Pain is associated with regional grey matter reduction in... : PAIN (original) (raw)
1. Introduction
Chronic pain is thought to be maintained in part by maladaptive functional or structural plasticity of the nociceptive system that can occur at various sites from the spinal cord to the cerebral cortex [36]. Magnetic resonance imaging (MRI)-based volumetric studies have repeatedly demonstrated that pain patients exhibit grey matter reductions in several brain areas belonging to the nociceptive system, providing a possible correlate of maladaptive structural plasticity. Altered brain morphology has been described in various types of pain, for example, chronic back pain [2,7,29], chronic tension-type headache [30], fibromyalgia [8,17,19,31], migraine [18,28,35], and somatoform pain disorder [34]. A common “brain signature” of chronic pain has been suggested because different types of pain show grey matter alterations in similar regions, that is, in the cingulate cortex, prefrontal cortex, insula, and dorsal pons [22].
In spite of these highly consistent results from different studies and pain types, causes and consequences of pain-related grey matter alterations are not well understood [23]. Structural differences in pain-processing areas might be preexisting, reflecting a predisposition for the development of pain disorders [1,5], or they may develop during the course of the disease, as suggested by studies finding a relation with pain duration [2,18,19,30]. It is also not clear if pain-related grey matter reductions represent reversible processes or irreversible atrophy of neurons, glial cells, and/or extracellular space [1,22,23]. Two recent studies found that grey matter alterations in patients with chronic painful hip osteoarthritis were partially reversed 4 to 9 months after pain relief by surgery [15,27], suggesting that grey matter changes are neither preexisting nor due to irreversible cell damage.
Previous studies have been performed in clinical pain populations representing the upper end on the continuum from no pain to severe pain [23]. It remains to be determined if pain-related grey matter alterations extend into the general population.
In the present study, instead of comparing highly select chronic pain patients with healthy controls, we used self-report of present and past pain complaints in a group of 172 older adults from the general population to identify pain-free control subjects, subjects with ongoing low back pain, headache, or lower extremity joint pain who had at least moderate pain on more than 3 days/month in the 12 months before investigation, and subjects with past pain (stopped for >12 months before investigation). High-resolution structural MRI followed by voxel-based morphometry was used to assess group differences in regional grey matter volume.
This approach allowed us to (1) investigate if pain-related grey matter changes extend beyond selected chronic pain populations into the general population and (2) investigate if subjects with pain that has stopped for more than 12 months show grey matter alterations. In addition, we performed an exploratory analysis, comparing patterns of grey matter decrease between different types of pain (low back pain, headache, joint pain).
2. Methods
2.1. Subjects
Structural MRI of the brain and self-report of pain complaints were initially obtained from 204 subjects enrolled in the Münster SEARCH study (Systematic Evaluation and Alteration of Risk factors for Cognitive Health). In this study, subjects from 40 to 85 years of age were randomly selected based on dates of birth from the population register of the city of Münster, Germany, then invited by letter to participate, and included in the study after giving written informed consent. The study was conducted in accordance with the Declaration of Helsinki and was approved by the local ethics committee of the University of Münster.
All participants, apart from completing the pain questionnaires, received a structured clinical face-to-face interview, a physical and neurological examination by a study physician (not a pain specialist), blood sampling, MRI brain imaging, and a comprehensive neuropsychological assessment. Thirty-two subjects were excluded because of (1) neurological disorders; (2) a Mini-Mental State Examination score <27, suggesting cognitive impairment; (3) incomplete questionnaires; (4) abnormal findings on T1 or T2 imaging; or (5) grey matter segmentation failure during MRI data preprocessing, leaving 172 subjects for analysis. Depressive mood according to the Beck Depression Inventory [4] was not an exclusion criterion, as pain is often associated with depressive mood.
