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Cited by 27Pain Mechanisms and TreatmentsFunctional Brain Connectivity StudiesPain Management and Treatment

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Transcriptional and cellular signatures of cortical morphometric remodelling in chronic pain

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Martins, Daniel, Dipasquale, Ottavia, Veronese, Mattia, Turkheimer, Federico, Loggia, Marco L., McMahon, Stephen, Howard, Matthew A., Williams, Steven C.R.·2022·Pain
DOI 10.1097/j.pain.0000000000002480PMC PMC8940732

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Abstract

Chronic pain is a highly debilitating and difficult to treat condition, which affects the structure of the brain. Although the development of chronic pain is moderately heritable, how disease-related alterations at the microscopic genetic architecture drive macroscopic brain abnormalities is currently largely unknown. Here, we examined alterations in morphometric similarity (MS) and applied an integrative imaging transcriptomics approach to identify transcriptional and cellular correlates of these MS changes, in 3 independent small cohorts of patients with distinct chronic pain syndromes (knee osteoarthritis, low back pain, and fibromyalgia) and age-matched and sex-matched pain-free controls. We uncover a novel pattern of cortical MS remodelling involving mostly small-to-medium MS increases in the insula and limbic cortex (none of these changes survived stringent false discovery rate correction for the number of regions tested). This pattern of changes is different from that observed in patients with major depression and cuts across the boundaries of specific pain syndromes. By leveraging transcriptomic data from Allen Human Brain Atlas, we show that cortical MS remodelling in chronic pain spatially correlates with the brain-wide expression of genes related to pain and broadly involved in the glial immune response and neuronal plasticity. Our findings bridge levels to connect genes, cell classes, and biological pathways to in vivo imaging correlates of chronic pain. Although correlational, our data suggest that cortical remodelling in chronic pain might be shaped by multiple elements of the cellular architecture of the brain and identifies several pathways that could be prioritized in future genetic association or drug development studies.

