Pain-free resting-state functional brain connectivity predicts individual pain sensitivity

Tamas Spisak,Zsigmond T Kincses,Tobias Schmidt-Wilcke,Balint Kincses,Matthias Zunhammer,Frederik Schlitt,Ulrike Bingel

doi:10.1038/s41467-019-13785-z

Abstract

Individual differences in pain perception are of interest in basic and clinical research as altered pain sensitivity is both a characteristic and a risk factor for many pain conditions. It is, however, unclear how individual sensitivity to pain is reflected in the pain-free resting-state brain activity and functional connectivity. Here, we identify and validate a network pattern in the pain-free resting-state functional brain connectome that is predictive of interindividual differences in pain sensitivity. Our predictive network signature allows assessing the individual sensitivity to pain without applying any painful stimulation, as might be valuable in patients where reliable behavioural pain reports cannot be obtained. Additionally, as a direct, non-invasive readout of the supraspinal neural contribution to pain sensitivity, it may have implications for translational research and the development and assessment of analgesic treatment strategies.

Highlights

Individual differences in pain perception are of key interest in clinical practice as altered pain sensitivity is both a characteristic and risk factor for many pain conditions[3,4,5]
Neuromarker development was based on intrinsic whole-brain functional brain connectivity, the degree to which resting-state brain activity in distinct neural regions is correlated over time
Cold and mechanical pain thresholds acquired according to the wellestablished quantitative sensory testing (QST) protocol[28] were aggregated into a composite pain-sensitivity score, as previously reported[8], to obtain a general estimate of pain sensitivity

Summary

Introduction

Individual differences in pain perception are of interest in basic and clinical research as altered pain sensitivity is both a characteristic and a risk factor for many pain conditions. It is, unclear how individual sensitivity to pain is reflected in the pain-free resting-state brain activity and functional connectivity. Mapping the resting-state network of pain sensitivity and exploiting its capacity to predict various aspects of pain processing would substantially advance the field—both from a basic research and translational perspective Contrasting it with experimental pain responses would extend our understanding of how the subjective experience of pain emerges from brain activity. Investigating how the hypothesised resting-state pain sensitivity network is embedded into the broader resting-state brain activity could extend our knowledge about the complex functional architecture of the resting brain

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Results

Conclusion