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

Wellcome Open Research
Hiroaki ManoBen Seymour

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 c...Continue Reading

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Citations

Dec 10, 2019·Cerebral Cortex·Weihao ZhengTor D Wager
Dec 14, 2018·Nature Reviews. Disease Primers·Johan W S VlaeyenSteven J Linton
Oct 4, 2019·Pain Reports·Maite M van der MiesenTor D Wager
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Oct 16, 2020·The International Journal of Neuroscience·Rutvi Prajapati, Isaac Arnold Emerson
Jul 24, 2021·Cerebral Cortex Communications·Camille FauchonRoland Peyron
Aug 11, 2021·Pain and Therapy·Diane ReckziegelA Vania Apkarian

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Methods Mentioned

BETA
feature extraction

Software Mentioned

DARTEL
Brain Connectivity Toolbox
SPM
LTSO
Matlab
python
SVM
Anatomical Automatic Labeling ( AAL )
learn
OpenPain

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