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Brain activations during pain: a neuroimaging meta-analysis of patients with pain and healthy controls

Jensen, Karin B.; Regenbogen, Christina; Ohse, Margarete C.; Frasnelli, Johannes; Freiherr, Jessica; Lundström, Johan N.

doi: 10.1097/j.pain.0000000000000517
Research Paper
Editor's Choice

In response to recent publications from pain neuroimaging experiments, there has been a debate about the existence of a primary pain region in the brain. Yet, there are few meta-analyses providing assessments of the minimum cerebral denominators of pain. Here, we used a statistical meta-analysis method, called activation likelihood estimation, to define (1) core brain regions activated by pain per se, irrelevant of pain modality, paradigm, or participants and (2) activation likelihood estimation commonalities and differences between patients with chronic pain and healthy individuals. A subtraction analysis of 138 independent data sets revealed that the minimum denominator for activation across pain modalities and paradigms included the right insula, secondary sensory cortex, and right anterior cingulate cortex (ACC). Common activations for healthy subjects and patients with pain alike included the thalamus, ACC, insula, and cerebellum. A comparative analysis revealed that healthy individuals were more likely to activate the cingulum, thalamus, and insula. Our results point toward the central role of the insular cortex and ACC in pain processing, irrelevant of modality, body part, or clinical experience; thus, furthering the importance of ACC and insular activation as key regions for the human experience of pain.

Supplemental Digital Content is Available in the Text.The minimum denominators of pain processing in the human brain were explored by means of activation likelihood estimation, suggesting that the insula and ACC are essential for the experience of pain.

aDepartment of Clinical Neuroscience, Karolinska Institutet, Stockholm, Sweden

bClinic of Diagnostic and Interventional Neuroradiology, RWTH Aachen University, Aachen, Germany

cMonell Chemical Senses Center, University of Pennsylvania, Philadelphia, PA, USA

dCogNAC, Department of Anatomy, Université du Québec à Trois-Rivières, Trois-Rivières, QC, Canada

eCÉAMS, Research Center, Sacré-Coeur Hospital, Montréal, QC, Canada

fDepartment of Psychology, University of Pennsylvania, Philadelphia, PA, USA

Corresponding author. Address: Department of Clinical Neuroscience, Karolinska Institutet, Nobels väg 9, D3, 17176 Stockholm, Sweden. Tel.: +46702130811; fax: +468311101. E-mail address: karin.jensen@ki.se (K. B. Jensen).

Sponsorships or competing interests that may be relevant to content are disclosed at the end of this article.

Supplemental digital content is available for this article. Direct URL citations appear in the printed text and are provided in the HTML and PDF versions of this article on the journal's Web site (www.painjournalonline.com).

Received October 06, 2015

Received in revised form January 03, 2016

Accepted January 25, 2016

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1. Introduction

The complexity of the subjective experience of pain entails involvement of multiple different brain regions.25,40 The communication between these different regions, and their reciprocal modulatory effects, is believed to largely account for the individual pain experience.61 Yet, a recent debate has discussed the existence of a primary pain cortex that would be comparable to, for example, the primary visual cortex.13 Because pain is defined as a subjective experience modulated by cognitive, affective, and contextual factors, scientists have long considered it unlikely that one primary brain area would respond to pain. Still, recent data suggest that the posterior insula plays a fundamental role in pain perception,58 a notion that finds support in earlier studies proposing that the insula is required for encoding and processing of nociceptive input.8,39

Literature reviews of the neural correlates to pain3,12,25,49,61 frequently report joint activation of the thalamus, primary and secondary somatosensory cortices (SI/SII), insula, anterior cingulate cortex (ACC), and prefrontal cortices.5 Despite the high prevalence of chronic pain in the general population and the vast number of available neuroimaging studies of chronic pain, there have been few systematic comparisons of neuroimaging findings from patients and healthy individuals.

