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

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.

Functional brain mapping in patients with chronic back pain shows age-related differences

“…The classification performance (>70%-75%) of our searchlight analysis using ALFF maps is as good as or better than the published literature using functional imaging data as features to discriminate between patients with CLBP and controls. 17,33,54,59,60,76 While we reported lower classification accuracy in the external validation data set (57%-59%, see Table 3), this accuracy was in the range reported for studies using an independent testing cohort. 60,84,106 For example, Zhang et al demonstrated accuracies ranging from 53% to 67% for classification of CLBP using multiple ALFF-based features in a second validation cohort.…”

Melatonin Moderates the Triangle of Chronic Pain, Sleep Architecture and Immunometabolic Traffic

“…Pain has been defined as "An unpleasant sensory and emotional experience associated with actual or potential tissue damage or described in terms of such damage" [33]. Pain significantly affects the quality of life and implies increased social costs for its manage-ment [34]. A relationship between pain and melatonin has beendelineated, since chronic pain patients have shown lower levels of melatonin in the blood and urine [35].…”

“…Analgesic effects of melatonin have been well appreciated across various chronic pain syndromes such as chronic pelvic pain, fibromyalgia, irritable bowel syndrome, tension and cluster headaches, migraine and others including chronic back pain [37,38]. Around 5-10% of the patients with acute back pain progress to subacute back pain category and then, finally develop chronic back pain, which is defined as back pain lasting for more than 3 months [34]. The next section will provide an overview of the melatonin's pain-relieving mechanisms before discussing the melatonin's effects in chronic pain disorders of different origin.…”

Brain Mapping of Behavioral Domains Using Multi-Scale Networks and Canonical Correlation Analysis

“…Here, we use brain images and neurobehavioral scores from N = 1,000 healthy participants and perform state-of-the-art machine learning analyses combining structural and functional brain networks at different spatial resolutions with comprehensive behavioral assessments within the domains of sensation, motor skills, and cognition. Previous studies addressed correlative relationships between brain network connectivity and neurobehavioral measures, such as motor function (Raichlen et al, 2016 ; Lo et al, 2017 ; Boyne et al, 2018 ), cognitive tasks (Zimmermann et al, 2018 ; Yu et al, 2020 ; Rasero et al, 2021 ), and sensory experiences (Yeung et al, 2016 ; Spisak et al, 2020 ; Park et al, 2020 ). Some relevant questions arise from these studies: Which modality is the one that dominates the association across behavioral domains?…”