Neuroimaging Approaches to Pain Perception

Advances in neuroimaging have transformed our understanding of how the brain encodes, modulates and learns from painful stimuli. Techniques such as functional magnetic resonance imaging, positron emission tomography, electroencephalography and magnetoencephalography offer complementary insights into the sensory, affective and cognitive dimensions of pain. Structural imaging reveals alterations in grey and white matter associated with chronic conditions, while functional and connectivity analyses characterise dynamic interactions among nociceptive pathways, limbic circuits and executive networks. Machine-learning approaches enable the extraction of multivariate signatures that predict individual pain responses and therapeutic outcomes. Together, these methods illuminate the neural mechanisms underpinning acute and persistent pain, inform the development of objective biomarkers and support personalised interventions. Ongoing work seeks to integrate multimodal data, refine predictive models and address ethical challenges in translating neuroimaging into clinical practice.

Research from Nature Portfolio

Recent studies have externally validated a resting-state brain connectivity model that accounts for inter-individual differences in pain-related learning. This predictive framework emphasises connections among the amygdala, posterior insula, sensorimotor, frontoparietal and cerebellar regions and explains a significant proportion of variance in reinforcement learning signals. It offers a non-invasive biomarker candidate for personalised pain management. Complementing this, a multivariate pattern signature has been defined to quantify cerebral contributions to pain beyond nociceptive input. This signature, comprising activity in the nucleus accumbens, lateral prefrontal and parahippocampal cortices, predicts trial-by-trial pain ratings and mediates expectancy effects, suggesting novel targets for cognitive and pharmacological interventions. A comprehensive review of brain imaging in chronic pain highlights requirements for standardisation, large-scale data, strict validation protocols and the ethical use of imaging as an adjunct to subjective report.

Neuroimaging Approaches to Pain Perception publication trend

The graph below shows the total number of articles in neuroimaging approaches to pain perception across all publications each year (not limited to Nature Index journals).

Technical terms

Functional magnetic resonance imaging (fMRI): Non-invasive method measuring blood oxygen level dependent changes to infer regional neural activity.

Resting-state functional connectivity: Assessment of spontaneous synchrony in neural signals between distinct brain regions when no task is performed.

Biomarker: Quantifiable indicator derived from biological measurements that reflects physiological or pathological states.

Predictive coding: Computational framework in which the brain minimises discrepancies between incoming sensory input and internal generative models.

Multivariate pattern analysis: Statistical technique that decodes cognitive or perceptual states by analysing distributed patterns of brain activity.

References

1. Chronic pain – A maladaptive compensation to unbalanced hierarchical predictive processing. NeuroImage (2024).
2. An externally validated resting-state brain connectivity signature of pain-related learning. Communications Biology (2024).
3. Pain perception as hierarchical Bayesian inference: A test case for the theory of constructed emotion. Annals of the New York Academy of Sciences (2024).
4. Brain imaging tests for chronic pain: medical, legal and ethical issues and recommendations. Nature Reviews Neurology (2017).
5. Quantifying cerebral contributions to pain beyond nociception. Nature Communications (2017).
6. Functional dissociation of stimulus intensity encoding and predictive coding of pain in the insula. eLife (2017).