Book contents
- The Cambridge Handbook of Human Affective Neuroscience
- Cambridge Handbooks in Psychology
- The Cambridge Handbook of Human Affective Neuroscience
- Copyright page
- Contents
- Figures
- Tables
- Contributors
- Introduction
- Section I Theoretical Models of Emotion
- Section II Measuring Emotional Processes
- Section III Emotion Perception and Elicitation
- Chapter 9 The Perception of Facial Expressions of Emotion
- Chapter 10 Body of Knowledge
- Chapter 11 A Lifespan Perspective of Emotion in Voice Perception
- Chapter 12 Pain in the Brain
- Chapter 13 Olfaction and Emotion
- Chapter 14 Music, Emotion, and Reward
- Chapter 15 Language and Emotion Concepts in the Predictive Brain
- Section IV Emotional Learning and Memory
- Section V Cognition–Emotion Interactions
- Section VI Social Emotions
- Section VII Individual Differences
- Index
- References
Chapter 12 - Pain in the Brain
from Section III - Emotion Perception and Elicitation
Published online by Cambridge University Press: 16 September 2025
Book contents
- The Cambridge Handbook of Human Affective Neuroscience
- Cambridge Handbooks in Psychology
- The Cambridge Handbook of Human Affective Neuroscience
- Copyright page
- Contents
- Figures
- Tables
- Contributors
- Introduction
- Section I Theoretical Models of Emotion
- Section II Measuring Emotional Processes
- Section III Emotion Perception and Elicitation
- Chapter 9 The Perception of Facial Expressions of Emotion
- Chapter 10 Body of Knowledge
- Chapter 11 A Lifespan Perspective of Emotion in Voice Perception
- Chapter 12 Pain in the Brain
- Chapter 13 Olfaction and Emotion
- Chapter 14 Music, Emotion, and Reward
- Chapter 15 Language and Emotion Concepts in the Predictive Brain
- Section IV Emotional Learning and Memory
- Section V Cognition–Emotion Interactions
- Section VI Social Emotions
- Section VII Individual Differences
- Index
- References
Summary
Pain is a complex experience that includes physical sensations and emotional responses. Research has shown that the central nervous system plays a significant role in how we experience pain. In this chapter, we review the current understanding of the neuroscience of pain, with a particular emphasis on pain processing in the brain. We cover early theories that emphasized the brain’s role in integrating and modulating pain, as well as modern approaches that view pain as distributed processing in the brain. We also introduce functional and computational frameworks for understanding the sensory and motivational aspects of pain and discuss various factors that contribute to the multidimensional nature of pain. The future direction of the study of pain neuroscience includes a deep sampling of subjective pain experience and the use of generative models.
Keywords
Information
- Type
- Chapter
- Information
- The Cambridge Handbook of Human Affective Neuroscience , pp. 248 - 265Publisher: Cambridge University PressPrint publication year: 2025
References
Aitchison, L., & Lengyel, M. (2017). With or without you: Predictive coding and Bayesian inference in the brain. Current Opinion in Neurobiology, 46, 219–227.CrossRefGoogle ScholarPubMed
Anderson, S. R., Gianola, M., Medina, N. A., Perry, J. M., Wager, T. D., & Losin, E. A. R. (2022). Doctor trustworthiness influences pain and its neural correlates in virtual medical interactions. Cerebral Cortex, 33, 3421–3436.Google Scholar
Apkarian, A. V., Bushnell, M. C., Treede, R. D., & Zubieta, J. K. (2005). Human brain mechanisms of pain perception and regulation in health and disease. European Journal of Pain, 9, 463–484.CrossRefGoogle Scholar
Apkarian, A. V., Sosa, Y., Sonty, S., Levy, R. M., Harden, R. N., Parrish, T. B., & Gitelman, D. R. (2004). Chronic back pain is associated with decreased prefrontal and thalamic gray matter density. The Journal of Neuroscience, 24, 10410–10415.CrossRefGoogle ScholarPubMed
Atlas, L. Y., Bolger, N., Lindquist, M. A., & Wager, T. D. (2010). Brain mediators of predictive cue effects on perceived pain. The Journal of Neuroscience, 30, 12964–12977.CrossRefGoogle ScholarPubMed
Atlas, L. Y., & Wager, T. D. (2012). How expectations shape pain. Neuroscience Letters, 520, 140–148.CrossRefGoogle ScholarPubMed
Baliki, M. N., Chialvo, D. R., Geha, P. Y., Levy, R. M., Harden, R. N., Parrish, T. B., & Apkarian, A. V. (2006). Chronic pain and the emotional brain: Specific brain activity associated with spontaneous fluctuations of intensity of chronic back pain. The Journal of Neuroscience, 26, 12165–12173.CrossRefGoogle ScholarPubMed
Baliki, M. N., Geha, P. Y., Fields, H. L., & Apkarian, A. V. (2010). Predicting value of pain and analgesia: Nucleus accumbens response to noxious stimuli changes in the presence of chronic pain. Neuron, 66, 149–160.CrossRefGoogle ScholarPubMed