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Figure 1. Overview of the analysis pipeline. (A) Morphometric similarity (MS) analysis. We constructed individual cortical MS matrices using 5 structural magnetic resonance imaging features (eg, gray matter volume [GM], surface area [SA], cortical thickness [CT], mean curvature [MC], and Gaussian curvature [GC]) extracted from 68 cortical regions of the Desikan–Killiany (DK) Atlas. For each individual, we produced a 68 × 68 MS matrix by correlating the normalized (z-scores) values of the 5 structural features between each pair of regions in the atlas. For each region, we averaged across all the edges involving that area to obtain a singular representation of the mean MS score for that region. We then computed case–control differences for each region, while accounting for age, gender, and total intracranial volume. (B) Gene expression analysis. We used abagen to obtain gene expression profiles from the Allen Human Brain Atlas (AHBA) in 68 regions (left hemisphere only) across the 6 postmortem brains sampled in this atlas. We excluded all genes with normalized expression values below the background (15,633 genes met this criterion). When more than one probe was available for a certain gene, we selected the probe with higher consistency in expression across the 6 donors. We used partial least squares regression (PLS) to rank all genes according to their association with the case–control changes in MS. Finally, we performed a set of enrichment analyses on the top genes positively or negatively associated with case–control differences in MS.
Figure 1. Overview of the analysis pipeline. (A) Morphometric similarity (MS) analysis. We constructed individual cortical MS matrices using 5 structural magnetic resonance imaging features (eg, gray matter volume [GM], surface area [SA], cortical thickness [CT], mean curvature [MC], and Gaussian curvature [GC]) extracted from 68 cortical regions of the Desikan–Killiany (DK) Atlas. For each individual, we produced a 68 × 68 MS matrix by correlating the normalized (z-scores) values of the 5 structural features between each pair of regions in the atlas. For each region, we averaged across all the edges involving that area to obtain a singular representation of the mean MS score for that region. We then computed case–control differences for each region, while accounting for age, gender, and total intracranial volume. (B) Gene expression analysis. We used abagen to obtain gene expression profiles from the Allen Human Brain Atlas (AHBA) in 68 regions (left hemisphere only) across the 6 postmortem brains sampled in this atlas. We excluded all genes with normalized expression values below the background (15,633 genes met this criterion). When more than one probe was available for a certain gene, we selected the probe with higher consistency in expression across the 6 donors. We used partial least squares regression (PLS) to rank all genes according to their association with the case–control changes in MS. Finally, we performed a set of enrichment analyses on the top genes positively or negatively associated with case–control differences in MS.CC BY · PubMed Central
Figure 2. Case–control differences in global morphometric similarity. In the upper panel, we present case and control distributions of the residual regional morphometric similarity (MS) strength (ie, the average similarity of each region to all other regions) after regressing out the effects of age, gender, and intracranial volume, for each data set separately. In the lower panel, we present case–control comparisons of the global MS. To calculate global MS, we averaged the residual regional MS strength across all regions for each subject. In all 3 data sets, there were no differences between groups in global MS. A, osteoarthritis dataset; B, chronic low back pain dataset; C, fibromyalgia dataset. CLBP, chronic low back pain; HC, healthy controls; OA, osteoarthritis.
Figure 2. Case–control differences in global morphometric similarity. In the upper panel, we present case and control distributions of the residual regional morphometric similarity (MS) strength (ie, the average similarity of each region to all other regions) after regressing out the effects of age, gender, and intracranial volume, for each data set separately. In the lower panel, we present case–control comparisons of the global MS. To calculate global MS, we averaged the residual regional MS strength across all regions for each subject. In all 3 data sets, there were no differences between groups in global MS. A, osteoarthritis dataset; B, chronic low back pain dataset; C, fibromyalgia dataset. CLBP, chronic low back pain; HC, healthy controls; OA, osteoarthritis.CC BY · PubMed Central
Figure 3. Case–control differences in regional morphometric similarity. In this figure, we present cortical maps of the distribution of Cohen d effect size quantifying case–control differences in regional morphometric similarity (Δ morphometric similarity) for each condition. In the upper panel, we present unthresholded maps. In the lower panel, we present only regions where we found case–control differences for a threshold of P < 0.05, uncorrected. Note that none of these regions survived FDR correction for the total number of regions tested within each condition. A, osteoarthritis dataset; B, chronic low back pain dataset; C, fibromyalgia dataset. FDR, false discovery rate.
Figure 3. Case–control differences in regional morphometric similarity. In this figure, we present cortical maps of the distribution of Cohen d effect size quantifying case–control differences in regional morphometric similarity (Δ morphometric similarity) for each condition. In the upper panel, we present unthresholded maps. In the lower panel, we present only regions where we found case–control differences for a threshold of P < 0.05, uncorrected. Note that none of these regions survived FDR correction for the total number of regions tested within each condition. A, osteoarthritis dataset; B, chronic low back pain dataset; C, fibromyalgia dataset. FDR, false discovery rate.CC BY · PubMed Central
Figure 4. Mapping case–control differences in regional morphometric similarity to established patterns of cytoarchitectonic cortical organization. To help us to contextualize the case–control differences in regional morphometric similarity we observed for the different chronic pain conditions, we mapped them in relation to the von Economo Atlas of the cortex classified by the cytoarchitectonic criteria. For each subject, we quantified MS within each parcel of these atlases (after regressing out age, sex, and intracranial volume) and then performed case–control comparisons using independent sample t tests. The colours in the tile plots represent the Cohen d effect size of case–control differences for each condition. *Highlights significant case–control differences, for P < 0.05, uncorrected. MS, morphometric similarity.
Figure 4. Mapping case–control differences in regional morphometric similarity to established patterns of cytoarchitectonic cortical organization. To help us to contextualize the case–control differences in regional morphometric similarity we observed for the different chronic pain conditions, we mapped them in relation to the von Economo Atlas of the cortex classified by the cytoarchitectonic criteria. For each subject, we quantified MS within each parcel of these atlases (after regressing out age, sex, and intracranial volume) and then performed case–control comparisons using independent sample t tests. The colours in the tile plots represent the Cohen d effect size of case–control differences for each condition. *Highlights significant case–control differences, for P < 0.05, uncorrected. MS, morphometric similarity.CC BY · PubMed Central
Figure 5. Chronic pain is associated with decoupling of morphometric similarity “hubs” and dedifferentiation of highly differentiated regions. In the upper panel, we present scatterplots depicting significant negative associations between case–control differences in regional morphometric similarity and the mean morphometric similarity of each region in healthy controls. In the lower panel, we subdivided these scatter plots in 4 quadrants according to the relationship between the distribution of case–control differences and mean regional morphometric similarity in healthy controls. The upper left quadrant includes regions of low MS in healthy controls (highly differentiated from the rest of the cortex) that increase their MS with the rest of the cortex during chronic pain (dedifferentiation). The upper right quadrant includes regions of high MS in healthy controls (highly connected with the rest of the cortex) that increase their MS during chronic pain (hypercoupling). The lower left quadrant includes regions of low MS in healthy controls that decrease their MS during chronic pain (hyperdifferentiation). The lower right quadrant includes region of high MS in healthy controls that decrease their MS during chronic pain (ecoupling). Across chronic pain conditions, we observed a predominant pattern of increases in regional MS in regions of low MS (dedifferentiation) and decreases in regions of high MS (decoupling) in healthy controls. Significance was assessed with spatial permutation testing. A, osteoarthritis dataset; B, chronic low back pain dataset; C, fibromyalgia dataset. MS, morphometric similarity.