Because of the complex integration of different brain regions and varying types of pain paradigms, neuroimaging studies often yield heterogeneous activation patterns. Many neuroimaging studies are also statistically underpowered,38 especially in clinical cohorts. Yet, systematic reviews may counteract this power problem by aggregating the data. Although descriptive literature reviews are well suited to characterize common activations between studies based on a given variable of interest, much of the 3-dimensional (3D) spatial information that voxel-based data consist of is lost. To counteract these problems, the meta-analytical method of activation likelihood estimation (ALE) analyses was developed.62 Activation likelihood estimation allows for formal statistical integration of unbiased voxel-based data from multiple studies to determine common activations across studies and to provide a formal estimate of activation likelihood.1 In a recent study,15 the authors reported likelihood maps derived from pain studies in healthy individuals; however, the study did not include patients with chronic pain. Chronic pain is a common health problem that affects more than 100 million adults in the United States alone at any given time and leads to large economic burdens for society.21 The aim of the present study was to use ALE to determine, based on statistical likelihoods, core brain regions that are activated by pain per se, irrelevant of pain modality and origin and to compare the likelihood maps from patients with chronic pain and healthy individuals.

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2. Materials and methods

2.1. Identification of publications

Only data from international peer-reviewed journals published between January 1990 and December 2014, using functional and spatially precise imaging methods (functional magnetic resonance imaging and positron emission tomography), were used for ALE analyses. We obtained these publications by searches in the National Center for Biotechnology Information's publications database PUBMED (http://www.ncbi.nlm.nih.gov/pubmed/) using the keywords pain*, ache*, laser, cold, and heat cross-paired with the keywords fMRI, PET, functional magnetic resonance imaging, or positron emission tomography. Similar searches were subsequently performed in the Thomson Reuters' ISI Web of Science publication database with all obtained results cross-referenced to sort out repeated entries. In addition to these 2 databases, the reference list within each analyzed article was searched for additional publications of interest. Data within publications were classified as originating from either healthy or patient populations based on the classification provided by the authors.

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2.2. Inclusion criteria

The inclusion criteria required a pain vs no-pain contrast where brain foci were derived from whole-brain volume analyses reported in 3D coordinates (x, y, and z) within a standardized stereotactic space (either Montreal Neurological Institute [MNI] or Talairach) and do not depend on region-of-interest analyses. Only studies on human participants using either functional magnetic resonance imaging or positron emission tomography imaging were included. The number of subjects in a group of healthy subjects or patients had to be larger than 5 in a given study. Postscreening of the included studies, regarding general methodological quality, was not performed because of 2 reasons. First, the broad inclusion strategy renders means that the result from the ALE analyses is a better representation of the published studies to date. Second, by adapting a broad inclusion strategy, incidences of methodologically weak studies will have limited impact on the ALE results because the relative contribution of each included study is smaller. Also, it is unlikely that a significant number of studies would suffer from such specific methodological problems that it would result in an anatomically precise colocalization between them, something that is needed to taint the ALE result.

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2.3. Activation likelihood estimation analyses

Activation likelihood estimation analyzes the given brain coordinates from published functional imaging studies and then searches for concordance by modeling each reported foci as the center of a 3D Gaussian probability distribution using permutation testing. These distributions are then used to create a whole-brain statistical map that estimates the likelihood of activation for each individual voxel, as determined by the entire set of studies included. Hence, the statistics reported in our ALE result tables are quantifications of the likelihood of activation because of a given task at a given voxel. The original ALE meta-analysis algorithm was originally developed by Turkeltaub et al. (2002) and continuously optimized. Detailed descriptions of the ALE algorithm can be found elsewhere16,17,35,62,63 and this algorithm is implemented in BrainMap (BrainMap GingerALE 2.3.2, http://brainmap.org/ale). The most recent algorithm allows not only automatic meta-analysis pipeline in both Talairach and MNI space, random-effects inference, and comparison of 2 different ALE maps but also cluster-based correction for multiple comparisons.

