Abstract
Sustained pain is a major characteristic of clinical pain disorders, but it is difficult to assess in isolation from co-occurring cognitive and emotional features in patients. In this study, we developed a functional magnetic resonance imaging signature based on whole-brain functional connectivity that tracks experimentally induced tonic pain intensity and tested its sensitivity, specificity and generalizability to clinical pain across six studies (total n = 334). The signature displayed high sensitivity and specificity to tonic pain across three independent studies of orofacial tonic pain and aversive taste. It also predicted clinical pain severity and classified patients versus controls in two independent studies of clinical low back pain. Tonic and clinical pain showed similar network-level representations, particularly in somatomotor, frontoparietal and dorsal attention networks. These patterns were distinct from representations of experimental phasic pain. This study identified a brain biomarker for sustained pain with high potential for clinical translation.
This is a preview of subscription content, access via your institution
Relevant articles
Open Access articles citing this article.
-
Associations between grip strength, brain structure, and mental health in > 40,000 participants from the UK Biobank
BMC Medicine Open Access 09 September 2022
-
Dissociation between individual differences in self-reported pain intensity and underlying fMRI brain activation
Nature Communications Open Access 22 June 2022
-
Antagonistic network signature of motor function in Parkinson’s disease revealed by connectome-based predictive modeling
npj Parkinson's Disease Open Access 22 April 2022
Access options
Access Nature and 54 other Nature Portfolio journals
Get Nature+, our best-value online-access subscription
$29.99 per month
cancel any time
Subscribe to this journal
Receive 12 print issues and online access
$189.00 per year
only $15.75 per issue
Rent or buy this article
Get just this article for as long as you need it
$39.95
Prices may be subject to local taxes which are calculated during checkout






Data availability
The dynamic functional connectivity data of Studies 3–5 are available at https://cocoanlab.github.io/tops and https://github.com/cocoanlab/tops as a part of the tutorial. In addition, all the data to generate main figures are available at the same GitHub repository. The data that were not used in the main figures will be shared upon reasonable request. The raw data of Studies 4 and 5 and Supplementary Data 1 are publicly available at http://www.openpain.org/. The tonic pain signature can also be shared through https://cocoanlab.github.io/tops.
Code availability
Code for generating the main figures is available at https://github.com/cocoanlab/tops. In-house MATLAB codes for fMRI data analyses (for example, preprocessing and predictive modeling) are available at https://github.com/canlab/CanlabCore and https://github.com/cocoanlab/cocoanCORE.
References
Dahlhamer, J. et al. Prevalence of chronic pain and high-impact chronic pain among adults - United States, 2016. MMWR Morb. Mortal. Wkly. Rep. 67, 1001–1006 (2018).
Gaskin, D. J. & Richard, P. The economic costs of pain in the United States. J. Pain 13, 715–724 (2012).
Chapman, C. R. & Gavrin, J. Suffering: the contributions of persistent pain. Lancet 353, 2233–2237 (1999).
Apkarian, A. V. Pain perception in relation to emotional learning. Curr. Opin. Neurobiol. 18, 464–468 (2008).
Hashmi, J. A. et al. Shape shifting pain: chronification of back pain shifts brain representation from nociceptive to emotional circuits. Brain 136, 2751–2768 (2013).
Baliki, M. N. & Apkarian, A. V. Nociception, pain, negative moods, and behavior selection. Neuron 87, 474–491 (2015).
Loggia, M. L. et al. Default mode network connectivity encodes clinical pain: an arterial spin labeling study. Pain 154, 24–33 (2013).
Kucyi, A. et al. Enhanced medial prefrontal-default mode network functional connectivity in chronic pain and its association with pain rumination. J. Neurosci. 34, 3969–3975 (2014).
Dubuisson, D. & Dennis, S. G. The formalin test: a quantitative study of the analgesic effects of morphine, meperidine, and brain stem stimulation in rats and cats. Pain 4, 161–174 (1977).
Stohler, C. S. & Kowalski, C. J. Spatial and temporal summation of sensory and affective dimensions of deep somatic pain. Pain 79, 165–173 (1999).