Figure 5. Chronic pain is associated with decoupling of morphometric similarity “hubs” and dedifferentiation of highly differentiated regions. In the upper panel, we present scatterplots depicting significant negative associations between case–control differences in regional morphometric similarity and the mean morphometric similarity of each region in healthy controls. In the lower panel, we subdivided these scatter plots in 4 quadrants according to the relationship between the distribution of case–control differences and mean regional morphometric similarity in healthy controls. The upper left quadrant includes regions of low MS in healthy controls (highly differentiated from the rest of the cortex) that increase their MS with the rest of the cortex during chronic pain (dedifferentiation). The upper right quadrant includes regions of high MS in healthy controls (highly connected with the rest of the cortex) that increase their MS during chronic pain (hypercoupling). The lower left quadrant includes regions of low MS in healthy controls that decrease their MS during chronic pain (hyperdifferentiation). The lower right quadrant includes region of high MS in healthy controls that decrease their MS during chronic pain (ecoupling). Across chronic pain conditions, we observed a predominant pattern of increases in regional MS in regions of low MS (dedifferentiation) and decreases in regions of high MS (decoupling) in healthy controls. Significance was assessed with spatial permutation testing. A, osteoarthritis dataset; B, chronic low back pain dataset; C, fibromyalgia dataset. MS, morphometric similarity.CC BY · PubMed Central
Figure 6. Gene expression profiles related to cross-condition changes in regional morphometric similarity during chronic pain. (A) We summarized case–control changes in regional morphometric similarity (MS) across chronic pain conditions using principal component analysis (PCA). The first component (PC1) explained the large majority of variance 64.45% and was the only component with an eigenvalue > 1. The cortical distribution of the scores of PC1 is depicted in the upper part of panel A. In the lower part, we show the cortical distribution of PLS1 scores summarizing the regional weighted expression of genes associated with cross-condition changes in regional MS during chronic pain. In the scatter plot on the right, we depict a significant positive correlation between PLS1 gene expression weights and the PC1 scores summarizing case–control regional MS differences across conditions. In panel B, we present the top 3 genes positively and negatively associated with PCA1, ranked by the respective loading into PLS1. Loading (Z-score) refers to the weight of each gene in PLS1. Genes with positive weights are highly expressed in regions where MS increases in patients, whereas genes with negative weights are highly expressed in regions where MS decreases. In the right part of panel B, we provide cortical maps summarizing the regional distribution of the top genes with the highest (CMTM3) or lowest (FAM126B) weights in PLS1.
Figure 6. Gene expression profiles related to cross-condition changes in regional morphometric similarity during chronic pain. (A) We summarized case–control changes in regional morphometric similarity (MS) across chronic pain conditions using principal component analysis (PCA). The first component (PC1) explained the large majority of variance 64.45% and was the only component with an eigenvalue > 1. The cortical distribution of the scores of PC1 is depicted in the upper part of panel A. In the lower part, we show the cortical distribution of PLS1 scores summarizing the regional weighted expression of genes associated with cross-condition changes in regional MS during chronic pain. In the scatter plot on the right, we depict a significant positive correlation between PLS1 gene expression weights and the PC1 scores summarizing case–control regional MS differences across conditions. In panel B, we present the top 3 genes positively and negatively associated with PCA1, ranked by the respective loading into PLS1. Loading (Z-score) refers to the weight of each gene in PLS1. Genes with positive weights are highly expressed in regions where MS increases in patients, whereas genes with negative weights are highly expressed in regions where MS decreases. In the right part of panel B, we provide cortical maps summarizing the regional distribution of the top genes with the highest (CMTM3) or lowest (FAM126B) weights in PLS1.CC BY · PubMed Central
Figure 7. Gene set and cell-type enrichment analyses of the top genes associated with cross-condition changes in regional morphometric similarity during chronic pain. In panel A, we present the results of gene set enrichment analyses on the top genes positively (PLS1+) and negatively (PLS1−) associated with cross-condition changes in regional morphometric similarity during chronic pain, as implemented in the Functional Mapping and Annotation of Genome-Wide Association Studies (FUMA) platform. For PLS1+, we present the top 10 Gene Ontology (GO)—biological pathways terms for which we found significant enrichment, ranked by P-value after FDR correction. For PLS1−, none of the GO terms survived correction, but we found significant enrichment for 4 terms from the Kyoto Encyclopaedia of Genes and Genomes (KEGG) pathways. Colour scale indicates –log(PFDR). The full results of the FUMA analysis can be found in supplementary data (available at http://links.lww.com/PAIN/B509, http://links.lww.com/PAIN/B510, http://links.lww.com/PAIN/B511). In panel B, we present the results of a cell-type enrichment analysis, where we investigated whether PLS1+ and PLS1− include overrepresentation of genes typically expressed in specific brain cell types. Enrichment was quantified using odds ratio (OR) and significance calculated with a Fisher exact test. Colour scale indicates OR. Higher ORs (in red) indicate higher enrichment in genes of a certain cell class. The asterisk denotes cell classes for which we found significant enrichment, after correcting for the number of cell types tested (*PFDR < 0.05). FDR, false discovery rate; OPCs, oligodendrocyte precursor cells.
Figure 7. Gene set and cell-type enrichment analyses of the top genes associated with cross-condition changes in regional morphometric similarity during chronic pain. In panel A, we present the results of gene set enrichment analyses on the top genes positively (PLS1+) and negatively (PLS1−) associated with cross-condition changes in regional morphometric similarity during chronic pain, as implemented in the Functional Mapping and Annotation of Genome-Wide Association Studies (FUMA) platform. For PLS1+, we present the top 10 Gene Ontology (GO)—biological pathways terms for which we found significant enrichment, ranked by P-value after FDR correction. For PLS1−, none of the GO terms survived correction, but we found significant enrichment for 4 terms from the Kyoto Encyclopaedia of Genes and Genomes (KEGG) pathways. Colour scale indicates –log(PFDR). The full results of the FUMA analysis can be found in supplementary data (available at http://links.lww.com/PAIN/B509, http://links.lww.com/PAIN/B510, http://links.lww.com/PAIN/B511). In panel B, we present the results of a cell-type enrichment analysis, where we investigated whether PLS1+ and PLS1− include overrepresentation of genes typically expressed in specific brain cell types. Enrichment was quantified using odds ratio (OR) and significance calculated with a Fisher exact test. Colour scale indicates OR. Higher ORs (in red) indicate higher enrichment in genes of a certain cell class. The asterisk denotes cell classes for which we found significant enrichment, after correcting for the number of cell types tested (*PFDR < 0.05). FDR, false discovery rate; OPCs, oligodendrocyte precursor cells.CC BY · PubMed Central