During standard analysis of neuroimaging data, functional images are spatially normalized to a stereotactic template that might differ between studies. These anatomical templates are not directly comparable because of minor anatomical deviances. Therefore, to facilitate direct comparisons between publications, all data included in the statistical meta-analysis were transformed into MNI space, as implemented in the GingerALE software. All coordinates were subsequently imported into the Java-based version of the ALE software and analyzed. A whole-brain ALE map was created by modeling a Gaussian probability distribution centered at each reported activation coordinate. A voxel-wise calculation of the probability that each activation was located within that particular voxel was then performed using a 3D Gaussian filter function with an empirically determined full-width, half-maximum value.17 The approach of histogram permutation testing was subsequently used to test the null hypothesis which states that activation foci are distributed uniformly across the brain.16,63 To compare ALE values in healthy subjects with patients, we matched a number of included contrasts to avoid weighting the resulting ALE output toward the group with more included contrasts. The ALE analysis in patients contained 40 contrasts (443 foci). As a first step, we randomly selected 40 contrasts (461 foci) including healthy subjects (see Supplementary Table 1, available online as Supplemental Digital Content at http://links.lww.com/PAIN/A239). Then, we calculated individual ALE maps of the 2 samples, as well as a pooled ALE map, and performed the actual subtraction. The local maxima of activation clusters were labeled using a cluster-analysis step within the ALE procedure and the MNI atlas included in Mango (http://ric.uthscsa.edu/mango/; version 2.5). Statistical maps were all corrected for multiple comparisons using P < 0.05 cluster-level corrected inference using P < 0.001 uncorrected at voxel-level as the cluster-forming threshold, unless explicitly noted.

For visualization purposes, the anatomical template provided on the GingerALE Website (Colin27_T1_seg_MNI.nii, http://brainmap.org/ale) was overlaid with the different threshold ALE maps using the Mango imaging software. To visualize overlaps between different pain modalities in healthy subjects, between different patients with chronic pain, or between patients and healthy individuals, the different ALE maps were superimposed.

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3. Results

Of the original search on the scientific search engines, 34,022 hits were narrowed down using the mentioned exclusion and inclusion criteria, rendering a total of 170 individual articles (see Supplementary Tables 1 and 2 for a detailed overview, available online as Supplemental Digital Content at http://links.lww.com/PAIN/A239). These articles were then divided into articles exploring neural activity of pain in healthy individuals (n = 138) and patients (n = 32).

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3.1. Literature search result—healthy individuals

A total of 138 functional imaging studies fulfilled all the stipulated inclusion criteria and were included in the final analyses (see Supplementary Table 1, available online as Supplemental Digital Content at http://links.lww.com/PAIN/A239). These 138 studies comprised a total of 183 contrasts and 2442 activation foci that were included in ALE analyses of pain processing in healthy individuals. Most of the 2442 included foci originated from studies using thermal pain as the means of stimulation (965 foci), followed by distension (356 foci), electrical (314 foci), and mechanical stimulation (eg, pressure pain; 226 foci). The most commonly stimulated body part was the arm (874 foci), followed by the hand (560 foci). For an overview of all pain modalities and stimulated body regions, see Supplementary Table 2 (available online as Supplemental Digital Content at http://links.lww.com/PAIN/A239).

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3.2. Literature search result—patients

Thirty-two studies, reporting a total of 40 contrasts and 443 activation foci, fulfilled the inclusion criteria and were included in the final ALE analysis of patients with chronic pain (see Supplementary Table 3, available online as Supplemental Digital Content at http://links.lww.com/PAIN/A239).

A total of 13 different categories of patients with pain were included in the 32 studies. Most foci were reported from patients with fibromyalgia (83 foci), followed by irritable bowel syndrome (76 foci), and headache (76 foci). The most common methods of experimental stimulation in patients with pain were pressure pain (167 foci), distension (116 foci), and thermal pain (80 foci). The most commonly stimulated body parts were the hand (144 foci), face or head (95 foci), and rectum (76 foci). For representation of the different patient categories, pain modalities, and stimulated body parts, see Supplementary Table 4 (available online as Supplemental Digital Content at http://links.lww.com/PAIN/A239).