Baliki, M. N., Petre, B., Torbey, S., Herrmann, K. M., Huang, L., Schnitzer, T. J., … Apkarian, A. V. (2012). Corticostriatal functional connectivity predicts transition to chronic back pain. Nature Neuroscience, 15, 1117–1119.CrossRefGoogle ScholarPubMed
Bashivan, P., Kar, K., & DiCarlo, J. J. (2019). Neural population control via deep image synthesis. Science, 364, eaav9436.CrossRefGoogle ScholarPubMed
Beecher, H. K. (1956). Relationship of significance of wound to pain experienced. Journal of American Medical Association, 161, 1609–1613.CrossRefGoogle ScholarPubMed
Berger, S. E., Branco, P., Vachon-Presseau, E., Abdullah, T. B., Cecchi, G., & Apkarian, A. V. (2021). Quantitative language features identify placebo responders in chronic back pain. Pain, 162, 1692–1704.CrossRefGoogle ScholarPubMed
Buchel, C., Geuter, S., Sprenger, C., & Eippert, F. (2014). Placebo analgesia: A predictive coding perspective. Neuron, 81, 1223–1239.CrossRefGoogle ScholarPubMed
Bushnell, M. C., Ceko, M., & Low, L. A. (2013). Cognitive and emotional control of pain and its disruption in chronic pain. Nature Reviews Neuroscience, 14, 502–511.CrossRefGoogle ScholarPubMed
Bushnell, M. C., Duncan, G. H., Hofbauer, R. K., Ha, B., Chen, J. I., & Carrier, B. (1999). Pain perception: Is there a role for primary somatosensory cortex? Proceedings of the National Academy of Sciences of the United States of America, 96, 7705–7709.Google Scholar
Cash, R. F. H., Weigand, A., Zalesky, A., Siddiqi, S. H., Downar, J., Fitzgerald, P. B., & Fox, M. D. (2021). Using brain imaging to improve spatial targeting of transcranial magnetic stimulation for depression. Biological Psychiatry, 90, 689–700.CrossRefGoogle ScholarPubMed
Ceko, M., Kragel, P. A., Woo, C. W., Lopez-Sola, M., & Wager, T. D. (2022). Common and stimulus-type-specific brain representations of negative affect. Nature Neuroscience, 25, 760–770.CrossRefGoogle ScholarPubMed
Che, X., Cash, R., Chung, S., Fitzgerald, P. B., & Fitzgibbon, B. M. (2018). Investigating the influence of social support on experimental pain and related physiological arousal: A systematic review and meta-analysis. Neuroscience & Biobehavioral Reviews, 92, 437–452.CrossRefGoogle ScholarPubMed
Cheng, J. C., Anzolin, A., Berry, M., Honari, H., Paschali, M., Lazaridou, A., … Napadow, V. (2022). Dynamic functional brain connectivity underlying temporal summation of pain in fibromyalgia. Arthritis & Rheumatology, 74, 700–710.CrossRefGoogle ScholarPubMed
Coghill, R. C. (2020). The distributed nociceptive system: A framework for understanding pain. Trends in Neurosciences, 43, 780–794.CrossRefGoogle ScholarPubMed
Coghill, R. C., Sang, C. N., Maisog, J. M., & Iadarola, M. J. (1999). Pain intensity processing within the human brain: A bilateral, distributed mechanism. Journal of Neurophysiology, 82, 1934–1943.CrossRefGoogle ScholarPubMed
Corns, J. (2017). The Routledge handbook of philosophy of pain. Routledge, Taylor & Francis Group.CrossRefGoogle Scholar
Craig, A. D. (2003). A new view of pain as a homeostatic emotion. Trends in Neurosciences, 26, 303–307.Google Scholar
Craig, A. D., Reiman, E. M., Evans, A., & Bushnell, M. C. (1996). Functional imaging of an illusion of pain. Nature, 384, 258–260.CrossRefGoogle ScholarPubMed
Craig, K. D. (2015). Social communication model of pain. Pain, 156, 1198–1199.CrossRefGoogle ScholarPubMed
Dallenbach, K. M. (1939). Pain: History and present status. The American Journal of Psychology, 52, 331–347.CrossRefGoogle Scholar
Derbyshire, S. W. G., Jones, A. K. P., Gyulai, F., Clark, S., Townsend, D., & Firestone, L. L. (1997). Pain processing during three levels of noxious stimulation produces differential patterns of central activity. Pain, 73, 431–445.CrossRefGoogle ScholarPubMed
Dobek, C. E., Beynon, M. E., Bosma, R. L., & Stroman, P. W. (2014). Music modulation of pain perception and pain-related activity in the brain, brain stem, and spinal cord: A functional magnetic resonance imaging study. Journal of Pain Research, 15, 1057–1068.Google ScholarPubMed
Dum, R. P., Levinthal, D. J., & Strick, P. L. (2009). The spinothalamic system targets motor and sensory areas in the cerebral cortex of monkeys. The Journal of Neuroscience, 29, 14223–14235.CrossRefGoogle ScholarPubMed
Eisenberger, N. I., Master, S. L., Inagaki, T. K., Taylor, S. E., Shirinyan, D., Lieberman, M. D., & Naliboff, B. D. (2011). Attachment figures activate a safety signal-related neural region and reduce pain experience. Proceedings of the National Academy of Sciences of the United States of America, 108, 11721–11726.Google ScholarPubMed
Eldar, E., Hauser, T. U., Dayan, P., & Dolan, R. J. (2016). Striatal structure and function predict individual biases in learning to avoid pain. Proceedings of the National Academy of Sciences of the United States of America, 113, 4812–4817.Google ScholarPubMed
