Project Outline

Pain is affecting 11–40% of the population worldwide and costs the EU more than €500 billion annually. Therefore, understanding how we respond to pain both when experienced first-hand and when observed in others (i.e., empathy) is vital for society. However, pain is highly subjective, and the perception of pain depends heavily on our prior expectation. For example, previous experiences of heat burn can certainly affect how we may understand pain in ourselves and show empathy for the pain of another person experiencing heat burn. There is still a lack of formal neurocomputational mechanisms of first-hand pain and empathy pain. The Bayesian predictive processing theory has been proposed to understand how the perception of pain is modulated by prior pain expectation and pain input. To date, however, only few studies have examined the neural implementation underlying such Bayesian integration. Furthermore, no studies have investigated whether the Bayesian integration process in first-hand pain also underpins empathy for pain.

Objectives and Methods

To close these gaps, our project will provide a systematic investigation into the neurocomputational mechanisms of how prior expectations (i.e., how much pain is expected) and noxious information (i.e., how much pain is received) are integrated for both first-hand pain and empathy for pain. We hypothesize that the variation in the degree of pain perception in oneself and others is associated with the differences in the degree of Bayesian integration between prior expectations and noxious input, modulated by the source of how prior expectation is formed. Methods will include novel experimental paradigm development, latest non-invasive neuroimaging techniques (brain and spine fMRI; uni- and multi-variate analyses), together with state-of-the-art Hierarchical Bayesian Modelling.

Training and Outcomes

This project brings together supervisory expertise in social learning and decision-making and Bayesian modelling (Dr. Zhang), motivation and cognitive control (Dr. Apps), and brain-spine fMRI imaging (Dr. Khatibi). The student will be primarily embedded in the Adaptive Learning Psychology & Neuroscience (ALPN) Lab (head: Dr. Zhang), that addresses the fundamental question of the “adaptive social brain” by studying the cognitive, computational, and neurobiological basis of social learning and decision-making in healthy individuals, and in psychiatric disorders. Students will get training in methods from psychology, neuroimaging and computational science, allowing them to develop a wide range of skills. Students will be given the independence to address their own questions of interest under the realm of the project.

Impact

This project employs rigorous approaches to address original research questions about the underlying neurocomputational mechanisms of Bayesian integration in first-hand pain and empathy for pain, which moves beyond previous verbal models that lack formal quantification. This will thus help us gain a better understanding of how we process pain in oneself, how we share and understand pain in others, and thus how we manage better to avoid harm to ourselves as well as others. All these will eventually enable us to develop better individualized health care for people with pain and similar chronic conditions.

Interested candidates are strongly encouraged to get in contact ( ) for informal discussion about projects before submitting an application.

References

Cohen-Adad, J., Alonso-Ortiz, E., Abramovic, M., Arneitz, C., Atcheson, N., Barlow, L., ... & Xu, J. (2021). Generic acquisition protocol for quantitative MRI of the spinal cord. Nature protocols, 16(10), 4611-4632. https://www.nature.com/articles/s41596-021-00588-0

Lockwood, P. L., Apps, M. A., Roiser, J. P., & Viding, E. (2015). Encoding of vicarious reward prediction in anterior cingulate cortex and relationship with trait empathy. Journal of neuroscience, 35(40), 13720-13727. https://www.jneurosci.org/content/35/40/13720

Zhang, L., & Gläscher, J. (2020). A brain network supporting social influences in human decision-making. Science advances, 6(34), eabb4159. https://www.science.org/doi/full/10.1126/sciadv.abb4159

Zhang, L., Lengersdorff, L., Mikus, N., Gläscher, J., & Lamm, C. (2020). Using reinforcement learning models in social neuroscience: frameworks, pitfalls and suggestions of best practices. Social Cognitive and Affective Neuroscience, 15(6), 695-707. https://academic.oup.com/scan/article-abstract/15/6/695/5864690

Zhao, Y., Zhang, L., Rütgen, M., Sladky, R., & Lamm, C. (2021). Neural dynamics between anterior insular cortex and right supramarginal gyrus dissociate genuine affect sharing from perceptual saliency of pretended pain. Elife, 10, e69994. https://elifesciences.org/articles/69994

Techniques

Depending on the aims of the project, the interests of the student, and how their independent research develops, there are multiple techniques that can be learnt in these projects, including:

• Paradigm/task development.
• Behavioural testing with humans.
• Statistical analysis of behavioural data.
• Functional magnetic resonance imaging (fMRI) of the Brain.
• Computational Modelling.
• Programming (Matlab/R/Python).
• Hierarchical Bayesian modelling.
• High performance computing.
• Patient testing.
• Additional opportunities for learning of cutting-edge cognitive neuroscience techniques with collaborators at University of Birmingham, University of Hamburg, and University of Vienna.