2.2. Self-report of pain
According to the idea that grey matter alterations are related to the pain itself rather than to specific diagnoses, the classification of subjects was not based on medical diagnoses but on perceived pain in one of the 3 locations. Self-report of pain complaints was assessed using a modified version of the official German Pain Questionnaire issued by the German chapter of the International Association for the Study of Pain (www.dgss.org). For each of the 3 types of pain studied here (back pain, headache, joint pain) and other types of pain (to be specified by the subject), the following information was obtained: (1) Do you currently suffer from this type of pain, or have you suffered from this type of pain at some period during your life? (Yes/No); (2) period when pain was present (eg, 2001 to present); (3) average days/month in pain during that period; (4) average intensity of pain (numerical rating scale [NRS], 0–10); (5) average interference of pain with daily life when pain is/was present (NRS, 0–10); (6) a schema displaying front and rear side of the body (or of the head, for headache) where the subject indicated location of their pain; (7) diagnosis/cause of the pain if known to (and as understood by) the subject. This last point was used only to exclude subjects with head pain purportedly due to secondary reasons (eg, nerve lesion) and to exclude subjects with joint pain who claimed to have a rheumatic diagnosis (see below).
2.3. Group assignments
Of the 172 subjects, 105 were categorised into one of the 3 nonoverlapping groups described below. Sixty-seven subjects did not fulfil the inclusion criteria for any of those groups and were excluded from further analysis.
2.3.1. Control group (n = 31)
The control group comprised subjects who indicated that they had never suffered from pain for ≥3 months.
2.3.2. Ongoing pain group (n = 45)
This group included subjects who indicated that they currently suffered from low back pain (64%), headache (33%, not differentiating between primary headache types but excluding those who indicated secondary headache causes), and/or joint pain (22%, oligoarticular, lower extremity joint pain; mostly uni- or bilateral knee pain, excluding those who claimed to have a rheumatic diagnosis) that was on average present on more than 3 days/month for at least the past 12 months and that was of at least moderate intensity (≥4 on the NRS).
Comparison between the different types of pain was performed only after excluding those subjects who suffered from more than one type of pain, leaving 22 subjects with low back pain, 8 subjects with headache, and 7 subjects with joint pain.
2.3.3. Past pain group (n = 29)
To address the question of whether changes in grey matter volume are reversible when the pain stops, we also studied subjects who indicated having suffered from low back pain (69%), headache (28%), and/or joint pain (10%; pain types as defied above) in the past but whose pain had stopped at least 12 months ago. As for the ongoing pain group, we included subjects whose pain had persisted for ≥12 months, had on average been present >3 days/month, and had been of at least moderate intensity (≥4 on the NRS).
2.4. MRI data acquisition
MRI was performed on a 3T MRI system (Gyroscan Intera T30, Philips Medical System, Best, The Netherlands) using a high-resolution structural T1-weighted 3D turbo-field-echo sequence (matrix 256 × 205 × 160 over a field of view of 25.6 × 25.6 × 16 cm3 reconstructed after zero filling to 512 × 410 × 320 cubic voxels with an edge length of 0.5 mm). The system was a whole body scanner equipped with master gradients (slew rate 150 mT/m/ms; maximal gradient strength 33 mT/m). A circularly polarised transmit-receive quadrature head coil with high frequency reflecting screen at the cranial end was used.
2.5. MRI data preprocessing
Because not the body coil but the head coil was used for spin excitation, no presaturation of arterial blood was performed. This led to high signal intensities of arteries so that standard segmentation routines failed. To solve this problem, we applied in advance to any further image processing in-house software for image bias correction that also accounted for hyperintense arteries. Afterwards, data preprocessing and analysis was performed with SPM8 (Statistical Parametric Mapping; Wellcome Department of Imaging Neuroscience, University College London, UK) running under Matlab 7.2 (Mathworks, Sherborn, MA, USA). The principles of voxel-based morphometry were employed as described by Ashburner and Friston [3]. Default settings were used unless otherwise indicated. In view of the later applied smoothing kernel of 12 mm full-width at half-maximum (see below) and with the intention to increase signal-to-noise ratio and reduce computing time, data were resliced to a voxel size of 2 × 2 × 2 mm3. The unified segmentation algorithm of SPM8 was used to segment and spatially normalise the structural MR images, setting bias regularisation to light (0.001) and cleanup to thorough. Voxel values of the resulting normalised grey matter segments indicate the probability (between 0 and 1) that a specific voxel belongs to grey matter. Images were corrected for volume changes during normalisation by modulating each voxel by the Jacobian determinant derived from the normalisation, allowing analysis of grey matter volumes instead of concentrations [14]. Finally, images were smoothed using an isotropic Gaussian kernel of 12 mm full-width at half-maximum.