Elfwing, S., & Seymour, B. (2017). “Parallel reward and punishment control in humans and robots: Safe reinforcement learning using the MaxPain algorithm.” Proceedings of the 2017 Joint IEEE International Conference on Development and Learning and Epigenetic Robotics (ICDL-EpiRob), Lisbon, 18–21 September 2017.CrossRefGoogle Scholar
Ellingsen, D. M., Isenburg, K., Jung, C., Lee, J., Gerber, J., Mawla, I., … Napadow, V. (2020). Dynamic brain-to-brain concordance and behavioral mirroring as a mechanism of the patient-clinician interaction. Science Advances, 6, eabc1304.CrossRefGoogle ScholarPubMed
Fanselow, M. S. (1994). Neural organization of the defensive behavior system responsible for fear. Psychonomic Bulletin & Review, 1, 429–438.CrossRefGoogle ScholarPubMed
Farmer, M. A., Baliki, M. N., & Apkarian, A. V. (2012). A dynamic network perspective of chronic pain. Neuroscience Letters, 520, 197–203.CrossRefGoogle ScholarPubMed
Feinstein, J. S., Khalsa, S. S., Salomons, T. V., Prkachin, K. M., Frey-Law, L. A., Lee, J. E., … Rudrauf, D. (2016). Preserved emotional awareness of pain in a patient with extensive bilateral damage to the insula, anterior cingulate, and amygdala. Brain Structure and Function, 221, 1499–1511.CrossRefGoogle Scholar
Fields, H. (2004). State-dependent opioid control of pain. Nature Reviews Neuroscience, 5, 565–575.CrossRefGoogle ScholarPubMed
Fields, H. L. (2006). ‘A motivation-decision model of pain: the role of opioids,’ in Proceedings of the 11th world congress on pain, Seattle: IASP press, pp. 449–459.Google Scholar
Fields, H. L. (2018). How expectations influence pain. Pain, 159, S3–S10.CrossRefGoogle ScholarPubMed
Goldstein, P., Weissman-Fogel, I., Dumas, G., & Shamay-Tsoory, S. G. (2018). Brain-to-brain coupling during handholding is associated with pain reduction. Proceedings of the National Academy of Sciences of the United States of America, 115, E2528–E2537.Google ScholarPubMed
Gordon, E. M., Laumann, T. O., Gilmore, A. W., Newbold, D. J., Greene, D. J., Berg, J. J., … Dosenbach, N. U. F. (2017). Precision functional mapping of individual human brains. Neuron, 95, 791–807.e7.CrossRefGoogle ScholarPubMed
Gratton, C., Laumann, T. O., Nielsen, A. N., Greene, D. J., Gordon, E. M., Gilmore, A. W., … Petersen, S. E. (2018). Functional brain networks are dominated by stable group and individual factors, not cognitive or daily variation. Neuron, 98, 439–452.e5.CrossRefGoogle ScholarPubMed
Greenspan, J. D., Lee, R. R., & Lenz, F. A. (1999). Pain sensitivity alterations as a function of lesion location in the parasylvian cortex. Pain, 81, 273–282.CrossRefGoogle ScholarPubMed
Harper, D. E., Shah, Y., Ichesco, E., Gerstner, G. E., & Peltier, S. J. (2016). Multivariate classification of pain-evoked brain activity in temporomandibular disorder. Pain Reports, 1, e572.CrossRefGoogle ScholarPubMed
Hashmi, J. A., Baliki, M. N., Huang, L., Baria, A. T., Torbey, S., Hermann, K. M., … Apkarian, A. V. (2013). Shape shifting pain: Chronification of back pain shifts brain representation from nociceptive to emotional circuits. Brain, 136, 2751–2768.CrossRefGoogle ScholarPubMed
Head, H., & Holmes, G. (1911). Sensory disturbances from cerebral lesions. Brain, 34, 102–254.CrossRefGoogle Scholar
Hong, Y. W., Yoo, Y., Han, J., Wager, T. D., & Woo, C. W. (2019). False-positive neuroimaging: Undisclosed flexibility in testing spatial hypotheses allows presenting anything as a replicated finding. NeuroImage, 195, 384–395.CrossRefGoogle ScholarPubMed
Horikawa, T., & Kamitani, Y. (2017). Generic decoding of seen and imagined objects using hierarchical visual features. Nature Communications, 8, 15037.CrossRefGoogle ScholarPubMed
Huang, G., Xiao, P., Hung, Y. S., Iannetti, G. D., Zhang, Z. G., & Hu, L. (2013). A novel approach to predict subjective pain perception from single-trial laser-evoked potentials. NeuroImage, 81, 283–293.CrossRefGoogle ScholarPubMed
Iannetti, G. D., & Mouraux, A. (2010). From the neuromatrix to the pain matrix (and back). Experimental Brain Research, 205, 1–12.CrossRefGoogle Scholar
Ingvar, M. (1999). Pain and functional imaging. Philosophical Transactions of the Royal Society of London. Series B, Biological Sciences, 354, 1347–1358.Google ScholarPubMed
Kastrati, G., Thompson, W. H., Schiffler, B., Fransson, P., & Jensen, K. B. (2022). Brain network segregation and integration during painful thermal stimulation. Cerebral Cortex, 32, 4039–4049.CrossRefGoogle ScholarPubMed
Kim, J., Loggia, M. L., Cahalan, C. M., Harris, R. E., Beissner, F. D. P. N., Garcia, R. G., … Napadow, V. (2015). The somatosensory link in fibromyalgia: Functional connectivity of the primary somatosensory cortex is altered by sustained pain and is associated with clinical/autonomic dysfunction. Arthritis & Rheumatology, 67, 1395–1405.CrossRefGoogle ScholarPubMed
