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

• Published in: Nature communications Vol. 11; no. 1; p. 187
• Main Authors: Spisak, Tamas, Kincses, Balint, Schlitt, Frederik, Zunhammer, Matthias, Schmidt-Wilcke, Tobias, Kincses, Zsigmond T., Bingel, Ulrike
• Format: Journal Article
• Language: English
• Published: London Nature Publishing Group UK 10.01.2020; Nature Publishing Group; Nature Portfolio
• Subjects: 59/36; 59/57; 631/378/3917; 692/308/53/2422; Adolescent; Adult; Analgesics; Brain; Brain - physiology; Brain mapping; Brain research; Connectome; Female; Functional magnetic resonance imaging; Humanities and Social Sciences; Humans; Individuality; Magnetic Resonance Imaging; Male; multidisciplinary; Nerve Net - physiology; Neural networks; Pain; Pain - physiopathology; Pain - psychology; Pain perception; Pain Perception - physiology; Pain sensitivity; Rest - physiology; Risk analysis; Risk factors; Science; Science (multidisciplinary); Sensitivity analysis; Young Adult
• DOI: 10.1038/s41467-019-13785-z
• ISSN: 2041-1723

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. An fMRI-based brain signature to predict an individual’s pain sensitivity could be useful clinically. Here the authors identify a network in the resting brain which can be used to predict responses to noxious stimuli in healthy subjects.