Skip to content
naulon

No retraction, correction or concern found when last checked on Oct 31, 2026.

Discovery and validation of biomarkers to aid the development of safe and effective pain therapeutics: challenges and opportunities

Karen D. Davis, Nima Aghaeepour, Andrew H. Ahn and 23 more

Nature Reviews Neurology · 2020 · Review

doi:10.1038/s41582-020-0362-2

Abstract

Pain medication plays an important role in the treatment of acute and chronic pain conditions, but some drugs, opioids in particular, have been overprescribed or prescribed without adequate safeguards, leading to an alarming rise in medication-related overdose deaths. The NIH Helping to End Addiction Long-term (HEAL) Initiative is a trans-agency effort to provide scientific solutions to stem the opioid crisis. One component of the initiative is to support biomarker discovery and rigorous validation in collaboration with industry leaders to accelerate high-quality clinical research into neurotherapeutics and pain. The use of objective biomarkers and clinical trial end points throughout the drug discovery and development process is crucial to help define pathophysiological subsets of pain, evaluate target engagement of new drugs and predict the analgesic efficacy of new drugs. In 2018, the NIH-led Discovery and Validation of Biomarkers to Develop Non-Addictive Therapeutics for Pain workshop convened scientific leaders from academia, industry, government and patient advocacy groups to discuss progress, challenges, gaps and ideas to facilitate the development of biomarkers and end points for pain. The outcomes of this workshop are outlined in this Consensus Statement. In 2018, the Discovery and Validation of Biomarkers to Develop Non-Addictive Therapeutics for Pain workshop convened to discuss strategies to facilitate the development of biomarkers and end points for pain. The outcomes of this workshop are outlined in this Consensus Statement.

Shown for reading. Its publisher allows quoting it, not republishing it.

References 246

  1. 1.

    Treede RD, et al. Chronic pain as a symptom or a disease: the IASP classification of chronic pain for the International Classification of Diseases (ICD-11). Pain. 2019;160:19-27.

    Not in our library10.1097/j.pain.0000000000001384

  2. 2.

    Von Korff M, et al. United States National Pain Strategy for population research: concepts, definitions, and pilot data. J. Pain. 2016;17:1068-1080.

    Not in our library10.1016/j.jpain.2016.06.009

  3. 3.
  4. 4.

    Nahin RL, Sayer B, Stussman BJ, Feinberg TM. Eighteen-year trends in the prevalence of, and health care use for, noncancer pain in the United States: data from the Medical Expenditure Panel Survey. J. Pain. 2019;20:796-809.

    Not in our library10.1016/j.jpain.2019.01.003

  5. 5.

    US Institute of Medicine. Relieving Pain in America: A Blueprint for Transforming Prevention, Care, Education, and Research (National Academies, 2011).

    Not in our library

  6. 6.
  7. 7.

    Gatchel RJ, et al. Research agenda for the prevention of pain and its impact: report of the Work Group on the Prevention of Acute and Chronic Pain of the Federal Pain Research Strategy. J. Pain. 2018;19:837-851.

    Not in our library10.1016/j.jpain.2018.02.015

  8. 8.

    World Health Organization. Management of substance abuse. Information sheet on opioid overdose (WHO, 2018).

    Not in our library

  9. 9.

    US Substance Abuse and Mental Health Services Administration. Facing addiction in America: the Surgeon General’s report on alcohol, drugs, and health (US Department of Health and Human Services, 2016).

    Not in our library

  10. 10.

    Mackey S, Kao MC. Managing twin crises in chronic pain and prescription opioids. BMJ. 2019;364:l917.

    Not in our library10.1136/bmj.l917

  11. 11.

    Pitcher MH, Von Korff M, Bushnell MC, Porter L. Prevalence and profile of high-impact chronic pain in the United States. J. Pain. 2019;20:146-160.

    Not in our library10.1016/j.jpain.2018.07.006

  12. 12.

    FDA Center for Drug Evaluation and Research. Advancing health through innovation: 2018 new drug therapy approvals (FDA, 2019).

    Not in our library

  13. 13.

    Fogel DB. Factors associated with clinical trials that fail and opportunities for improving the likelihood of success: a review. Contemp. Clin. Trials Commun. 2018;11:156-164.

    Not in our library10.1016/j.conctc.2018.08.001

  14. 14.

    Thomas D, Wessel C. The state of innovation in highly prevalent chronic disease. BIO Ind. Anal. 2018;II:1-15.

    Not in our library

  15. 15.

    Ferber G. Biomarkers and proof of concept. Methods Find. Exp. Clin. Pharmacol. 2002;24(Suppl. C):35-40.

    Not in our library

  16. 16.

    Morgan P, et al. Impact of a five-dimensional framework on R&D productivity at AstraZeneca. Nat. Rev. Drug Discov. 2018;17:167-181.

    Not in our library10.1038/nrd.2017.244

  17. 17.

    Thomas, D. W. et al. Clinical development success rates 2006–2015 (BIO, 2016).

    Not in our library

  18. 18.

    Nagakura Y. The need for fundamental reforms in the pain research field to develop innovative drugs. Expert Opin. Drug Discov. 2017;12:39-46.

    Not in our library10.1080/17460441.2017.1261108

  19. 19.

    Niculescu AB, et al. Towards precision medicine for pain: diagnostic biomarkers and repurposed drugs. Mol. Psychiat. 2019;24:501-522.

    Not in our library10.1038/s41380-018-0345-5

  20. 20.

    Wideman TH, et al. The multimodal assessment model of pain: a novel framework for further integrating the subjective pain experience within research and practice. Clin. J. Pain. 2019;35:212-221.

    Not in our library10.1097/ajp.0000000000000670

  21. 21.

    Treede RD. The International Association for the Study of Pain definition of pain: as valid in 2018 as in 1979, but in need of regularly updated footnotes. Pain. Rep. 2018;3:e643.

    Not in our library10.1097/pr9.0000000000000643

  22. 22.

    Bonafe FSS, de Campos LA, Maroco J, Campos J. Brief pain inventory: a proposal to extend its clinical application. Eur. J. Pain. 2019;23:565-576.

    Not in our library10.1002/ejp.1330

  23. 23.

    Main CJ. Pain assessment in context: a state of the science review of the McGill pain questionnaire 40 years on. Pain. 2016;157:1387-1399.

    Not in our library10.1097/j.pain.0000000000000457

  24. 24.

    Bullock L, et al. Pain assessment and pain treatment for community-dwelling people with dementia: a systematic review and narrative synthesis. Int. J. Geriatr. Psychiat. 2019;34:807-821.

    Not in our library10.1002/gps.5078

  25. 25.

    Birnie KA, Hundert AS, Lalloo C, Nguyen C, Stinson JN. Recommendations for selection of self-report pain intensity measures in children and adolescents: a systematic review and quality assessment of measurement properties. Pain. 2019;160:5-18.

    Not in our library10.1097/j.pain.0000000000001377

  26. 26.

    Dansie EJ, Turk DC. Assessment of patients with chronic pain. Br. J. Anaesth. 2013;111:19-25.

    Not in our library10.1093/bja/aet124

  27. 27.