Knecht, S., Kunesch, E., & Schnitzler, A. (1996). Parallel and serial processing of haptic information in man: Effects of parietal lesions on sensorimotor hand function. Neuropsychologia, 34, 669–687.CrossRefGoogle ScholarPubMed
Koban, L., Jepma, M., Geuter, S., & Wager, T. D. (2017). What’s in a word? How instructions, suggestions, and social information change pain and emotion. Neuroscience & Biobehavioral Reviews, 81, 29–42.CrossRefGoogle Scholar
Koban, L., Jepma, M., Lopez-Sola, M., & Wager, T. D. (2019). Different brain networks mediate the effects of social and conditioned expectations on pain. Nature Communications, 10, 4096.CrossRefGoogle ScholarPubMed
Kober, H., Buhle, J., Weber, J., Ochsner, K. N., & Wager, T. D. (2019). Let it be: Mindful acceptance down-regulates pain and negative emotion. Social Cognitive and Affective Neuroscience, 14, 1147–1158.CrossRefGoogle ScholarPubMed
Kong, J., Jensen, K., Loiotile, R., Cheetham, A., Wey, H. Y., Tan, Y., … Gollub, R. L. (2013). Functional connectivity of the frontoparietal network predicts cognitive modulation of pain. Pain, 154, 459–467.CrossRefGoogle ScholarPubMed
Krishnan, A., Woo, C. W., Chang, L. J., Ruzic, L., Gu, X., Lopez-Sola, M., … Wager, T. D. (2016). Somatic and vicarious pain are represented by dissociable multivariate brain patterns. eLife, 5, e15166.CrossRefGoogle ScholarPubMed
Kucyi, A., & Davis, K. D. (2015). The dynamic pain connectome. Trends in Neurosciences, 38, 86–95.CrossRefGoogle ScholarPubMed
Kucyi, A., Salomons, T. V., & Davis, K. D. (2013). Mind wandering away from pain dynamically engages antinociceptive and default mode brain networks. Proceedings of the National Academy of Sciences of the United States of America, 110, 18692–18697.Google ScholarPubMed
Lee, J., Mawla, I., Kim, J., Loggia, M. L., Ortiz, A., Jung, C., … Napadow, V. (2019). Machine learning-based prediction of clinical pain using multimodal neuroimaging and autonomic metrics. Pain, 160, 550–560.CrossRefGoogle ScholarPubMed
Lee, J. J., Kim, H. J., Ceko, M., Park, B. Y., Lee, S. A., Park, H., … Woo, C. W. (2021). A neuroimaging biomarker for sustained experimental and clinical pain. Nature Medicine, 27, 174–182.CrossRefGoogle ScholarPubMed
Lee, J. J., Lee, S., Lee, D. H., & Woo, C. W. (2022). Functional brain reconfiguration during sustained pain. eLife, 11, e74463.CrossRefGoogle ScholarPubMed
Lee, S. W., & Seymour, B. (2019). Decision-making in brains and robots – the case for an interdisciplinary approach. Current Opinion in Behavioral Sciences, 26, 137–145.CrossRefGoogle Scholar
Leknes, S., Berna, C., Lee, M. C., Snyder, G. D., Biele, G., & Tracey, I. (2013). The importance of context: When relative relief renders pain pleasant. Pain, 154, 402–410.CrossRefGoogle ScholarPubMed
Lim, M., O’Grady, C., Cane, D., Goyal, A., Lynch, M., Beyea, S., & Hashmi, J. A. (2020). Threat prediction from schemas as a source of bias in pain perception. Journal of Neuroscience, 40, 1538–1548.CrossRefGoogle ScholarPubMed
Loeser, J. D., & Treede, R. D. (2008). The Kyoto protocol of IASP basic pain terminology. Pain, 137, 473–477.CrossRefGoogle ScholarPubMed
Lopez-Sola, M., Koban, L., & Wager, T. D. (2018). Transforming pain with prosocial meaning: A functional magnetic resonance imaging study. Psychosomatic Medicine, 80, 814–825.CrossRefGoogle ScholarPubMed
Lopez-Sola, M., Woo, C. W., Pujol, J., Deus, J., Harrison, B. J., Monfort, J., & Wager, T. D. (2017). Towards a neurophysiological signature for fibromyalgia. Pain, 158, 34–47.CrossRefGoogle ScholarPubMed
Ma, Q. (2022). A functional subdivision within the somatosensory system and its implications for pain research. Neuron, 110, 749–769.CrossRefGoogle ScholarPubMed
Mano, H., Kotecha, G., Leibnitz, K., Matsubara, T., Sprenger, C., Nakae, A., … Seymour, B. (2018). Classification and characterisation of brain network changes in chronic back pain: A multicenter study. Wellcome Open Research, 3, 19.CrossRefGoogle ScholarPubMed
Mano, H., & Seymour, B. (2015). Pain: A distributed brain information network? PLoS Biology, 13, e1002037.CrossRefGoogle Scholar
Marshall, J. (1951). Sensory disturbances in cortical wounds with special reference to pain. Journal of Neurology, Neurosurgery, and Psychiatry, 14, 187.CrossRefGoogle ScholarPubMed
Matthewson, G. M., Woo, C. W., Reddan, M. C., & Wager, T. D. (2019). Cognitive self-regulation influences pain-related physiology. Pain, 160, 2338–2349.CrossRefGoogle ScholarPubMed
May, E. S., Nickel, M. M., Ta Dinh, S., Tiemann, L., Heitmann, H., Voth, I., … Ploner, M. (2019). Prefrontal gamma oscillations reflect ongoing pain intensity in chronic back pain patients. Human Brain Mapping, 40, 293–305.CrossRefGoogle ScholarPubMed
Mayr, A., Jahn, P., Deak, B., Stankewitz, A., Devulapally, V., Witkovsky, V., … Schulz, E. (2022). Individually unique dynamics of cortical connectivity reflect the ongoing intensity of chronic pain. Pain, 163, 1987–1998.CrossRefGoogle ScholarPubMed
