A neuroimaging marker for predicting longitudinal changes in pain intensity of subacute back pain based on large-scale brain network interactions
Identification of predictive neuroimaging markers of pain intensity changes is a crucial issue to better understand macroscopic neural mechanisms of pain. Although a single connection between the medial prefrontal cortex and nucleus accumbens has been suggested as a powerful marker, how the complex...
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Veröffentlicht in: | Scientific reports 2020-10, Vol.10 (1), p.17392-17392, Article 17392 |
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Sprache: | eng |
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Zusammenfassung: | Identification of predictive neuroimaging markers of pain intensity changes is a crucial issue to better understand macroscopic neural mechanisms of pain. Although a single connection between the medial prefrontal cortex and nucleus accumbens has been suggested as a powerful marker, how the complex interactions on a large-scale brain network can serve as the markers is underexplored. Here, we aimed to identify a set of functional connections predictive of longitudinal changes in pain intensity using large-scale brain networks. We re-analyzed previously published resting-state functional magnetic resonance imaging data of 49 subacute back pain (SBP) patients. We built a network-level model that predicts changes in pain intensity over one year by combining independent component analysis and a penalized regression framework. Connections involving top-down pain modulation, multisensory integration, and mesocorticolimbic circuits were identified as predictive markers for pain intensity changes. Pearson’s correlations between actual and predicted pain scores were
r
= 0.33–0.72, and group classification results between SBP patients with persisting pain and recovering patients, in terms of area under the curve (AUC), were 0.89/0.75/0.75 for visits four/three/two, thus outperforming the previous work (AUC 0.83/0.73/0.67). This study identified functional connections important for longitudinal changes in pain intensity in SBP patients, providing provisional markers to predict future pain using large-scale brain networks. |
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ISSN: | 2045-2322 2045-2322 |
DOI: | 10.1038/s41598-020-74217-3 |