Rainville, P., Feine, J. S., Bushnell, M. C. & Duncan, G. H. A psychophysical comparison of sensory and affective responses to four modalities of experimental pain. Somatosens. Mot. Res. 9, 265–277 (1992).
Huang, T. et al. Identifying the pathways required for coping behaviours associated with sustained pain. Nature 565, 86–90 (2019).
Wiech, K. et al. Modulation of pain processing in hyperalgesia by cognitive demand. Neuroimage 27, 59–69 (2005).
Reckziegel, D. et al. Deconstructing biomarkers for chronic pain: context- and hypothesis-dependent biomarker types in relation to chronic pain. Pain 160, S37–S48 (2019).
Davis, K. D. et al. Brain imaging tests for chronic pain: medical, legal and ethical issues and recommendations. Nat. Rev. Neurol. 13, 624–638 (2017).
Khoutorsky, A. & Price, T. J. Translational control mechanisms in persistent pain. Trends Neurosci. 41, 100–114 (2018).
Basbaum, A. I. Distinct neurochemical features of acute and persistent pain. Proc. Natl Acad. Sci. USA 96, 7739–7743 (1999).
Woo, C. W., Chang, L. J., Lindquist, M. A. & Wager, T. D. Building better biomarkers: brain models in translational neuroimaging. Nat. Neurosci. 20, 365–377 (2017).
Lindquist, M. A., Xu, Y., Nebel, M. B. & Caffo, B. S. Evaluating dynamic bivariate correlations in resting-state fMRI: a comparison study and a new approach. Neuroimage 101, 531–546 (2014).
Fan, L. et al. The human brainnetome atlas: a new brain atlas based on connectional architecture. Cereb. Cortex 26, 3508–3526 (2016).
Hastie, T., Tibshirani, R. & Friedman, J. H. The Elements of Statistical Learning: Data Mining, Inference, and Prediction (Springer, 2009).
Rosenberg, M. D., Finn, E. S., Scheinost, D., Constable, R. T. & Chun, M. M. Characterizing attention with predictive network models. Trends Cogn. Sci. 21, 290–302 (2017).
Scheinost, D. et al. Ten simple rules for predictive modeling of individual differences in neuroimaging. Neuroimage 193, 35–45 (2019).
Baliki, M. N. et al. Corticostriatal functional connectivity predicts transition to chronic back pain. Nat. Neurosci. 15, 1117–1119 (2012).
Vachon-Presseau, E. et al. Corticolimbic anatomical characteristics predetermine risk for chronic pain. Brain 139, 1958–1970 (2016).
Mano, H. et al. Classification and characterisation of brain network changes in chronic back pain: a multicenter study. Wellcome Open Res. 3, 19 (2018).
Yeo, B. T. et al. The organization of the human cerebral cortex estimated by intrinsic functional connectivity. J. Neurophysiol. 106, 1125–1165 (2011).
Senkowski, D., Hofle, M. & Engel, A. K. Crossmodal shaping of pain: a multisensory approach to nociception. Trends Cogn. Sci. 18, 319–327 (2014).
Ploner, M., Lee, M. C., Wiech, K., Bingel, U. & Tracey, I. Flexible cerebral connectivity patterns subserve contextual modulations of pain. Cereb. Cortex 21, 719–726 (2011).
Kong, J. et al. Functional connectivity of the frontoparietal network predicts cognitive modulation of pain. Pain 154, 459–467 (2013).
Roy, M., Piche, M., Chen, J. I., Peretz, I. & Rainville, P. Cerebral and spinal modulation of pain by emotions. Proc. Natl Acad. Sci. USA 106, 20900–20905 (2009).
Ploghaus, A. et al. Exacerbation of pain by anxiety is associated with activity in a hippocampal network. J. Neurosci. 21, 9896–9903 (2001).
Ossipov, M. H., Dussor, G. O. & Porreca, F. Central modulation of pain. J. Clin. Invest. 120, 3779–3787 (2010).
Basbaum, A. I. & Fields, H. L. Endogenous pain control systems: brainstem spinal pathways and endorphin circuitry. Annu. Rev. Neurosci. 7, 309–338 (1984).