Mayr, A., Jahn, P., Stankewitz, A., Deak, B., Winkler, A., Witkovsky, V., … Schulz, E. (2022). Patients with chronic pain exhibit individually unique cortical signatures of pain encoding. Human Brain Mapping, 43, 1676–1693.CrossRefGoogle ScholarPubMed
Mazzola, L., Isnard, J., Peyron, R., & Mauguiere, F. (2012). Stimulation of the human cortex and the experience of pain: Wilder Penfield’s observations revisited. Brain, 135, 631–640.CrossRefGoogle ScholarPubMed
Melzack, R. (1990). Phantom limbs and the concept of a neuromatrix. Trends in Neurosciences, 13, 88–92.CrossRefGoogle ScholarPubMed
Melzack, R. (1993). Pain: Past, present and future. Canadian Journal of Experimental Psychology, 47, 615–629.CrossRefGoogle ScholarPubMed
Melzack, R., & Loeser, J. D. (1978). Phantom body pain in paraplegics: Evidence for a central “pattern generating mechanism” for pain. Pain, 4, 195–210.Google ScholarPubMed
Melzack, R., & Wall, P. D. (1962). On the nature of cutaneous sensory mechanisms. Brain, 85, 331–356.CrossRefGoogle ScholarPubMed
Melzack, R., & Wall, P. D. (1965). Pain mechanisms: A new theory. Science, 150, 971–979.CrossRefGoogle ScholarPubMed
Misra, G., Wang, W. E., Archer, D. B., Roy, A., & Coombes, S. A. (2017). Automated classification of pain perception using high-density electroencephalography data. Journal of Neurophysiology, 117, 786–795.CrossRefGoogle ScholarPubMed
Moayedi, M., & Davis, K. D. (2013). Theories of pain: From specificity to gate control. Journal of Neurophysiology, 109, 5–12.CrossRefGoogle ScholarPubMed
Moseley, G. L., & Arntz, A. (2007). The context of a noxious stimulus affects the pain it evokes. Pain, 133, 64–71.CrossRefGoogle ScholarPubMed
Moseley, G. L., & Vlaeyen, J. W. S. (2015). Beyond nociception: The imprecision hypothesis of chronic pain. Pain, 156, 35–38.CrossRefGoogle ScholarPubMed
Mouraux, A., Diukova, A., Lee, M. C., Wise, R. G., & Iannetti, G. D. (2011). A multisensory investigation of the functional significance of the “pain matrix.” NeuroImage, 54, 2237–2249.CrossRefGoogle ScholarPubMed
Mouraux, A., & Iannetti, G. D. (2009). Nociceptive laser-evoked brain potentials do not reflect nociceptive-specific neural activity. Journal of Neurophysiology, 101, 3258–3269.CrossRefGoogle Scholar
Mouraux, A., & Iannetti, G. D. (2018). The search for pain biomarkers in the human brain. Brain, 141, 3290–3307.CrossRefGoogle ScholarPubMed
Nummenmaa, L., Tuominen, L., Dunbar, R., Hirvonen, J., Manninen, S., Arponen, E., … Sams, M. (2016). Social touch modulates endogenous mu-opioid system activity in humans. NeuroImage, 138, 242–247.CrossRefGoogle ScholarPubMed
Palermo, S., Benedetti, F., Costa, T., & Amanzio, M. (2015). Pain anticipation: An activation likelihood estimation meta-analysis of brain imaging studies. Human Brain Mapping, 36, 1648–1661.CrossRefGoogle ScholarPubMed
Penfield, W., & Boldrey, E. (1937). Somatic motor and sensory representation in the cerebral cortex of man as studied by electrical stimulation. Brain, 60, 389–443.CrossRefGoogle Scholar
Perl, E. R. (2011). Pain mechanisms: A commentary on concepts and issues. Progress in Neurobiology, 94, 20–38.CrossRefGoogle ScholarPubMed
Ploner, M., Lee, M. C., Wiech, K., Bingel, U., & Tracey, I. (2011). Flexible cerebral connectivity patterns subserve contextual modulations of pain. Cerebral Cortex, 21, 719–726.CrossRefGoogle ScholarPubMed
Price, D. D., Greenspan, J. D., & Dubner, R. (2003). Neurons involved in the exteroceptive function of pain. Pain, 106, 215–219.CrossRefGoogle ScholarPubMed
Qi, S., Hassabis, D., Sun, J., Guo, F., Daw, N., & Mobbs, D. (2018). How cognitive and reactive fear circuits optimize escape decisions in humans. Proceedings of the National Academy of Sciences of the United States of America, 115, 3186–3191.Google ScholarPubMed
Rainville, P., Duncan, G. H., Price, D. D., Carrier, B., & Bushnell, M. C. (1997). Pain affect encoded in human anterior cingulate but not somatosensory cortex. Science, 277, 968–971.CrossRefGoogle Scholar
Raja, S. N., Carr, D. B., Cohen, M., Finnerup, N. B., Flor, H., Gibson, S., … Vader, K. (2020). The revised international association for the study of pain definition of pain: Concepts, challenges, and compromises. Pain, 161, 1976–1982.CrossRefGoogle Scholar
Reddan, M. C., & Wager, T. D. (2019). Brain systems at the intersection of chronic pain and self-regulation. Neuroscience Letters, 702, 24–33.CrossRefGoogle ScholarPubMed
Reddan, M. C., Young, H., Falkner, J., Lopez-Sola, M., & Wager, T. D. (2020). Touch and social support influence interpersonal synchrony and pain. Social Cognitive and Affective Neuroscience, 15, 1064–1075.CrossRefGoogle ScholarPubMed
