Pain-free resting-state functional brain connectivity predicts individual pain sensitivity
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 activ...
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Published in | Nature communications Vol. 11; no. 1; p. 187 |
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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.
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. |
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AbstractList | 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. 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. 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. 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. 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. |
ArticleNumber | 187 |
Author | Zunhammer, Matthias Schmidt-Wilcke, Tobias Kincses, Zsigmond T. Kincses, Balint Spisak, Tamas Bingel, Ulrike Schlitt, Frederik |
Author_xml | – sequence: 1 givenname: Tamas surname: Spisak fullname: Spisak, Tamas email: tamas.spisak@uk-essen.de organization: Department of Neurology, University Hospital Essen – sequence: 2 givenname: Balint surname: Kincses fullname: Kincses, Balint organization: Department of Neurology, University of Szeged – sequence: 3 givenname: Frederik surname: Schlitt fullname: Schlitt, Frederik organization: Department of Neurology, University Hospital Essen – sequence: 4 givenname: Matthias orcidid: 0000-0002-3680-9675 surname: Zunhammer fullname: Zunhammer, Matthias organization: Department of Neurology, University Hospital Essen – sequence: 5 givenname: Tobias surname: Schmidt-Wilcke fullname: Schmidt-Wilcke, Tobias organization: Institute of Clinical Neuroscience and Medical Psychology, University of Düsseldorf, Mauritius Therapieklinik – sequence: 6 givenname: Zsigmond T. surname: Kincses fullname: Kincses, Zsigmond T. organization: Department of Neurology, University of Szeged – sequence: 7 givenname: Ulrike surname: Bingel fullname: Bingel, Ulrike organization: Department of Neurology, University Hospital Essen |
BackLink | https://www.ncbi.nlm.nih.gov/pubmed/31924769$$D View this record in MEDLINE/PubMed |
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Snippet | 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... Abstract Individual differences in pain perception are of interest in basic and clinical research as altered pain sensitivity is both a characteristic and a... 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... |
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Title | Pain-free resting-state functional brain connectivity predicts individual pain sensitivity |
URI | https://link.springer.com/article/10.1038/s41467-019-13785-z https://www.ncbi.nlm.nih.gov/pubmed/31924769 https://www.proquest.com/docview/2342998932 https://search.proquest.com/docview/2336249605 https://pubmed.ncbi.nlm.nih.gov/PMC6954277 https://doaj.org/article/272aca92147441f0ad646b5b5072cd03 |
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