RT Journal Article
SR Electronic
T1 A NEURAL BIOMARKER FOR CHRONIC PAIN BASED ON DECODED BRAIN NETWORKS
JF Journal of Neurology, Neurosurgery & Psychiatry
JO J Neurol Neurosurg Psychiatry
FD BMJ Publishing Group Ltd
SP e4
OP e4
DO 10.1136/jnnp-2015-312379.20
VO 86
IS 11
A1 Gopal Kotecha
A1 Hiroaki Mano
A1 Kenji Leibnitz
A1 Aya Nakae
A1 Valerie Voon
A1 Wako Yoshida
A1 Toshio Yanagida
A1 Mitsuo Kawato
A1 Maria Joao Rosa
A1 Ben Seymour
YR 2015
UL http://jnnp.bmj.com/content/86/11/e4.108.abstract
AB The lack of a biomarker for chronic pain remains an important impediment to clinical and translational pain research. The problem stems from the multiple parallel but subtle abnormalties thought to represent the chronic pain state, yielding the emerging view of chronic pain as a ‘network disorder’. This suggests analysis approaches that aim to identify distributed patterns of data (multivariate, machine learning methods) might offer the best opportunity to discover biomarkers. Here, we performed a multi-center functional brain imaging study to record state functional brain networks resting in 41 patients with chronic back pain and 33 healthy control subjects. We calculated with functional covariance matrix from 160 regions of interest, and used Sparse Multinomial Logistic Regression to classify subjects as patient or control using a leave-one-out cross validation. Diagnostic accuracy was 91.9%, with sensitivity and specificity 90.2% and 93.9% respectively. We then used graph theoretic measures to characterise the pattern of network differences between the groups, and showed that the chronic pain state was associated with disrupted network ‘assortativity’. These data provide evidence to support an accurate functional biomarker of chronic pain, and open the door to the development of translatable biomarkers using similar methodologies in animals.