    Smith SM, et al. Pain intensity rating training: results from an exploratory study of the ACTTION PROTECCT system. Pain. 2016;157:1056-1064.

    Not in our library10.1097/j.pain.0000000000000502

  28. 28.

    Vollert J, et al. Quantitative sensory testing using DFNS protocol in Europe: an evaluation of heterogeneity across multiple centers in patients with peripheral neuropathic pain and healthy subjects. Pain. 2016;157:750-758.

    Not in our library10.1097/j.pain.0000000000000433

  29. 29.
  30. 30.

    Haanpaa M, et al. NeuPSIG guidelines on neuropathic pain assessment. Pain. 2011;152:14-27.

    Not in our library10.1016/j.pain.2010.07.031

  31. 31.

    Group BDW. Biomarkers and surrogate endpoints: preferred definitions and conceptual framework. Clin. Pharmacol. Ther. 2001;69:89-95.

    Not in our library10.1067/mcp.2001.113989

  32. 32.

    US Food and Drug Administration–National Institutes of Health Biomarker Working Group. BEST (Biomarkers, EndpointS, and other Tools) Resource (FDA, 2016).

    Not in our library

  33. 33.

    European Medicines Agency. Guideline on the clinical investigation of medicines for the treatment of Alzheimer’s disease (EMA, 2018).

    Not in our library

  34. 34.

    FDA Center for Drug Evaluation and Research. Biomarker qualification: evidentiary framework (FDA, 2018).

    Not in our library

  35. 35.

    Wager TD, et al. An fMRI-based neurologic signature of physical pain. N. Engl. J. Med. 2013;368:1388-1397.

    Not in our library10.1056/nejmoa1204471

  36. 36.

    Woo C-W, Wager TD. Neuroimaging-based biomarker discovery and validation. Pain. 2015;156:1379-1381.

    Not in our library10.1097/j.pain.0000000000000223

  37. 37.

    Davis KD, et al. Brain imaging tests for chronic pain: medical, legal and ethical issues and recommendations. Nat. Rev. Neurol. 2017;13:624-638.

    Not in our library10.1038/nrneurol.2017.122

  38. 38.

    Kragel PA, Koban L, Barrett LF, Wager TD. Representation, pattern information, and brain signatures: from neurons to neuroimaging. Neuron. 2018;99:257-273.

    Not in our library10.1016/j.neuron.2018.06.009

  39. 39.

    Woo C-W, Chang LJ, Lindquist MA, Wager TD. Building better biomarkers: brain models in translational neuroimaging. Nat. Neurosci. 2017;20:365-377.

    Not in our library10.1038/nn.4478

  40. 40.

    Kohoutová L, et al. Toward a unified framework for interpreting machine-learning models in neuroimaging. Nat. Protoc. 2020;15:1399-1435.

    Not in our library10.1038/s41596-019-0289-5

  41. 41.

    Varoquaux G. Cross-validation failure: small sample sizes lead to large error bars. Neuroimage. 2018;180:68-77.

    Not in our library10.1016/j.neuroimage.2017.06.061

  42. 42.

    Hastie, T., Tibshirani, R. & Friedman, J. The Elements of Statistical Learning: Data Mining, Inference, and Prediction (Springer, 2013).

    Not in our library

  43. 43.
  44. 44.
  45. 45.

    Zunhammer M, Bingel U, Wager TD. Placebo effects on the neurologic pain signature: a meta-analysis of individual participant functional magnetic resonance imaging data. JAMA Neurol. 2018;75:1321-1330.

    Not in our library10.1001/jamaneurol.2018.2017

  46. 46.

    Borsook D, Becerra L, Hargreaves R. Biomarkers for chronic pain and analgesia. Part 1: the need, reality, challenges, and solutions. Discov. Med. 2011;11:197-207.

    Not in our library

  47. 47.

    Borsook D, Becerra L, Hargreaves R. Biomarkers for chronic pain and analgesia. Part 2: how, where, and what to look for using functional imaging. Discov. Med. 2011;11:209-219.

    Not in our library

  48. 48.

    Bair E, et al. Identification of clusters of individuals relevant to temporomandibular disorders and other chronic pain conditions: the OPPERA study. Pain. 2016;157:1266-1278.

    Not in our library10.1097/j.pain.0000000000000518

  49. 49.

    Rolke R, et al. Quantitative sensory testing in the German Research Network on Neuropathic Pain (DFNS): standardized protocol and reference values. Pain. 2006;123:231-243.

    Not in our library10.1016/j.pain.2006.01.041

  50. 50.

    Diatchenko L, Fillingim RB, Smith SB, Maixner W. The phenotypic and genetic signatures of common musculoskeletal pain conditions. Nat. Rev. Rheumatol. 2013;9:340-350.

    Not in our library10.1038/nrrheum.2013.43

  51. 51.

    Smith SM, et al. The potential role of sensory testing, skin biopsy, and functional brain imaging as biomarkers in chronic pain clinical trials: IMMPACT considerations. J. Pain. 2017;18:757-777.

    Not in our library10.1016/j.jpain.2017.02.429

  52. 52.

    Ashraf AB, et al. The painful face — pain expression recognition using active appearance models. Image Vis. Comput. 2009;27:1788-1796.

    Not in our library10.1016/j.imavis.2009.05.007

  53. 53.

    Bartlett MS, Littlewort GC, Frank MG, Lee K. Automatic decoding of facial movements reveals deceptive pain expressions. Curr. Biol. 2014;24:738-743.

    Not in our library10.1016/j.cub.2014.02.009

  54. 54.

    LaChapelle DL, Hadjistavropoulos T, Craig KD. Pain measurement in persons with intellectual disabilities. Clin. J. Pain. 1999;15:13-23.

    Not in our library10.1097/00002508-199903000-00004

  55. 55.

    Sikka K, et al. Automated assessment of children’s postoperative pain using computer vision. Pediatrics. 2015;136:e124-e131.

    Not in our library10.1542/peds.2015-0029

  56. 56.

    Branco A, Fekete SMW, Rugolo LMSS, Rehder MI. The newborn pain cry: descriptive acoustic spectrographic analysis. Int. J. Pediatr. Otorhinolaryngol. 2007;71:539-546.

    Not in our library10.1016/j.ijporl.2006.11.009

  57. 57.

    Cohn, J. F. et al. Detecting depression from facial actions and vocal prosody. Int. Conf. Affect. Comput. Intell. Interact. Workshops10.1109/ACII.2009.5349358 (2009).

    Not in our library10.1109/acii.2009.5349321

  58. 58.

    Gholami B, Haddad WM, Tannenbaum AR. Relevance vector machine learning for neonate pain intensity assessment using digital imaging. IEEE Trans. Biomed. Eng. 2010;57:1457-1466.

    Not in our library10.1109/tbme.2009.2039214

  59. 59.

    Yang M, et al. A machine learning approach to assessing gait patterns for complex regional pain syndrome. Med. Eng. Phys. 2012;34:740-746.

    Not in our library10.1016/j.medengphy.2011.09.018

  60. 60.

    Nguyen QC, et al. Social media indicators of the food environment and state health outcomes. Public Health. 2017;148:120-128.

    Not in our library10.1016/j.puhe.2017.03.013

  61. 61.