Bushnell, M. C., Ceko, M. & Low, L. A. Cognitive and emotional control of pain and its disruption in chronic pain. Nat. Rev. Neurosci. 14, 502–511 (2013).
Apkarian, A. V., Bushnell, M. C., Treede, R. D. & Zubieta, J. K. Human brain mechanisms of pain perception and regulation in health and disease. Eur. J. Pain 9, 463–484 (2005).
Ayoub, L. J., Seminowicz, D. A. & Moayedi, M. A meta-analytic study of experimental and chronic orofacial pain excluding headache disorders. Neuroimage Clin. 20, 901–912 (2018).
Lorenz, J., Minoshima, S. & Casey, K. L. Keeping pain out of mind: the role of the dorsolateral prefrontal cortex in pain modulation. Brain 126, 1079–1091 (2003).
Wiech, K., Ploner, M. & Tracey, I. Neurocognitive aspects of pain perception. Trends Cogn. Sci. 12, 306–313 (2008).
Seminowicz, D. A. & Davis, K. D. Pain enhances functional connectivity of a brain network evoked by performance of a cognitive task. J. Neurophysiol. 97, 3651–3659 (2007).
Wager, T. D. et al. An fMRI-based neurologic signature of physical pain. N. Engl. J. Med. 368, 1388–1397 (2013).
Woo, C. W., Roy, M., Buhle, J. T. & Wager, T. D. Distinct brain systems mediate the effects of nociceptive input and self-regulation on pain. PLoS Biol. 13, e1002036 (2015).
Gabrieli, J. D. E., Ghosh, S. S. & Whitfield-Gabrieli, S. Prediction as a humanitarian and pragmatic contribution from human cognitive neuroscience. Neuron 85, 11–26 (2015).
Woo, C. W. et al. Quantifying cerebral contributions to pain beyond nociception. Nat. Commun. 8, 14211 (2017).
Coghill, R. C., Sang, C. N., Maisog, J. M. & Iadarola, M. J. Pain intensity processing within the human brain: a bilateral, distributed mechanism. J. Neurophysiol. 82, 1934–1943 (1999).
Seminowicz, D. A. & Moayedi, M. The dorsolateral prefrontal cortex in acute and chronic pain. J. Pain 18, 1027–1035 (2017).
Baars, B. J. Global workspace theory of consciousness: toward a cognitive neuroscience of human experience. Prog. Brain Res. 150, 45–53 (2005).
Wager, T. D., Atlas, L. Y., Leotti, L. A. & Rilling, J. K. Predicting individual differences in placebo analgesia: contributions of brain activity during anticipation and pain experience. J. Neurosci .31, 439–452 (2011).
Tetreault, P. et al. Brain connectivity predicts placebo response across chronic pain clinical trials. PLoS Biol. 14, e1002570 (2016).
Segerdahl, A. R., Mezue, M., Okell, T. W., Farrar, J. T. & Tracey, I. The dorsal posterior insula subserves a fundamental role in human pain. Nat. Neurosci. 18, 499–500 (2015).
Mazzola, L., Isnard, J., Peyron, R. & Mauguiere, F. Stimulation of the human cortex and the experience of pain: Wilder Penfield’s observations revisited. Brain 135, 631–640 (2012).
Bushnell, M. C. et al. Pain perception: is there a role for primary somatosensory cortex? Proc. Natl Acad. Sci. USA 96, 7705–7709 (1999).
Rainville, P., Duncan, G. H., Price, D. D., Carrier, B. & Bushnell, M. C. Pain affect encoded in human anterior cingulate but not somatosensory cortex. Science 277, 968–971 (1997).
Atlas, L. Y., Bolger, N., Lindquist, M. A. & Wager, T. D. Brain mediators of predictive cue effects on perceived pain. J. Neurosci. 30, 12964–12977 (2010).
Horing, B., Sprenger, C. & Buchel, C. The parietal operculum preferentially encodes heat pain and not salience. PLoS Biol. 17, e3000205 (2019).
Zhang, S. et al. The control of tonic pain by active relief learning. eLife 7, e31949 (2018).
Seymour, B. et al. Opponent appetitive-aversive neural processes underlie predictive learning of pain relief. Nat. Neurosci. 8, 1234–1240 (2005).
