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 coefficientand 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. 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.
Highlights
Maladaptive brain processing of pain is thought to have a primary or facilitative role in many types of chronic pain
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
To obtain the depression-related labels we divided the subjects according to their Beck Depression Inventory (BDI) scores: BDI ≥ 3, BDI < 3
Summary
Maladaptive brain processing of pain is thought to have a primary or facilitative role in many types of chronic pain. One way to tackle this is to use machine learning and deep learning methods, and a number of studies have shown how this can be used to successfully build biomarkers (i.e. classifiers) in a range of psychiatric disease (Takagi et al, 2017; Watanabe et al, 2017; Yahata et al, 2016; Yamada et al, 2017). These methods need to be validated on genuinely independent data sets to be convincing, and current evidence of generalisable classifiers for chronic pain is lacking. 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
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