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A neuroimaging marker for predicting longitudinal changes in pain intensity of subacute back pain based on large-scale brain network interactions

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Title
A neuroimaging marker for predicting longitudinal changes in pain intensity of subacute back pain based on large-scale brain network interactions
Author(s)
Bo-yong park; Jae-Joong Lee; Hong Ji Kim; Choong-Wan Woo; Hyunjin Park
Subject
INDEPENDENT COMPONENT ANALYSIS, ; MIDBRAIN DOPAMINERGIC-NEURONS, ; SEX-BASED DIFFERENCES, ; NUCLEUS-ACCUMBENS, ; FUNCTIONAL CONNECTIVITY, ; REPRESENTATION, ; TRANSITION, ; SIGNATURE, ; REWARD, ; FMRI
Publication Date
2020-10
Journal
SCIENTIFIC REPORTS, v.10, no.1, pp.17392
Publisher
NATURE PUBLISHING GROUP
Abstract
© 2020, The Author(s). 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
URI
https://pr.ibs.re.kr/handle/8788114/8464
DOI
10.1038/s41598-020-74217-3
ISSN
2045-2322
Appears in Collections:
Center for Neuroscience Imaging Research (뇌과학 이미징 연구단) > 1. Journal Papers (저널논문)
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