Summary: Facial expressions triggered by pain are deeply tied to brain activity, revealing distinct neural mechanisms compared to verbal pain reports. Researchers developed a neurobiological model, using MRI and machine learning, to predict pain-induced facial expressions.
These findings highlight the potential of facial expressions as an objective tool for assessing pain. This research could pave the way for improved pain management, especially in non-verbal or chronic pain patients.
Key Facts:
- Pain Facial Signature: A machine-learning model predicts pain-induced facial expressions based on brain activity.
- Distinct Neural Pathways: The brain mechanisms for pain-related facial expressions differ from those for verbal pain reports.
- Clinical Potential: Facial expression analysis could enhance pain assessments in patients unable to verbalize their discomfort.
Source: University of Montreal
Stubbing your toe on a table leg or fracturing your wrist will probably make you wince in pain (and possibly curse). It’s a natural reaction; facial expressions play an important role in communicating the unpleasant sensory and emotional experience of pain. Among other things, they signal to others that we are hurt and may need help.
The neural processes associated with this form of nonverbal expression have received little attention although they are known to play an important role in the experience of pain. Marie-Eve Picard, a doctoral student in the laboratory of Pierre Rainville, a professor in the Faculty of Dentistry at Université de Montréal and a researcher at the Montreal University Institute of Geriatrics Research Centre, decided to investigate.
In a new study, Picard and Rainville show that facial expressions triggered by painful stimuli can be predicted from brain activity.
Their findings reveal that the neural mechanisms underlying these expressions are largely distinct from those associated with other manifestations of pain, such as subjective verbal reports of perceived intensity.
Analyzing facial muscles
Picard and her colleagues developed a neurobiological model that predicts facial expressions elicited by painful stimuli. Using machine-learning algorithms trained on magnetic resonance brain imaging data, they created a Facial Expression Pain Signature.
Healthy volunteers underwent painful thermal stimulation and their facial expressions were measured using the Facial Action Coding System, a standardized tool that analyzes facial movements based on the activity of several groups of facial muscles.
Activation of each muscle group causes a specific change in facial expression. For example, pain-related expressions often include furrowed brows, elevated cheeks, squinting, wrinkled nose and raised upper lip.
Towards more precise assessments
In clinical settings, accurately assessing a patient’s pain is important for appropriate pain management.
“The importance of facial expression in pain assessment receives less attention than the role it plays in social interactions,” said Picard.
“However, our results suggest that this behavioural indicator of pain can be a valuable complement to verbal reports of perceived intensity.”
The study was informed by an understanding of pain as multidimensional, meaning that considering its various manifestations can improve assessments of its severity.
Picard’s work shows the existence of brain signatures, or patterns of brain activity, that are predictive of pain-related facial responses. While these results advance our understanding of the brain mechanisms behind pain and nonverbal communication, further research will be needed to test their generalizability and determine their applicability to conditions such as chronic pain.
About this pain and neuroscience research news
Author: Béatrice St-Cyr-Leroux
Source: University of Montreal
Contact: Béatrice St-Cyr-Leroux – University of Montreal
Image: The image is credited to Neuroscience News
Original Research: Open access.
“A distributed brain response predicting the facial expression of acute nociceptive pain” by Marie-Eve Picard et al. eLife
Abstract
A distributed brain response predicting the facial expression of acute nociceptive pain
Pain is a private experience observable through various verbal and non-verbal behavioural manifestations, each of which may relate to different pain-related functions.
Despite the importance of understanding the cerebral mechanisms underlying those manifestations, there is currently limited knowledge on the neural correlates of the facial expression of pain.
In this functional magnetic resonance imaging (fMRI) study, noxious heat stimulation was applied in healthy volunteers and we tested if previously published brain signatures of pain were sensitive to pain expression.
We then applied a multivariate pattern analysis to the fMRI data to predict the facial expression of pain. Results revealed the inability of previously developed pain neurosignatures to predict the facial expression of pain.
We thus propose a Facial Expression of Pain Signature (FEPS) conveying distinctive information about the brain response to nociceptive stimulations with minimal or no overlap with other pain-relevant brain signatures associated with nociception, pain ratings, thermal pain aversiveness, or pain valuation.
The FEPS may provide a distinctive functional characterization of the distributed cerebral response to nociceptive pain associated with the socio-communicative role of non-verbal pain expression.
This underscores the complexity of pain phenomenology by reinforcing the view that neurosignatures conceived as biomarkers must be interpreted in relation to the specific pain manifestation(s) predicted and their underlying function(s).
Future studies should explore other pain-relevant manifestations and assess the specificity of the FEPS against simulated pain expressions and other types of aversive or emotional states.
