(264) Altered Brain Network Topology in Chronic Low Back Pain Patients on Prescription Opioid Analgesics

Abstract

Opioid prescribing for chronic pain conditions such as Chronic Low Back Pain (CLBP) in the United States has increased substantially in the past two decades. However, the effects of opioid analgesics on the brain network topology in CLBP remains unknown. The present study therefore provides the first test of the hypothesis that opioid status impacts the activity of the whole-brain network using graph theory methods. Resting state fMRI data were collected on a 3T scanner from 10 CLBP patients on long-term opioid regimens (CLBP+; 5 males, mean age ± SD = 48.5 ± 14.8 years) and 10 matched opioid-naive CLBP patients (CLBP-, 5 males, mean age ± SD = 43.6 ± 12.6 years) according to a protocol approved by the Stanford IRB. Following image quality assurance in MRIQC, and preprocessing in SPM12, we performed graph theoretical network analysis using CONN toolbox. For each participant, global network efficiency — a graph theory measure for integrative capacity of complex systems, was calculated and correlated with individual differences in sensory pain scores from Short Form McGill Pain Questionnaire (SF-MPQ). We focused on global efficiency as it reflects effective information transfer (i.e., small-worldness) within a network of nodes (i.e., regions of interests) and edges (i.e., correlation). Results revealed that the global efficiency values were positively correlated with pain in CLBP- but not in CLBP+ (r = 0.49 vs r = - 0.06, p = 0.05). This suggests that as sensory dimension of the pain intensity increased, CLBP- exhibited more efficient information transfer across a network of brain regions, including the core nodes of the salience network (anterior insula), frontoparietal central executive network (dorsolateral prefrontal cortices), and bilateral sensorimotor networks. Follow up studies with larger sample size are required to corroborate these observations and to formulate appropriate strategies for opioid prescribing guidelines, accordingly. Supported by NIH P01AT006651, and NIH T32DA035165.