Baliki, M. N. et al. Chronic pain and the emotional brain: specific brain activity associated with spontaneous fluctuations of intensity of chronic back pain. J. Neurosci. 26, 12165–12173 (2006).
Rodriguez, E. et al. A craniofacial-specific monosynaptic circuit enables heightened affective pain. Nat. Neurosci. 20, 1734–1743 (2017).
Garcia, J., Hankins, W. G. & Rusiniak, K. W. Behavioral regulation of the milieu interne in man and rat. Science 185, 824–831 (1974).
Buckner, R. L., Krienen, F. M., Castellanos, A., Diaz, J. C. & Yeo, B. T. The organization of the human cerebellum estimated by intrinsic functional connectivity. J. Neurophysiol. 106, 2322–2345 (2011).
Choi, E. Y., Yeo, B. T. & Buckner, R. L. The organization of the human striatum estimated by intrinsic functional connectivity. J. Neurophysiol. 108, 2242–2263 (2012).
Eickhoff, S. B. et al. A new SPM toolbox for combining probabilistic cytoarchitectonic maps and functional imaging data. Neuroimage 25, 1325–1335 (2005).
Diedrichsen, J. et al. Imaging the deep cerebellar nuclei: a probabilistic atlas and normalization procedure. Neuroimage 54, 1786–1794 (2011).
Glasser, M. F. et al. A multi-modal parcellation of human cerebral cortex. Nature 536, 171–178 (2016).
Allen, E. A. et al. Tracking whole-brain connectivity dynamics in the resting state. Cereb. Cortex 24, 663–676 (2014).
Shen, X. et al. Using connectome-based predictive modeling to predict individual behavior from brain connectivity. Nat. Protoc. 12, 506–518 (2017).
Cochran, W. G. Sampling Techniques (Wiley, 1977).
Acknowledgements
We thank M. C. Reddan for help with conducting experiments; the OpenPain Project (principal investigator: A. V. Apkarian) for data sharing; and M. A. Lindquist for helpful comments on using dynamic connectivity analysis tools. This work was supported by IBS-R015-D1 (Institute for Basic Science; to C.-W.W.), 2019R1C1C1004512 (National Research Foundation of Korea; to C.-W.W.), 18-BR-03 and 2019-0-01367-BabyMind (Ministry of Science and ICT, Korea; to C.-W.W.); by R01DA035484 and R01MH076136 (National Institutes of Health; to T.D.W.); and by 2018H1A2A1059844 (National Research Foundation of Korea; to J.-J.L.).
Author information
Authors and Affiliations
Contributions
C.-W.W. and T.D.W. conceived and designed the experiment for Studies 1 and 2, and J.-J.L. and C.-W.W. designed the Study 3 experiment. J.-J.L. and C.-W.W. analyzed the data and interpreted the results. J.-J.L. and C.-W.W. wrote the manuscript. C.-W.W. and T.D.W. edited the manuscript and provided the supervision. C.-W.W. and T.D.W. contributed to Study 1 data. M.C., C.-W.W. and T.D.W. contributed to Study 2 data. J.-J.L., H.J.K., S.-G.K. and C.-W.W. contributed to Study 3 data. M.R. and T.D.W. contributed to Study 6 data. S.A.L. and C.-W.W. contributed to Supplementary Data 2. B.Y.P. and H.P. helped with aggregation and preprocessing of the Studies 4 and 5 data and Supplementary Data 1.
Corresponding authors
Ethics declarations
Competing interests
The authors declare no competing interests.
Additional information
Peer review information Jerome Staal and Kate Gao were the primary editors on this article and managed its editorial process and peer review in collaboration with the rest of the editorial team.
Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Extended data
Extended Data Fig. 1 Behavioral data of Study 1.
a, Pain intensity and unpleasantness ratings of Study 1 (n = 19). b, Heart rate (HR; beat-per-minute) and skin conductance response (SCR; μS) of Study 1 (n = 14). Note that physiological data of five participants were discarded, because they were not recorded during scan or the data quality was too bad. Error bars represent within-subject standard errors of the mean (s.e.m.).
Extended Data Fig. 2 Overview of the signature development and test procedure.