    Olausson H, Wessberg J, Morrison I, McGlone F, Vallbo A. The neurophysiology of unmyelinated tactile afferents. Neurosci. Biobehav. Rev. 2010;34:185-191.

    Not in our library10.1016/j.neubiorev.2008.09.011

  62. 62.

    Serra J. Microneurography: towards a biomarker of spontaneous pain. Pain. 2012;153:1989-1990.

    Not in our library10.1016/j.pain.2012.07.008

  63. 63.

    Serra J, et al. Hyperexcitable C nociceptors in fibromyalgia. Ann. Neurol. 2014;75:196-208.

    Not in our library10.1002/ana.24065

  64. 64.

    Waxman, S. G. Chasing Men on Fire: The Story of the Search for a Pain Gene (MIT Press, 2018).

    Not in our library

  65. 65.
    Brain Rhythms of Pain · 2017
    No notice
  66. 66.

    Ploner M, May ES. Electroencephalography and magnetoencephalography in pain research — current state and future perspectives. Pain. 2018;159:206-211.

    Not in our library10.1097/j.pain.0000000000001087

  67. 67.
  68. 68.

    Peng W, et al. Brain oscillations reflecting pain-related behavior in freely moving rats. Pain. 2018;159:106-118.

    Not in our library10.1097/j.pain.0000000000001069

  69. 69.

    Nickel MM, et al. Brain oscillations differentially encode noxious stimulus intensity and pain intensity. Neuroimage. 2017;148:141-147.

    Not in our library10.1016/j.neuroimage.2017.01.011

  70. 70.

    May ES, et al. Prefrontal gamma oscillations reflect ongoing pain intensity in chronic back pain patients. Hum. Brain Mapp. 2018;40:293-305.

    Not in our library10.1002/hbm.24373

  71. 71.

    Leblanc BW, Lii TR, Silverman AE, Alleyne RT, Saab CY. Cortical theta is increased while thalamocortical coherence is decreased in rat models of acute and chronic pain. Pain. 2014;155:773-782.

    Not in our library10.1016/j.pain.2014.01.013

  72. 72.

    LeBlanc BW, Bowary PM, Chao YC, Lii TR, Saab CY. Electroencephalographic signatures of pain and analgesia in rats. Pain. 2016;157:2330-2340.

    Not in our library10.1097/j.pain.0000000000000652

  73. 73.

    LeBlanc BW, et al. T-type calcium channel blocker Z944 restores cortical synchrony and thalamocortical connectivity in a rat model of neuropathic pain. Pain. 2016;157:255-263.

    Not in our library10.1097/j.pain.0000000000000362

  74. 74.
  75. 75.

    Koyama S, et al. An electroencephalography bioassay for preclinical testing of analgesic efficacy. Sci. Rep. 2018;6:16402.

    Not in our library10.1038/s41598-018-34594-2

  76. 76.

    Llinas RR, Ribary U, Jeanmonod D, Kronberg E, Mitra PP. Thalamocortical dysrhythmia: a neurological and neuropsychiatric syndrome characterized by magnetoencephalography. Proc. Natl Acad. Sci. USA. 1999;96:15222-15227.

    Not in our library10.1073/pnas.96.26.15222

  77. 77.

    Sarnthein J, Stern J, Aufenberg C, Rousson V, Jeanmonod D. Increased EEG power and slowed dominant frequency in patients with neurogenic pain. Brain. 2006;129:55-64.

    Not in our library10.1093/brain/awh631

  78. 78.

    Stern J, Jeanmonod D, Sarnthein J. Persistent EEG overactivation in the cortical pain matrix of neurogenic pain patients. Neuroimage. 2006;31:721-731.

    Not in our library10.1016/j.neuroimage.2005.12.042

  79. 79.

    Saab CY, Barrett LF. Thalamic bursts and the epic pain model. Front. Comput. Neurosci. 2016;10:147.

    Not in our library10.3389/fncom.2016.00147

  80. 80.

    LeBlanc BW, et al. Thalamic bursts down-regulate cortical theta and nociceptive behavior. Sci. Rep. 2017;7:2482.

    Not in our library10.1038/s41598-017-02753-6

  81. 81.

    Mamas M, Dunn WB, Neyses L, Goodacre R. The role of metabolites and metabolomics in clinically applicable biomarkers of disease. Arch. Toxicol. 2011;85:5-17.

    Not in our library10.1007/s00204-010-0609-6

  82. 82.

    Ramsden CE, et al. A systems approach for discovering linoleic acid derivatives that potentially mediate pain and itch. Sci. Signal. 2017;10:eaal5241.

    Not in our library10.1126/scisignal.aal5241

  83. 83.

    Dorsey SG, et al. Whole blood transcriptomic profiles can differentiate vulnerability to chronic low back pain. PLOS ONE. 2019;14:e0216539.

    Not in our library10.1371/journal.pone.0216539

  84. 84.

    Roses AD. Apolipoprotein E alleles as risk factors in Alzheimer’s disease. Annu. Rev. Med. 1996;47:387-400.

    Not in our library10.1146/annurev.med.47.1.387

  85. 85.
  86. 86.

    Blennow K, Mattsson N, Scholl M, Hansson O, Zetterberg H. Amyloid biomarkers in Alzheimer’s disease. Trends Pharmacol. Sci. 2015;36:297-309.

    Not in our library10.1016/j.tips.2015.03.002

  87. 87.

    Mattsson N, Cullen NC, Andreasson U, Zetterberg H, Blennow K. Association between longitudinal plasma neurofilament light and neurodegeneration in patients with Alzheimer disease. JAMA Neurol. 2019;76:791-799.

    Not in our library10.1001/jamaneurol.2019.0765

  88. 88.

    McIntosh AM, et al. Genetic and environmental risk for chronic pain and the contribution of risk variants for major depressive disorder: a family-based mixed-model analysis. PLOS Med. 2016;13:e1002090.

    Not in our library10.1371/journal.pmed.1002090

  89. 89.

    Gormley P, et al. Common variant burden contributes to the familial aggregation of migraine in 1,589 families. Neuron. 2018;99:1098.

    Not in our library10.1016/j.neuron.2018.08.029

  90. 90.

    Zorina-Lichtenwalter K, Meloto CB, Khoury S, Diatchenko L. Genetic predictors of human chronic pain conditions. Neuroscience. 2016;338:36-62.

    Not in our library10.1016/j.neuroscience.2016.04.041

  91. 91.

    Tracey I, Woolf CJ, Andrews NA. Composite pain biomarker signatures for objective assessment and effective treatment. Neuron. 2019;101:783-800.

    Not in our library10.1016/j.neuron.2019.02.019

  92. 92.

    Sandoval J, Peiro-Chova L, Pallardo FV, Garcia-Gimenez JL. Epigenetic biomarkers in laboratory diagnostics: emerging approaches and opportunities. Exp.Rev. Mol. Diagn. 2013;13:457-471.

    Not in our library10.1586/erm.13.37

  93. 93.

    Douglas SR, et al. Analgesic response to intravenous ketamine is linked to a circulating microRNA signature in female patients with complex regional pain syndrome. J. Pain. 2015;16:814-824.

