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Classification and characterisation of brain network changes in chronic back pain: A multicenter study.

ABSTRACT: Background. Chronic pain is a common, often disabling condition thought to involve a combination of peripheral and central neurobiological factors. However, the extent and nature of changes in the brain is poorly understood. Methods. We investigated brain network architecture using resting-state fMRI data in chronic back pain patients in the UK and Japan (41 patients, 56 controls), as well as open data from USA. We applied machine learning and deep learning (conditional variational autoencoder architecture) methods to explore classification of patients/controls based on network connectivity. We then studied the network topology of the data, and developed a multislice modularity method to look for consensus evidence of modular reorganisation in chronic back pain. Results. Machine learning and deep learning allowed reliable classification of patients in a third, independent open data set with an accuracy of 63%, with 68% in cross validation of all data. We identified robust evidence of network hub disruption in chronic pain, most consistently with respect to clustering coefficient and betweenness centrality. We found a consensus pattern of modular reorganisation involving extensive, bilateral regions of sensorimotor cortex, and characterised primarily by negative reorganisation - a tendency for sensorimotor cortex nodes to be less inclined to form pairwise modular links with other brain nodes. Furthermore, these regions were found to display increased connectivity with the pregenual anterior cingulate cortex, a region known to be involved in endogenous pain control. In contrast, intraparietal sulcus displayed a propensity towards positive modular reorganisation, suggesting that it might have a role in forming modules associated with the chronic pain state. Conclusion. The results provide evidence of consistent and characteristic brain network changes in chronic pain, characterised primarily by extensive reorganisation of the network architecture of the sensorimotor cortex.

SUBMITTER: Mano H

PROVIDER: S-EPMC5930551 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:Treatment of chronic lower back pain (CLBP) should be stratified for best medical and economic outcome. To improve the targeting of potential participants for exclusive therapy offers from payers, Freytag et al. developed a tool to classify back pain chronicity classes (CC) based on claim data. The aim of this study was to evaluate the criterion validity of the model. Administrative claim data and self-reported patient information from 3,506 participants (2014-2021) in a private health insurance health management programme in Germany were used to validate the tool. Sensitivity, specificity, and Matthews' correlation coefficient (MCC) were calculated comparing the prediction with actual grades based on von Korff's graded chronic pain scale (GCPS). The secondary outcome was an updated view on direct health care costs (€) of patients with back pain (BP) grouped by GCPS. Results showed a fair correlation between predicted CC and actual GCPS grades. A total of 69.7% of all cases were correctly classified. Sensitivity and specificity rates of 54.6 and 76.4% underlined precision. Correlation between CC and GCPS with an MCC of 0.304 also indicated a fair relationship between prediction and observation. Cost data could be clearly grouped by GCPS: the higher the grade, the higher the costs and the use of health care. This was the first study to compare the predicted severity of BP using claim data with the actual severity of BP by GCPS. Based on the results, the usage of CC as a single tool to determine who receives CLBP treatment cannot be recommended. CC is a good tool to segment candidates for specific types of intervention in BP. However, it cannot replace a medical screening at the beginning of an intervention, as the rate of false negatives is too high. Trial registration The study was conducted using routinely collected data from an intervention, which was previously evaluated and registered retrospectively in the German Registry of Clinical Trials under DRKS00015463 (04/09/2018). Informed consent and the self-reported questionnaire have remained unchanged since the study and, therefore, are still valid according to the ethics proposal.

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Classification and characterisation of brain network changes in chronic back pain: A multicenter study.

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