Step 1: Model training. Using Study 1 data, we trained a large number of candidate models for each outcome variable (pain intensity and unpleasantness) with multiple combinations of different parcellation solutions (features), connectivity estimation methods (feature engineering), algorithms, and hyperparameters. We generated a total of 5916 candidate models for each outcome variable. Step 2: Model competition. To select the best model, we conducted a competition among the candidate models based on the pre-defined criteria including sensitivity, specificity, and generalizability based on cross-validated performance in Study 1 and also using Study 2 data as a validation dataset. For the full description of the competition procedure, please see Methods, and for the full report of the competition results, please see Extended Data Fig. 3. Step 3: Independent testing. To further characterize the final model in multiple test contexts, we tested the final model on multiple independent datasets, including two additional tonic pain dataset (Study 3 and Supplementary Data 2), three clinical pain datasets (Studies 4-5 and Supplementary Data 1), and one experimental phasic pain dataset (Study 6). Gray boxes represent locally collected datasets, and green boxes represent publicly available datasets. Different font colors indicate different scan sites.
Extended Data Fig. 3 Model competition results of pain intensity models.
Using multiple candidate models generated from the model development step (see Extended Data Fig. 2 and Methods for details), we conducted a model competition using 7 predefined criteria. The criteria consist of 4 correlation coefficients (within- and between-individual prediction-outcome correlations of Studies 1 and 2; shown in the top panel), and 3 classification accuracy values (for Capsaicin vs. Control in Studies 1 and 2, and for Capsaicin vs Quinine in Study 2; shown in the middle panel). Dotted lines separate different parcellations, and colored bars on the top of the plots (gray, light green, green, red, orange, and pink) represent different options of connectivity calculation methods and algorithms (see the top right for detailed description for each color bar). For CPM-based models (gray and red color bars), thresholds become more stringent from the left to the right. For PCR-based models (light green, green, orange, and pink), more PCs were used from the left to the right. To combine the 7 different performance metrics, we used a percentile-based scoring method (ranging from 0 to 100 for each criterion). The combined score (possible range: 0 to 700) was shown in the bottom panel, and the selected best model was indicated with the red arrow on the plots. Here we show the competition results only for the predictive model for pain intensity.
Extended Data Fig. 4 Specificity tests using a prediction approach (Study 3).
We used the ToPS to predict the avoidance ratings while participants were given a, bitter taste (quinine) or b, aversive odor. Left: Actual versus predicted ratings (that is, signature response) are shown in the plot. Signature response was calculated using the dot product of the model with the data. Signature response is using an arbitrary unit. Each colored line (or symbol) represents an individual participant’s data for across the treatment (quinine or aversive odor) and control runs (red: higher r, yellow: lower r, blue: r < 0). The exact P-values were P = 0.014 for bitter taste and P = 0.372 for aversive odor, two-tailed, bootstrap tests, n = 48. Right: Mean avoidance rating (black) and signature response (red) across the treatment and control runs. Shading represents within-subject s.e.m. Note that the left and right plots were based on averaging within five and ten time bins, respectively. ns = non-significant, *P < 0.05.
Extended Data Fig. 5 Noise analysis.