    Not in our library10.1016/j.jpain.2015.05.008

  94. 94.

    Ramanathan S, Ajit SK. MicroRNA-based biomarkers in pain. Adv. Pharmacol. 2016;75:35-62.

    Not in our library10.1016/bs.apha.2015.12.001

  95. 95.

    Lopez-Gonzalez MJ, Landry M, Favereaux A. MicroRNA and chronic pain: from mechanisms to therapeutic potential. Pharmacol. Ther. 2017;180:1-15.

    Not in our library10.1016/j.pharmthera.2017.06.001

  96. 96.

    Descalzi G, et al. Epigenetic mechanisms of chronic pain. Trends Neurosci. 2015;38:237-246.

    Not in our library10.1016/j.tins.2015.02.001

  97. 97.

    Raoof R, Willemen H, Eijkelkamp N. Divergent roles of immune cells and their mediators in pain. Rheumatology. 2018;57:429-440.

    Not in our library10.1093/rheumatology/kex308

  98. 98.

    Ji RR, Chamessian A, Zhang YQ. Pain regulation by non-neuronal cells and inflammation. Science. 2016;354:572-577.

    Not in our library10.1126/science.aaf8924

  99. 99.

    Tsai AS, et al. A year-long immune profile of the systemic response in acute stroke survivors. Brain. 2019;142:978-991.

    Not in our library10.1093/brain/awz022

  100. 100.

    Aghaeepour N, et al. A proteomic clock of human pregnancy. Am. J. Obstet. Gynecol. 2018;218:347.e1-347.e14.

    Not in our library10.1016/j.ajog.2017.12.208

  101. 101.
  102. 102.

    Ganz P, et al. Development and validation of a protein-based risk score for cardiovascular outcomes among patients with stable coronary heart disease. JAMA. 2016;315:2532-2541.

    Not in our library10.1001/jama.2016.5951

  103. 103.

    Erez O, et al. The prediction of late-onset preeclampsia: results from a longitudinal proteomics study. PLOS ONE. 2017;12:e0181468.

    Not in our library10.1371/journal.pone.0181468

  104. 104.

    Aghaeepour N, et al. Deep immune profiling of an arginine-enriched nutritional intervention in patients undergoing surgery. J. Immunol. 2017;199:2171-2180.

    Not in our library10.4049/jimmunol.1700421

  105. 105.

    Gaudilliere B, et al. Clinical recovery from surgery correlates with single-cell immune signatures. Sci. Transl. Med. 2014;6:255ra131.

    Not in our library10.1126/scitranslmed.3009701

  106. 106.

    Fragiadakis GK, et al. Patient-specific immune states before surgery are strong correlates of surgical recovery. Anesthesiology. 2015;123:1241-1255.

    Not in our library10.1097/aln.0000000000000887

  107. 107.

    Wallace DJ, Gavin IM, Karpenko O, Barkhordar F, Gillis BS. Cytokine and chemokine profiles in fibromyalgia, rheumatoid arthritis and systemic lupus erythematosus: a potentially useful tool in differential diagnosis. Rheumatol. Int. 2015;35:991-996.

    Not in our library10.1007/s00296-014-3172-2

  108. 108.

    LaPaglia DM, et al. RNA-Seq investigations of human post-mortem trigeminal ganglia. Cephalalgia. 2018;38:912-932.

    Not in our library10.1177/0333102417720216

  109. 109.

    Jacob M, Lopata AL, Dasouki M, Abdel Rahman AM. Metabolomics toward personalized medicine. Mass. Spectrom. Rev. 2017;38:221-238.

    Not in our library10.1002/mas.21548

  110. 110.

    Parker KS, et al. Urinary metabolomics identifies a molecular correlate of interstitial cystitis/bladder pain syndrome in a Multidisciplinary Approach to the Study of Chronic Pelvic Pain (MAPP) research network cohort. EBioMedicine. 2016;7:167-174.

    Not in our library10.1016/j.ebiom.2016.03.040

  111. 111.
  112. 112.

    Lu G, Fei B. Medical hyperspectral imaging: a review. J. Biomed. Opt. 2014;19:10901.

    Not in our library10.1117/1.jbo.19.1.010901

  113. 113.

    Marcu L, Boppart SA, Hutchinson MR, Popp J, Wilson BC. Biophotonics: the big picture. J. Biomed. Opt. 2017;23:1-7.

    Not in our library10.1117/1.jbo.23.2.021103

  114. 114.
  115. 115.

    van der Miesen MM, Lindquist MA, Wager TD. Neuroimaging-based biomarkers for pain: state of the field and current directions. Pain. Rep. 2019;4:e751.

    Not in our library10.1097/pr9.0000000000000751

  116. 116.

    Vachon-Presseau E, et al. Corticolimbic anatomical characteristics predetermine risk for chronic pain. Brain. 2016;139:1958-1970.

    Not in our library10.1093/brain/aww100

  117. 117.

    Fischer TZ, Waxman SG. Neuropathic pain in diabetes — evidence for a central mechanism. Nat. Rev. Neurol. 2010;6:462-466.

    Not in our library10.1038/nrneurol.2010.90

  118. 118.

    Kuner R, Flor H. Structural plasticity and reorganisation in chronic pain. Nat. Rev. Neurosci. 2016;18:20-30.

    Not in our library10.1038/nrn.2016.162

  119. 119.

    Reddan MC, Wager TD. Brain systems at the intersection of chronic pain and self-regulation. Neurosci. Lett. 2018;702:24-33.

    Not in our library10.1016/j.neulet.2018.11.047

  120. 120.

    O’Muircheartaigh J, et al. Multivariate decoding of cerebral blood flow measures in a clinical model of on-going postsurgical pain. Hum. Brain Mapp. 2015;36:633-642.

    Not in our library10.1002/hbm.22652

  121. 121.

    Marshall TM, et al. Activation of descending pain-facilitatory pathways from the rostral ventromedial medulla by cholecystokinin elicits release of prostaglandin-E2 in the spinal cord. Pain. 2012;153:86-94.

    Not in our library10.1016/j.pain.2011.09.021

  122. 122.

    Xie JY, et al. Cholecystokinin in the rostral ventromedial medulla mediates opioid-induced hyperalgesia and antinociceptive tolerance. J. Neurosci. 2005;25:409-416.

    Not in our library10.1523/jneurosci.4054-04.2005

  123. 123.

    Martucci KT, Weber KA, Mackey SC. Altered cervical spinal cord resting-state activity in fibromyalgia. Arthritis Rheumatol. 2019;71:441-450.

    Not in our library10.1002/art.40746

  124. 124.

    Weber KA, et al. Thermal stimulation alters cervical spinal cord functional connectivity in humans. Neuroscience. 2018;369:40-50.

    Not in our library10.1016/j.neuroscience.2017.10.035

  125. 125.

    Islam H, Law CSW, Weber KA, Mackey SC, Glover GH. Dynamic per slice shimming for simultaneous brain and spinal cord fMRI. Magn. Reson. Med. 2019;81:825-838.

    Not in our library10.1002/mrm.27388

  126. 126.

    Davis KD, Moayedi M. Central mechanisms of pain revealed through functional and structural MRI. J. Neuroimmune Pharmacol. 2013;8:518-534.