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3.3. Significant activation likelihood estimation results—healthy individuals

The ALE analysis for all 2442 foci revealed 7 clusters that had a significant likelihood of activation during pain, namely, bilateral thalamus, bilateral insula, left SI and SII, right ACC, right prefrontal cortices (middle-frontal gyrus), and cerebellum (Fig. 1A, Table 1; Supplementary Fig. 1 for complete brain coverage, available online as Supplemental Digital Content at http://links.lww.com/PAIN/A239).

Figure 1

Figure 1

Table 1

Table 1

To assess commonly reported brain regions independent of pain modality, we first calculated the individual ALE maps for the 4 most common methods of pain stimulation (thermal, distension, electrical, and mechanical stimulation). We then calculated areas of overlap between these individual ALE maps to represent areas commonly activated by pain stimulation. The commonly activated brain regions in healthy individuals were further explored by a common subtraction method, ie, every voxel in the brain that did have a significant ALE value in all the individual analyses was kept; all others were given a value of zero, and thus removed. Although this is not a statistical approach stricto sensu, it has the potential to elucidate core brain regions that process pain through its common presence in all conditions. For a visual representation of the overlap between the 4 different modalities (conjunction analysis), see Figure 2 for discussed areas and Supplementary Figure 2 for complete brain coverage (available online as Supplemental Digital Content at http://links.lww.com/PAIN/A239). The overlap image reveals an overlap across the 4 most common pain modalities in the right insula, bilateral SII, and right ACC.

Figure 2

Figure 2

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3.4. Significant activation likelihood estimation results—patients with chronic pain

The ALE analysis in patients with chronic pain, including 443 foci, revealed 10 different clusters that were likely activated during pain: the bilateral thalamus, insula, SI, SII, right-cingulate gyrus, and left cerebellum (Fig. 1B, Table 1).

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3.5. Significant activation likelihood estimation results—overlap between healthy individuals and patients with chronic pain

A conjunction analysis of common ALE results for healthy individuals and patients with chronic pain revealed a considerable pain-processing overlap in 14 clusters, eg, in the insula, cingulum, thalamus, and cerebellum (Fig. 2, Table 2).

Table 2

Table 2

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3.6. Significant activation likelihood estimation results—differences between healthy individuals and patients with chronic pain

Because of the large amount of literature suggesting that chronic pain is associated with functional brain aberrations, we investigated the differences in activation likelihood between patients and healthy individuals. Statistical subtraction was used to elucidate the differences in ALE results between healthy individuals and patients with chronic pain (P < 0.05 uncorrected at voxel-level). Ten different clusters had a significantly higher likelihood of activation in healthy individuals than in patients, including the cingulate gyrus, thalamus, insula, middle-frontal gyrus, and the cerebellum (Fig. 3, Table 3). The reverse contrast, ie, assessing where patients had a higher likelihood of activity than healthy individuals, revealed 4 different clusters reaching statistical significance for higher likelihood of activation: the cerebellum, inferior-frontal gyrus, precentral gyrus, and the cingulate gyrus (Fig. 3, Table 3; for complete brain coverage, see Supplementary Fig. 3, available online as Supplemental Digital Content at http://links.lww.com/PAIN/A239).

Figure 3

Figure 3

Table 3

Table 3

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4. Discussion

Here, a quantitative assessment of brain regions involved in human processing of pain, and a unique comparison of brain activations in patients with pain and healthy individuals, was performed using ALE. The insula, ACC, SII, and thalamus were commonly activated by pain, irrelevant of pain modality or stimulated body part, both in patients and healthy individuals. The high predominance of insular and ACC activations in our analyses furthers their role as key regions for the experience of pain. Our results are also made available as a spatial brain mask for free download and use in future neuroimaging analyses, eg, when analyses are confined to the core pain network or when independently and functionally defined pain areas for region of interest analyses are desired.