a, Univariate comparisons of head motion (framewise displacement, FD) and physiological measures (heart rate, HR, and respiratory rate, RR) between the capsaicin versus control conditions with the independent test dataset (Study 3, n = 48). We used the violin and box plots to show the distributions of the values. The box was bounded by the first and third quartiles, and the whiskers stretched to the greatest and lowest values within median ±1.5 interquartile range. The data points outside of the whiskers were marked as outliers. Note that 10 participants’ physiological data were excluded due to technical issues with acquisition (remaining n = 38). For statistical testing, paired t-tests (two-tailed) were conducted. b, Noise analysis 1. To examine whether the nuisance and physiology variables explain the Tonic Pain Signature (ToPS) response, we trained a model to predict the ToPS scores based on 34 nuisance variables + 2 additional physiology variables with Study 3 data. The 34 nuisance variables included 24 head motion parameters (6 movement parameters including x, y, z, roll, pitch, and yaw, their mean-centered squares, their derivatives, and squared derivative), 5 principal component scores derived from white matter (WM), and 5 principal component scores derived from cerebrospinal fluid (CSF). The 2 physiological variables were heart rate and respiratory rate. Because the effects of these confounding variables can be different across individuals and conditions, we trained predictive models for each condition and for each individual. To achieve more stable and unbiased predictive performance, we divided the data into 40 time-bins (each bin was 30 seconds) and conducted 10-fold cross-validation. Prediction-outcome correlation coefficients are visualized with violin and box plots. For statistical testing, we used one-sample t-test, two-tailed. c, Noise analysis 2. To test whether the ToPS responses can explain tonic pain ratings above and beyond the nuisance and physiology variables, we conducted multi-level general linear model (GLM) analysis (n = 38) using data averaged within 10 time bins for each individual across capsaicin and control condition (5 bins per condition), which is the same binning scheme as our main prediction results (Fig. 2c). To obtain standardized beta coefficients, all the features were z-scored. The exact P-values were (from left to right) 1.86 × 10−8 (ToPS), 0.231 (FD), 0.145 (mean WM), 0.270 (mean CSF), 0.270 (HR), and 0.272 (RR), two-tailed, multi-level GLM with bootstrap tests, 10,000 iterations. Overall, participants moved more and showed heart-rate acceleration during capsaicin (a), but the ToPS model was independent of movement and physiological variables (b). ToPS predicted pain avoidance ratings controlling for movement and physiological nuisance variables, but the nuisance variables themselves did not predict avoidance ratings. ns = not significant; ****P < 0.0001.
Extended Data Fig. 6 Predictive performance of the clinical pain models.
a, Predictive performance of the SBP model, which was derived using a half of SBP patients’ spontaneous pain rating task data (n = 35, training set). The SBP model was then tested on the remaining half of the SBP data (n = 35, hold-out test set) to obtain an unbiased estimate of the predictive performance. Leave-one-subject-out cross-validation was used for predicting pain scores within the training dataset. The exact P-values for the prediction performance was 0.0002 for the training set (left) and 0.032 for the hold-out test set (right), two-tailed, one-sample t-test. b, Cross-validated performance of the CBP model, which was derived using the whole CBP patients’ resting state data (n = 17, after excluding data with insufficient brain coverages). Because the CBP model showed poor predictive performance even within the training dataset, further testing of the CBP model was discontinued. The exact P-value of the prediction performance was P = 0.269, two-tailed, one-sample t-test). ns = not significant, *P < 0.05, ***P < 0.001.
Extended Data Fig. 7 Circular plot representation of the ToPS.
From outermost to innermost, the first layer of the circle represents different functional groups, and the second and third layers each represent the sum of positive and negative predictive weights coming from each brain region.
Extended Data Fig. 8 Ventral striatum seed-based connectivity analysis.
a, We used the bilateral ventral striatum (VS) ROIs from the ToPS model as a seed to construct whole-brain seed-based connectivity maps for each time-bin of Study 1 data (n = 19). We had a particular interest in the weight patterns within the two medial prefrontal regions, dorsomedial and ventromedial prefrontal cortices (dmPFC and vmPFC). With the whole-brain connectivity maps, b, we first conducted the univariate GLM analysis. For each individual, we regressed the VS seed-based functional connectivity (Y) on pain intensity ratings (X) across capsaicin and control runs and performed second-level t-tests on the beta maps, treating participant as a random effect. Here we show the results for FDR-corrected q < 0.05 (corresponding to uncorrected P = 0.001), pruned with uncorrected P < 0.01 and 0.05 (two-tailed). c, We also conducted a multivariate analysis, in which we used the principal component regression (PCR) with reduced number of PCs to predict pain intensity ratings based on VS seed-based connectivity across capsaicin and control condition. The number of PCs was selected based on cross-validated within-individual predictive performance (#PC = 45; mean prediction-outcome r = 0.25, P = 0.002, two-tailed, bootstrap test). To identify important brain regions, we conducted the bootstrap test for the PCR with 10,000 iterations. Here we show the results for P < 0.005 uncorrected, pruned with P < 0.01 and 0.05, two-tailed. d, Regression weights in the medial prefrontal regions, focusing on the dorsomedial and ventromedial prefrontal cortices (that is, dmPFC and vmPFC). The left panel shows the unthresholded univariate map from b, and the right panel shows the unthresholded multivariate regression map from c. The pie chart represents the proportion of positive (red) and negative (blue) weights in each of the medial prefrontal regions. Across both univariate and multivariate maps, a dorsal-ventral gradient (dorsal: more positive, ventral: more negative) was found in the medial prefrontal cortex. Black lines show the contours of dmPFC and vmPFC regions.