    Not in our library10.1007/s11481-012-9386-8

  127. 127.

    Geuter, S., et al. in Handbook of Psychophysiology (eds Cacioppo, J. T. et al.) 41–73 (Cambridge Univ. Press, 2017).

    Not in our library

  128. 128.

    Cherry SR. Fundamentals of positron emission tomography and applications in preclinical drug development. J. Clin. Pharmacol. 2001;41:482-491.

    Not in our library10.1177/00912700122010357

  129. 129.

    Jones AKP, Watabe H, Cunningham VJ, Jones T. Cerebral decreases in opioid receptor binding in patients with central neuropathic pain measured by [11C] diprenorphine binding and PET. Eur. J. Pain. 2004;8:479-485.

    Not in our library10.1016/j.ejpain.2003.11.017

  130. 130.

    Zubieta JK, et al. Regional μ opioid receptor regulation of sensory and affective dimensions of pain. Science. 2001;293:311-315.

    Not in our library10.1126/science.1060952

  131. 131.

    Loggia ML, et al. Evidence for brain glial activation in chronic pain patients. Brain. 2015;138:604-615.

    Not in our library10.1093/brain/awu377

  132. 132.

    Notter T, Coughlin JM, Sawa A, Meyer U. Reconceptualization of translocator protein as a biomarker of neuroinflammation in psychiatry. Mol. Psychiat. 2018;23:36-47.

    Not in our library10.1038/mp.2017.232

  133. 133.

    Gent YYJ, et al. Macrophage positron emission tomography imaging as a biomarker for preclinical rheumatoid arthritis: findings of a prospective pilot study. Arthritis Rheum. 2012;64:62-66.

    Not in our library10.1002/art.30655

  134. 134.
  135. 135.

    Marquand A, et al. Quantitative prediction of subjective pain intensity from whole-brain fMRI data using Gaussian processes. Neuroimage. 2010;49:2178-2189.

    Not in our library10.1016/j.neuroimage.2009.10.072

  136. 136.

    López-Solà M, et al. Towards a neurophysiological signature for fibromyalgia. Pain. 2017;158:34-47.

    Not in our library10.1097/j.pain.0000000000000707

  137. 137.

    Mano H, et al. Classification and characterisation of brain network changes in chronic back pain: a multicenter study. Wellcome Open. Res. 2018;3:19.

    Not in our library10.12688/wellcomeopenres.14069.1

  138. 138.

    Mansour A, et al. Global disruption of degree rank order: a hallmark of chronic pain. Sci. Rep. 2016;6:34853.

    Not in our library10.1038/srep34853

  139. 139.

    Cheng JC, et al. Multivariate machine learning distinguishes cross-network dynamic functional connectivity patterns in state and trait neuropathic pain. Pain. 2018;159:1764-1776.

    Not in our library10.1097/j.pain.0000000000001264

  140. 140.

    Nan J, et al. Whole-brain functional connectivity identification of functional dyspepsia. PLOS ONE. 2013;8:e65870.

    Not in our library10.1371/journal.pone.0065870

  141. 141.
  142. 142.

    Bagarinao E, et al. Preliminary structural MRI based brain classification of chronic pelvic pain: a MAPP network study. Pain. 2014;155:2502-2509.

    Not in our library10.1016/j.pain.2014.09.002

  143. 143.

    Ung H, et al. Multivariate classification of structural MRI data detects chronic low back pain. Cereb. Cortex. 2014;24:1037-1044.

    Not in our library10.1093/cercor/bhs378

  144. 144.

    Baliki MN, et al. Corticostriatal functional connectivity predicts transition to chronic back pain. Nat. Neurosci. 2012;15:1117-1119.

    Not in our library10.1038/nn.3153

  145. 145.

    Kutch JJ, et al. Resting-state functional connectivity predicts longitudinal pain symptom change in urologic chronic pelvic pain syndrome: a MAPP network study. Pain. 2017;158:1069-1082.

    Not in our library10.1097/j.pain.0000000000000886

  146. 146.

    Hashmi JA, et al. Brain networks predicting placebo analgesia in a clinical trial for chronic back pain. Pain. 2012;153:2393-2402.

    Not in our library10.1016/j.pain.2012.08.008

  147. 147.
  148. 148.

    Bosma RL, et al. Brain dynamics and temporal summation of pain predicts neuropathic pain relief from ketamine infusion. Anesthesiology. 2018;129:1015-1024.

    Not in our library10.1097/aln.0000000000002417

  149. 149.

    Hung PS, Chen DQ, Davis KD, Zhong J, Hodaie M. Predicting pain relief: use of pre-surgical trigeminal nerve diffusion metrics in trigeminal neuralgia. Neuroimage Clin. 2017;15:710-718.

    Not in our library10.1016/j.nicl.2017.06.017

  150. 150.

    Rosa MJ, Seymour B. Decoding the matrix: benefits and limitations of applying machine learning algorithms to pain neuroimaging. Pain. 2014;155:864-867.

    Not in our library10.1016/j.pain.2014.02.013

  151. 151.

    Davis KD. Is chronic pain a disease? Evaluating pain and nociception through self-report and neuroimaging. J. Pain. 2013;14:332-333.

    Not in our library10.1016/j.jpain.2013.01.002

  152. 152.

    Hemington KS, Wu Q, Kucyi A, Inman RD, Davis KD. Abnormal cross-network functional connectivity in chronic pain and its association with clinical symptoms. Brain Struct. Funct. 2016;221:4203-4219.

    Not in our library10.1007/s00429-015-1161-1

  153. 153.

    Marbach D, et al. Wisdom of crowds for robust gene network inference. Nat. Methods. 2012;9:796-804.

    Not in our library10.1038/nmeth.2016

  154. 154.

    Aghaeepour N, et al. Critical assessment of automated flow cytometry data analysis techniques. Nat. Methods. 2013;10:228-238.

    Not in our library10.1038/nmeth.2365

  155. 155.

    Aghaeepour N, et al. A benchmark for evaluation of algorithms for identification of cellular correlates of clinical outcomes. Cytometry A. 2016;89:16-21.

    Not in our library10.1002/cyto.a.22732

  156. 156.

    Halilaj E, Hastie TJ, Gold GE, Delp SL. Physical activity is associated with changes in knee cartilage microstructure. Osteoarthr. Cartil. 2018;26:770-774.

    Not in our library10.1016/j.joca.2018.03.009

  157. 157.

    Tibshirani, R. & Friedman, J. A pliable lasso. Preprint at arXivhttps://arxiv.org/abs/1712.00484v4 (2018).

    Not in our library10.1080/10618600.2019.1648271

  158. 158.

    Choo J, Liu S. Visual analytics for explainable deep learning. IEEE Comput. Graph. Appl. 2018;38:84-92.

    Not in our library10.1109/mcg.2018.042731661

  159. 159.

    Aghaeepour N, et al. GateFinder: projection-based gating strategy optimization for flow and mass cytometry. Bioinformatics. 2018;34:4131-4133.

    Not in our library10.1093/bioinformatics/bty430

  160. 160.

