Connectome-based predictive modelling can predict follow-up craving after abstinence in individuals with opioid use disorders

Background Individual differences have been detected in individuals with opioid use disorders (OUD) in rehabilitation following protracted abstinence. Recent studies suggested that prediction models were effective for individual-level prognosis based on neuroimage data in substance use disorders (SUD).Aims This prospective cohort study aimed to assess neuroimaging biomarkers for individual response to protracted abstinence in opioid users using connectome-based predictive modelling (CPM).Methods One hundred and eight inpatients with OUD underwent structural and functional magnetic resonance imaging (fMRI) scans at baseline. The Heroin Craving Questionnaire (HCQ) was used to assess craving levels at baseline and at the 8-month follow-up of abstinence. CPM with leave-one-out cross-validation was used to identify baseline networks that could predict follow-up HCQ scores and changes in HCQ (HCQfollow-up−HCQbaseline). Then, the predictive ability of identified networks was tested in a separate, heterogeneous sample of methamphetamine individuals who underwent MRI scanning before abstinence for SUD.Results CPM could predict craving changes induced by long-term abstinence, as shown by a significant correlation between predicted and actual HCQfollow-up (r=0.417, p<0.001) and changes in HCQ (negative: r=0.334, p=0.002；positive: r=0.233, p=0.038). Identified craving-related prediction networks included the somato-motor network (SMN), salience network (SALN), default mode network (DMN), medial frontal network, visual network and auditory network. In addition, decreased connectivity of frontal-parietal network (FPN)-SMN, FPN-DMN and FPN-SALN and increased connectivity of subcortical network (SCN)-DMN, SCN-SALN and SCN-SMN were positively correlated with craving levels.Conclusions These findings highlight the potential applications of CPM to predict the craving level of individuals after protracted abstinence, as well as the generalisation ability; the identified brain networks might be the focus of innovative therapies in the future.