Romero, Y. R., Straube, T., Nitsch, A., Miltner, W. H. R., & Weiss, T. (2013). Interaction between stimulus intensity and perceptual load in the attentional control of pain. Pain, 154, 135–140.CrossRefGoogle ScholarPubMed
Roy, M., Lebuis, A., Peretz, I., & Rainville, P. (2011). The modulation of pain by attention and emotion: A dissociation of perceptual and spinal nociceptive processes. European Journal of Pain, 15, 641.e1–641.e10.Google ScholarPubMed
Roy, M., Piche, M., Chen, J. I., Peretz, I., & Rainville, P. (2009). Cerebral and spinal modulation of pain by emotions. Proceedings of the National Academy of Sciences of the United States of America, 106, 20900–20905.Google ScholarPubMed
Roy, M., Shohamy, D., Daw, N., Jepma, M., Wimmer, G. E., & Wager, T. D. (2014). Representation of aversive prediction errors in the human periaqueductal gray. Nature Neuroscience, 17, 1607–1612.CrossRefGoogle ScholarPubMed
Sajid, N., Ball, P. J., Parr, T., & Friston, K. J. (2021). Active inference: Demystified and compared. Neural Computation, 33, 674–712.CrossRefGoogle ScholarPubMed
Salomons, T. V., Iannetti, G. D., Liang, M., & Wood, J. N. (2016). The “pain matrix” in pain-free individuals. JAMA Neurology, 73, 755–756.CrossRefGoogle Scholar
Schrimpf, M., Blank, I. A., Tuckute, G., Kauf, C., Hosseini, E. A., Kanwisher, N., … Fedorenko, E. (2021). The neural architecture of language: Integrative modeling converges on predictive processing. Proceedings of the National Academy of Sciences of the United States of America, 118, e2105646118.Google ScholarPubMed
Schulz, M. A., Yeo, B. T. T., Vogelstein, J. T., Mourao-Miranada, J., Kather, J. N., Kording, K., … Bzdok, D. (2020). Different scaling of linear models and deep learning in UKBiobank brain images versus machine-learning datasets. Nature Communications, 11, 4238.CrossRefGoogle ScholarPubMed
Schwartz, N., Miller, C., & Fields, H. L. (2017). Cortico-accumbens regulation of approach-avoidance behavior is modified by experience and chronic pain. Cell Reports, 19, 1522–1531.CrossRefGoogle ScholarPubMed
Seth, A. K., & Tsakiris, M. (2018). Being a beast machine: The somatic basis of selfhood. Trends in Cognitive Sciences, 22, 969–981.CrossRefGoogle ScholarPubMed
Seymour, B. (2019). Pain: A precision signal for reinforcement learning and control. Neuron, 101, 1029–1041.CrossRefGoogle Scholar
Seymour, B., Daw, N. D., Roiser, J. P., Dayan, P., & Dolan, R. (2012). Serotonin selectively modulates reward value in human decision-making. Journal of Neuroscience, 32, 5833–5842.CrossRefGoogle ScholarPubMed
Seymour, B., & Mancini, F. (2020). Hierarchical models of pain: Inference, information-seeking, and adaptive control. NeuroImage, 222, 117212.CrossRefGoogle ScholarPubMed
Sherrington, C. S. (1906). The integrative action of the nervous system. A. Constable.Google Scholar
Shirvalkar, P., Prosky, J., Chin, G., Ahmadipour, P., Sani, O. G., Desai, M., … Chang, E. F. (2023). First-in-human prediction of chronic pain state using intracranial neural biomarkers. Nature Neuroscience, 26, 1090–1099.CrossRefGoogle ScholarPubMed
Silver, D., Hubert, T., Schrittwieser, J., Antonoglou, I., Lai, M., Guez, A., … Hassabis, D. (2018). A general reinforcement learning algorithm that masters chess, shogi, and Go through self-play. Science, 362, 1140–1144.CrossRefGoogle ScholarPubMed
Singer, T., Seymour, B., O’Doherty, J., Kaube, H., Dolan, R. J., & Frith, C. D. (2004). Empathy for pain involves the affective but not sensory components of pain. Science, 303, 1157–1162.CrossRefGoogle Scholar
Starr, C. J., Sawaki, L., Wittenberg, G. F., Burdette, J. H., Oshiro, Y., Quevedo, A. S., & Coghill, R. C. (2009). Roles of the insular cortex in the modulation of pain: Insights from brain lesions. Journal of Neuroscience, 29, 2684–2694.CrossRefGoogle ScholarPubMed
Ta Dinh, S., Nickel, M. M., Tiemann, L., May, E. S., Heitmann, H., Hohn, V. D., … Ploner, M. (2019). Brain dysfunction in chronic pain patients assessed by resting-state electroencephalography. Pain, 160, 2751–2765.CrossRefGoogle ScholarPubMed
Tabor, A., & Burr, C. (2019). Bayesian learning models of pain: A call to action. Current Opinion in Behavioral Sciences, 26, 54–61.CrossRefGoogle Scholar
Tabor, A., Thacker, M. A., Moseley, G. L., & Kording, K. P. (2017). Pain: A statistical account. PLoS Computational Biology, 13, e1005142.CrossRefGoogle Scholar
Tetreault, P., Mansour, A., Vachon-Presseau, E., Schnitzer, T. J., Apkarian, A. V., & Baliki, M. N. (2016). Brain connectivity predicts placebo response across chronic pain clinical trials. PLoS Biology, 14, e1002570.CrossRefGoogle ScholarPubMed
Timmers, I., Park, A. L., Fischer, M. D., Kronman, C. A., Heathcote, L. C., Hernandez, J. M., & Simons, L. E. (2018). Is empathy for pain unique in its neural correlates? A meta-analysis of neuroimaging studies of empathy. Frontiers in Behavioral Neuroscience, 12, 289.CrossRefGoogle ScholarPubMed