    Taylor J, Tibshirani R. Post-selection inference for ℓ1-penalized likelihood models. Can. J. Stat. 2018;46:41-61.

    Not in our library10.1002/cjs.11313

  161. 161.

    Cagney DN, et al. The FDA NIH Biomarkers, EndpointS, and other Tools (BEST) resource in neuro-oncology. Neuro Oncol. 2018;20:1162-1172.

    Not in our library10.1093/neuonc/nox242

  162. 162.

    Dworkin RH, et al. Core outcome measures for chronic pain clinical trials: IMMPACT recommendations. Pain. 2005;113:9-19.

    Not in our library10.1016/j.pain.2004.09.012

  163. 163.

    Edwards RR, et al. Patient phenotyping in clinical trials of chronic pain treatments: IMMPACT recommendations. Pain. 2016;157:1851-1871.

    Not in our library10.1097/j.pain.0000000000000602

  164. 164.

    Bennett M. The LANSS pain scale: the Leeds Assessment of Neuropathic Symptoms and Signs. Pain. 2001;92:147-157.

    Not in our library10.1016/s0304-3959(00)00482-6

  165. 165.

    Bennett MI, et al. Using screening tools to identify neuropathic pain. Pain. 2007;127:199-203.

    Not in our library10.1016/j.pain.2006.10.034

  166. 166.

    Bouhassira D, et al. Neuropathic pain phenotyping as a predictor of treatment response in painful diabetic neuropathy: data from the randomized, double-blind, COMBO-DN study. Pain. 2014;155:2171-2179.

    Not in our library10.1016/j.pain.2014.08.020

  167. 167.

    Forstenpointner J, Rehm S, Gierthmuhlen J, Baron R. Stratification of neuropathic pain patients: the road to mechanism-based therapy? Curr. Opin. Anaesthesiol. 2018;31:562-568.

    Not in our library10.1097/aco.0000000000000642

  168. 168.

    Turk DC, et al. Identifying important outcome domains for chronic pain clinical trials: an IMMPACT survey of people with pain. Pain. 2008;137:276-285.

    Not in our library10.1016/j.pain.2007.09.002

  169. 169.

    Taylor AM, et al. Assessment of physical function and participation in chronic pain clinical trials: IMMPACT/OMERACT recommendations. Pain. 2016;157:1836-1850.

    Not in our library10.1097/j.pain.0000000000000577

  170. 170.

    Turk DC, Fillingim RB, Ohrbach R, Patel KV. Assessment of psychosocial and functional impact of chronic pain. J. Pain. 2016;17:T21-T49.

    Not in our library10.1016/j.jpain.2016.02.006

  171. 171.

    Perrot S, Lanteri-Minet M. Patients’ global impression of change in the management of peripheral neuropathic pain: clinical relevance and correlations in daily practice. Eur. J. Pain. 2019;23:1117-1128.

    Not in our library10.1002/ejp.1378

  172. 172.

    Jamison RN, Dorado K, Mei A, Edwards RR, Martel MO. Influence of opioid-related side effects on disability, mood, and opioid misuse risk among patients with chronic pain in primary care. Pain. Rep. 2017;2:e589.

    Not in our library10.1097/pr9.0000000000000589

  173. 173.

    Lauria G, et al. European Federation of Neurological Societies/Peripheral Nerve Society guideline on the use of skin biopsy in the diagnosis of small fiber neuropathy. Report of a joint task force of the European Federation of Neurological Societies and the Peripheral Nerve Society. Eur. J. Neurol. 2010;17:903-912.

    Not in our library10.1111/j.1468-1331.2010.03023.x

  174. 174.

    Devigili G, et al. The diagnostic criteria for small fibre neuropathy: from symptoms to neuropathology. Brain. 2008;131:1912-1925.

    Not in our library10.1093/brain/awn093

  175. 175.

    Themistocleous AC, et al. The Pain in Neuropathy Study (PiNS): a cross-sectional observational study determining the somatosensory phenotype of painful and painless diabetic neuropathy. Pain. 2016;157:1132-1145.

    Not in our library10.1097/j.pain.0000000000000491

  176. 176.

    Zhou L, et al. Correlates of epidermal nerve fiber densities in HIV-associated distal sensory polyneuropathy. Neurology. 2007;68:2113-2119.

    Not in our library10.1212/01.wnl.0000264888.87918.a1

  177. 177.

    von Hehn CA, Baron R, Woolf CJ. Deconstructing the neuropathic pain phenotype to reveal neural mechanisms. Neuron. 2012;73:638-652.

    Not in our library10.1016/j.neuron.2012.02.008

  178. 178.

    Costigan M, Scholz J, Woolf CJ. Neuropathic pain: a maladaptive response of the nervous system to damage. Annu. Rev. Neurosci. 2009;32:1-32.

    Not in our library10.1146/annurev.neuro.051508.135531

  179. 179.

    Zunhammer M, Bingel U, Wager TD, Placebo Imaging Consortium. Placebo effects on the neurologic pain signature: a meta-analysis of individual participant functional magnetic resonance imaging data. JAMA Neurol. 2018;75:1321-1330.

    Not in our library10.1001/jamaneurol.2018.2017

  180. 180.

    Campbell CM, et al. Randomized control trial of topical clonidine for treatment of painful diabetic neuropathy. Pain. 2012;153:1815-1823.

    Not in our library10.1016/j.pain.2012.04.014

  181. 181.

    Rowbotham MC, et al. Oral and cutaneous thermosensory profile of selective TRPV1 inhibition by ABT-102 in a randomized healthy volunteer trial. Pain. 2011;152:1192-1200.

    Not in our library10.1016/j.pain.2011.01.051

  182. 182.

    Serra J, et al. Effects of a T-type calcium channel blocker, ABT-639, on spontaneous activity in C-nociceptors in patients with painful diabetic neuropathy: a randomized controlled trial. Pain. 2015;156:2175-2183.

    Not in our library10.1097/j.pain.0000000000000249

  183. 183.

    Yarnitsky D, et al. Remote electrical neuromodulation (REN) relieves acute migraine: a randomized, double-blind, placebo-controlled, multicenter trial. Headache. 2019;59:1240-1252.

    Not in our library10.1111/head.13551

  184. 184.

    US Food and Drug Administration. Statement by FDA Commissioner Scott Gottlieb, MD on the agency’s ongoing work to forcefully address the opioid crisis (FDA, 2018).

    Not in our library

  185. 185.

    Canadian Institutes of Health Research. Institute of Musculoskeletal Health and Arthritis IMHA Strategic Plan 2014–2018: enhancing musculoskeletal, skin and oral health. CIHRhttps://cihr-irsc.gc.ca/e/48830.html (2014).

    Not in our library

  186. 186.

    Heath Canada. Responding to Canada’s opioid crisis (Government of Canada, 2019).

    Not in our library

  187. 187.

    International Council for Harmonisation of Technical Requirements for Pharmaceuticals for Human Use. E16 biomarkers related to drug or biotechnology product develoment: context, structure and format of quantification submissions. ICHhttps://www.ich.org/page/efficacy-guidelines (2010).

    Not in our library

  188. 188.

    European Medicines Agency. Essential considerations for successful qualification of novel methodologies (EMA, 2017).

