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

Wellcome Open Research

article

Hiroaki Mano¹  Gopal Kotecha²  Kenji Leibnitz¹  Takashi Matsubara³  Christian Sprenger⁴  Aya Nakae⁵  Nicholas Shenker²  Masahiko Shibata⁵  Valerie Voon⁷  Wako Yoshida⁸  Michael Lee⁷  Toshio Yanagida¹  Mitsuo Kawato⁸  Maria Joao Rosa⁹  Ben Seymour¹ 

[1] Center for Information and Neural Networks, National Institute of Information and Communications Technology;Cambridge University Hospitals NHS Foundation Trust;Graduate School of System Informatics, Kobe University;Computational and Biological Learning Laboratory, Department of Engineering, University of Cambridge;Osaka University School of Medicine;Immunology Frontiers Research Center, Osaka University;School of Clinical Medicine, University of Cambridge;Advanced Telecommunications Research Center International;Max-Planck UCL Centre for Computational Psychiatry and Ageing Research, University College London;Department of Computer Science, University College London

关键词: Chronic pain;    Nociception;    Connectomics;    graph theory;    deep learning;    sensorimotor;    multislice modularity;    hub disruption;    osteoarthritis;    arthritis;    rostral ACC;    endogenous modulation;   

DOI  :  10.12688/wellcomeopenres.14069.2

学科分类：内科医学

来源: Wellcome

【 摘 要 】

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.

【 授权许可】

CC BY