Torta, D. M., Legrain, V., Mouraux, A., & Valentini, E. (2017). Attention to pain! A neurocognitive perspective on attentional modulation of pain in neuroimaging studies. Cortex, 89, 120–134.CrossRefGoogle Scholar
Toye, F., Seers, K., Hannink, E., & Barker, K. (2017). A mega-ethnography of eleven qualitative evidence syntheses exploring the experience of living with chronic non-malignant pain. BMC Med Research Methodology, 17, 116.CrossRefGoogle ScholarPubMed
Tracey, I. (2021). Neuroimaging enters the pain biomarker arena. Science Translational Medicine, 13, eabj7358.CrossRefGoogle ScholarPubMed
Tracey, I., & Mantyh, P. W. (2007). The cerebral signature for pain perception and its modulation. Neuron, 55, 377–391.CrossRefGoogle ScholarPubMed
Vachon-Presseau, E., Berger, S. E., Abdullah, T. B., Griffith, J. W., Schnitzer, T. J., & Apkarian, A. V. (2019). Identification of traits and functional connectivity-based neurotraits of chronic pain. PLoS Biology, 17, e3000349.CrossRefGoogle ScholarPubMed
Vachon-Presseau, E., Berger, S. E., Abdullah, T. B., Huang, L., Cecchi, G. A., Griffith, J. W., … Apkarian, A. V. (2018). Brain and psychological determinants of placebo pill response in chronic pain patients. Nature Communications, 9, 3397.CrossRefGoogle ScholarPubMed
Vachon-Presseau, E., Tetreault, P., Petre, B., Huang, L., Berger, S. E., Torbey, S., … Apkarian, A. V. (2016). Corticolimbic anatomical characteristics predetermine risk for chronic pain. Brain, 139, 1958–1970.CrossRefGoogle ScholarPubMed
Valentini, E., Shindy, A., Witkovsky, V., Stankewitz, A., & Schulz, E. (2023). Interindividual variability and individual stability of pain- and touch-related neuronal gamma oscillations. Journal of Neurophysiology, 129, 1400–1413.CrossRefGoogle ScholarPubMed
Vanneste, S., Song, J. J., & De Ridder, D. (2018). Thalamocortical dysrhythmia detected by machine learning. Nature Communications, 9, 1103.CrossRefGoogle ScholarPubMed
Vierck, C. J., Whitsel, B. L., Favorov, O. V., Brown, A. W., & Tommerdahl, M. (2013). Role of primary somatosensory cortex in the coding of pain. Pain, 154, 334–344.CrossRefGoogle ScholarPubMed
Vijayakumar, V., Case, M., Shirinpour, S., & He, B. (2017). Quantifying and characterizing tonic thermal pain across subjects from EEG data using random forest models. IEEE Transactions of Biomedical Engineering, 64, 2988–2996.CrossRefGoogle ScholarPubMed
Villemure, C., & Bushnell, M. C. (2009). Mood influences supraspinal pain processing separately from attention. Journal of Neuroscience, 29, 705–715.CrossRefGoogle ScholarPubMed
Vlaeyen, J. W. S., & Linton, S. J. (2012). Fear-avoidance model of chronic musculoskeletal pain: 12 years on. Pain, 153, 1144–1147.CrossRefGoogle Scholar
Wager, T. D., & Atlas, L. Y. (2015). The neuroscience of placebo effects: Connecting context, learning and health. Nature Reviews Neuroscience, 16, 403–418.CrossRefGoogle ScholarPubMed
Wager, T. D., Atlas, L. Y., Leotti, L. A., & Rilling, J. K. (2011). Predicting individual differences in placebo analgesia: Contributions of brain activity during anticipation and pain experience. Journal of Neuroscience, 31, 439–452.CrossRefGoogle ScholarPubMed
Wager, T. D., Atlas, L. Y., Lindquist, M. A., Roy, M., Woo, C. W., & Kross, E. (2013). An fMRI-based neurologic signature of physical pain. New England Journal of Medicine, 368, 1388–1397.CrossRefGoogle ScholarPubMed
Wager, T. D., Rilling, J. K., Smith, E. E., Sokolik, A., Casey, K. L., Davidson, R. J., … Cohen, J. D. (2004). Placebo-induced changes in FMRI in the anticipation and experience of pain. Science, 303, 1162–1167.CrossRefGoogle ScholarPubMed
Whittington, J. C. R., Muller, T. H., Mark, S., Chen, G., Barry, C., Burgess, N., & Behrens, T. E. J. (2020). The Tolman-Eichenbaum machine: Unifying space and relational memory through generalization in the hippocampal formation. Cell, 183, 1249–1263.e23.CrossRefGoogle ScholarPubMed
Winston, J. S., Vlaev, I., Seymour, B., Chater, N., & Dolan, R. J. (2014). Relative valuation of pain in human orbitofrontal cortex. Journal of Neuroscience, 34, 14526–14535.CrossRefGoogle ScholarPubMed
Woo, C. W., Chang, L. J., Lindquist, M. A., & Wager, T. D. (2017). Building better biomarkers: Brain models in translational neuroimaging. Nature Neuroscience, 20, 365–377.CrossRefGoogle ScholarPubMed
Woo, C. W., Roy, M., Buhle, J. T., & Wager, T. D. (2015). Distinct brain systems mediate the effects of nociceptive input and self-regulation on pain. PLoS Biology, 13, e1002036.CrossRefGoogle ScholarPubMed
Woo, C. W., Schmidt, L., Krishnan, A., Jepma, M., Roy, M., Lindquist, M. A., … Wager, T. D. (2017). Quantifying cerebral contributions to pain beyond nociception. Nature Communications, 8, 14211.CrossRefGoogle ScholarPubMed