    Not in our library

  189. 189.

    US Food and Drug Administration. Table of surrogate endpoints that were the basis of drug approval or licensure. FDAhttps://www.fda.gov/drugs/development-resources/table-surrogate-endpoints-were-basis-drug-approval-or-licensure (2019).

    Not in our library

  190. 190.

    Innovative Medicines Initiative. Innovative Medicines Initiative IMI1 Final project report public summary: Europain. Understanding chronic pain and improving its treatment (IMI, 2015).

    Not in our library

  191. 191.

    Fitzgerald M, Walker SM. Infant pain management: a developmental neurobiological approach. Nat. Clin. Pract. Neurol. 2009;5:35-50.

    Not in our library10.1038/ncpneuro0984

  192. 192.
  193. 193.

    Hicks CL, von Baeyer CL, Spafford PA, van Korlaar I, Goodenough B. The faces pain scale — revised: toward a common metric in pediatric pain measurement. Pain. 2001;93:173-183.

    Not in our library10.1016/s0304-3959(01)00314-1

  194. 194.

    Zamzmi G, et al. A review of automated pain assessment in infants: features, classification tasks, and databases. IEEE Rev. Biomed. Eng. 2018;11:77-96.

    Not in our library10.1109/rbme.2017.2777907

  195. 195.

    Boly M, et al. Perception of pain in the minimally conscious state with PET activation: an observational study. Lancet Neurol. 2008;7:1013-1020.

    Not in our library10.1016/s1474-4422(08)70219-9

  196. 196.

    Monti MM, et al. Willful modulation of brain activity in disorders of consciousness. N. Engl. J. Med. 2010;362:579-589.

    Not in our library10.1056/nejmoa0905370

  197. 197.

    Cole LJ, et al. Pain sensitivity and fMRI pain-related brain activity in Alzheimer’s disease. Brain. 2006;129:2957-2965.

    Not in our library10.1093/brain/awl228

  198. 198.

    de Knegt N, Scherder E. Pain in adults with intellectual disabilities. Pain. 2011;152:971-974.

    Not in our library10.1016/j.pain.2010.11.001

  199. 199.

    de Knegt NC, et al. Behavioral pain indicators in people with intellectual disabilities: a systematic review. J. Pain. 2013;14:885-896.

    Not in our library10.1016/j.jpain.2013.04.016

  200. 200.

    Fanurik D, Koh JL, Dale Harrison R, Conrad TM, Tomerun C. Pain assessment in children with cognitive impairment. Clin.Nurs. Res. 1998;7:103-119.

    Not in our library10.1177/105477389800700202

  201. 201.

    Wolff BB, Langley S. Cultural factors and the response to pain: a review. Am. Anthropol. 1968;70:494-501.

    Not in our library

  202. 202.

    Zborowski M. Cultural components in responses to pain. J. Soc. Issues. 1952;8:16-30.

    Not in our library

  203. 203.

    Anderson SR, Reynolds Losin EA. A sociocultural neuroscience approach to pain. Cult. Brain. 2017;5:14-35.

    Not in our library

  204. 204.

    Loeb S, et al. Overdiagnosis and overtreatment of prostate cancer. Eur. Urol. 2014;65:1046-1055.

    Not in our library10.1016/j.eururo.2013.12.062

  205. 205.

    Cannon A, Kurklinsky S, Guthrie KJ, Riegert-Johnson DL. Advanced genetic testing comes to the pain clinic to make a diagnosis of paroxysmal extreme pain disorder. Case Rep. Neurol. Med. 2016;2016:9212369.

    Not in our library10.1155/2016/9212369

  206. 206.

    Drenth JP, Waxman SG. Mutations in sodium-channel gene SCN9A cause a spectrum of human genetic pain disorders. J. Clin. Invest. 2007;117:3603-3609.

    Not in our library10.1172/jci33297

  207. 207.

    Carey TS, Garrett JM. The relation of race to outcomes and the use of health care services for acute low back pain. Spine. 2003;28:390-394.

    Not in our library10.1097/01.brs.0000048499.25275.51

  208. 208.

    Quartana PJ, Campbell CM, Edwards RR. Pain catastrophizing: a critical review. Expert Rev. Neurother. 2009;9:745-758.

    Not in our library10.1586/ern.09.34

  209. 209.

    Clarke TK, et al. Low frequency genetic variants in the μ-opioid receptor (OPRM1) affect risk for addiction to heroin and cocaine. Neurosci. Lett. 2013;542:71-75.

    Not in our library10.1016/j.neulet.2013.02.018

  210. 210.

    Petersen KK, Arendt-Nielsen L, Simonsen O, Wilder-Smith O, Laursen MB. Presurgical assessment of temporal summation of pain predicts the development of chronic postoperative pain 12 months after total knee replacement. Pain. 2015;156:55-61.

    Not in our library10.1016/j.pain.0000000000000022

  211. 211.

    Lauria G, et al. Intraepidermal nerve fiber density at the distal leg: a worldwide normative reference study. J. Peripher. Nerv. Syst. 2010;15:202-207.

    Not in our library10.1111/j.1529-8027.2010.00271.x

  212. 212.

    Freeman R, Baron R, Bouhassira D, Cabrera J, Emir B. Sensory profiles of patients with neuropathic pain based on the neuropathic pain symptoms and signs. Pain. 2014;155:367-376.

    Not in our library10.1016/j.pain.2013.10.023

  213. 213.

    Reimer M, et al. Prediction of response to tapentadol in chronic low back pain. Eur. J. Pain. 2017;21:322-333.

    Not in our library10.1002/ejp.926

  214. 214.

    Starkey Lewis PJ, et al. Circulating microRNAs as potential markers of human drug-induced liver injury. Hepatology. 2011;54:1767-1776.

    Not in our library10.1002/hep.24538

  215. 215.

    Serra J, et al. Microneurographic identification of spontaneous activity in C-nociceptors in neuropathic pain states in humans and rats. Pain. 2012;153:42-55.

    Not in our library10.1016/j.pain.2011.08.015

  216. 216.

    Ackerley R, Watkins RH. Microneurography as a tool to study the function of individual C-fiber afferents in humans: responses from nociceptors, thermoreceptors, and mechanoreceptors. J. Neurophysiol. 2018;120:2834-2846.

    Not in our library10.1152/jn.00109.2018

  217. 217.

    Pascal MMV, et al. DOLORisk: study protocol for a multi-centre observational study to understand the risk factors and determinants of neuropathic pain. Wellcome Open. Res. 2019;3:63.

    Not in our library10.12688/wellcomeopenres.14576.1

  218. 218.

    Levitt J, Saab CY. What does a pain ‘biomarker’ mean and can a machine be taught to measure pain? Neurosci. Lett. 2019;702:40-43.

    Not in our library10.1016/j.neulet.2018.11.038

  219. 219.

    Schulman J, Ramirez R, Zonenshayn M, Ribary U, Llinas RR. Thalamocortical dysrhythmia syndrome: MEG imaging of neuropathic pain. Thalamus Relat. Syst. 2005;31:33-39.

    Not in our library

  220. 220.

    Juottonen K, et al. Altered central sensorimotor processing in patients with complex regional pain syndrome. Pain. 2002;98:315-323.