2.6. Voxel-based analysis
Voxel-wise comparisons between the control group and the pain groups were performed using the general linear model as implemented in SPM8 while controlling for age, sex, and the volume scaling factor provided by SIENAX (www.fmrib.ox.ac.uk/fsl) as a measure for brain size. Voxels with a grey matter probability <0.2 in the mean unmodulated grey matter segment (averaged over all included subjects) were excluded from statistical analysis. As pain-related grey matter alterations are small, voxel-by-voxel family-wise error (FWE) correction of statistical thresholds is, in many cases, too conservative for whole-brain analysis [7,27,29,30]. We therefore used statistical inference corrected for multiple comparisons at cluster level that allows detection of low-intensity but spatially extended grey matter alterations [26]. At cluster level, significance was determined by assessing SPM{t} images using the nonstationary random field theory and adjusting cluster sizes according to the local smoothness at each voxel [16,24]. This approach has been shown to be valid for experiments where degrees of freedom are >30 and image smoothness (full-width at half-maximum) >3 * voxel sampling resolution [16,24]. A primary (voxel-intensity) threshold of P < 0.01 (uncorrected) was used to sensitise the cluster test for spatially extended clusters. Then, an FWE-corrected cluster size threshold of P < 0.05 was applied.
2.7. Region of interest-based analysis
To allow analysis of grey matter alterations within functional regions, we complemented the analysis by comparing grey matter within 10 regions of interest (ROIs) that were defined according to previous knowledge on locations of grey matter alterations in pain states, using masks generated by the Wake Forest University PickAtlas tool (Functional MRI Laboratory, Wake Forest University School of Medicine, Winston-Salem, NC, USA). ROIs were defined as follows: orbitofrontal cortex (OFC, defined as the conjunction of Brodmann areas [BA] 10 and 11), dorsolateral prefrontal cortex (DLPFC, BA46/9), anterior cingulate cortex (ACC, BA24/32), insular cortex, (pre)motor areas including supplementary motor areas and the frontal eye field (BA4/6/8), primary sensory cortex (SI, BA1/2/3), posterior cingulate cortex (PCC, BA23/31), posterior parietal cortex (BA5/7/40), parahippocampal cortex, and thalamus.
Individual grey matter volume within each ROI was calculated by summing voxel values of the smoothed modulated normalised images within the corresponding ROI. Similarly, global grey matter volume was determined by summing voxel values within a mask, including all voxels with a grey matter probability >0.2 (see above).
ROI-based analysis, taking into account the total grey matter within a given ROI, has the advantage of allowing quantitative analysis within functional regions. Effect sizes (eg, Cohen’s d) within functional regions are independent of groups sizes and provide a measure of group differences that is easier to interpret than voxel-wise T-maps. When interpreting the results of both voxel-wise and ROI-based statistics performed on images preprocessed with a smoothing kernel (here: 12 mm full-width at half-maximum), it has to be kept in mind that the results within each voxel or ROI partially reflect grey matter volumes in neighbouring voxels/regions. This is most conspicuous when small clusters of voxels or small ROIs are considered.
2.8. Statistics
Outside SPM, statistical analysis was performed with SPSS, version 15 (SPSS Inc, Chicago, IL, USA). Grey matter volumes within ROIs and global grey matter volume were corrected for age, sex, and brain size by calculating linear regression residuals over the entire cohort and then compared between groups using Student’s _t_-test followed by Bonferroni adjustment to correct for the number of regions tested. Correlations between grey matter volume residuals and pain characteristics were determined using Pearson’s correlation coefficient. P < 0.05 was considered significant.
3. Results
Characteristics of the control group and the pain groups are shown in Table 1.
Characterisation of the study population.
3.1. Comparison between the control group and the ongoing pain group
3.1.1. Global grey matter volume
After correction for age, sex, and brain size, global grey matter volumes were smaller in the ongoing pain group than in the control group (T[74] = 2.81, P < 0.01), corresponding to a global grey matter volume reduction by 3.3% compared with the mean raw global grey matter volume of the control group.
