1. A neuroimaging marker for predicting longitudinal changes in pain intensity of subacute back pain based on large-scale brain network interactions
- Author
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Choong-Wan Woo, Hong Ji Kim, Jae-Joong Lee, Bo-yong Park, and Hyunjin Park
- Subjects
0301 basic medicine ,Male ,medicine.medical_specialty ,lcsh:Medicine ,Nucleus accumbens ,Article ,03 medical and health sciences ,0302 clinical medicine ,Physical medicine and rehabilitation ,Text mining ,Neuroimaging ,Image processing ,Neural Pathways ,Back pain ,Medicine ,Humans ,Prefrontal cortex ,lcsh:Science ,Pain Measurement ,Multidisciplinary ,medicine.diagnostic_test ,business.industry ,lcsh:R ,Multisensory integration ,Brain ,Magnetic Resonance Imaging ,Intensity (physics) ,030104 developmental biology ,Back Pain ,Female ,lcsh:Q ,medicine.symptom ,Chronic Pain ,business ,Functional magnetic resonance imaging ,030217 neurology & neurosurgery ,Neuroscience - Abstract
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.
- Published
- 2020
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