    Not in our library10.1016/s0304-3959(02)00119-7

  221. 221.

    Kim JA, et al. Neuropathic pain and pain interference are linked to alpha-band slowing and reduced beta-band magnetoencephalography activity within the dynamic pain connectome in patients with multiple sclerosis. Pain. 2019;160:187-197.

    Not in our library10.1097/j.pain.0000000000001391

  222. 222.
  223. 223.

    Goadsby PJ, et al. Pathophysiology of migraine: a disorder of sensory processing. Physiol. Rev. 2017;97:553-622.

    Not in our library10.1152/physrev.00034.2015

  224. 224.

    Oaklander AL, Herzog ZD, Downs HM, Klein MM. Objective evidence that small-fiber polyneuropathy underlies some illnesses currently labeled as fibromyalgia. Pain. 2013;154:2310-2316.

    Not in our library10.1016/j.pain.2013.06.001

  225. 225.

    Vlckova-Moravcova E, Bednarik J, Dusek L, Toyka KV, Sommer C. Diagnostic validity of epidermal nerve fiber densities in painful sensory neuropathies. Muscle Nerve. 2008;37:50-60.

    Not in our library10.1002/mus.20889

  226. 226.

    Carragee EJ, Alamin TF, Miller JL, Carragee JM. Discographic, MRI and psychosocial determinants of low back pain disability and remission: a prospective study in subjects with benign persistent back pain. Spine J. 2005;5:24-35.

    Not in our library10.1016/j.spinee.2004.05.250

  227. 227.

    Szabo N, et al. White matter microstructural alterations in migraine: a diffusion-weighted MRI study. Pain. 2012;153:651-656.

    Not in our library10.1016/j.pain.2011.11.029

  228. 228.

    Woodworth D, et al. Unique microstructural changes in the brain associated with urological chronic pelvic pain syndrome (UCPPS) revealed by diffusion tensor MRI, super-resolution track density imaging, and statistical parameter mapping: a MAPP network neuroimaging study. PLOS ONE. 2015;10:e0140250.

    Not in our library10.1371/journal.pone.0140250

  229. 229.

    Griebel AJ, Trippel SB, Emery NC, Neu CP. Noninvasive assessment of osteoarthritis severity in human explants by multicontrast MRI. Magn. Res. Med. 2014;71:807-814.

    Not in our library10.1002/mrm.24725

  230. 230.

    Staikopoulos V, et al. Hyperspectral imaging of endogenous fluorescent metabolic molecules to identify pain states in central nervous system tissue. Proc. SPIE. 2016;10013:1001306.

    Not in our library

  231. 231.

    Aarnio M, et al. Visualization of painful inflammation in patients with pain after traumatic ankle sprain using [11C]-d-deprenyl PET/CT. Scand. J. Pain. 2017;17:418-424.

    Not in our library10.1016/j.sjpain.2017.10.008

  232. 232.

    Uceyler N, et al. Increased cortical activation upon painful stimulation in fibromyalgia syndrome. BMC Neurol. 2015;15:210.

    Not in our library10.1186/s12883-015-0472-4

  233. 233.

    Vrana A, Meier ML, Hotz-Boendermaker S, Humphreys BK, Scholkmann F. Cortical sensorimotor processing of painful pressure in patients with chronic lower back pain — an optical neuroimaging study using fNIRS. Front. Hum. Neurosci. 2016;10:578.

    Not in our library10.3389/fnhum.2016.00578

  234. 234.

    Demant DT, et al. The effect of oxcarbazepine in peripheral neuropathic pain depends on pain phenotype: a randomised, double-blind, placebo-controlled phenotype-stratified study. Pain. 2014;155:2263-2273.

    Not in our library10.1016/j.pain.2014.08.014

  235. 235.

    Geuter S, Gamer M, Onat S, Büchel C. Parametric trial-by-trial prediction of pain by easily available physiological measures. Pain. 2014;155:994-1001.

    Not in our library10.1016/j.pain.2014.02.005

  236. 236.

    Kalliomaki J, et al. Evaluation of a novel chemokine receptor 2 (CCR2)-antagonist in painful diabetic polyneuropathy. Scand. J. Pain. 2013;4:77-83.

    Not in our library10.1016/j.sjpain.2012.10.003

  237. 237.

    Kalliomaki J, et al. A randomized, double-blind, placebo-controlled trial of a chemokine receptor 2 (CCR2) antagonist in posttraumatic neuralgia. Pain. 2013;154:761-767.

    Not in our library10.1016/j.pain.2013.02.003

  238. 238.

    Quiding H, et al. TRPV1 antagonistic analgesic effect: a randomized study of AZD1386 in pain after third molar extraction. Pain. 2013;154:808-812.

    Not in our library10.1016/j.pain.2013.02.004

  239. 239.

    Miller F, Bjornsson M, Svensson O, Karlsten R. Experiences with an adaptive design for a dose-finding study in patients with osteoarthritis. Contemp. Clin. Trials. 2014;37:189-199.

    Not in our library10.1016/j.cct.2013.12.007

  240. 240.

    Gimbel JS, et al. Long-term safety and effectiveness of tanezumab as treatment for chronic low back pain. Pain. 2014;155:1793-1801.

    Not in our library10.1016/j.pain.2014.06.004

  241. 241.

    Juhasz G, et al. Sumatriptan causes parallel decrease in plasma calcitonin gene-related peptide (CGRP) concentration and migraine headache during nitroglycerin induced migraine attack. Cephalalgia. 2005;25:179-183.

    Not in our library10.1111/j.1468-2982.2005.00836.x

  242. 242.

    Yarnitsky D, Granot M, Nahman-Averbuch H, Khamaisi M, Granovsky Y. Conditioned pain modulation predicts duloxetine efficacy in painful diabetic neuropathy. Pain. 2012;153:1193-1198.

    Not in our library10.1016/j.pain.2012.02.021

  243. 243.

    Yarnitsky D, et al. Nonpainful remote electrical stimulation alleviates episodic migraine pain. Neurology. 2017;88:1250-1255.

    Not in our library10.1212/wnl.0000000000003760

  244. 244.

    Nahman-Averbuch H, et al. Waning of “conditioned pain modulation”: a novel expression of subtle pronociception in migraine. Headache. 2013;53:1104-1115.

    Not in our library10.1111/head.12117

  245. 245.

    Yarnitsky D. Conditioned pain modulation (the diffuse noxious inhibitory control-like effect): its relevance for acute and chronic pain states. Curr. Opin. Anaesthesiol. 2010;23:611-615.

    Not in our library10.1097/aco.0b013e32833c348b

  246. 246.

    Petropoulos IN, et al. Corneal confocal microscopy: ready for prime time. Clin. Exp. Optom. 2019;103:265-277.

    Not in our library10.1111/cxo.12887

Cited by papers we hold 1

Reads

Read by people on this page

None yet.

Checked or read by AI through naulon

None yet.

A person counts once a day per paper. We keep no cookie and no address: a reader is a hash that changes every day. Any month under five reads shows no number.

Are you an author?

Is this your paper?

Claim it to add your note and be paid when agents cite it. Free.