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.
Highlights
Identification of predictive neuroimaging markers of pain intensity changes is a crucial issue to better understand macroscopic neural mechanisms of pain
Interpretable independent components (ICs) were mapped to the visual network (VN), default mode network (DMN), frontoparietal network (FPN), salience network (SN), sensorimotor network (SMN), auditory network (AN), basal ganglia (BG), and cerebellum/brainstem
Comparing with brainnetome atlas[37], VN consists of cuneus, lingual gyrus, superior/middle/inferior occipital gyrus, and fusiform gyrus, DMN involves medial/lateral prefrontal cortex, precuneus, anterior/posterior cingulate cortex, and parahippocampal gyrus, FPN contains dorsolateral/ventrolateral prefrontal cortex, superior/inferior parietal lobule, and posterior superior temporal sulcus, SN consists of anterior insula, anterior cingulate cortex, and lateral orbitofrontal cortex, SMN involves precentral/postcentral gyri, paracentral lobule, superior parietal lobule, and dorsal inferior parietal lobule, AN contains superior/medial temporal gyrus, dorsal inferior temporal gyrus, and ventral inferior parietal lobule, and BG consists of amygdala, caudate, putamen, and thalamus
Summary
Identification of predictive neuroimaging markers of pain intensity changes is a crucial issue to better understand macroscopic neural mechanisms of pain. 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. A seminal study by Baliki et al.[9] predicted pain transition from SBP to CBP using the mPFC–NAc connectivity measured with functional magnetic resonance imaging (fMRI) This connection contributed to distinguishing the SBP persisted (SBPp) group from the recovered (SBPr) group with high accuracy. We aimed to develop a functional connectivity-based model to predict longitudinal changes in pain intensity of SBP patients using large-scale brain networks. To this end, we re-analyzed previously published[9] resting-state fMRI (rs-fMRI) data from 49 SBP patients. Functional connections among brain networks were used to construct a model to predict longitudinal changes in pain intensity for SBP patients
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