3.1.2. Regional grey matter volume: voxel-based comparison
Voxel-based comparisons revealed that relative to the control group, the ongoing pain group exhibited significantly reduced grey matter volume within a large, rather symmetrical cluster covering parts of the prefrontal (including OFC and DLPFC) and motor/premotor cortex, the ACC and PCC and primary sensory as well as posterior parietal cortex (Fig. 1, Table 2, cluster extent P < 0.001, FWE-corrected, T-value of peak voxel: 4.04). No regions of increased grey matter volume were found at a cluster extent threshold of P < 0.05 (FWE-corrected, T-value of peak voxel: 1.42).
Regional grey matter volume decrease in the ongoing pain group as determined by voxel-based morphometry. Regions of significant (cluster extent P < 0.05, FWE-corrected) grey matter decrease as compared to the control group, corrected for age, sex, and brain size, are illustrated superimposed on the Montreal Neurological Institute (MNI) high-resolution single-subject T1 image. Coordinates are according to the MNI atlas. Right side in the figure corresponds to right side of the brain. FWE, family-wise error.
Regions with decreased grey matter in the ongoing pain group.
3.1.3. Regional grey matter volume: ROI-based comparison
Voxel-wise comparison between the control group and the ongoing pain group revealed a single large cluster encompassing multiple functional regions. To enable separate analysis of functional regions, we compared total grey matter volume within 10 ROIs, including the major regions covered by the cluster identified above and regions that have previously been reported to be altered in chronic pain patients (Fig. 2). Compared with the control group, total grey matter in the DLPFC, the ACC, and the motor/premotor cortex ROIs was significantly reduced in the ongoing pain group, showing a mean grey matter volume decrease of 5.3%, 4.3%, and 5.4%, respectively, relative to the mean regional raw grey matter volume of the control group.
Regional grey matter volume alterations in ongoing and past pain as determined by region of interest (ROI)-based analysis. (A) Examples of grey matter volumes (mean ± SEM) across groups within 2 selected ROIs, the ACC and the motor/premotor cortex. The left scale shows residuals after controlling for age, sex and brain size. The right scale shows percentual decrease in grey matter in relation to the mean raw grey matter value of the control group within the corresponding ROI. (B) Illustration of grey matter volume reduction across ROIs in the ongoing pain group and the past pain group. Group sizes are indicated in parentheses. Greyscales code for effect size (Cohen’s d) as indicated. ROIs are as defined in Methods (Section 2.7). ∗ P < 0.05; ∗∗ P < 0.01; (T-test against control group, Bonferroni-corrected for the number of regions tested). OFC, orbitofrontal cortex; DLPFC, dorsolateral prefrontal cortex; ACC, anterior cingulate cortex; SI, primary sensory cortex; PCC, posterior cingulate cortex.
When the analysis was limited to the subgroup of subjects without intake of (co-) analgesic drugs (nonsteroidal anti-inflammatory drugs, antidepressants, or anticonvulsants, control: n = 30, ongoing pain: n = 33), results were similar (DLPFC, ACC, SI: P < 0.05, motor/premotor cortex: P < 0.01, corrected for multiple comparisons).
3.2. Relationship between regional grey matter decrease and pain severity
We used correlation analysis to test if there was a linear relationship between pain characteristics and regional grey matter decrease within the ongoing pain group. No significant correlations were found between the extent of regional grey matter decrease in the DLPFC, ACC, or motor/premotor cortex ROIs and pain duration, pain intensity, or pain frequency.
3.3. Regional grey matter volume in subjects with past pain
We next investigated if grey matter volume decreases can be detected in a group of subjects who had suffered from pain in the past, but whose pain had stopped more than 12 months ago. Compared with the control group, the past pain group showed no regions of significant grey matter reduction (Fig. 2). However, pain duration was significantly shorter in the past pain group than in the ongoing pain group (Table 1). To make the past and ongoing pain groups more comparable, we limited the analysis to subjects with low back pain and matched groups for pain duration, leaving 18 subjects within each group. Pain characteristics in these 2 groups were similar (Table 3). While significant grey matter volume reductions were detected in the ongoing low back pain group, no such reductions were found in the past low back pain group (Fig. 3).
Comparison of matched ongoing and low back pain groups.
