous brain activity in patients with idiopathic trigeminal neuralgia: a resting-state functional MRI study. Clin J Pain 2018;34:600–9.
[28]. Zhang J, Li X, Jin Z, et al. Spontaneous brain activity and connectivity in female patients with temporomandibular joint synovitis pain: a pilot functional magnetic resonance imaging study. Oral Surg Oral Med Oral Pathol Oral Radiol 2018;126:363–74.
[29]. Xiang CQ, Liu WF, Xu QH, et al. Altered spontaneous brain activity in patients with classical trigeminal neuralgia using regional homogeneity: a resting-state functional MRI study. Pain Pract 2019;19:397–406.
[30]. Alshelh Z, Di Pietro F, Youssef AM, et al. Chronic neuropathic pain: it's about the rhythm. J Neurosci 2016;36:1008–18.
[31]. Lin Y, Bai Y, Liu P, et al. Alterations in regional homogeneity of resting-state cerebral activity in patients with chronic prostatitis/chronic pelvic pain syndrome. PLoS One 2017;12:e0184896.
[32]. Albajes-Eizagirre A, Solanes A, Vieta E, et al. Voxel-based meta-analysis via permutation of subject images (PSI): theory and implementation for SDM. Neuroimage 2019;186:174–84.
[33]. Shamseer L, Moher D, Clarke M, et al. Preferred reporting items for systematic review and meta-analysis protocols (PRISMA-P) 2015: elaboration and explanation. BMJ 2015;350:g7647.
[34]. Treede RD, Rief W, Barke A, et al. A classification of chronic pain for ICD-11. Pain 2015;156:1003–7.
[35]. Nicholas M, Vlaeyen JWS, Rief W, et al. The IASP classification of chronic pain for ICD-11: chronic primary pain. Pain 2019;160:28–37.
[36]. Albajes-Eizagirre A, Solanes A, Fullana MA, et al. Meta-analysis of voxel-based neuroimaging studies using seed-based d mapping with permutation of subject images (SDM-PSI). J Vis Exp 2019;153:e59841.
[37]. Gorgolewski KJ, Poldrack RA. A practical guide for improving transparency and reproducibility in neuroimaging research. PLoS Biol 2016;14:e1002506.
Keywords:

chronic pain; coordinate-based meta-analysis; regional homogeneity; resting-state functional MRI; Seed-based d Mapping

Copyright © 2020 the Author(s). Published by Wolters Kluwer Health, Inc.