Extended Data Fig. 9 Predicting tonic pain ratings with fMRI activation-based signatures for EPP.
To examine whether the fMRI activation-based pain markers could achieve similar levels of predictive performance, we tested existing fMRI activation-based models, including a, the Neurologic Pain Signature (NPS) and b, the Stimulus Intensity Independent Pain Signature-1 (SIIPS1). The top panel shows the predictive performances on the validation dataset (Study 2), and the bottom panel shows the predictive performances on the independent dataset (Study 3). In the plots on the left-side, the color of dots and lines represented the levels of correlation (r) for each participant’s pain prediction (red: higher r; yellow: lower r, blue: r < 0). The plots on the right-side show mean values of the actual avoidance ratings (black) and signature responses (red). The capsaicin run was shown before the control run for the display purpose, and in the real experiment, the order of the runs was counterbalanced across participants. Shading represents the standard errors of the mean (s.e.m.). Note that the left and right plots were based on averaging within five and ten time bins, respectively.
Extended Data Fig. 10 Testing the ToPS on an independent dataset with a different time-course of tonic pain and bitter taste (Supplementary Data 2).
We used the ToPS to predict the unpleasantness ratings while participants were given capsaicin, quinine (bitter taste), or saline (‘Control’). Here, the capsaicin and quinine stimuli were delivered to participants’ mouths using an MR-compatible gustometer system. This experimental setup allowed us to evoke capsaicin-induced orofacial tonic pain or quinine-induced bitter taste during two separate epochs within one run. a, Experimental paradigm for Supplementary Data 2 (n = 58). Each run lasts for around 14.5 minutes, and each stimulus (capsaicin for ‘Capsaicin’, quinine for ‘Bitter Taste’, and saline for ‘Control’ condition) was delivered two times within each run (1.5–3 min, and 7–8.5 min). The order of all conditions was counterbalanced across participants. b, Left: Actual versus predicted ratings (that is, signature responses) are shown in the plot. Signature response was calculated using the dot product of the model with imaging data. Each colored line (or symbol) represents an individual participant’s data across the capsaicin and control runs (red: higher r, yellow: lower r, blue: r < 0). Right: Mean avoidance rating (black) and signature response (red) across the capsaicin and control runs. Black arrows indicate when taste stimuli were delivered to participants. Shading represents within-subject s.e.m. The capsaicin and quinine runs are shown before the control run for the display purpose, regardless of the actual order of the two runs. Note that the left and right plots were based on averaging within five and ten time bins, respectively. The exact P-values were P = 3.32 × 10−9 for the capsaicin condition (top) and 0.710 for the bitter taste condition (bottom), two-tailed, bootstrap tests. ****P < 0.0001.
Supplementary information
Supplementary Information
Supplementary Methods, Supplementary Results, Supplementary Figs. 1–9 and Supplementary Tables 1–4.
Rights and permissions
About this article
Cite this article
Lee, JJ., Kim, H.J., Čeko, M. et al. A neuroimaging biomarker for sustained experimental and clinical pain. Nat Med 27, 174–182 (2021). https://doi.org/10.1038/s41591-020-1142-7
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1038/s41591-020-1142-7
This article is cited by
-
Associations between grip strength, brain structure, and mental health in > 40,000 participants from the UK Biobank
BMC Medicine (2022)
-
Individual variability in brain representations of pain
Nature Neuroscience (2022)
-
Disrupted stepwise functional brain organization in overweight individuals
Communications Biology (2022)
-
Dissociation between individual differences in self-reported pain intensity and underlying fMRI brain activation
Nature Communications (2022)
-
Antagonistic network signature of motor function in Parkinson’s disease revealed by connectome-based predictive modeling
npj Parkinson's Disease (2022)