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4.1. Activation likelihood during pain processing

In line with recent data suggesting that the insula plays a fundamental role in human pain,11,58 we report robust likelihood of activations of the entire insula during pain, with peaks in the anterior insula (AI). Although the dorsal-posterior insula has been described as the cortical representation of incoming nociceptive signals,11 the AI has been associated with the integration of emotional and interoceptive states.10,46 For example, exposure to noxious stimuli may induce the activation of both the posterior and anterior insula, whereas the subjective evaluation of these stimuli is represented in the AI.31 Based on the significant likelihood of activating the insula across pain modalities, with peaks in the AI, we suggest that the AI is an essential component for the subjective experience of pain. Previous ALE meta-analyses also found insular involvement in pain processing,15,36,43 with peaks in anterior, middle, and the posterior insula. Yet, is there coherence between experimental studies suggesting that the posterior insula plays a fundamental role in pain and ALE analyses suggesting that the AI is key for pain processing? It has been suggested that the posterior insula is a nonspecific way station for sensory input and coding of stimulus intensity, rather than a specific pain center,13 as opposed to the evaluative role of the AI. Hence, the 2 regions reflect different aspects of pain processing. Neuroanatomical studies support the notion that the right AI is part of an afferent path for interoceptive representations of pain and homeostatic drive.9 The AI is thereby thought to provide meta-representations of the state of the body, or “the feeling self”, combined with motivational drive for bodily protection.9 Involvement of the AI in cognitive meta-representations of pain has also been found in studies of empathy for pain where the AI is activated both by one's own pain and by watching pain in others.47,60 Interestingly, Ochsner et al.47 found that the right AI was more engaged during the processing of one's own pain than watching others' pain.47 Combined with the central role of the AI suggested here, the AI could be essential for attributing pain to one's own body.

Conclusive ALE findings support the importance of ACC activation for pain processing. The ACC is a limbic brain structure that shares afferent projections from the same spinothalamic tract as the AI.10 The AI and ACC are often jointly activated, consistent with the idea that they represent complementary limbic sensory and motor regions that work together.10 More specifically, the AI might be the site for pain awareness because of its afferent representation of bodily interoception and the ACC, the site for the initiation of behavioral response. The ACC has been frequently associated with the emotional-motivational aspect of pain, as supported by lesion and imaging studies.19,20,54 For example, selective changes in pain unpleasantness, but not pain intensity, have been associated with ACC modulation.50

In line with analyses by Duerden and Albanese,15 we found that the AI, ACC, and thalamus were consistently activated across pain modalities, suggesting a consistent core network of brain regions involved in pain processing.

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4.2. Comparison between pain processing in health and disease

As cumulating evidence suggests that chronic pain is manifested in the central nervous system,22,44,57 a number of studies have investigated the cerebral changes associated with chronic pain2,55,56; including structural,4,14,18,32,52 functional,7,24,45 and neurochemical brain alterations.23,29,65 Here, a formal comparison between the ALE results from a large number of patients and healthy individuals demonstrates that patients are less likely to activate the AI and thalamus. As previously discussed, the AI is involved in the subjective experience and interoceptive representation of pain.9 As patients had a less likelihood to activate the AI, it may reflect a disrupted pain interoceptive function, which in turn could explain the absence of pain inhibitory activations in patients during evoked pain.26,28

Patients' less likelihood to activate key nociceptive regions may seem counterintuitive because patients have persistent pain. One methodological explanation could be that patients' relatively lower likelihood to activate, eg, the AI and cingulate gyrus, indicates a ceiling effect, as patients have constant noxious input to nociceptive brain regions and are thus not able to display additional activation during added experimental pain.36 Furthermore, recent data suggest that the transition from acute to chronic pain entails a shift from lateral (sensory-discriminative) to medial (affective-motivational) neural activity.3 Our results may thus support the hypothesis that chronic pain involves decreased sensory and enhanced emotional processes because ALE differences were also found in the thalamus, a region with a significantly less likelihood of pain activation in patients. For several different categories of patients with pain, there are reports of thalamic structural changes,14,53,59 attenuated thalamic activity during rest,34,41 and attenuated pain-evoked activations.26,33 Our results thereby support the notion that pain pathophysiology may involve thalamocortical dysrhythmia.6,30,37 According to the dysrhythmia theory, thalamocortical disruption may lead to disturbances of sensation, motor performance, cognition, and ultimately, to disabling chronic disorders, such as chronic pain. In a longitudinal neuroimaging study,27 where fibromyalgia patients were scanned before and after treatment with cognitive behavioral therapy, there was increased connectivity between the thalamus and lateral prefrontal cortex compared to waitlist controls. Because chronic pain is associated with disrupted thalamocortical connectivity, the increase in thalamocortical connectivity may reflect a normalization of pain pathophysiology.

