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Plastic Changes in Pain and Motor Network Induced by Chronic Burn Pain.

ABSTRACT: Musculoskeletal diseases with chronic pain are difficult to control because of their association with both central as well as the peripheral nervous system. In burn patients, chronic pain is one of the major complications that cause persistent discomfort. The peripheral mechanisms of chronic pain by burn have been greatly revealed through studies, but the central mechanisms have not been identified. Our study aimed to characterize the cerebral plastic changes secondary to electrical burn (EB) and non-electrical burn (NEB) by measuring cerebral blood volume (CBV). Sixty patients, twenty with electrical burn (EB) and forty with non-electrical burn (NEB), having chronic pain after burn, along with twenty healthy controls, participated in the study. Voxel-wise comparisons of relative CBV maps were made among EB, NEB, and control groups over the entire brain volume. The CBV was measured as an increase and decrease in the pain and motor network including postcentral gyrus, frontal lobe, temporal lobe, and insula in the hemisphere associated with burned limbs in the whole burn group. In the EB group, CBV was decreased in the frontal and temporal lobes in the hemisphere associated with the burned side. In the NEB group, the CBV was measured as an increase or decrease in the pain and motor network in the postcentral gyrus, precentral gyrus, and frontal lobe of the hemisphere associated with the burn-affected side. Among EB and NEB groups, the CBV changes were not different. Our findings provide evidence of plastic changes in pain and motor network in patients with chronic pain by burn.

SUBMITTER: Joo SY

PROVIDER: S-EPMC8230805 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description: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.

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Plastic Changes in Pain and Motor Network Induced by Chronic Burn Pain.

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