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Metadata

Authors
Martins, D., Dipasquale, O., Veronese, M., Turkheimer, F., Loggia, M. L., McMahon, S., Howard, M. A., & Williams, S. C. R.
Affiliations
  • King's College London (GB)
  • Harvard University (US)
Corresponding author
Martins, Daniel
ORCID
Venue
Pain
Publisher
Ovid Technologies (Wolters Kluwer Health)
Published
2021-09-23
Volume
163
Issue
6
Pages
e759-e773
ISSN
  • 0304-3959 (ISSN-L)
  • 1872-6623
DOI
10.1097/j.pain.0000000000002480
PMC
PMC8940732
OpenAlex
W3201512041
Cited by
27
References
11
Citations by year
2026: 9 · 2025: 4 · 2024: 4 · 2023: 4 · 2022: 2 · 2021: 4
FWCI
2.20
Type
journal article
Language
en
Open access
Licence
Locations
  • Pain (publishedVersion, cc-by)
  • bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • Research Portal (King's College London) (publishedVersion, cc-by)
  • Padua Research Archive (University of Padova) (submittedVersion)
Funders
  • National Institute for Health and Care Research
  • National Institutes of Health (1r01ns094306-01a1)
  • Medical Research Council (MR/J005142/1, MR/N026969/1)
  • National Institute on Drug Abuse (R01DA047088)
  • National Institute of Neurological Disorders and Stroke (R01‐NS‐094306, R01NS095937)
Topics
  • Health Sciences › Medicine › Physiology (medical) › Pain Mechanisms and Treatments
  • Life Sciences › Neuroscience › Cognitive Neuroscience › Functional Brain Connectivity Studies
  • Health Sciences › Medicine › Anesthesiology and Pain Medicine › Pain Management and Treatment
Keywords
chronic pain, cortical morphometry, insula, limbic cortex, knee osteoarthritis, low back pain, fibromyalgia, neuroplasticity, Allen Human Brain Atlas, gene expression, brain structure
MeSH terms
Brain, Brain Mapping, Fibromyalgia, Humans, Magnetic Resonance Imaging, Syndrome, Chronic Pain

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