Xu, A., Larsen, B., Baller, E. B., Scott, J. C., Sharma, V., Adebimpe, A., … Satterthwaite, T. D. (2020). Convergent neural representations of experimentally-induced acute pain in healthy volunteers: A large-scale fMRI meta-analysis. Neuroscience & Biobehavioral Reviews, 112, 300–323.CrossRefGoogle ScholarPubMed
Yekkirala, A. S., Roberson, D. P., Bean, B. P., & Woolf, C. J. (2017). Breaking barriers to novel analgesic drug development. Nature Reviews Drug Discovery, 16, 545–564.Google ScholarPubMed
Yoshida, W., Seymour, B., Koltzenburg, M., & Dolan, R. J. (2013). Uncertainty increases pain: Evidence for a novel mechanism of pain modulation involving the periaqueductal gray. Journal of Neuroscience, 33, 5638–5646.CrossRefGoogle ScholarPubMed
Younger, J., Aron, A., Parke, S., Chatterjee, N., & Mackey, S. (2010). Viewing pictures of a romantic partner reduces experimental pain: Involvement of neural reward systems. PLoS ONE, 5, e13309.CrossRefGoogle ScholarPubMed
Zebhauser, P. T., Hohn, V. D., & Ploner, M. (2023). Resting-state electroencephalography and magnetoencephalography as biomarkers of chronic pain: A systematic review. Pain, 164, 1200–1221.CrossRefGoogle ScholarPubMed
Zhang, S., Mano, H., Ganesh, G., Robbins, T., & Seymour, B. (2016). Dissociable learning processes underlie human pain conditioning. Current Biology, 26, 52–58.CrossRefGoogle ScholarPubMed
Zhang, S., Mano, H., Lee, M., Yoshida, W., Kawato, M., Robbins, T. W., & Seymour, B. (2018). The control of tonic pain by active relief learning. eLife, 7, e31949.CrossRefGoogle ScholarPubMed
Zheng, W., Woo, C. W., Yao, Z., Goldstein, P., Atlas, L. Y., Roy, M., … Wager, T. D. (2020). Pain-evoked reorganization in functional brain networks. Cerebral Cortex, 30, 2804–2822.CrossRefGoogle ScholarPubMed
Zunhammer, M., Bingel, U., Wager, T. D., & Placebo Imaging Consortium (2018). Placebo effects on the neurologic pain signature: A meta-analysis of individual participant functional magnetic resonance imaging data. JAMA Neurology, 75, 1321–1330.CrossRefGoogle ScholarPubMed
Accessibility standard: WCAG 2.0 A
The PDF of this book
conforms to version 2.0 of the
Web Content Accessibility Guidelines
(WCAG), ensuring core accessibility principles are addressed and meets the basic
(A)
level of WCAG compliance, addressing essential accessibility barriers.
Content Navigation
Table of contents navigation
Allows you to navigate directly to chapters, sections, or non‐text items through a linked table of contents, reducing the need for extensive scrolling.
Allows you to navigate directly to chapters, sections, or non‐text items through a linked table of contents, reducing the need for extensive scrolling.
Index navigation
Provides an interactive index, letting you go straight to where a term or subject appears in the text without manual searching.
Provides an interactive index, letting you go straight to where a term or subject appears in the text without manual searching.
Reading Order & Textual Equivalents
Single logical reading order
You will encounter all content (including footnotes, captions, etc.) in a clear, sequential flow, making it easier to follow with assistive tools like screen readers.
You will encounter all content (including footnotes, captions, etc.) in a clear, sequential flow, making it easier to follow with assistive tools like screen readers.
Short alternative textual descriptions
You get concise descriptions (for images, charts, or media clips), ensuring you do not miss crucial information when visual or audio elements are not accessible.
You get concise descriptions (for images, charts, or media clips), ensuring you do not miss crucial information when visual or audio elements are not accessible.
Full alternative textual descriptions
You get more than just short alt text: you have comprehensive text equivalents, transcripts, captions, or audio descriptions for substantial non‐text content, which is especially helpful for complex visuals or multimedia.
You get more than just short alt text: you have comprehensive text equivalents, transcripts, captions, or audio descriptions for substantial non‐text content, which is especially helpful for complex visuals or multimedia.
Visual Accessibility
Use of high contrast between text and background colour
You benefit from high‐contrast text, which improves legibility if you have low vision or if you are reading in less‐than‐ideal lighting conditions.
You benefit from high‐contrast text, which improves legibility if you have low vision or if you are reading in less‐than‐ideal lighting conditions.
Structural and Technical Features
ARIA roles provided
You gain clarity from ARIA (Accessible Rich Internet Applications) roles and attributes, as they help assistive technologies interpret how each part of the content functions.
You gain clarity from ARIA (Accessible Rich Internet Applications) roles and attributes, as they help assistive technologies interpret how each part of the content functions.