Managing Pain

From Cognitive Sciences to Clinical Pactice. The mechanisms underlying professional pain management. The ability of appropriately diagnosing others’ pain is critical in social communities, and it is a cornerstone for an efficient health care system. Differently from most medical conditions, which are diagnosed on the basis of reliable biomarkers or radiological imaging, pain is an experience that it is difficult to quantify objectively. Consequently, it is often underestimated and undertreated, even in specialized emergency departments. The main goal of this project is to exploit results from fundamental research in social/cognitive psychology and neuroscience, to identify the processes explaining how healthcare providers diagnose and treat pain in hospital environments. Our long-term ambition is to apply our findings to develop of new educational protocols, grounded on solid psychological and neuroscience research, focused at improving pain management in everyday life.

Automatic diagnosis of Pain

Dirupo et al., 2020, IEEE Transactions on Affective ComputingPain inadequate treatment is frequent in modern society, with major medical, ethical, and financial implications. In many healthcare environments, pain is quantified prevalently through subjective measures, such as self-reports from patients or health care providers’ personal experience. Recently, automatic diagnostic tools have been developed to detect and quantify pain more “objectively” from facial expressions. However, it is still unclear if these approaches can distinguish pain from other aversive (but painless) states. In the present study, we analyzed the facial responses from a database of video-recorded facial reactions evoked by comparably-unpleasant painful and disgusting stimuli. We modeled this information as function of subjective unpleasantness, as well as the specific state evoked by the stimuli (pain vs. disgust). Results show that a machine learning algorithm could predict subjective pain unpleasantness from facial information, but mistakenly detected unpleasant disgust, especially in those models relying in great extent on the brow lowerer. Importantly, pain and disgust could be disentangled using an ad hoc algorithm that rely on combined information from the eyes and the mouth. Overall, the facial expression of pain contains both specific and unpleasantness-related information shared with disgust. Automatic diagnostic tools should be guided to account for this confounding effect.

Effect of Medical Education in Pain Diagnosis

Dirupo et al., 2021, eLife Sciences. Healthcare providers often underestimate patients’ pain, sometimes even when aware of their reports. This could be the effect of experience reducing sensitivity to others pain, or distrust towards patients’ self-evaluations. Across multiple experiments (375 participants), we tested whether senior medical students differed from younger colleagues and lay controls in the way they assess people’s pain and take into consideration their feedback. We found that medical training affected the sensitivity to pain faces, an effect shown by the lower ratings and highlighted by a decrease in neural response of the insula and cingulate cortex. Instead, distrust towards the expressions’ authenticity affected the processing of feedbacks, by decreasing activity in the ventral striatum whenever patients’ self-reports matched participants’ evaluations, and by promoting strong reliance on the opinion of other doctors. Overall, our study underscores the multiple processes which might influence the evaluation of others’ pain at the early stages of medical career.

Clinical Decision-Making

Corradi-Dell'Acqua et al., 2019, BJAPain undertreatment, or oligoanalgesia, is frequent in the emergency department (ED), with major medical, ethical, and financial implications. Across different hospitals, healthcare providers have been reported to differ considerably in the ways in which they recognize and manage pain, with some prescribing analgesics far less frequently than others. However, factors that could explain this variability remain poorly understood. Here, we employed neuroscience approaches for neural signal modelling to investigate whether individual decisions in the ED could be explained in terms of brain patterns related to empathy, risk-taking, and error monitoring. For fifteen months, we monitored the pain management behaviour of ED nurses at triage, and subsequently invited them to a neuroimaging study involving three well-established tasks probing relevant cognitive and affective dimensions. Univariate and multivariate regressions were used to predict pain management decisions from neural activity during these tasks. We found that the brain signal recorded when empathizing with others predicted the frequency with which nurses documented pain in their patients. In addition, neural activity sensitive to errors and negative outcomes predicted the frequency with which nurses denied analgesia by registering potential side effects. These results highlight the multiple processes underlying pain management, and suggest that the neural representations of others' states and one's errors play a key role in individual treatment decisions. Neuroscience models of social cognition and decision-making are a powerful tool to explain clinical behaviour and might be used to guide future educational programs to improve pain management in ED.


Dirupo, G., Totaro, S., Richard, J., & Corradi-Dell'Acqua, C. (2021). Medical education and distrust modulate the response of insular-cingulate network and ventral striatum in pain diagnosis. eLife Sciences, 10, e63272 doi: 10.7554/eLife.63272 internet_earth.png application-pdf.png  

Dirupo G., Garlasco P., Chappuis C., Sharvit G., & Corradi-Dell'Acqua C. (2020) State-specific and supraordinal components of facial response to pain. IEEE Transactions of Affective Computing. doi: 10.1109/TAFFC.2020.2965105 internet_earth.png application-pdf.png 

Corradi-Dell'Acqua C., Foester M., Sharvit G., Trueb L., Foucault E., Fournier Y., Vuilleumier P., & Hugli O. (2019) Pain management decisions in emergency hospitals are predicted by brain activity during empathy and error monitoring. British Journal of Anaesthesia, 123, e284-e292. doi: 10.1016/j.bja.2019.01.039 application-pdf.png internet_earth.png - Supplementary Information: application-pdf.png