Background & Aims
Chronic pain is a global public health crisis, yet its underlying pathophysiology remains unclear. Several MRI studies have reported reduced gray matter volumes associated with various chronic pain conditions(1, 2, 3) however early studies are based on small samples and produce inconsistent findings in recent meta-analyses(4,5). Moreover, recent studies of gray-matter relationships with various traits indicate effects small enough to require thousands of individuals for replicable findings(6,7). Large-sample MRI studies of gray matter and chronic pain have only recently become available (8,9,10). Additionally, whether these neural anomalies result from, lead to, or simply co-occur with chronic pain remains unclear. To explore the relationship between chronic pain progression and morphological brain changes in a large population, we examined structural brain measures and their correlations with chronic pain trajectories in the UK Biobank Cohort.
Methods
We used longitudinal data from 40,000 U. K. Biobank participants, grouping them into one of four categories: (1) Chronic pain at baseline and MRI session 4+ years later (Unresolved Pain); (2) Chronic pain at baseline with no pain at MRI session (Resolved Pain); (3) Chronic pain at MRI session with no pain at baseline (New Pain); (4) Pain-free controls with no pain at baseline or MRI session. The categories were assigned within and across eight body sites (head, face, neck/shoulder, back, stomach, hip, knee, and widespread). We used multiple linear regression models (one per brain region) to estimate the associations between pain categories and regional cortical and subcortical gray matter volume (GMV), cortical thickness (CT), and surface area (SA) using the Freesurfer Destrieux(11) and Aseg(12) atlases (165 ROIs total) while controlling for demographics (sex, age, age^2, intracranial volume) and scanning procedure (scanner site, table position).
Results
Unresolved Pain was associated with widespread GMV reduction relative to Controls across 68 cortical regions and SA reduction across 88 regions. The largest reductions in GMV and SA were observed in the somatosensory cortex (q < 0.05 FDR-corrected). Among subcortical regions, we observed increased GMV in the right thalamus and bilateral striatum (caudate and putamen) and decreased GMV in bilateral cerebellar cortices. CT changes were found in 59 regions, including 17 with decreased CT and 42 with increased CT, with largest increases in the parietal cortex. Compared with Controls, New Pain was associated with GMV reduction in 18 regions and SA reduction across 26 regions, with largest effects in somatosensory and occipito-temporal regions. Compared with Controls, Resolved Pain was associated with SA reduction in two parcellations of the somatosensory cortex and no changes in GMV. No changes in CT were associated with New or Resolved Pain.
Conclusions
Our findings reveal a pervasive decrease in GMV and SA, along with patterns of both reduced and increased CT in individuals experiencing unresolved chronic pain. Additionally, we observed limited GMV and SA reductions associated with new chronic pain that largely overlapped with areas most affected in unresolved chronic pain, and SA reductions in somatosensory areas associated with resolved pain. Taken together, our findings suggest a reversible spreading of gray matter and surface area loss across cortical areas that correlates with chronic pain trajectories. However, reductions in SA persist in somatosensory regions even after pain resolution, potentially indicating irreversible gray matter reductions associated with chronic pain, or, alternatively, a predisposition to pain chronification. Our study identifies brain markers of chronic pain progression and resolution, which may be targeted in novel treatment approaches such as neuromodulation.
References
1. Geha, P. Y., Baliki, M. N., Harden, R. N., Bauer, W. R., Parrish, T. B., & Apkarian, A. V. (2008). The brain in chronic CRPS pain: abnormal gray-white matter interactions in emotional and autonomic regions. Neuron, 60(4), 570-581.
2. Smallwood, R. F., Laird, A. R., Ramage, A. E., Parkinson, A. L., Lewis, J., Clauw, D. J., … & Robin, D. A. (2013). Structural brain anomalies and chronic pain: a quantitative meta-analysis of gray matter volume. The Journal of Pain, 14(7), 663-675.
3. Ninneman, J. V., Gretzon, N. P., Stegner, A. J., Lindheimer, J. B., Falvo, M. J., Wylie, G., Dougherty, R. J., Almassi, N. E., Van Riper, S. M., Boruch, A. E., Dean, D. C., Koltyn, K. F., & Cook, D. B. (2022). Pain, but not Physical Activity, is Associated with Gray Matter Volume Differences in Gulf War Veterans with Chronic Pain. The Journal of Neuroscience, JN-RM-2394-21.
4. Henn, A. , Larsen, B. , Frahm, L. , Xu, A. , Adebimpe, A. , Scott, J. , Linguiti, S. , Sharma, V. , Basbaum, A. , Corder, G. , Dworkin, R. , Edwards, R. , Woolf, C. , Habel, U. , Eickhoff, S. , Eickhoff, C. , Wagels, L. & Satterthwaite, T. (2023). Structural imaging studies of patients with chronic pain: an anatomical likelihood estimate meta-analysis. PAIN, 164 (1), e10-e24.
5. Ayoub, L. J., Seminowicz, D. A., & Moayedi, M. (2018). A meta-analytic study of experimental and chronic orofacial pain excluding headache disorders. NeuroImage: Clinical, 20, 901-912.
6. Marek, S., Tervo-Clemmens, B., Calabro, F. J., Montez, D. F., Kay, B. P., Hatoum, A. S., … & Dosenbach, N. U. (2022). Reproducible brain-wide association studies require thousands of individuals. Nature, 603(7902), 654-660.
7. Spisak, T., Bingel, U., & Wager, T. D. (2023). Multivariate BWAS can be replicable with moderate sample sizes. Nature, 615(7951), E4-E7.
8. Bhatt, R. R., Haddad, E., Zhu, A. H., Thompson, P. M., Gupta, A., Mayer, E. A., & Jahanshad, N. (2022). Brain Structural Differences in Adults Reporting Localized Chronic Pains Mediate Risk for Suicidal Behaviors. medRxiv, 2022-10.
9. Tanguay-Sabourin, C., Fillingim, M., Guglietti, G. V., Zare, A., Parisien, M., Norman, J., Sweatman, H., Da-Ano, R., Heikkala, E., PREVENT-AD Research Group, Perez, J., Karppinen, J., Villeneuve, S., Thompson, S. J., Martel, M. O., Roy, M., Diatchenko, L., & Vachon-Presseau, E. (2023). A prognostic risk score for development and spread of chronic pain. Nature medicine, 29(7), 1821–1831.
10. Farrell, S. F., Campos, A. I., Kho, P. F., de Zoete, R. M., Sterling, M., Rentería, M. E., … & Cuéllar-Partida, G. (2021). Genetic basis to structural grey matter associations with chronic pain. Brain, 144(12), 3611-3622.
11. Destrieux, C., Fischl, B., Dale, A., & Halgren, E. (2010). Automatic parcellation of human cortical gyri and sulci using standard anatomical nomenclature.
12. Fischl, B., Salat, D. H., Busa, E., Albert, M., Dieterich, M., Haselgrove, C., … & Dale, A. M. (2002). Whole brain segmentation: automated labeling of neuroanatomical structures in the human brain. Neuron, 33(3), 341-355.
Presenting Author
Carmen I. Bango
Poster Authors
Carmen Bango
Oregon Health and Sciences University
Lead Author
Katerina Zorina-Lichtenwalter
University of Colorado Boulder
Lead Author
Marta ?eko
PhD
University of Colorado Boulder
Lead Author
Lydia Rader
MA
University of Colorado Boulder
Lead Author
Martin Lindquist
Johns Hopkins University
Lead Author
Naomi Friedman
PhD
University of Colorado Boulder
Lead Author
Tor Wager
Dartmouth College
Lead Author
Topics
- Pain Imaging