Regional grey matter volume alterations in the past and ongoing low back pain groups. Groups were matched for pain characteristics. (A) Examples of grey matter volumes (mean ± SEM) across groups within 2 regions of interest (ROIs). The left scale shows residuals after controlling for age, sex and brain size. The right scale shows percentual decrease in grey matter in relation to the mean raw grey matter value of the control group within the corresponding ROI. (B) Illustration of grey matter volume reduction across ROIs and groups. Group sizes are indicated in parentheses. Greyscales code for effect size (Cohen’s d) as indicated. ROIs are as defined in Methods (Section 2.7). ∗ P < 0.05; ∗∗ P < 0.01 (T-test against control group, Bonferroni-corrected for the number of regions tested). OFC, orbitofrontal cortex; DLPFC, dorsolateral prefrontal cortex; ACC, anterior cingulate cortex; SI, primary sensory cortex; PCC, posterior cingulate cortex.
3.4. Comparison between pain types
We next differentiated grey matter alterations between the 3 pain types included in the ongoing pain group. After exclusion of those subjects that suffered from more than one type of pain, 22 subjects with low back pain, 8 subjects with headache, and 7 subjects with joint pain were available for analysis. Compared with the control group, only the low back pain group showed significant grey matter volume reductions at a corrected level of significance (Fig. 4). By way of an exploratory analysis, we lowered the significance threshold to uncorrected levels. While the low back pain and the headache group showed grey matter reductions centred on frontal regions, the joint pain group showed grey matter reductions centred on parietal regions. Direct comparisons between the 3 groups did not reach significance.
Regional grey matter volume alterations in the low back pain, headache, and joint pain groups. Groups are nonoverlapping subgroups of the ongoing pain group after exclusion of subjects suffering from more than one type of pain. Grey matter volume reduction is illustrated across regions of interest (ROIs) and pain types. Group sizes are indicated in parentheses. Greyscales code for effect size (Cohen’s d) as indicated. ROIs are as defined in Methods (Section 2.7). ∗ P < 0.05 (T-test against control group, corrected for the number of regions tested); (∗) P < 0.05 (T-test against control group, uncorrected). OFC, orbitofrontal cortex; DLPFC, dorsolateral prefrontal cortex; ACC, anterior cingulate cortex; SI, primary sensory cortex; PCC, posterior cingulate cortex.
4. Discussion
The main result of the present study is that pain-related grey matter reductions were found in individuals from the general population with ongoing pain but not in subjects with pain that had stopped for more than 12 months before investigation.
4.1. Regional grey matter volume reduction in subjects with ongoing pain
Pain-related grey matter alterations have repeatedly been reported in several clinical pain cohorts, including those suffering from back pain, headache, and joint pain [2,7,15,18,27–29,35]. The present study shows that pain-related grey matter alterations are not limited to highly select clinical pain cohorts but are also present in subjects from the general population. According to currently used definitions of chronic pain (pain on most days of the past 3 or 6 months [1,20,25]), about half of the subjects included in the pain groups of the present study suffered from chronic pain, while the remainder did not suffer from pain on “most days,” that is, more than 50% of the days.
No direct correlation between grey matter volume reduction and pain duration, frequency, or intensity was found. Some previous studies have reported positive correlations between grey matter decrease and pain duration [2,13,18,19,30], while others have not found such a relationship [8,17,29,31]. One recent study reported an inverse relationship between insular cortex thickness and pain duration, interpreting these findings as evidence for a preexisting structural abnormality in the nociceptive system, and subsequent grey matter increase induced by the continuous nociceptive input [5]. In contrast, present and other previous [15,27] results suggest that grey matter volume reductions develop during prolonged pain states, but the time course is unknown at present. Maybe changes are fully developed after a certain time in pain, so that a ceiling effect precludes the detection of correlations with pain duration when subjects with long-standing pain (≥5 years in >70% of the subjects in the present study) are examined.