We found that patients had a less likelihood to activate parts of the middle-frontal gyrus, possibly reflecting patients' decreased activation of the brain's pain inhibitory network.26,28 The prefrontal cortex is a key region for pain inhibition, eg, during reappraisal of pain and placebo analgesia,48,64 suggesting that the attenuated activation of the frontal gyrus in patients may represent decreased pain inhibition. Yet, our results were inconclusive, as patients displayed an increased likelihood of activating some parts of the prefrontal cortex, whereas the same structure had a decreased likelihood of activation on the right side. Because the laterality and specificity of the different prefrontal subregions during noxious stimulation is not fully understood, further studies of the role of the prefrontal cortex in pain chronification are needed.

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4.3. Future outlook and limitations

Most neuroimaging paradigms in human subjects do not allow for the distinction between nociception and pain because the experimental stimulation of peripheral nociceptors is inherently coupled to the subjective experience of pain. Therefore, our study will not be able to shed light on the possible segregation between nociception and pain. Furthermore, it is important to note that pain activations share a considerable overlap with other cognitive and emotional processes.42 Even if we strive to describe the minimum denominators of pain, specific patterns of brain activity are merely correlated to the subjective experience of pain, and not pain per se.51

The results reported within this meta-analysis are based entirely on results reported in previous publications. This means that although the analyses performed are random-effect analyses in a statistical sense, the ALE values reported are referring to the likelihood that these voxels are activated in any given neuroimaging study on pain processing. Hence, our results should be interpreted and used within balanced limits of previous probabilities and reverse inferences in neuroimaging studies.

In this study, we only included data originating from publications reporting their results using 3D coordinates within a standardized stereotactic space. Although studies assessing links between brain areas and pain processing in patients with localized lesions are very informative and, some argue, with a stronger causality, they fall outside the scope of this statistical meta-analysis. Future attempts should be made to quantify lesion extensions in coordinate space and merge the information with that from coordinate-based inference studies to render a more inclusive meta-analysis.

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5. Conclusions

The results from the present study suggest that (1) insular and ACC activation are central for pain perception and (2) functional differences in pain processing between patients with chronic pain and healthy individuals may explain behavioral differences and further our understanding of pain pathology, especially when pain is viewed as a homeostatic function or as a transition from lateral (sensory) to medial (emotional) processing of pain.

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Conflict of interest statement

The authors have no conflicts of interest to declare.

This material is based on work supported by a grant from the Knut and Alice Wallenberg Foundation (KAW 2012.0141) awarded to J. N. Lundström. Johnson & Johnson Inc provided partial salary support during the data collection phase. C. Regenbogen is supported by a postdoctoral fellowship of the DAAD (German Academic Exchange Service). J. Frasnelli is supported by grants from the Research Center of Sacré-Coeur Hospital, Montréal, the University of Québec in Trois-Rivières, the Natural Sciences and Engineering Research Council (Canada), and the Fonds de Recherche du Québec—Santé. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

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Acknowledgements

The authors thank Dr Marco Loggia for valuable comments on a previous version of this manuscript as well as Kajsa Forsberg and Anna Sjöholm Norling for assisting in data collection and extraction.

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Supplemental Digital Content

Supplemental Digital Content associated with this article can be found online at http://links.lww.com/PAIN/A239.

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Keywords:

Pain; Meta-analysis; Chronic pain; Neuroimaging; Thalamus; Cingulate cortex

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