It has recently been proposed that there is a “brain signature” of grey matter alterations common to different pain syndromes, involving changes in the cingulate cortex, insular cortex, OFC/DLPFC, and brainstem [22]. In the present study, grey matter reductions in the ongoing pain group overlapped with the postulated brain signature of chronic pain in the cingulate cortex and several prefrontal regions. These regions are involved in cognitive and emotional pain processing and likely also in the activation of endogenous descending pain control mechanisms [32,33]. However, no significant grey matter reductions were detected in the brainstem or insula. The latter is consistent with a previous study showing that pain-related grey matter reductions in the insula are present in younger but not in older adults [13]. In addition to alterations in regions belonging to the proposed brain signature of chronic pain, the ongoing pain group also showed prominent grey matter reductions in motor, premotor, and supplementary motor regions. Although pain often hinders exercise, it has recently been emphasised [22] that only few studies in pain populations found grey matter reductions extending to motor areas [2,10,18]. Our cohort was older than in most previous studies. It might therefore be hypothesised that in older adults, pain has a larger impact on physical activity, or reduced physical activity has a larger impact on (pre)motor grey matter compared to younger adults. Grey matter alterations in the posterior parietal cortex might be associated with attention and orientation towards painful stimuli [12].
It has repeatedly been discussed whether pain-related grey matter alterations may be the result of (co-)analgesic drug intake [21,27,29,31]. In the present study, grey matter decreases in the ongoing pain group were still present after excluding subjects that reported intake of nonsteroidal anti-inflammatory drugs, antidepressants, or anticonvulsants. Consistently, several previous studies have not found an association between grey matter decreases and medication [2,19,30].
4.2. Lack of grey matter reduction in subjects with past pain
Grey matter decreases could not be detected in subjects whose pain had stopped more than 12 months before investigation. These results have to be regarded with caution because they rely on recalled past pain experiences and are based on a cross-sectional design. Nonetheless, they are consistent with those of recent longitudinal studies showing that grey matter alterations in patients with chronic painful hip osteoarthritis reverse after pain relief by surgery [15,27]. Thus, evidence is accumulating that grey matter changes develop during prolonged pain states and reverse after cessation of the pain. It is therefore unlikely that they are due to irreversible neuronal damage or part of a preexisting disposition to develop chronic pain as proposed, for example, in previous studies [1,5,19]. Instead, pain-related grey matter alterations may be due to reversible changes in neuronal or glial size, decreases of synaptic density, or even modifications of the extracellular space [11,22,23].
4.3. Patterns of regional grey matter volume reduction in different pain types
Due to low numbers of subjects, these results were significant only at uncorrected levels and have to be considered with due caution. However, due to the high effect sizes reached (Fig. 4), we believe that they are worth reporting by way of an exploratory analysis. Results suggest that grey matter reductions in the low back pain and headache groups are centred on frontal regions, while grey matter reductions in the joint pain group may be more centred on parietal regions and the PCC. A tempting interpretation of such a difference would be that dysfunctional pain (eg, low back pain, headache [9]) is associated with grey matter reductions in areas related to endogenous pain control (prefrontal cortex, ACC), while this is not the case for osteoarthritic joint pain, which is thought to be primarily nociceptive because of its generally excellent response to surgery [6]. Previous studies on osteoarthritic pain had conflicting results, one showing a grey matter decrease limited to the thalamus [15] and the other showing grey matter decreases centred on frontal and insular regions [27]. In conclusion, it is currently not clear if osteoarthritis patients exhibit a pattern that is similar to or different from that of more dysfunctional pain disorders.
4.4. Limitations
Classification of subjects relied entirely on self-report of pain sites and pain characteristics; based on the idea that pain itself (rather than the associated medical diagnosis) has an impact on the central nervous system. Pain is an inherently subjective phenomenon, but memories of past pain and pain duration may be less reliable than review of medical charts. On the other hand, this approach allowed us to classify subjects from the general population based on their pain experience rather than on if and when they went to seek medical care.
The present study is cross-sectional, allowing only indirect conclusions on causes and time course of the reported grey matter changes. Clearly, more studies with a longitudinal design, such as the recent work on hip osteoarthritis patients, are needed [15,27].
5. Conclusion
The present study showed that pain-associated regional grey matter decreases are not limited to clinical pain cohorts, suggesting that they may be a common phenomenon that affects a considerable percentage of the general population. These grey matter alterations seem to develop during prolonged pain states and recede after cessation of the pain (present results and [15,27]). The functional implications of pain-related grey matter alterations are not clear at present, but may include disturbance of endogenous pain inhibitory systems [22].
Acknowledgments
This work was supported by the Volkswagen Stiftung (Az.: I/80 708), the Deutsche Forschungsgemeinschaft (DFG SFB TR3 Project A08), and the BMBF (01GW0520).
The authors declare no conflict of interest.
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